Techno-Societal 2020

Facial recognition is a biometric software category that mathematically maps the facial features of a person and stores the data as a face-print. Using machine learning algorithms, the software compares a live capture or digital image to the stored face print to verify an individual's identity and help automate authentication. Facial recognition will increase protection, recognize unauthorized entry and keep a track of visitors. ID passes are yesterday’s technology Our project's main task is to identify if the person is an employee or a visitor by using a face recognition system where in security guards job is to watch over the process and stepping in only when the system says that the person is not an employee or when they see something suspicious.

This paper mainly emphasis on the use of machine learning algorithms such as self-organizing maps (SOM) and support vector machines (SVM) for classifying text documents. We have to classify documents effectively and accurately to different classes based on their content. We tested classification of self-organizing map on Reuters R-8 data set and compared the results to three other popular machine learning algorithms: k-means clustering, k nearest neighbor searching, and Naive Bayes classifier. Self-organizing map yielded the highest accuracies as an unsupervised method. Furthermore, the accuracy of self-organizing maps was improved when used together with support vector machines.

In this research paper, Bandwidth improvement is investigated in E-shaped Micro strip patch antennas by using multilayer and capacitive feed techniques. Capacitive feeding technique has been used to cancel the inductive impedance of probe by capacitive patch. The Rectangular capacitive patch has been used separately near the radiating E-shaped design model. Performance analysis of E-shaped conventional and suspended capacitive feed Micro Strip Patch Antenna is done to enhance its parameters like Bandwidth, Gain, and Directivity. The proposed E-shaped multilayer capacitive feed Micro strip patch Antenna is shown improved bandwidth of 238 MHz and Gain of 6.071 dB as compared with conventional candidates. The proposed antenna design models have a working center frequency in the range of 2.36 GHz to 2.59 GHz, which can be used for wireless applications. The Return Loss and VSWR have been investigated in an acceptable range. E-field and Current flow of the antenna are within the desired radiating patch. It has observed that the capacitive feed technique E-shaped design model improves the Bandwidth, Gain, and other antenna parameters over the conventional probe feed antenna.

Identifying speaker from the whispered voice is difficult task contrasted to neutral as voiced phonations are absent in the whisper. The accomplishment of the speaker identification system for the most part relies on the selection of proper audio features reasonable for the type of database and type of application. This paper examines the various audio features available and emphasizes on the use of selected timbrel features which are sorted by Hybrid Selection Algorithm. The limited number of timbrel features namely MFCC, Roll-off, Brightness, Roughness, and irregularity which are found outperforming when tested on CHAIN database. Likewise, the possibility of using the MEDIAN based features is investigated by analysis. The use of Median timbrel features reported an enhancement in speaker identification accuracy by 2.4% compared to timbrel features only in whisper train-whisper test scenario.

The CAN bus has emerged as vital means of communication within the automotive sector. Intrinsically varied applications are being implemented using the Controller Area Network Protocol. In this paper, two major automotive applications namely engine temperature monitoring and control and airbag deployment mechanism are implemented with the assistance of ARM7 based microcontroller nodes. Thus the essential CAN protocol has been implemented for real time embedded automotive applications.

Temporomandibular joint disorder (TMD) includes specifically a series of musculoskeletal disorders that may affect the masticating system. Roughly 30–40 percent of adults today have oral problems, and the most common cause of oral problems is TMJ. This disorder is very prevalent in the general population, but it affects more women and young people. The focus of this research review was on the methods for detecting TMJ disorder using machine learning algorithms. Propelled with the rise in use of machine learning techniques in the research dimensions of medical diagnosis, in this paper there is an attempt to explore different classification for predicting the TMJ disorder. The proposed techniques are evaluated on real time TMJ datasets. Dataset related to TMJ screening in subjects had 84 instances and 11 attributes. After applying different machine learning techniques, results suggest that Naïve Bayes and Adaboost models work better with higher accuracy of 93% and 92%.

In cognitive radio network some of the important functionalities is spectrum sensing. It plays a very vital role for unlicensed system to operate efficiently and to provide the required improvement in spectrum efficiency. If the spectrum, which is sensed is in idle state allow the unauthorized users (secondary users) to use the spectrum. Machine learning algorithms are used for spectrum sensing in cognitive radio networks. They are weighted K-nearest neighbor, Support Vector Machine (SVM) which comes under supervised learning and Gaussian Mixture Model (GMM), K-means clustering which comes under unsupervised learning-based classification techniques. In this paper rigorous survey is done by using machine learning algorithms to review various methodologies used in spectrum sensing like K-nearest -neighbor, GMM, K-means clustering and SVM.

For the time being, blood disorders are defined by examination of microscopic images of blood cells. It may lead to identity the blood disorders class of blood-associated diseases. This paper discusses an initial look at the creation of Microscopic Blood Pattern Images to detect leukemia forms. Pixel reading can be very critical since it is possible to detect and classify pixel diseases at an earlier stage. From there, more steps, such as disease control, surveillance and prevention, can be completed. Photos are used because they can be fairly priced and do not require costly check-out and laboratory equipment. White blood cell deficiency, leukemia, will be identified by the gadget. The computer can use microscopic image capabilities to look at texture, geometry, colour and statistical evaluation changes. Changes in these characteristics may be used as feedback to a classifier. A literature review has been undertaken and it is suggested to categories types of leukemia by reinforcement learning to know. A brief debate about the problems involved has also been prepared by researchers.

Brain tumor identification is an essential task for assessing the tumors and its classification based on the size of tumor. There are various types of imaging strategies such as X-rays, MRI, CT-scan used to recognize brain tumors. Computed Tomography (CT) scan images are used for in this work for Brain tumor Image Identification. CT-scan images are used, because as it gives size, shape and blood vessels detailing and is non-invasive technique. CT-scan is commonly utilized because of the superior quality of image. Deep learning (DL) is the most recent technology which gives higher efficiency results in recognition, classification. In this paper, the model is developed by using Convolution neural network to detect the tumor of brain image from a dataset from Kaggle. The dataset contains near about 1000 images. Tumor is identified by image processing algorithm using CNN, time complexity is 90 m sec, and the accuracy of the present system is 97.87%.

The need for multiband, bigger addition and low profile radio wires to help numerous remote applications prompted the plan of Microstrip reception apparatuses. Microstrip radio wires because of their little profile configuration take less zone. This paper presents a straightforward rectangular Microstrip Patch Antenna, E-Shaped, U-Shaped, +-Shaped radio wires work at 2.2 to 3.8 GHz. The Proposed reception apparatus will be in lightweight, keen and conservative unit contrast and comprises of metallic fix and ground between which is a dielectric medium called the substrate. This various structures of MSA are utilized for military, remote and common applications. The CADFEKO programming is utilized to register the increase, power, radiation example and S11 of receiving wire.

Wireless sensor network is defined as homogeneous or heterogeneous system containing large number of sensors namely called as nodes used to monitor different environments in cooperatives. WSN is composed of sensor nodes (SN), base stations (BS) and cluster head (CH). The popularity of wireless sensor network has been increased day by day exponentially due to its wide range of application. The applications of wireless sensor networks are air traffic control, healthcare systems, home services, military services, industrial & building automations, network communications, VAN etc. The advantage of WSN is that it is very easy to install in critical regions where normal network cannot be set. Thus the wide range of applications attracts attacker. To secure from different types of attacks mainly intruder, intrusion detection system based on dynamic state context and hierarchical trust in WSNs (IDSHT) is proposed. The trust evaluation is carried out in hierarchical way. The trust of sensor nodes is evaluated by cluster head (CH) whereas trust of cluster head is evaluated by neighbor cluster head or base stations. Hence the content trust, honest trust and interactive trust is put forward by combining direct evaluation and feedback based evaluation in the fixed hop range. In this way the complexity of trust management is carried in hierarchical manner and trust evaluation overhead is minimized. This proposed work addresses the security issues of wireless sensor network. A more prominent intrusion detection system based on context level and trust level is introduced. This mechanism achieves more than 90% accuracy in detection of routing attack and sinkhole attack. The architecture suggested in this paper is used to develop two level of trust model. Accuracy of 90% and 95% is expected in intrusion detection and context text detection respectively.

We propose a camera-based mechanical man app. This app helps the blind persons to browse the text on explicit objects. In this system the camera captures the actual text on the object. Multiple techniques square measure applied to its text. Such as Optical Character Recognition that supply the operation of scanning and recognition of text and a few have integrated voice output. From a grayscale image, thresholding are often accustomed produce binary pictures i.e. image with solely black or white colors, Filtering are often accustomed cut back the noise of image, Next image segmentation technique is employed to perform the method of partitioning a digital image into multiple segments. The goal of segmentation is to modify and/or amendment the illustration of a picture into one thing that’s a lot of significant and easier to analyses. Image scaling is the method of resizing a digital image. Next technique employed in this project is template matching. Temples matching is a way in the digital image process for locating tiny components of a picture that match a template image. Also template extraction are often employed in producing as a vicinity of internal control, some way to navigate a mobile golem or as some way to notice edges in images then finally voice output are going to be generated then blind man will simply listen to the text on it explicit object.

At the outer edge a solid white ring circling iris exist which is known as sodium ring or deposit normally located around iris of an eye which symbolizes the existence of high level cholesterol in the human body. There is a half-circle of gray, white and yellow deposits in the outer edge of cornea known as Arcus senilis or arcus senilis cornea occurred because of fat and cholesterol deposits in people under 45 years of age. The high cholesterol is also an indication of the presence of hyperlipidemia which indicates the increased amount of fats in the blood. There is a risk of developing heart disease which leads to stroke and death. There is a chance to occur a common type of heart disease known as coronary artery disease (CAD) when the arteries that supply blood to heart muscle become hardened and narrowed. This is also due to the buildup of cholesterol. Iridology approach is the different type that also helps to identify diseases with the help of pattern of an iris. Whenever there is a excess deposition of the cholesterol occur in the body it will create a whitish sodium ring around the iris, By analyzing the sodium ring we can identify the existence of cholesterol in the human body.

In the era of modern digitalization world, it is a simple to monitor and control the substation equipment remotely using expensive PLC and SCADA system, but it is desirable to design a system which is cost-effective, smart and reliable. So that IoT is an effective solution as the real-time capability of IoT is considered as a key feature for monitoring and control applications of power systems. The IoT is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human to human or human to computer interaction. These devices capable of interacting with one another directly or indirectly and also data collection are performed locally or remotely via centralized servers or cloud-based applications. This paper aims to design a low-cost energy monitoring and controlling system using IoT devices. This paper shows a result of the effective use of IoT devices in a power system to obtain better efficiency with less time. The use of IoT devices improves the power system performance remotely without any human intervention. In this project, the prototype of the system using Raspberry pi has been designed. The use of Raspberry pi reduces manpower and maintenance cost. It performed mainly two functions such as oil quality and oil level sensing and transformer differential protection.

Human body is the magical creation of god. It carries many interconnected systems. Changes in one system replicate changes in another system. Due to such interconnection of systems, we can analyze one system by observing changes in another system. Iridology supports the same theory. Iridology tells the relation between iris and other systems present in the body. Evaluation can be done in the form of the iris that speaks about the physical condition of different body parts. In the proposed method by observing different iris images without doing any complicated and time-consuming test, we can perform diagnosis for the brain tumour. This method can be used as a pre diagnosis tool.

In current period, Light fidelity network is becoming very famous and catching the interest of some of customers with that one advanced technology-based totally features. It brings a brand new decision inside the Wi-Fi communication exchange and those are showing so much curiosity to recognize approximately it. Light fidelity is a wireless communication-primarily based expertise which transmits records over the community through a light source like led’s as opposed to radio frequency indicators (RF) with very excessive information charge. The aim of paper Wireless and optical networks are widely used nowadays because the network performance is a very important issue to supply services to a good variety of users whereas reassuring users’ quality of service necessities. It aims to analysis the wireless and optical networks performance. The learning toward research the act as an Light fidelity Network system in wireless communication consumes remained created and achieve a knowledge speed of 10 k rate over 40 cm distances.

The system “Smart Trolley with Automatic Master Follower and Billing System”, has been developed for shopping malls. Once the client enters the mall, the trolley follows the client with the help of Bluetooth and Ultrasonic sensors placed in the cart. All the trolleys are designed with a RFID reader, digital display screen, Bluetooth and ultrasonic sensors. All the products within the shopping complex are connected with RFID tags. Once the customer puts any item into the cart, its distinctive ID range is detected. At the same time, the chosen product name with the cost, expiry date, discount amount if any available are displayed on the liquid digital display screen, thereby the value gets added to the whole bill. If the client wants to take away the item from the cart, the client must press the button which is available on the trolley and then the client has to scan the product once again and therefore the cost of that specific product gets subtracted from total quantity bill. Once the purchase is completed, the customer has to select the upload button and the whole quantity bill is send to the billing counter. With the assistance of payment app the payment is completed. Once the bill amount is paid, the data is distributed to the exit gate and the trolley gets disconnected to the customer and the client can exit the gate.

The microstrip antenna play important role in wireless communication, because microstrip antennas are light in weight and compact in size. Now days, the wearable microstrip antenna is emerging technology widely used to perform the communication between the one human body to another human body and this technology is called Wireless Body Area Network (WBAN). The wearable microstrip antennas are used in various wireless applications such as medical, military and satellite etc. The geometry of the designed microstrip textile antenna consist of E shaped rectangular patch the top and the full ground plane at bottom. The dimension of designed antenna is of 35 mm × 31 mm by its width and length. These two planes are separated by the cotton fabric as dielectric material of (ɛr) of 1.6 and the thickness of 1 mm. In this paper the E shaped microstrip textile antenna is designed which is resonating at dual frequencies of 3.7 and 7.4 GHz with high directivity. The proposed antenna is used for wireless communication in S and C band. The proposed antenna has been simulated in CST software and the various antenna parameters such as return loss, VSWR and directivity have been analyzed.

Due to ever increasing population and growth in pollution there has been considerable increase in the cancer-causing agents due to this there is a need to detect cancer at its early stages so that it becomes easier to cure it. The precautions to be taken to cure or prevent cancer or other tumor is that cause death very less or completely negligible this is why there is a need to create a system that can suggest the early stages of cancer and the precautions that will be taken in order to prevent this cancer from growing more. Cancer as it is cannot be cured hundred percent when it is in the moderate stages and above as there are very few treatments for undertaking or curing cancer completely. We have undertaken the job of detecting lung cancer as it is the major cause for death in Cancer diseases. Lung cancer when it is in the initial stages is very accurate and is very difficult to detect and it spreads rapidly and the chances to stop this growth are very less or completely negligible so to avoid this, we have decided to create a system that can detect lung cancer at its early stages. Lung cancer comes in stages and to cure this we need to understand each and every stage precisely the lung cancer has around Four stages. After looking at the stages of cancer we can clearly see that the first stage is very important in the recovery process and for cancer treatment. So, if we detect the cancer at its early stages it becomes easier for us to cure it and the patient suffers very less due to this cancer. In some cases, the microscopic examination of this cancer shows wrong results as the cancer is very less and cannot be easily detected even at microscopic level. If we record such occurrences it becomes easier for us to detect for the next time and the chances of error reduces. Here comes the role of Technology and our system. As we are using machine learning for the development of a system which is Computer aided design for early lung cancer detection it becomes easier to train the machine for intangible occurrences of results which cannot be neglected as life of each and every patient is precious. Our system is completely based on the data analysis of multiple cases of cancer where lung cancer differs from first stage to last stage. By considering the occurrences of results we can analyze and design the pattern for this disease and can easily predict whether the underlying patient being monitored is suffering from cancer. We are using machine learning based on Python programming language as it becomes easier to monitor data as well as the detection process of cancer. Python makes it easier to use machine learning and we can train the system for the future. Python has multiple directories which we can use for our project and which makes our project very simple and accurate as we are going to take the data set which includes arrays of data.

Dr. Manu Prakash Professor, Department of Biotechnology, Stanford University invented foldscope known to be paper microscope. A low cost, portable and foldable microscope. It is ultra-affordable and durable and quality is almost similar to conventional microscopes. It has various applications in various fields. However, its importance is still obscured by many. Foldscope can be used as an efficient tool to study pollen viability and stomata, to visualize cells and detect seed viability was demonstrated. Foldscopes are used in projects by students to detect, visualize and learn real life examples. In this paper we will use Foldscope to detect the growth of microorganisms on various materials and on vessels. It can be a preventive measure and various diseases can hence be prevented.

Building architectures are growing towards increased complexity, with countless people moving through them. Not all amongst the crowd could possibly be familiar with the building to escape a fire danger zone. Even if the infrastructure complies to safety standards, decision making for fire evacuation, while ensuring safety, is utmost critical. Tailoring to these constraints, it is essential to protect lives by efficient and complete evacuation. For fire emergency, the proposed evacuation routing system is inputted by a group of wireless sensor nodes present across the considered floor plan; a MATLAB based central server to find/calculate better safe evacuation routes for the imperiled people, at a remote location in the building; a Wi-Fi based network that communicates this calculated route from the sensor network to server and server to the occupant, on evacuee’s cell phone. The information from the sensors is transmitted by a Wi-Fi network and is aggregated by the Thingspeak server. The real-time evacuation route is calculated by the server, towards the nearest and safest exit door from the occupant’s instantaneous location, by deploying A* algorithm for route optimization, along with data from sensor network that informs about origin and fire spread regarding hazard’s location. The server transmits the route information to the occupants through Wi-Fi connectivity. The endangered evacuees aretherebyenabled to view and follow this information of dynamic and real time active maps using a Smartphone. The proposed framework is prototyped and analyzed for their future inclusion into existing fire evacuation systems.

In an automated manner, In an automated manner, holistic traffic management is essential to enhance management in metro cities and even in two-tier cities. Detection of Vehicle flow is deemed to be crucial in the management of Traffic. In fact, flow of the Traffic shows the state of the Traffic in a definite amount and helps to rectify situations leading to traffic jam. Particularly this project intends to elucidate a traffic TV for non-chaotic vehicular traffic management. The fundamental idea includes five steps: subtraction of background, detection of the blob, blob analysis, pursuit of blob and reckoning of vehicle. Ideally, a vehicle is considered as associate rectangular patch and classified via blob analysis. After analyzing the blob of vehicles, the pertinent choices unit of mensuration extracted. The pursuit of moving targets is achieved by examination the extracted choices and activity. The experimental results show that the projected system can give a huge amount of useful information for traffic investigation.

Early detection of Diabetic Retinopathy shields patients from losing their vision because Diabetic Retinopathy may be a typical eye disorder in diabetic patients. The elemental explanation for a visual deficiency within the populace. Thus, this paper proposes an automated method for image-based classification of diabetic retinopathy. The technique is separated into three phases: image processing, feature extraction, and image classification. The target is to naturally group the evaluation of non-proliferative diabetic retinopathy at any retinal image. For that, an underlying image preparing stage separates blood vessels, microaneurysms, and hard exudates, so on extricate highlights utilized by a calculation to make sense of the retinopathy grade.

We live in an era full of unprecedented opportunities, and deep learning technology can help us achieve new breakthroughs. Deep learning plays a pivotal role in the exploration of exponents, the development of new drugs, the diagnosis of diseases, and the detection of subatomic particles. It can fundamentally enhance our understanding of biology (including genomics, proteomics, metabolomics, immunohisics, etc.).This era of our lives is also facing severe challenges. Climate change threatens food production, and may even one day explode because of limited resources. The challenge of environmental change will also be further exacerbated by the growing population, with a global population expected to reach 9 billion by 2050. Coupled with the ever-evolving ability of biological neural networks to process visual information, vision provides animals with a map of their surroundings, improving their ability to perceive the outside world. Today, the combination of artificial eye cameras and neural networks that can handle the visual information captured by these artificial eyes detonates the explosion of data-driven artificial intelligence applications. Just as vision plays a key role in the evolution of Earth's life, deep learning and neural networks will enhance the capabilities of robots. The ability of robots to understand the surrounding environment will become stronger and stronger, and they can make decisions on their own, collaborate with humans, and enhance human capabilities.

Artificial Intelligence (AI) is an umbrella consisting of many small blocks like machine learning, evolution computation, robotics, vision, natural language process and planning, speech processing etc. In the past years AI has developed a lot and given its share to make human life better, easy and compact. The word “technology” is a term which can be defined in many ways. The definition of the word keeps evolving with the continuous development in various fields. Decades ago, it used to take an entire room to accommodate a single computer but now we use the same computer at the ease of our fingertips. With the rapid development in technology mankind’s expectations and needs have also increased. Human race demands accurate results in less time with easier methods. The current rapidly developing field is AI which has also shown some extra ordinary results like the robot Sophie, Alexa, Siri etc. Mankind has very willingly adapted the use of AI in daily life and wants to excel more in the field. Neural Networks which is a very small section of AI but can be used vastly in various fields. The healthcare section has also improved its facilities and increased life expectancy. The motive behind this project is to use neural networks in the medical field for greater accuracy and instant results.

High-resolution images decide the performance of the system. Image resolution enhancement is the process to extend no of pixels. During this, we increase the quality of the image to form it suitable for all applications. Within the proposed method, we’ve used a combination of DWT, SWT and Interpolation, i.e. combination of 3 traditional methods. DWT has edge loss problem, so alongside it, SWT is employed to beat edge loss problem. Initially, DWT of a low-resolution image is taken. The LL band of this is often interpolated by a factor of two and it’s combined with SWT of the input low-resolution image. Again inverse DWT is taken of the resultant image.

In computer vision application, the style transfer is a most active area, where deep generative networks have been used to achieve desired results. The development of adversarial networks training produces a high-quality image result in terms of face age progression and regression that is face aging and de-aging. Inspired by Ian Goodfellow, in this paper, we have designed the combinational network using the residual block, convolution and transpose convolutional in CycleGAN for face age progression and regression. Face aging is an image to image translation concept which is used in many applications such as cross-age verification and recognition, entertainment, in smart devices like biometric system for verification purpose etc. The proposed architecture preserves the original identity as it is and converts young people to old and vice versa. The network consists of residual blocks to extract deep features. The UTKFace unpaired image dataset is used to do experiments. The qualitative analysis of proposed methods in terms of performance metrics which gives better results. The performance metrics calculated such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Structured Similarity Index (SSIM) to the quality of image.

This paper summarizes the novel approach of designing MIMO antenna working at dual-band frequencies with wide bandwidth. The 2-Element, circular-shaped MIMO antenna proposed here is operating at C-band frequencies which can be used for satellite communication, cellular communication such as 4G and 5G, Wi-MAX, and WBAN wearable applications. Two circular shaped microstrip patches fed with the tapered feed line, are placed diagonally opposite of each other. The designed MIMO antenna resonates at 3.1 and 6.9 GHz frequencies. The first operating band is from 2.83 to 5.58 GHz with a maximum bandwidth of 2.75 GHz (88.6% Bandwidth) and the second operating band is from 6.19 to 7.95 GHz with a maximum bandwidth of 1.76 GHz (29% Bandwidth). Reflection Coefficients at those frequencies are less than −10 dB and VSWR value is between 1 and 2 which is necessary and sufficient condition for any antenna to radiate efficiently. The radiation efficiency of this antenna is 58% and observed directivity is 3.4 dBi.

Automation is a crucial thing about an industry which manufactures product within the mass quantity. After manufacturing product; to form the decision of rejecting or accepting is taken by measuring quality parameters. To test quality parameters like dimensions and features of manufactured product inspection is mostly done manually in manufacturing industries. Manual assessment is time-consuming, costly, sometimes inaccurate and manual assessment for elegant shapes is incredibly difficult. To resolve these problems, control and quality management of the commercial product is feasible by the use of image processing techniques.

In the current era of digital technology, the information security is the challenging task. For the secrete communication information hiding is an essential element. The current information steganography system uses objects like audio, image and video. The audio steganography is the technique that convey hidden message by modifying an audio signal in an unnoticeable manner. It is a technique for the hiding secret message in the host audio signal. The original audio message before steganography and after encoding message is having uniform characteristics. The embedding secrete audio message in the original audio file is a more challenging and difficult task. This paper presents the comprehensive survey of audio steganography techniques for information security. The experiment was tested using proposed LSB technique for audio steganography. This paper extended towards quality measure of steganography message. The quality of audio steganography measures using energy score, Mean square error, Peak signal to noise ratio. From this experiment the quality of audio steganography is observed as 92.759% for MSE and 94.971% for PSNR technique. Audio information hiding is the one of the robust and dynamic ways of protecting the privacy and secretes communication.

This paper addresses the use of HOG (Histogram of Oriented Gradients) and SVM (Support Vector Machine) for boundary regression for biomedical image segmentation and comparing the output with image processing segmentation techniques. Here we will use MATLAB for computation of HOG and SVM. MATLAB image processing toolbox will be extensively used for reading, processing, visualizing and saving the images.

People who suffer from complete or partial paralysis often require the help of a second individual to move around from one place to another even with automated traveling mechanisms. This dependence on another person takes a toll on the individual. Thus, a framework is proposed wherein eye blinks and brain waves are used to decide the decisions made by the individual. This requires no extra physical exertion. These signals will be collected by an Electroencephalogram Headset (Neurosky Mindwave Mobile) and will then be passed to an android application which will then transfer necessary data to the Arduino, which will use these data to run the device. Such a framework can be executed on mechanized wheelchairs to uphold the impaired and help in their transition of becoming independent.

The analysis of soil wetness systems in inexperienced agricultural houses supported IoT automation management. This type of intelligent soil wetness system helps manage the wetness level of providing the water. During this analysis, embedding, a sway system into AN automatic pump controller relies upon the soil’s wetness. To assess the types and amounts of nutrients in food in the region resulting from plant growth on contaminated land, and to identify such contaminants’ sources. After classifying soil nutrients, we know how much the soil is getting polluted and how to recover it—different crops. Farmers and landowners will realize how not to use more chemical fertilizer which directly affects the growth of crops.

The healthiness of bone is decided by organic as well as inorganic contents. Inappropriate proportions of organic (collagen) and inorganic (minerals) contents give rise to fractures and diseases like arthritis, osteopenia, and osteoporosis. Bone Mineral Density (BMD) is a commonly used inorganic clinical indicator to determine the quality of bone. There are invasive methods like chemical quantitative analysis, and noninvasive methods include imaging techniques, to find the composition of bone. This paper is to review different medical imaging techniques like X-Ray, DEXA, SR µCT, Photo Acoustic Imaging, Quantitative Ultrasound to do bone mineral density analysis. The study concludes that the imaging technique Dual Energy X-ray Absorptiometry (DEXA) will be a more useful diagnostic modality for rapid investigation of bone health. It is a two-dimensional imaging tool that calculates a ratio of Bone Mineral Contents (BMC) with an area of bone is known as areal BMD. But the shortcoming of DEXA technique is that the bones of different lengths may produce the same BMD results by maintaining the equal ratio thereby indicating incorrect strength of bones. Hence along with the area, we have to take into account the thickness of the bone to predict BMD.

In Today’s world, security frames the most essential segment of our lives. Face Recognition is an important part of the purpose of the security and surveillance field. A small project which does face detection using OpenCV library on Raspberry Pi. Face Recognition/Facial Recognition is a category of biometric software that identifies people by their faces. The face is captured by the digital camera and the system is trained and then it is capable of identifying the person. This paper focuses on the implementation of a face detection system for human identification based on the open-source computer vision library (OpenCV) with python. We also proposed a hierarchical image processing approach to reduce the training or testing time while improving recognition accuracy.

The Adoption of digital content by the institutional members and students have seen rapid growth in recent years. The students could access the content across various devices, platform and applications, which has a direct implication on the physical presence of the student. The institutions follow old and very traditional base approach like a manual record of attendance to track the company of students which consumes a lot of time and efforts from the staff members. The study looks at the various technologies available in the market and present the implementation of the best possible solution. The latest use of technology of Facial Recognition with a combination of RFID will enhance the tracking process and also provide valuable insight into student behaviour. The data collected by the system can further utilize to improve the efficiency and effectiveness of student behaviour patterns and predict the learning trend, which will help the institutions to make the correct decisions.

Today, the entire world is frequently facing a challenging and speedy environment everywhere. The main problem we all are going through is the energy crisis. Actual energy demand made on existing energy supply is Electric energy consumption. Early India has recorded a swift growth in electricity generation since 1985 which is increasing from 179 TWh in 1985 to 1057 TWh in 2012. The rapid increase of this came from non-conventional renewable energy resources (RES) and coal-fired plants. In India gross utility of electricity generation was about 1384 billion kWh in 2019–20 which represents 1.0% growth yearly as compared to 2018–2019. A country’s electric power development is an important measure by annual electricity consumption per capita. It reached 3084 kWh, up 42.3% from 1990. Sensor networks have been incorporated in the management of buildings for organizations and cities. In recent it has led to an exponential increase in the volume of data available, and monetary savings. For this purpose, new approaches, alignments and techniques are required to investigate and analyze information in big data environments. For this problem, having a relevant system to monitor the power usage is the only solution. This paper proposes an analytical and prediction model by using different energy profiles, which will provide the user power consumption chart of a consumer over a period of time, to perform quantitative analysis using smart meters that automatically acquire context information. There are devices which are capable of measuring a customer's energy consumption, for example smart meters. In this paper, there are two modules. The first one focuses on receiving the data from the smart meter and also transfers it to the data analyst. The second module is the predictive module which uses consumption data and information of the consumer in order to understand the behavioral patterns of the consumption of electricity. These models can be used to predict energy consumption and also identify irregularities and outliers. The customer gets acknowledged about abnormal usage. On getting acknowledged the consumer will get to know where and when exactly the power usage is increasing and can control power consumption which will ultimately result in reduction of expenses.

Visible Light Communication (VLC), also known as Optical Wireless Communication (OWC) has emerged as a viable candidate with a wide range of applications due to its promising features like license-free channels, large bandwidth, immunity to interference and security and, less power utilization. Light Fidelity is a potential application of OWC mainly used for indoor applications to provide high-speed internet up to 10 Gbps. Shadowing effect and data loss in Li-Fi is a major concern that can be solved by using the hybrid network and different methods like access point selection (APS) and resource allocation (RA). In this paper, we have discussed a hybrid network of Li-Fi and carried out a comparative study of different techniques used for access point selection and resource allocation. Finally, we concluded that Reinforcement learning provides better throughput with less computational complexity.

Since the last three decades, face detection and recognition have become very active and a huge part of image processing research. In real-time applications like video surveillance, front views cannot be guaranteed as input. Hence the failure rates can degrade the performance of the face recognition system. The proposal aims to introduce a novel PFR method termed as DFM that combine Sparse Representation Classification (SRC) and FCN for resolving the partial face recognition issues. As the major contribution, this proposal aims to tune the sparse coefficient of DFM in an optimal manner, such that the reconstruction error should be minimal. Moreover, this proposal introduces Jaccard Similarity Index measure to calculate the similarity scores among the gallery sub feature map and probe feature map. For optimization purpose, this work deploys a hybrid algorithm that hybrids both the concepts of Grey Wolf Optimization (GWO) and Sea Lion Optimization (SLnO) algorithm.

Secure communication network is the demand of today’s transportation due to excess number of vehicles and limitation to the existing communication protocol. This Paper discusses the secure communication protocol between the vehicles and the detection and prevention of the Sybil attack. Fake node request, multiple routing entries these are the common example of Sybil attack which can be detected and prevented using existing proposed method. Virtual nodes are created and messages are sent to nodes. The proposed method detects the identity of received message and number of fake entries of that particular massage in the routing buffer. If messages are validated then communication is possible otherwise request is terminated. Results showed that the existing proposed method is able to detect and prevent Sybil attack by maintaining single node identity.

From the last decade, a tremendous spotlight is on the giving quality medical services because of the exponentially growing of life threatening illnesses of the patients. There are numerous components that influence the health condition of each person and a few illnesses are more dangerous and cause death of the patient. And in the present age, the most common reason of death is heart disease. This research work presents the IoT based system for heart disease detection using the Machine Learning (ML) technique. It consists a novel preprocessing stage that provides more accurate classification of the ECG signal. Also, this novel preprocessing method removes the noise effectively from the raw ECG data. The classification performance was evaluated using the various classifiers such as KNN, Naïve Bayes and Decision tree that detects the normal and abnormal heart-beat rhythms. With the obtained results, we have observed that the preprocessing has improved the classification performance. This technique further proves that the decision tree has good performance over the KNN and Naïve Bayes with respect to the accuracy, sensitivity and precision.

Internet of things (IoT) is another worldview converging with the social networks, permitting data sharing between the individuals and electronic gadgets. Likewise, it is expected for omnipresent connectivity among different entities or things using Internet. Anyway, security and privacy issues are the major concerns for IoT. The heterogeneous technological advancements, inherent vulnerabilities of IoT devices, poor design IoT standard invites the cyber attack. This research work mainly aims to address the security threats and issues on different layers of IoT architectures and their possible mitigations. Also, it provides a taxonomic representation of the major 3-layers of IoT architecture with their protocol stack. Finally, we have highlighted the most challenging security threats and their mitigations with some future research work proposals.

Now a days, age and gender classification became relevant to growing applications such as human-computer interaction (HCI), biometrics, video surveillance, electronic customer relationship management, forensic, and many more. The social networks have a huge amount of data available, but often, people do not provide some of their data, such as age, gender, and other demographics. Age and gender of a person from face image is one such significant demographic attribute. Face recognition is an extremely difficult issue in the field of image analysis and computer vision on the grounds. Although Face recognition had been done over a couple of decades with traditional image processing features, recently Deep Convolutional Neural Networks (CNNs) has been proved that it is the descent method for the classification task, especially of uncontrolled, real-world face images. In this research, we propose a deep learning CNN method to extract discriminative features from the uncontrolled, real-world dataset and classify those images into gender and age group. In this paper, learning representation is shown through the use of the deep learning-based convolutional neural network (CNN), essential growth in the performance can be achieved on these works. To this end, we would like to mention that our CNN model can be used even when the number of learning features is restricted. We evaluate our technique on two prominent datasets. Accuracy up to 80% obtained for the Adience datasets and 95% for the Gender Classification dataset from Kaggle.

Study of auxiliaries from a morphological perspective is extremely interesting from the semantic and pragmatic points of view, and they still await detailed and careful study.This paper deals with the role played by auxiliaries in Kannada Morphology. Two kinds of auxiliaries are indicated in the Kannada language, namely, aspect auxiliaries and modal auxiliaries. These auxiliaries are useful information of derivative stems in Kannada verbs. Many of these aspect auxiliaries are used as verbalizers to derive verbs from nouns and verbs from adjectives. Auxiliaries play a very important role in Morphology of Dravidian languages like Kannada. They are useful in the complex verb formation process. Aspect markers have a major role in verb morphology. This paper explores the distribution of non-finite auxiliaries and modal auxiliaries as part of the morphological analyser in Standard Kannada. Auxiliaries form the basis for the formation of multiple stems in Morphology when they are not used separately like in the English language.

Patients suffering from dysarthria have trouble controlling their muscles involved in speaking, thereby leading to spoken speech that is indiscernible. There have been a number of studies that have addressed speech impairments; however additional research is required in terms of considering speakers with the same impairment though with variable condition of the impairment. The type of impairment and the level of severity will help in assessing the progression of the dysarthria and will also help in planning the therapy.This paper proposes the use of Convolutional Neural Network based model for identifying whether a person is suffering from dysarthria. Early diagnosis is a step towards better management of the impairment. The proposed model makes use of several speech features viz. zero crossing rates, MFCCs, spectral centroids, spectral roll off for analysis of the speech signals. TORGO speech signal database is used for the training and testing of the proposed model. CNN shows promising results for early diagnosis of dysarthric speech with an accuracy score of 93.87%.

COVID-19 situations are getting worse day by day. Buying behaviors and economic priorities for purchasing new goods have changed. In these days, maintenance of existing equipment has gained more importance. For the purpose of training the maintenance workers or end users in this pandemic situation, augmented reality is like a blessing today. The maintenance procedures can be communicated remotely on demand with the help of web based AR and android apps. This paper describes advantages of using AR based instruction over paper based instruction to train maintenance operators. The authors have developed web based AR and android apps for augmenting 3D models. Many benefits observed by using this technology are reduced human errors reduced execution time, reduced downtime of equipment, reduced cost, and increased productivity. Paper mainly focuses on ways of delivering maintenance instructions remotely.

Face recognition based attendance based system will be used in the near future in classrooms instead of the traditional system; it may replace even biometric attendance systems. The purpose of the present work is to devise a novel attendance system using cv2. Facebook also uses face recognition technology as it tags the names of faces as soon as you upload photos which have been tagged by you previously. The algorithm identifies the unique features of the faces in the database and encodes them into pattern image. Python modules are used to Then the machine learning algorithm called classifier is used to find the name of the person. Image capture, facial features, face recognition and attendance system, are the stages of the procedure.

Proposed paper put light on the automation in distribution of goods by using IOT based ration distribution system which uses the biometric verification and the cloud storage technology. Proposed system looks like an Automated Teller machine (ATM). We can simplify the process by using an interactive approach. Aadhar cards contain details like contact number, residential details, details of bank account and available scheme. Details of customers are stored and maintained as the database in the cloud storage by the government. Here we are storing the customer’s data in the cloud using storage technology such as Google Firebase. This database contains the necessary information such as Aadhar details, allotted goods, bank account information, and ration card type. To carry out the transaction and the withdrawal of goods one needs the One time password (OTP) which is sent through the SMS or E-mail to notify and alert the user during the process. After entering the OTP required amount regarding grains purchased will get deducted from the linked bank account. Proposed system will help to minimize the issues like lack of rationing material which was caused due to the smuggling goods by the ration shop owner, workers and the dealers in order to gain profit. This system will be transparent and will help customers by giving smooth and automized experience of purchasing rationing. This proposed system will be the solution to One Ration One Nation.

The Controlled Infotainment system is based on a single board computer Raspberry Pi 3 Model B+ . This system is inspired by popular products in the market \ALEXA”, \MBUX-Mercedes Benz” and \Hyundai-TUCSON". As Infotainment system is the combination of ‘Information and ‘Entertainment, this includes voice-controlled multimedia such as online music player. In a hands-busy and eyes-busy activity such as driving, spoken language technology is an important component of the multimodal human–machine interface. Adding speech to the HMI introduces two distinct challenges: (1) accurately acquiring the user’s speech in a noisy car environment (2). Creating a spoken dialog system that does not require the driver’s full attention In order to provide security spy camera is used to capture the image of a person entering inside the car and accordingly the email will be sent to the owner of the car.

To Identify the Particular image from a huge dataset is a key problem in Image Processing. For image retrieval, a block truncation coding based method is used. The image features derived from colour quantizer as well as a Bitmap image. Using colour quantizer Color Histogram Features (CHF) is obtained, and using Bitmap image Bit Pattern Feature (BPF) is obtained. The various distance measures are employed to match the similarity between images. Simulated result shows better in term of Average Recall Rate (ARR) and Average Precision rate (APR).

Machine learning plays a vital role in Object classification due to its various applications viz autonomous vehicle, driverless cars. In our research work we have considered machine learning algorithm, linear support vector machine (SVM) and K-Nearest Neighborhood (KNN) for classification of object like car and truck which are essential for Autonomous vehicle applications. We have performed RGB to gray conversion followed by histogram of gradient (HOG) for feature extraction before applying to KNN and SVM for classification. The dataset required for the experimentations for training and testing are utilized from kaggle website and the performance of SVM and KNN have been evaluated on these testing data. Results show that SVM outperforms the KNN providing accuracy of 71.3%.

Developing a robust bone fracture segmentation technique using deep learning is an important step in the medical imaging system. Bone fracture segmentation is the technique to separate out the various fracture and Non-fracture tissues. The fracture can occur in upper extremity parts of the human body like elbow, shoulder, finger, wrist, hand, humerus and forearm etc. X-ray is one of the widely used imaging modality for visualizing and assessing bone anatomy of the upper extremity. X-ray is used in the diagnosis and planning of the treatment for the bone fracture. The problem of computational bone fracture segmentation has gained researchers attention over a decade because of high variation in fracture size, shape, location, variation in intensities and variation textures. Many semi-automatic and fully automatic methods have been proposed and they are becoming more and more mature. A recent technique that is CNN based deep learning gives the promising result of the segmentation. In this Method, MURA (Musculoskeletal Radiographs) database is used. The CNN based U-Net model is trained using the MURA Database. After the training, the Model is tested on the test images. The Evaluation parameters Like Dice Coefficient and Validation Dice coefficient are found out to check the robustness of the technique. The CNN based U-Net architecture gives the training dice coefficient of 95.95% and validation dice coefficient of 90.29% for whole bone fracture segmentation.

This paper highlights an attempt for addressing the issue of imbalanced classification resulted due to deployment of machine learning algorithms over an imbalanced dataset. It has used Synthetic Minority Oversampling Technique (SMOTE). This type of augmentation of the dataset is extremely necessary as it leads to poor performance in the minority class. Four machine learning algorithms were deployed on the Twitter dataset using the Python platform. Standard data preprocessing including data cleaning, data integration, data transformations, and data reduction was carried out first as the most necessary arrangement before experimentations.

One of the best methods for Breast cancer diagnosis is histopathological images from the visual analysis of histology, but pathologist requires lots of experience and training to an accurate diagnosis. Therefore, computer-aided diagnosis (CAD) is an automated and more precise method. Recent developments in computer vision and deep learning (DL), DL based models are popular in analyzing the hematoxylin–eosin (H&E) stained breast cancer digital slides. This paper proposed a deep learning-based framework, called multi-classification of breast histopathological image using Xception: Deep Learning with Depth wise Separable Convolutions model (MCBHIX). Xception based on depthwise separable convolution layer. We trained this network from scratch for binary classes and for multi-classes BreakHis dataset. The accuracy achieved by MCBHIX- 99.01% for binary type and 96.57% for Multiclass.

Pictures caught in murky climate show up low conversely. Debasement in the picture contrast is because of lessening in the light energy reflected from the scene object. In this paper, we propose a picture de-right of passage network which upgrades the perceivability of pictures caught in murky climate. The proposed network comprises of multi-scale convolution channels consolidated by commencement module to extricate the multi-scale highlights. Alongside the multi-scale highlight extraction, we propose a utilization of thick associations with engender learned highlights inside the origin modules. Combinely, the proposed network is planned by joining the standards of both initiation and thick module, along these lines, named as beginning thick organization. To prepare the proposed network for picture de-inception, we utilize primary similitude list metric alongside the L1 misfortune. Existing benchmark information bases are used to assess the favorable to presented network for picture de-right of passage. Exploratory examination shows that the proposed network beats the current methodologies for picture de-preliminaries.

This work aims to establish a new automated Diabetic Retinopathy (DR) recognition scheme, which involves phases such as “Preprocessing, Blood Vessel Segmentation, Feature Extraction, and Classification”. Initially, Contrast Limited Adaptive Histogram Equalization (CLAHE) and median filter aids in pre-processing the image. For blood vessels segmentation, Fuzzy C Mean (FCM) thresholding is deployed that offers improved threshold values. As the next process, feature extraction is performed, where local, morphological transformation oriented features and Gray-Level Run-Length Matrix (GLRM) is based on extracted features. Further, the optimal features are selected using a new FireFly Migration Operator-based Monarch Butterfly Optimization (FM-MBO) model. Finally, Convolutional Neural Network (CNN) is deployed for classification purposes. Moreover, to attain better accuracy, the count of convolutional neurons of CNN is optimally elected using the proposed FM-MBO algorithm.

According to the Ministry of Education, 2010 “The Education service is that which moulds young generation into good citizens, who become conscious with their responsibilities towards the family, society and country. The present COVID 19 Pandemic has largely affected the lives of scholars round the world. They missed the possibility to interact one on one basis with their teachers as all the governments temporarily closed the academic Institutions. Due to this, the importance for technology based learning has become more prominent which resulted in ICT enabled learning called Virtual learning. It particularly becomes important to grasp how this ICT enabled learning helps the scholars, who lay strong foundation for better Society, therefore the present research aimed to look at the student’s perception towards virtual class room learning, their satisfaction, besides determining the challenges faced by virtual learning. The survey method is employed for data collection. The scope of the study involves only post graduate students from Hyderabad city. The information collected from a convenient sample size of 142 students is analyzed using SPSS 26. Descriptive statistics, correlation, ANOVA & T-test are used to draw the inferences because of this pandemic situation it was found that students have positive perception towards virtual learning. When measured satisfaction there are few issues that require to be addressed. So based on the findings, a model has been proposed which include stakeholders of virtual learning and their prime responsibility through which ICT enabled learning ecosystem may be made a beautiful learning place.

A system based on android application is proposed in this paper to manage appointment slots for businesses like Automobile Service Center or Beauty Parlours/Saloons etc. in order to manage spread through social gathering. The proposed system has features such as a single app for service providers and customers. Verification of genuine customers and service providers will be done through OTP and location based photographs of shop respectively. Owner can maintain details of their staff through registration and can keep track on regular customers. It facilitates easy booking and cancellation of appointments. Customer can view non-working days through an event calendar and services offered with their respective charges, time required etc. The system also provides customer payment handling option, generation of invoices, reports for analysis helps in maintaining a database and provides appointment reminder to customer. Thus the system will do proper scheduling and reduce efforts and time of customer and owner both.

This System (Website and App) is aimed at developing a Bandobast Allocation and Attendance System (BAAS) that is of importance to Pandharpur Police for various Bandobast like VIP Bandobast, Election Bandobast, and mostly for Wari Bandobast. This system is used for the management of any Bandobast in Pandharpur (or in any City). This system is being developed for CO’s to allocate duties to all police, and further heads can take attendance of their subordinates. Later the control room can print the entire report in just one click. During any Bandobast it is very hard to allocate duties manually (informing each one about it) and also it may not be possible to take attendance manually of more than 5000 police staff. Besides, it is very hard to change the duty allocation or to know whether everyone is at their allocated point.

Fire accident results in catastrophic injuries and devastating damage. The death rate in India due to fire accidents was almost 2.5 times more than in other parts of the world. Fire fighting is a highly difficult and challenging task for human beings to access the remote target areas. By using unmanned aerial vehicle, quick response to the fire affected area can be achieved and also firefighters will get the visual information of the fire accident. This work focuses on the implementation of Un-manned Air Vehicles (UAV)s that can extinguish the fire. The proposed fire fighting UAV system consists of Hexacopter as a platform. Hexacopter is a UAV that works with six motors to achieve stable flight and better lift loading capability. The goal of this work is achieved with stable and robust hexacopter along with dropping mechanism which is used to drop the fire extinguishing ball on fire-affected area and camera interface for live video footage. The description of the proposed work is briefly described as well as determines the principle functionality.

In this paper, Positional Ternary Pattern features based Human Age classification using Artificial Neural Network for Forensic science application. The classification of human age from facial pictures plays an important role in pc vision, scientific discipline, and forensic Science. The various machine and mathematical models, for classifying facial age together with Principal Component Analysis (PCA), Positional Ternary Pattern (PTP) are planned yields higher performance. This paper proposes a completely unique technique of classifying the human age group exploitation Artificial Neural Network. This is often done by preprocessing the face image initially and so extracting the face options exploitation PCA. Then the classification of human age is finished exploitation Artificial Neural Network (ANN). The method of combining PCA and ANN performs higher rather than victimization separately.

In this paper, object recognition is proposed using combined DRLBP and SIFT features for high efficient signal transfer system applications. The aim of this research is to develop a non-real-life application of a security lock system employing object recognition methodology. DRLBP is chosen for the object recognition algorithmic program. Arduino microcontroller is employed to represent the response to object identification. USB serial communication is employed to inter-object between the MATLAB and Arduino UNO Microcontroller. First, the image of the individual is captured then the captured image is then transferred to the information developed in MATLAB during this stage, the captured image compares to the training image within the database to see the individual standing. If the system acknowledges the individual as an authentication person or un-authentication person, the result is sent to the Arduino UNO microcontroller.

Requirement fulfilment is an essential factor in the success of software. The various stakeholders specify the requirements should be satisfied with each point of the development of the software. DevOps is a mutual directorial endeavor to automate continuous rescue of new software renew while assurance their accuracy and consistency. Requirement traceability helps software engineers to trace the requirement from its starting point to its completion. In the software development process, traceability helps in various ways, like change management, software maintenance, and confusion prevention. However, many of the challenges can be overcome through organizational policy, quality requirements traceability tool support remains the open problem. The traceability links become outdated throughout software updating and maintenance since the developers can modify or remove some features of the source code. The proposed method is based on automatically find out characteristic principle as of explicit links. The presented system proves to give superior quality results by comparison. It is also a low cost, the very flexible method to apply regarding preprocessing the source code and documentation.

Clustering Algorithm is an unsupervised machine learning technique. Unsupervised Machine learning well defined unknown patterns in data. Clustering is the process of organizing data into specific groups. Clustering is mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering has been studied for a long time by many researchers with different methods. In this project work, we deal with object clustering problem. Here, we proposed a K-means algorithm. K-means algorithm is the easiest and prominent unsupervised machine learning algorithm. We apply the K-means algorithm for grouping the fruits as per these features. The experiment conducted on small clustering dataset and results found that the K-means algorithm s help for clustering object.

Augmented reality (AR) is an virtual experience of the real world, sometimes across multiple sensory modalities, including visual, auditory, and haptic (Umeda et al. in ICIIBMS 2017, track 3: bioinformatics, medical imaging and neuroscience, Okinawa, Japan, pp 146–149, 2017 [1]) Augmented reality is expounded largely to synonymous terms: mixed reality and computer-mediated reality. So we are thinking to vary over the instruction into present day or computerized training with the help of Augmented Reality. Nowadays everybody has mobile phone additionally children utilize the cell phones, with the help of mobile phone we are going to propose our framework. Considering a book there are numerous pictures yet kids can’t envision these items in genuine so with our application we’ll founded a framework which tells this specific picture and also the comparing data to find out understudies effectively and adequately with no issues.

Today oss becomes most popular in software community due to its wide use. But some issues are also identified by users while using such softwares. Due to free availability major advantage of such software is even if vendors stop developing such softwares other programmers can contribute to develop such softwares. Nowdays there is big challenge to outline the policy for usage of open source software. This paper includes challenges faced in the use of oss, features of oss, and emerging applications of oss.

Because of the exponential increment of records on the web, clients need all the related information in a difficult situation. Finding the significant data from such huge information is examining undertakings, hence the data recovery turns out to be progressively crucial for looking through the applicable information viably These oversee via Automatic content summarization. It is a procedure that perceives the significant focuses from all the important reports to display a compact summary. The proper text summarization and dimensionality reduction (TSDR) of summarized text can lead to a notable reduction in accessing time for the input elements. The proposed method produces the summarization task by dimensionality reduction with rank (TSDRR) using training methodology. The consequence of a string of words in a data text is appraised by the assistant of the PageRank algorithm. The subject is first pre-processed to tokenize the determinations and perform stemming operations. Then descent-based text summarization involves selecting determinations of high connection according to the level of the report depends on word also determination characteristics and established them collectively to make a report. The test results determine that this approach has more reliable production than other current classifications.

The main aim of this paper is to study various properties of extended binary Hamming [8, 4, 4] code, when we know its generator matrix. Using MATLAB, we can study syndrome decoding, weight of a codeword, error correction and error detection of binary Hamming [8, 4, 4] code.

Because of the uncommon development of data on the web, it is getting difficult to decode reality from the bogus. Accordingly, this demonstrates the serious issue of phony news. This test thinks about past and current strategies for counterfeit news identification. The executed framework manages the uses of NLP (Regular Language Handling) methods for recognizing the ‘Phony News’, that is, misinforming reports that originate from the offensive sources. Just by developing a model dependent on a tally victimiser (utilizing word counts) or a (Term Recurrence Backwards Record Recurrence) tfidf lattice, (word counts comparative with how frequently they are utilized in various articles in your dataset) can just contact you up until this point. Yet, these models don’t concentrate on significant characteristics like word requesting and setting. It is truly practical that two articles that are comparative in their promise will be totally extraordinary in their importance. Restricting the phony news is the exemplary content order venture with a direct hypothes. So a proposed chip away at bunching a dataset of both phony and genuine news enlist a Credulous Bayes classifier to make a model to characterize an article into phony or genuine dependent on its words. Right now two techniques are utilized Credulous Bayes, Bolster Vector Machine (SVM). The standardization system is a fundamental advance for refining information before utilizing AI strategies to order information.

Life is becoming more fast and hazardous while driving. Moreover life is valuable so, we need to have some automation techniques to secure life. In this paper we attempted to plan our thought in which the system that can recognize the person worn or not the helmet, spot accident place and immediate response, finding whether the person consumed liquor while riding, identify petrol level of tank and predicting the crash or collision between the vehicles in order to avoid road side accidents. The interaction between bike and helmet part takes place wirelessly by making utilization of RF transmitter and receiver. RF transmitter is appended at helmet and receiver at bike.

The main aim of this paper is to study various properties of extended binary Golay [24, 12, 8] code, when we know its generator matrix. Using MATLAB, we can study syndrome decoding, weight of a codeword, error correction and error detection of extended binary Golay [24, 12, 8] code.

The main aim of this paper is to study the encoding process of binary Schubert code [43,7] using MATLAB, when one knows its generator matrix. Using MATLAB, we can study syndrome decoding, weight of a codeword, error correction and error detection of the code.

The rising numbers of users over the network have also raised the privacy risk and incidents of various types of theft and attacks. Hence, the social networks have been the major victims. The users over these networking sites share the information under various attributes like gender, location, contact information etc. This personal information can get compromised due to malicious act that severely violates the integrity of the data and privacy protection policy. As a result, it has become mandatory for a service provider to offer privacy protection before publishing any kind of data over the network. In this research, we have proposed data mining techniques for user’s privacy preservation in social network sites using Support Vector Machines (SVMs). Social media datasets ARNET and SDFB are used for the analysis of privacy preservation models by calculating Average Path Length parameter. Finally, the proposed model shows 1.72% and 1.46% less information loss and 1.42% to 5.09% reduction in APL with these datasets as compared to previous works.

Near Field Communication (NFC) as a form of wireless technology has seen many improvements in recent years due to the increasing availability of NFC enabled devices. NFC is a recently emerging technology for short range communications aimed to enhance existing near field technologies such as RFID (Radio Frequency Identification). NFC is a standards-based, short-range (a few centimeters) wireless connectivity technology that enables simple and safe two-way interactions between electronic devices, allowing consumers to perform contactless transactions, access digital content, and connect electronic devices with a single touch. In this review paper, NFC technology is put forward with respect to its implementation, operating modes, its application in the form of tags as well as payments and its standards and protocols.

As lung cancer is one of the significant causes of death, there is a need for the development of algorithms for early detection of these cancers. Early detection of lung cancer helps to provide appropriate treatment and reduce morbidity. Accurate segmentation of the lung is an essential step in every computer-aided diagnosis (CAD) system to provide an accurate lung CT image analysis. This study is focused on the design of the appropriate architecture of the convolution neural network (CNN) using suitable combinations of CNN blocks to improve lung segmentation efficiency. Based on the scientific intuition, three CNN architectures are proposed for effective segmentation of lung parts from CT images. These CNN architectures are varied by the depth of down sampling of images as 32 × 32, 16 × 16 and 8 × 8. The performances of these CNN are obtained as under segmentation or over-segmentation by comparing the segmented lung part with ground truth lung images. This performance analysis shows the segmentation efficiency greatly affected by appropriate selection of downsampling of these images.

In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). Group data sharing in block chain technology and cloud computing has become a hot topic in recent. With the popularity of cloud computing, how to achieve secure data sharing in cloud environments is an urgent problem to be solved. Although encryption techniques have been used to provide data confidentiality and data security in cloud computing, current technique cannot enforce privacy concerns over encrypted data associated with multiple data owners, which makes co-owners unable to appropriately control whether data distributor can actually distribute their data. Data Sharing in Cloud Computing, in which data owner can share private data with a group of users via the cloud in a secure way, and data distributor can distribute the data to a new group of users if the attributes satisfy the access policies in the encrypted data. Further present a multiparty access control mechanism over the distributed encrypted data, in which the data co-owners can append new access policies to the encrypted data due to their privacy preferences.

This paper we are written for XML document to store XML data in XML formats for Security purpose. Here we compute the problem of efficiently creating ranked result for keyword search query in XML document. In old methods there are Xlink, Xpath and Xquery are query methods available to search data in XML file of XML DB. Here the method new users are not able to understand syntax of query when accessing the query. In this steps first write query, put forward to the system and retrieve relevant results. In case of keyword search there is the fuzzy type ahead search over XML data that user write a keyword search on fly way and access a new information pattern, This method are optional to old methods. The users didn’t need to know knowledge of XML query languages and its syntax. We are also adding a user study confirming that keyword-based search in SQL for a range of DB retrieval task. The query time, the text index carry keyword-based searches with giving interactive answer. Successful keyword search is valuable for top-k in XML document, these are user simply manage, semantic and steer into documents.

The Internet of Things (IoT) is the network of physical devices with electronics, software, sensors and other objects which consists of an embedded system that enables to collect and exchange data. It is mainly intended for transferring user data in real time security monitoring system, e.g. to monitor and control traffic and road condition. Unquestionably, everyday day-life and behavior of potential subscribers have adhered to IOT. From this perspective domestic and work areas will have effects of IoT. It is anticipated that there will about 50 billion internet-enabled devices by 2020. The aim of this paper is to introduce security alarm system for detecting motion and get an image or notification using low processing power when motion is detected. The user is alerted by sending snapshots through mail or notification via text message. In case of unavailability of the network service, Raspberry Pi will store the data locally and send that data when the internet is available. Raspberry Pi is a low-cost device as compared to other available present systems. It is a small sized computer used to process a captured image or data as and when motion is detected. Passive Infrared (PIR) sensors are used to detect the motion; the image is captured through the camera and provisionally stored in the raspberry pi module. This system is suitable for small personal area i.e. personal office cabin, bank locker room, parking entrance.

In the recent scenario, the technology of the Internet of Things (IoT) acting a vital part in Precision agriculture, Military, Engineering applications. The main resource of our country is the agriculture field. IoT is widely adopted in the Precision Agriculture field to count the dissimilar environmental constraints such as soil moisture, humidity, temperature, PH value of soil, etc. for increasing the yield of the crop. While using the IoT in Precision Agriculture it aided to decrease the consumption of the natural assets (freshwater, clean air, healthy soils, etc.) used in farming. Therefore, the purpose of this work is to implement the several IoT technologies accepted for Precision agriculture. This work has also points to the different communication technologies and wireless sensors available for Precision Agriculture. The proposed system will very helpful to our farmers because these technologies applied for utilize the limited resources to increase the yield of crop. These technologies applied cognitively at exact location and at exact time so quantity and quality of crop will definitely increase.

Flooring is one of the built features which is playing a crucial role in elderly housing and also reported as one of the major cause for fall-related injuries among elderly people. A house with in appropriate flooring leads to falls which is a major serious accident to people in old age (Designing supportive spaces for the elderly with the right floors, https://professionals.tarkett.com/en_EU/node/designing-supportive-spaces-for-the-elderly-with-the-right-floors-1092 , [1]). The study aimed to ascertain the gap between existing flooring design and necessities of the elderly people. The elderly women aged sixty and above were the subjects of the study. The sample was drawn from Kurnool district of Andhra Pradesh. Most of the elderly people houses were not provided with flooring that has minimum chances for falls. The elderly people preferred to have non-slippery flooring, provision of sound-absorbing materials and floor with different colors at various levels to avoid risk of falls in houses.

Highways contribute greatly to the economy and growth of a country. For highway building projects to complete on time and on budget, it is therefore necessary. However, when the project begins prematurely, interruption often occurs, resulting in delays, which have had numerous negative results on all project shareholders. Previous origins are one of the causes of construction hold up. Before studying the information, however, there is little understanding of whether a highway is premature or ready to build. The purpose of this research is to find out the criteria consumed in action to determine whether a road construction project is prepared or not. To this end, interviews are conducted and analyzed with sixteen practitioners working on road construction projects. Important attached (1) building readiness can be evaluate even during the launch phase; and (2) failure to comply with construction preparation requirements can lead to job delay, wasteful operation, reworking, and labour, equipment or material deficiencies. The study provides to the current knowledge by defining criteria which point out whether a road project is ready to be built or not. Learnings from this study will prevent premature initiation of road construction projects by industry.

Biogas is produced using agricultural waste and cow dung with help of the anaerobic co-digestion in a biogas digester. Generally cow dung is used as the raw material for the Biogas generation. In this biogas production using different composition of the cow dung and agricultural waste as right now cow dung is indispensable member for the biogas production with agricultural waste. Also other factors such as retention time, temperature can also be observed while taking different compositions. Theoretical maximum biogas yield is calculated for different raw material composition for biogas production. It is an extensive study on design of the biogas digester and how agricultural waste can be used to produce biogas. Design of experiment was performed with computer software to predict the biogas yield and to decide important factor responsible for biogas generation.

High modulus bituminous mix is one of attractive option to increase load-bearing capacity of pavement structure against conventional structural distresses, such as rutting and fatigue crack. A comparative study was carried out between five combinations of flexible pavement mentioned in IRC guidelines with conventional perpetual pavement design criteria. All five combinations were subjected to high modulus bituminous mix of 3000 MPa to 8000 MPa. The perpetual pavements were designed by using mechanistic-empirical design software IITPAVE. The results were further validated using the design software KENPAVE and WESLEA. The study investigates the effect of high modulus bituminous mix in conventional perpetual pavement design with respect to life cycle cost analysis and overall pavement thickness. It is found that use high modulus bituminous mix results in substantial decrease in pavement thickness.

From ancient times the jaggery production has been done by the same traditional process. Jaggery industry provides good employment to the village people in the rural area. But efficiency for the traditional plant is minimum due to the wall heat losses in the furnace, stack losses through a chimney. The efficiency of the jaggery plant is very much less looked upon as compared to other worldwide industries. Different researchers work on these problems to enhance the thermal performance of the plant and to minimize the extra usage of bagasse. The introduction of a thermic fluid heater along with an open pan heat exchanger to the replacement of conventional furnace in the industry, improves production and efficiency. Besides the thermic fluid heat exchanger, multiple pans are also introduced for a better heat transfer rate. From the new method developed the total cycle time reduces up to 40% of the total time in the traditional method.

The world utilization of petroleum derivatives is expanding quickly and it influences nature by green house gases causing health risks. Biodiesel is rising as a significant promising optional energy source which can be utilized to lessen or even substitute the petroleum utilization. As it is principally produced from vegetable oils or animal fat that can be produced enormously. Anyway the broad use of the biofuels can make deficiencies in the food life. This research work analyzes the Sunflower Methyl Ester (SFME) and its mixtures as a substitute of fuel for any diesel engine. Biodiesel can be made from sunflower oil in the lab in a little biodiesel set-up (30Litres) by base transesterification. Four cylinder diesel engine was utilized for testing on different mixtures of sunflower bio-diesel. The emissions of CO, HC are lesser than diesel fuel for all mixes experimented. The NOx emission is more because of the higher volatility and viscosity of bio-diesel.

In an Engineering scenario, it is necessary to increase the efficiency and life of every component in the machine. A particular vehicle is selected based on its working ability to increase the efficiency and life span. In this project, I have chosen the piston ring material with the Jatropha as lubricating oil to increase the efficiency and the life span of the engine. The life span and working ability are governed by the friction and wear characteristics of components of the engine of the vehicle. The life span of any vehicle is calculated by the life of its sensitive part that is susceptible to wear. In tribological system “piston -ring-liner” observed 45–55% of frictional mechanical losses. To reduce such losses, lubrication performs a vital role. Lubricant is the main part of lubrication to maintain reliable functions of the machine, provide smooth operations and chances of failure is less. Crude oil is a vital source to manufacture lubricant. But in today’s condition, the prices of crude oil increasing and depletion of crude oil reserves in the world, and global concerning protecting the environment from pollution have renewed interesting developing and using environment-friendly lubricants derived from alternative sources.

Now each day we face the matter of garbage which is scattered everywhere in buildings also as in villages the Gathering of that garbage isn't done on time. So, we've proposed the system of garbage pickup & Monitoring. This system, it's alleged to be collect the bins from every registered home. The worker should collect Bin from the house and scan the RFID tag with the RFID Reader. The RFID Reader is going to be interfaced with the Wi-Fi module. After scanning of RFID tag the message "Bin collected" are going to be delivered to the respective customer. If any home is going to be missed for collection of Bin, then the actual customer can call to the customer care with the given toll-free number. After interaction with care, the message “Bin collection is remaining is sent to the nearby bin collector worker.

In today’s society Depression, Stress and Gloominess are some of the most broadly perceived and increasing mental issue influencing us. The presence of a system that is automatically capable of identifying a users mental state is of great benefit. Due to users spending a lot of time on social media using that to check his well-being will be helpful in many ways. There are various algorithms such as Random Forest, SVM, ANN, CNN, RNN present using which this can be achieved. Sentiment Analysis and deep learning techniques could provide us robust algorithms and structure for a target also a chance for observing mental issues which are, specifically of depression and stress. In this paper different ways of dealing with depression shown on social media platform are studied. This will enable in achievement of better understanding of the various mechanisms used in depression detection.

Cardiovascular diseases are one of the leading causes of mortality and morbidity worldwide which is a major concern to be dealt with, but it is difficult to identify cardiovascular diseases in the earlier stages because of several contributory factors which posses risk such as high blood pressure, high cholesterol, diabetes, and many other factors. Prediction of cardiovascular diseases is considered as one of the important aspects of healthcare analysis given a large amount of raw data available, waiting to be converted into valuable information. On the other hand, the majority of internet users are adopting one or more messenger platforms which enable us to deploy AI-based conversational bots that can be developed to cater to individual users to provide user satisfaction, high level of accessibility and flexibility. The aim of this paper is to demonstrate the use of Machine Learning models using Google Cloud Platform to assist users in the prediction of cardiovascular diseases using a chatbot with the help of Flutter framework on Android and other messenger platforms such as Telegram and Facebook.

Diabetic Retinopathy is a common retinal complication associated with diabetes. Early detection of Diabetic Retinopathy shields patients from losing their vision. Thus this paper proposes an automated method for image-based classification of diabetic retinopathy. The method is divided into three stages: image processing, feature extraction and image classification. The objective is to naturally group the evaluation of non-proliferative diabetic retinopathy at any retinal image. For that an underlying image preparing stage separate blood vessels microaneurysm, and hard exudates so as to extricate highlights that can be utilized by calculation to make sense of retinopathy grade.

The product lifecycle concept is acknowledged in both the general economy and management studies. In line with the concept, every product features an estimative lifecycle. The car's lifecycle is divided into four stages; they are, introduction, growth, maturity, and decline. Moreover, after a decline, it is sent to the scrap yard. Electric vehicles, when linked with low-carbon electricity sources, offer the perspective for diminishing greenhouse emissions. The sales of the vehicles also are different in indifferent stages. Consistent with the concept, every product features an estimative lifecycle. The present study addresses these concepts of the product lifecycle for automobiles.

Contamination of heavy metals in sediments and soils are of increasing concern. Heavy metals sources in sediments and soils mainly include presence of sediments as well as anthropogenic sources. Heavy metals are not biodegradable they tend to persist over long-term periods in the sediments and soil template. They can be mixed or elated to underground and surface water as well as uptake by agricultural crops or food crop which leads to concerns over animal and human health. Heavy metal contaminated sediments have direct adverse effects on aquatic life and ecosystems. Poisoning of food chain and loss of recreational enjoyment are the most potential problems caused by contaminated sediments.

Rapid and accurate detection of Infectious disease at rural setup is one of the most challenging tasks. Advancement in molecular biology-based techniques had revolutionized the field of diagnosis. Among several advantages, these techniques are restricted to tertiary healthcare centers due to high cost and specific operating procedures. We had developed a fast, battery-operated, portable device which can be clubbed with conventional PCR for screening of Infectious diseases at rural setup. The proof of concept was tested on standard reference samples of Mycobacterium tuberculosis (H37RV strain) DNA as a positive control and ATCC Strain of E.coli as a negative control in duplicates by clubbing the system with Conventional PCR machine (For Amplification) and compared the result with Real-Time PCR and Fluorescence microscopy techniques. The results of the detection system were found consistent with conventional diagnostic techniques and can be used for screening of infectious diseases at rural setup.

When a vibrating system is damped with more than one type of damping, it is necessary to determine which of these types of damping are more effective to control the resonant response. In such a case, identification of damping parameters from the responses of a vibrating system becomes an important factor. Therefore when the system is damped due to coulomb friction, viscous friction, it is necessary to develop theoretical and experimental methods for identification of these damping parameters from the responses of the vibrating system. As such, for identification of coulomb and viscous friction (and also with particle damping) parameters, it is proposed to develop methods irresponsible for the control of resonant response of vibrating systems. The paper contains experimental setup and results about these different types of mechanical vibrations.

Water resources are sources of water that useful or potentially useful to humans. Uses of water are including domestic, agricultural, industrial, recreational as well as environmental activities. Virtually, all these human uses require fresh water. The water samples from the Godavari River of the five sampling site are taken and analyzed for the physicochemical parameters such as Colour, Odour, Temperature, pH, Electrical Conductivity (EC), Total Solids (TS), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness (TH), Calcium (Ca), Magnesium (Mg), Chloride (Cl), Alkalinity (TA), Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD) and Turbidity, etc. parameters in seven weeks. Godavari River water is one of the important sources of water in Nanded city. As the Nanded is drought-prone area alteration within major or minor in the characteristics of River water results in great attention of day to day life of citizens of Nanded. There are five samples taken weeks of March and April months of Godavari River to access the quality of water and the results are compared with WHO standards.

More than hundred million tons of coal fly ash is produced annually in India from combustion of coal in power plants. It is expected that about 150 million tons of coal ash will be produced due to burning of coal in power plants by the year of 2015. This will require about 30,000 hectare of land for the disposal of ash. One of the biggest problems due to disposal of large quantities of coal ash is the possible leaching of different hazardous pollutants, including TDS, fluoride and sulphate. The present study investigated the leaching of soluble compounds in ash ponds and its impact on the groundwater quality in the sub-watershed around the ash ponds in the vicinity of Koradi and Khaperkheda near Nagpur in Maharashtra, India. A network of twenty three observation wells set up for monitoring of water level and groundwater quality for major cations and anions during pre-monsoon season, 2010. The results indicate that the SO4 concentration is very high (>1000 mg/L) in the samples which is much closer to the ash ponds. The TDS and Fluoride concentration is also elevated with respect to BIS standards in few samples.

In this work, a process sequence module in a CAPP system using feature-based modeling (FBM) for prismatic and axis-symmetric parts is developed. It extracts feature information in Extensible Markup Language (XML) format and thereon generates the process sequence for efficient manufacturing of the micro parts. The system consists of two components: (1) development of FBM through design by feature (DBF) approach and automatically extract the feature information and verify micro machine tool capability feature details and (2) Process sequence determination to produce the micro features in prismatic and axis-symmetric micro parts using a knowledge-based system (KBS) approach. A CAPP system for prismatic and axis-symmetric parts can be realized only with the incorporation of many micro features and the development of other activities in process planning.

Abrasive water jet machining (AWJM) is a non-conventional manufacturing process, has a potential to cut wide range of materials. For processing various engineering materials abrasive water jet cutting has been proven to be an effective technology. The motive of the paper is to analyze the process parameters on taper angle in abrasive water jet machining having grade type of 304 stainless steel material. Design of experiment were conducted according to response surface methodology (RSM), based on Box-Behnken design. Influence of process parameters on taper angle is shown by main effect plots and 3D surface plots. Evaluation of process parameters were done by ANOVA technique. For optimization of process parameters so as to achieve minimum taper angle, multi-objective response methodology is used which resulted desirability 0.9195 of the developed model. The optimal process parameters obtained were traverse rate 80 mm/min, abrasive flow rate 300 gm/min, and stand-off distance 1 mm. For validation of results, confirmation analysis is performed and resulted percentage error showed is less than 6% for taper angle.

Now a days to produce stress free and burr free micro components, photochemical machining is one of the emerging technologies. The micro level etching of components of different materials is carried out by using chemical etching process. The very precision parts such as microchannels, heat sinks, Fine mesh or screens and printed circuit boards are developed by this process. In this work, the cartridge brass was selected as a base metal which has good electrical and thermal conductivity. The process parameters are etching temperature and time and responses are etching depth and surface roughness. The objective of this study is to achieve etching depth on the Cartridge brass plate having thickness 0.5 mm. The etching depth and surface roughness of material increases with increase in time and temperature. The study of copper and brass were reported by many researchers. Very few literatures are available on an alloy of copper and brass. The effect of process parameters is changing by changing composition of material. This article is focused on the study of etching depth and surface roughness of cartridge brass material at different time and temperature. The cartridge brass has been widely used in industrial applications.

The circular two-phase closed thermosyphon with acetone as working fluid is analyzed in this study. TPCT pipe is made up of Aluminium. The filling ratio is 30%. The objective of this study is analyzing aluminium and acetone combination at increasing heat input. The vertical position TPCT is analyzed for six different heat input. Thermal efficiency and mean temperature difference between evaporator and condenser is determined and plot. Thermal performance of TPCT is increasing with an increase in input heat supply.

We investigate the effect of slip on the formation of recirculation zone near the two-part cylinder with the same sign zeta potential placed in a microchannel. The external electric field is used to actuate the electroosmotic flow (EOF). The governing transport equations are solved using a finite element based numerical solver. The vortex formation takes place near the upstream part of the cylinder. The strength of the vortex is analyzed in terms of the maximum magnitude of reversed flow velocity $$\left( {U_{R} } \right)$$ U R . It is found that the extent of the recirculation zone is smaller for the slip case as compared to the no-slip case. The magnitude of $$U_{R}$$ U R increases with the slip coefficient ( $$\beta$$ β ) for smaller values of $$\beta$$ β . Also there is a decrement in $$U_{R}$$ U R at larger values of slip coefficient and the decrement is amplified at higher values of zeta potential. The flow rate monotonically increases with the slip coefficient and zeta potential.

The objective of this study is to perform multi-characteristic optimization of WEDM parameters while machining Nimonic -75 (Ni-75) alloy using an integrated approach of AHP-MOORA method. Taguchi’s L27 orthogonal matrix is used to conduct the experiments. The cutting factors selected for this research work are the pulse on time (Pon), pulse off time (Poff), gap voltage (GV), peak current (IP), wire velocity (WV) and wire tension (WT) while the outcomes are MRR, surface roughness, and kerf width. AHP method is used to find out weights for the chosen quality characteristics, and MOORA technique is employed to determine the most favorable cutting conditions. The optimum combination of explanatory factors are Pon = 110 machine units (mu), Poff = 51 mu, GV = 40 V, IP = 230 Amp., WV = 5 m/min and WT = 8 mu. Finally, authentication trials are accomplished to validate the results.

The wind energy is most of the promising renewable energy source. In wind turbine technology, the turbine blades play an important role as it directly comes in contact with the wind. Many researchers have concentrated on improving the aerodynamic performance of wind turbine blade through testing and theoretical studies. In general, moderate to high-speed winds, typically from 5 m/s to about 25 m/s are considered favourable for most wind turbines in India. But in rural areas, wind speed is near about 3–9 m/s. The present investigation aims is to compare the performance of eight blade novel mini Horizontal Axis Wind Turbine (NMHAWT) blades of novel airfoils over National Advisory Committee for Aeronautics (NACA) eight blade profiles in terms of loads and performance. Therefore blade design of well known series of NACA airfoil was selected from Q blade and Mat-Lab software. Analytical calculations are help out for the selection of NACA 4418 is suitable for the comparison. The objective of this paper is to decide the novel profile of the blade for the development. The comparison is done through software for predicting the performance of a novel mini horizontal axis wind turbine.

Micro-mixer is a device which is used to carry the mixing of two or more than two fluids. Any one dimension of it is in micrometer. Due to low Reynolds’ Number Micro-mixer design is challenge for the designers. To address this challenge, novel methods of mixing enhancement within micro-fluidic devices have been explored for a variety of applications. For Passive Micro-mixer mixing will be carried out with the help of geometry of the micro-mixer. In this study fabrication of a tree type Micro-mixer with Circular baffles using CO2 Laser machining is discussed. The Circular baffles are responsible for making diversion of flow of the fluid in Convergent & Divergent manner due to which proper mixing of fluids become possible. This type of micro-mixer is suitable for Bio-medical applications such as Urine testing, Blood testing, in preparation & in testing of Drugs. This paper focuses on design and fabrication of tree type Micro-mixer with Circular Baffles. The parametric study for CO2 laser machining by considering the speed and power as the control parameters and the depth as a performance measure for engraving different width micro channels is presented in this paper. The tree type Micro-mixer with Circular Baffles fabricated by CO2 laser machining is found to be suitable for master molds used in soft lithography process.