Proceedings of First International Conference on Smart System, Innovations and Computing

There are various challenges in underwater wireless communication such as limited bandwidth, limited energy of sensor nodes, node mobility, high error rate, and high propagation delay. The main problem in underwater wireless sensor networks has limited power of battery as underwater sensor nodes cannot get charged due to deficiency of solar energy. Another main issue is the reliability. In this paper, an approach is proposed for energy-efficient communication among sensor nodes based upon the energy efficiency, reliability, and throughput of sensor node in UWSN (Underwater wireless sensor networks). It uses two step methods. First, it uses a method to identify node called as head node from a cluster which is required for effective communication. This node is responsible for communication with other nodes and further it sends the sensed data to surface station. Second, it finds path between the head node and underwater sink calculated by using Euclidean distance.

Food acts as an energy source for the organisms that help them to grow and sustain life. In order to maintain the proper hygiene and the safe supply of food products, the food quality should be checked and monitored regularly. To quench the thirst of greed, people add adulterants in the food products to get the monetary benefits by selling the low-quality food at the higher price. So, to avoid any compromise to the human health, food adulteration monitoring system can be used to detect the presence of adulterants in the food product. This system is governed by the Raspberry pi which controls the use of sensors in the system. The recorded data is transferred using the ZigBee module and results are displayed. The IoT technology has been introduced in the system for the purpose of making the system as smart device. With the use of this system, consumption of poor-quality food can be avoided. Moreover, the simplicity of the system can help everyone (commoner, food inspectors, and shop owners) to use food adulteration monitoring system.

One of the most crucial and anticipated problems of underwater sensor network (UWSN) is of node mobility issue. It is a problem that arises due to mobile nature of nodes. In a communication, there is a situation when either of the source or the destination nodes displaces from their original position, thus resulting in a condition of communication failure. We have proposed an approach named SEE THROUGH to overcome this problem. To explain the same, few cases have also been considered. The objective of this paper is to present a new approach to node mobility issue.

Underwater wireless sensor network (UWSN) finds various issues such as low bandwidth, node mobility, high error rate, limited energy. UWSN have a dynamic topology due to the movement of sensor nodes which results in improper communication between sensor nodes and underwater-sinks (uw-sinks). In this paper, we have studied various routing protocols which has been proposed for underwater wireless sensor based on some of the parameter, i.e., packet delivery ratio (PDR), end-to-end delay (E2E delay) and energy efficiency. A comparison among these protocols is shown under different network scenario. The overall performance of routing protocol is evaluated by using these parameters and best possible protocols among them are to be identified.

The major findings of this paper are as follows. After completion, it is evident that an Arduino is fully capable of controlling an automated chessboard using a permanent magnet. The MEGA used for this purpose does so by analyzing the incoming Bluetooth signals. A java code is used to design a virtual chess playing program, and the Bluetooth signals are sent through a laptop. The Arduino program uses the concept of “Least Hindrance Path” in order to move and capture the pieces in the board.

Due to lack of energy resources, renewable energy sources have increased fabulous consideration and settled rapidly in recent years. It is a very decisive issue to integrate hybrid energy system to the microgrid. In this paper, a control model for integration of hybrid energy sources to the microgrid and the effectiveness of the control algorithm for DC/DC converter under variation of different load demands are presented. A nearly constant dc voltage at the output of hybrid energy system is preferred for high efficiency So, there is a necessity for developing control techniques for a grid integration hybrid system including a method for output voltage control that stabilizes the voltage and dc-link capacitance. The simulation results of MATLAB/SIMULINK model report that the proposed control algorithm has a good performance. Therefore an idea of an effective technique for integration of solar/wind system to microgrid has been given.

Purple fringe aberration (PFA) patterns stem from specific defects in certain camera sensor grids, leading to the fraying of edges near high-contrast regions. Much of the literature deploys predefined, absolute, experimentally determined static thresholds for detecting purple fringes. Given the potential diversity in the spectral signature of the local light source, these fringes may not have a static distribution. It is therefore important to make the detection procedure content-adaptable and in tune with the environmental settings. In this paper, we propose a PFA detection procedure in the Y-Cb-Cr CHROMATIC space, by first using a global relativistic Y-channel-gradient threshold for detecting the high-contrast regions and then use the fact that PURPLE and GREEN are antipodes of each other to segregate PURPLE fringes reliably. Comparisons with the state-of-the-art detection approaches are presented. The advantage with the proposed approach rests with the fact that the threshold is content-adaptable and non-static and can therefore be used to pick up diverse fringe patterns (not necessarily confined to the seat of PURPLE).

Hand movement (both physical and imaginary) is linked to the motor cortex region of human brain. This paper aims to compare the left–right hand movement classification performance of different classifiers with respect to different feature extraction techniques. We have deployed four types of feature extraction techniques—wavelet-based energy–entropy, wavelet-based root mean square, power spectral density-based average power, and power spectral density-based band power. Elliptic bandpass filters are used to discard noise and to extract alpha and beta rhythm which corresponds to limb movement. The classifiers used are Bayesian logistic regression, naive Bayes, logistic, variants of support vector machine, and variants of multilayered perceptron. Classifier performance is evaluated using area under ROC curve, recall, precision, and accuracy.

Block matching is the most efficient technique for motion estimation (ME) in video compression and there are many algorithms to implement block matching. This paper discusses the block matching algorithms based on differential evolution (DE) and artificial bee colony (ABC) and proposes a new algorithm hybridizing these two algorithms aiming to get better results in block matching than the individual algorithms. In the proposed algorithm, food source generation operation of ABC is replaced by mutation and crossover operations of DE with the objective to utilize the search space exploration ability of DE and the solution exploitation ability of ABC.

With the advent of technology, the business process, worldwide, has shifted from a slow—person-to-person physical interaction to a better and faster service platform—the Internet. The biggest advantage of this platform is that it allows the people to undergo their business transactions without meeting with the other party/parties and still making the benefits out of the agreement between them. Though this approach has a major edge over the conventional methods, still has many drawbacks to be pondered upon. The Internet has also emerged as a platform to commit crimes at a very high rate. Frauds committed online has put a question on this very approach of revolutionized business since no proposed model yet has given a sound and complete way to handle these issues. This paper is an extension of the previously generated OHM model, and enhances the parameters and flow of the entire OHM system, to prevent online frauds. The key frauds where Enhanced Online Hybrid Model (EOHM) can be used would be auction frauds, no-delivery frauds, and identity theft frauds.

Three-Dimensional (3D) Underwater Wireless Sensor Networks (UWSN) different from the terrestrial Network forms various challenges like propagation delay, battery, packet loss. Sensor deployed in water is mobile and hence is the prominent challenge while transferring the data packet. Various routing protocols have emerged but still finding a static and reliable route remains a foremost challenge. We propose Robust and Efficient Routing Protocol (RERP) attaining a more static route for the communication between two nodes. In RERP, we assign a rank to each node by formulating important aspects of the sensor in order to form a stable network. So, by selecting least rank node for communication, a more efficient and reliable routing path is constructed. 2-Hop Acknowledgment along with the mobility factor also ensures high packet delivery ratio.

Mobile ad hoc network (MANET) diverges from the conventional wireless Internet infrastructure. MANETs are deployed in an environment which has no preexisting infrastructure along with the irregular movement of nodes. Because of the inconsistent movement of nodes, routes break frequently and the reestablishment of routes utilizes more network resources and energy. So, it is essential to provide a routing protocol which handles the mobility of nodes and helps in reducing packet drops. Ad Hoc On-Demand Multipath Distance Vector (AOMDV) is a preexisting routing protocol for MANET whose performance may degrade due to high velocity of mobile nodes. Furthermore, the AOMDV protocol incorporates excessive flooding of RREQ packets at the time of route discovery phase. So to deal with these issues, we are introducing a new routing protocol named Velocity Constrained Multipath Routing Protocol (VC-AOMDV). And it also results in better QoS performance simultaneously. The only routes that are velocity constrained and having better link reliability will get chosen for information transmission. The simulation results prove that the proposed protocol VC-AOMDV will perform better with delay, PDF, packet loss, etc., as compared to AOMDV protocol. It will also reduce the packet drop and delay with the increment of successfully delivered packets.

Handwriting is one of the behavioral biometric techniques for person identification. Our paper proposes a novel grapheme-based handwriting recognition system. First, the images of handwritings are segmented into graphemes and then graphemes are represented as projection profile. Then, dictionary is learnt from the images of handwriting using k-means clustering. Now using dictionary, writer feature vectors are generated and stored with writer label. Finally, query document features are matched with writer feature vectors using k-nearest neighbor. The outcome is the writer label with the highest similarity score.

Cloud computing enables the users to outsource and access data economically from the distributed cloud server. Cloud provider offers data storage as a service and share the data across multiple authorized users in a cost-effective manner. In the cloud-computing model, once the data leaves the owner premises, there is no control of data to the data owner. To provide security for the outsources, data is one of the challenging tasks in cloud computing. Public auditing of outsourced data to verify the integrity of the data provides the data security. In this paper, we proposed a multisector public auditing system by utilizing a linear combination of homomorphic linear authenticator tags of the file blocks. Our proposed method performs the multiple auditing tasks simultaneously, without retrieving the entire file, which reduces the computation and communication overheads in the auditing. The analysis of the proposed system shows that, multisector public auditing method is better than the existing auditing method in terms of data security, communication, and computation overheads by utilizing linear combination of homomorphic linear authenticator tags of the file blocks. Our proposed method performs the multiple auditing tasks simultaneously, without retrieving the entire file, which reduces the computation and communication overheads in the auditing. The analysis of the proposed system shows that, multisector public auditing method is better than the existing auditing method in terms of data security, communication, and computation overheads.

The data handling and processing capabilities of current computing systems are increasing, owing to applications involving the bigger size of data. Hence, the services have become more expensive. To maintain the popularity of cloud environment due to less cost for such requirements, an appropriate scheduling technique is essential, which will decide what task will be executed on which resource in a manner that will optimize the overall costs. This paper presents an application of the Bat Algorithm (BA) for scheduling a workflow application (i.e., a data intensive application), in cloud computing environment. The algorithm is successfully implemented and the results compared with two popular existing algorithms, namely Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The proposed BA algorithm gives an optimal processing cost with better convergence and fair load distribution.

This paper approaches toward the standardization of telecom customer’s behavior by specifying the call activities like frequency, duration, time of calls with the type of calls like local, national, and international. In the same way specifying the SMS/MMS activities as behavior of customers plus the rate of data pack and talk-time recharge. It is an attempt to identify meaningful attributes to describe behavior of customer plus study the available call detail records in big data environment and recognize the procedure that uses customer behavior for the designing of data product which is tariff plan.

Linguists have been the interesting area for researcher from many years but sign language linguists become a new challenge. Contrasted with spoken language phonology, the field of sign-based phonology is quite young. In the fundamental work for sign-based communication, some researchers have shown the need of grammatical structure for the sign language as well. In this paper, we included the linguistic structure of Indian Sign Language (ISL) with phonological counterparts. ISL phonology has different aspects in which few are illustrated and discussed as phonemic list, allophones, and internal prosodic structure.

Outdoor air pollution has emerged as a serious threat to public health across the globe. Air quality monitoring and forecasting are required to provide the policy makers a scientific basis for formulating a robust policy on abatement of air pollution. Moreover, if air pollution forecasts are issued to the public, they can take preventive measures to minimize their exposure to unsafe levels of air pollutants. In this paper, an intelligent air pollution prediction system using Extreme Learning Machine (ELM) has been proposed to predict the air quality index for five pollutants (PM₁₀, PM_2.5, NO₂, CO, O₃) for the next day. It is found that the prediction of ELM-based proposed system is better than the existing air pollution prediction systems.

To make MANETs energy efficient and to balance the load swarm intelligence along with clustering as Bee-Ad Hoc-C has been chosen as the best method in our previous work with improvized routing by the use of BCN. But in this routing technique optimum load balancing and energy efficiency have not been improved much. In the present paper, one new routing algorithm has been introduced which takes care of the above factors. Here, the energy efficiency is increased by taking into consideration the number of nodes in the cluster and remaining battery power of the nodes. Also to avoid any redundancy in the routing alternate router has been initiated if any shortest path is busy. The work is carried using the NS2 (Network Simulator-2). By the proposed method, the MANET routing can be properly balanced and improved in terms of Energy Efficiency, End-to-end delay, Throughput, Packet Delivery Ratio, Route Discovery Time.

In this research paper, a new approach to image encryption has been proposed. The technique involves using a seed to generate a random list of prime numbers, which is then used to generate subsequent lists recursively. In this manner, a random number generation is created by recursive seeding, rather than one direct seed. This final set of lists is used to encrypt each pixel of the image. Moreover, each pixel is encrypted with its counterpart pixel in the original image, to reduce the possibility of deducing the original image without knowledge of the key. Statistical analysis tests are used to justify the strength of the algorithm.

Roles in WSCDL choreography define the behavior of participants in collaboration to achieve business objectives. These roles may be realized by one or more web services. The quality of service (QoS) of roles and choreography are dependent on these QoS of competing web services. Service providers may or may not publish the values of attributes of the QoS of their services to enable their selection. Consequently, it is desirable to have an alternative strategy to compute values of attributes of QoS of web services. Further, WSDL descriptions of web services do not contain an order of invocation of operations. This invocation protocol may be needed by the client either for adaption or selection of web services. In this paper, I propose a method to discover invocation protocol of a web service and to capture values of attributes of QoS from its execution traces of web services. I have represented execution traces as Labeled Transition Systems (LTS) and merge these LTSs to obtain service invocation protocol. My LTS model defines a set of functions for attributes of QoS. These functions take a transition as input and return the value for a specific attribute of QoS. These functions are used to compute the range of values of QoS attributes. I have demonstrated my methodology through an example.

Video content has become one of the most noticeable online trends of recent times. It is no secret that videos have become a staple of everyone’s life. WCAG 2.0 suggested that closed captioning is needed to provide accessible audio content for the audience who are Deaf and Hard of Hearing due to their disability in pursuing the auditory information. Caption files are plain text files with time codes information for each frame. Readability of caption is one major challenge, due to variation in speaking rate and reading rate. The time duration of caption frame affects the flow of text perceived by the reader. This paper proposes a novel model for caption readability based on time dimension. The proposed algorithm calculates the readability of each frame corresponds to the time duration. As a result, we analyzed the caption frames with respect to time, word count, and syllables and identified that the time gap enhances the caption readability.

Readability of text is an important factor to be considered during the design of contents which are aimed to transfer information to a large population. Texts which are easy to comprehend improves retention, reading persistence and reading speed. The paper presents evaluation result of 60 departmental websites of Rajasthan government by using various readability tools such as Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, Gunning Fog Index, Simple Measure of Gobbledygook, Coleman–Liau Index and Automatic Readability Index. Also, the classification of websites into four categories are presented such as Type-1 falls in very easy to understand, Type-2 falls in easy to understand, Type-3 falls in hard to understand and Type-4 falls in very hard to understand the text. By providing readability training and awareness programme to developers and designers in terms of standard guidelines, to achieve this readability-cum-accessibility in a better way. This analysis provides the feedback to website administrator to improve the readability-cum-accessibility and helps to achieve the accessibility for all.

There are many technologies that are already available for searching items with mobile phone. But in this method, user should have a mobile phone in working condition. The proposed system is based on wireless sensor network. The wireless sensor network is a network of nodes that can sense and control the environment and also has interaction between the user, computer, and environment. This research paper has been developed to search the frequently used homely products during emergencies through alarm with transmitter and Receiver connection. If any user wants to search items, this application is very much useful during emergency.

The exigency of many people with ataxia can be fulfilled with hand operated or not automatic bed segment of ataxia people society chance on it hard or laborious to use bed. There is broad on computer-controlled bed where sensors and intellectual algorithms have been used to minimize the level of interference. This paper describes an innovative, motorized, voice controlled wheel bed for physically challenged people using the embedded system. Proposed design supports the voice activation system for physically challenged persons which also have manual operation. Arduino microcontroller and voice recognition processor have been used to support the guide of the wheel bed. The wheel bed does not respond to a wrong speech command. Microcontroller controls the wheel bed directions through the voice commands, at the same time it inactivates the wheel bed for the commands which are not trained and using ultrasonic sensors impediments are avoided.

As there is enormous increase or rise in the data exchange by the electronic system, the requirements of information security have become a compulsion. The most important concern in the communication system which is between sender and receiver is the security of the information which is to be transmitted. To get rid of the intruders, various cryptographic algorithms are used for example, AES, DES, Triple DES, Blowfish, etc. In this paper, authors have attempted to transfer file securely using randomly generated keys with Blowfish symmetric algorithm for encryption process. A unique transaction id is also generated which is used to fetch the random key which will be used for decryption at the receiver end. A validation key is also proposed which is used to validate that file is decrypted by the intended user only. The combination of random key, validation key, and transaction id as proposed will provide more robustness to secure transfer of file using Blowfish symmetric key encryption.

The smart homes are the need of emerging infrastructure in order to reduce the energy needs of society. Room heating is among the major source of energy consumption in such homes. Hence a setup is required in centrally controlled homes to manage the energy consumption. One such arrangement has been discussed in which several heaters in a single room and different rooms can be controlled along with a smart thermostat relay control algorithm so that energy is properly utilized. The controllers have been connected using Zigbee communication for the sake of simplicity and efficiency. The indoor and outdoor temperatures are monitored using appropriate sensors. Alongside, the energy savings and the heating duration in a typical Indian scenario is discussed.

The current generation of digitalized lifestyle demands intelligent systems to support all real-time applications. The footpaths of the cloud technology have led to the developments many smart systems which are developed on grounds of machine learning techniques. The health care domain demands smart systems in order to automate functionalities to ease out the patient’s problems. The proposed Healthcare As A Service (HAAS) solution describes the smart health care solution which would provide automated functionalities to reach out nearby health care centers to take treatments for their disorders. The predictive analysis of the patient’s health information is done based on bagging algorithm in order to alert them regarding their health disorder. In case of emergency treatments, the alerts to the nearby health care centers can be done and the relevant treatments are provided based on the patient’s historical information tracked from their unique smart id generated by the HAAS.

In this paper, we present a compact slotted microstrip patch antenna whose multiband characteristics have been studied. This antenna is compact in shape and size having a dimension of 24 × 24 mm2. This antenna consists of two inverted F-shaped slots in the patch and a defected ground plane for enhancement of impedance bandwidth. This patch is fed by a coaxial probe. The antenna shows multi band characteristics, i.e., from 1.94–1.98 GHz, 2.4–2.52 GHz, 3.2–3.38 GHz, 5.07–5.41 GHz, 5.73–6.09 GHz. After analyzing all necessary characteristics in perspective of gain, bandwidth, polarization, and return loss, the proposed patch is well applicable for WLAN (2.4/3.2/5.2/5.8 GHz) and WiMAX (5.5 GHz) communications.

The computation of speed in sensorless AC drive has gained much importance over the last few years. The main focus lies in evaluating the accurate value of speed for vector-controlled drives thereby making the use of an encoder at the machine shaft obsolete. Considered to be most simple of its kind, the model-based approach utilizes the linear time invariant and nonlinear time variant systems for speed estimation. In the proposed work two fuzzy controllers are adopted. One fuzzy-based adaptation scheme in the nonlinear feedback is used for obtaining proper speed estimate. In a vector-controlled induction motor, the problem that perseveres even after the evaluation of speed is the harmonics generated due to switching action which decreases the overall efficiency of the drive system. For an improved performance of the drive control system, a second fuzzy-based control is supplemented along with the PI Controller in the outer speed control loop. The significant feature of this work is the implementation of an intuitive fuzzy logic-based learning approach which is fast and effective. The type-1 fuzzy logic-based controller is designed which is less computationally intensive. An artificial intelligence-based machine model scheme which utilizes two fuzzy controllers (Dual Fuzzy) offers most satisfactory performance reducing the Total Harmonic Distortion (THD) to a great extent.

In the modern era of digital advancement of images, all information are exchanged through Internet, so for exchanging any secret message, either cryptography or steganography is used. Cryptography is a process of converting data in unreadable form. We can embed even the existence of data itself. In this paper, we proposed a new robust and secure method to embed secret message in an image. To increase robustness of message, intermediate significant bits (ISB) are used instead of using LSBs. To increase the security of message, encryption technique is used. The objective of the paper is to embed a message in bit plane such that it is robust against various attack and transformation (like scaling, cropping, filtering, etc.) and also maintaining the perceptual transparency of stego-image.

In the current scenario of deregulated power system congestion in the network is one of the critical issues, which need to be resolved for better economic and system efficiency. Several measures have been adopted to relieve the system from congestion for maximization of social benefit. Fuel cost minimization is considered as the key factor for social welfare maximization, which provides benefits to both buyer and consumer of electricity. The fuel cost minimization for power system is achieved based on many parameters one of them being generator allocation. This paper uses GA-based optimization algorithm to obtain optimal generator allocation to find the best fit having minimum fuel cost. The proposed approach for fuel cost minimization is tested on IEEE 14 bus standard system.

This paper presents a framework for modeling the behavior of virtual agents participating in human–computer interaction. For a given input statement, this framework derives the response of the virtual agent from a combination of 2 parameters, context and the emotional state. A stack based automaton is employed to record and track the values of these parameters for constructing the behavior and response of the agent. The approach is later implemented on a quiz based virtual agent response system. The framework can be utilized to enhance the quantum of emotional expressiveness in the response of the virtual agents for more human-like interaction with the machine.

This paper presents an internet of thing (IoT)-based innovative real-time pH monitoring and control of municipal wastewater for agriculture and gardening application. During the past few decades after the green revolution in India, water requirement is increased exponentially in all sectors, viz., agriculture, gardening, industry, etc. The demand and supply relationship is very essential for every country in present time, and is also a big challenge to satisfy this requirement around the world. Regular change in the climate and the urbanization makes the lavish use of the available resources has exhausted the available resources. Water is necessary for the survival of the human being on the earth. So for the survival the conservation and management of the available water resource are also equally important. Moreover, for the healthier society, the access of the clean and safe water resource is also imperative. Nowadays municipal wastewater is recycled and reused for agriculture and gardening application after treatment. This paper describes a smart solution to control the water quality through its pH, thus to treat the municipal wastewater and its reuse in the agriculture and gardening purpose. The idea is to develop a low-cost electronic system and its application with such a quality of maintaining (monitoring and control) the water quality within the prescribed standard.

Modular feedforward network method is introduced to detect diabetic retinopathy in retinal images. In this paper, the authors present classification method; the Modular Feedforward Neural Network (MNN) to classify retinal images as normal and abnormal. Publically available database such as DIARETDB0 including high-quality normal and abnormal retinal images is taken for detection of diabetic retinopathy. Modular Feedforward Neural Network is designed based on the extracted features of retinal images and the train N times method. The classification accuracy by MNN classifier was 100% for normal retinal images and 86.67% for abnormal retinal images. In this paper, the authors have explored such a method using MNN classifier which can detect diabetic retinopathy by classifying retinal images as normal and abnormal.

The Interactive automation system provides a user-friendly and secure environment, utilized the virtual instrument based control system. The LabVIEW software offers a software centric approach to define the controls of home appliances and utilities. The DAQ-6009 used for providing an interface between sensors-actuators assembly and software. The overall automated system comprises of independent modules that can be controlled in manual and automated mode. The scheduler system and authenticated log-in scheme add features of optimum utilization with safe and secure environment.

Interline Power Flow Controller (IPFC) is a series–series type of device belongs to FACTS family. The main purpose of using IPFC instead of remaining FACTS devices is that it can provide series compensation for a required transmission line in system, and also IPFC can control power flow across different lines simultaneously in system. In actual IPFC is a combination of two Voltage Source Converters (VSC’s) connected in series by a common dc link in between. By compensating particular line in transmission system, the power flow management becomes effective. It can also possible to exchange reactive power through series compensation within the transmission system. The Voltage Source Converters makes use of snubber capacitance and resistance for reactive power compensation. In this paper a three-phase system is simulated and analyzed by connecting with IPFC Results of the proposed system with output as reactive power are verified for a particular line with and without IPFC.

Touchstroke dynamics is an essential component of computer security. In recent years, we are heavily dependent on computers for communication, banking, security applications, and many other areas. This dependency has increased the chances of malicious attacks, so there is a need for high security to protect user’s secured data from unauthorized access. Currently, we are using PINs and passwords for access in computers, but these methods are not sufficient as the computer systems are accessed globally. So we propose a method for touchstroke dynamics in touchscreen mobile devices to improve security. The behavioral biometric gives a confidence measurement instead of accept/reject measurements. We have used an android mobile device for assessing the security using the touchstroke behavior of users. This provides us with confidence measurements for security purpose as compared to physiological biometric in which FRR/FAR cannot be changed by varying threshold at individual level.

Topological sort referred to as topo sort or topological ordering is defined as constraint-based ordering of nodes (vertices) of graph G or DAG (Directed Acyclic Graph). In other words, it gives a linearized order of graph nodes describing the relationship between the graph vertices. Many applications of various fields in computer science require a constraint-based ordering of tasks and, thus, topological sorting holds a big place of importance for many applications like semantic analysis in compiler design, Gantt chart generation in software project management and many more. In this paper, a parallel version of this ordering algorithm over CUDA (Compute Unified Device Architecture) has been discussed by identifying an approach to process-independent portions of the graph simultaneously for load flow analysis over radial distribution networks. The serial implementation of topological sort has been first discussed followed by its implementation on thread-block architecture of CUDA modifying the serial algorithm. Finally, the efficiency of this parallel version of topo sort has been investigated on various structures of graph modeled from radial distribution networks and has been reported.

Traditionally, software was developed by writing a main method which invoked many subroutines. Each subroutine was programmed as a specific part of the program based on the given requirements and function partitions. Software engineers called for enhanced software quality, timely, at reduced costs and hence adopted the use of reusable components. This work intends at designing and augmenting generic software components for admission management system domain using OOPs methods. The analysis of major admission management system functions, data and behaviors has been taken herewith. Also, pattern-based domain engineering was conducted so as to identify the structure points thereby factoring out generically reusable components.

Cognitive Radio (CR) mechanism brings the solution to the difficulty of spectrum inadequacy by distributing the unexploited spectrum to unlicensed users (Secondary Users) when the licensed users (Primary Users) are not using them so that transmission by primary user would not be distorted. To attain this, the CR dynamically regulates their physical and transmission parameters. The key characteristics of Cognitive radio, Cognitive Capability and Reconfigurability make the radio network vulnerable to security threats. One of the foremost intimidations is Primary User Emulation Attack (PUEA) performed by secondary user (or an attacker) by imitating the primary user characteristics at physical layer. In this paper, an algorithm is projected to distinguish primary user signal from others, through an energy-efficient localization mechanism and channel parameter variance, which is simulated in MATLAB environment and it is found to be an effective mechanism to authenticate primary user and mitigate the primary user emulation attack.

Elimination of the gallstones is gaining popularity due to significant increase in the count of the people suffering from Cholelithiasis. Cholelithiasis is one of the most reported diseases in India. This paper presents a comparative study of various approaches of gallstones detection and further analyzing the ultrasound images of the patients suffering from Cholelithiasis. Our research mainly focuses on examining the fissures in the identification of the shapes of the Gall Bladder stones automatically by applying various techniques such as image processing, segmentation, and a combination of other preprocessing morphological techniques to scrutinize gallstones with a motive of aiding medical science with more competent techniques for the proficient, effortless and cost-effective removal of gallstones.

As per the statistics of World Health Organization, a major percentage of elderly society across the globe is affected with dementia (age related memory loss). Dementia care entails prolonged effort in terms of money, time and manpower. Assistive health care system is therefore essential and is probable through a smart home, that offers Ambient Assisted Living (AAL) to its occupants. The objective of this work is to model an Intelligent Decision Support System (IDSS) for dementia care through the smart home. The innovation in the design of IDSS is to offer two levels of decision-making (1) Short-Term Decision-Making (STDM)—to raise suitable alerts for the abnormality detected in ADL (2) Long-Term Decision-Making (LTDM)—to decide on the progress in occupant’s developmental stage of dementia. The novelty in the design of STDM is to assimilate Random Forest (data driven) decision-making and Rule-based (knowledge-driven) decision-making within a single framework. Random Forest modeling provides better predictive accuracy through ensemble learning which is later combined with the domain specific knowledge to offer context-based decision-making. On the other hand, LTDM decides on occupants developmental stage of dementia through automation of Barthel score. Barthel score is a clinical measure to assess the stage of dementia through the level of dependency required by the occupant to complete his activities. The experimental analysis confirms the proficiency of the proposed IDSS in decision-making is better than existing approaches.

The design of a fault tolerant and eminent Wireless Body Sensor Network (WBSN) has become very necessary since Wireless Sensor Network (WSN) serves as a leading solution to all sorts of monitoring and surveillance problems. The Body Sensor Network (BSN) has reduced the occupancy of patients in the hospital. The sophistication and remote monitoring of BSN make the system more vulnerable to attacks and faults. Designing a trusted fault tolerant system is mandatory to meet these issues. A Fault Aware Trust Determination (FATD) algorithm for WBSN is proposed in this paper. The trust of the node is identified based on the battery terminal voltage, receiver signal strength, and speed of movement of the node. A value is assigned for each node in the range of −1 to 1 called trust value, to rate the trustworthiness. This trust value of the node is assigned as per the trustworthy algorithm. The Hierarchical Hidden Markov Model (HHMM) predicts the transition between states and the packet classifier classifies the packet and communicates it to the sink through a high trusted path. The simulation results show that the proposed Fail Aware Trust determination algorithm outperforms the existing LEACH and QPRR protocols in terms of lifetime and throughput.

Paroxysmal atrial fibrillation is a life threatening arrhythmia which leads to sudden cardiac death. Cardiac professionals are always looking to obtain a maximum accuracy in identifying and treating heart disorders. The new method of automatic feature extraction and classification of paroxysmal atrial fibrillation is proposed in this paper. The first step toward classifying paroxysmal disorder is to decompose the ECG signals (healthy and unhealthy) using wavelet transformation techniques. Corresponding to these decomposed levels, the values of ECG signals are computed on the basis of entropy by using the method of cross recurrence quantification analysis. The classification was implemented by probabilistic neural network (PNN) concept. Overall gained accuracy by using PNN classifier is 86.6%. The purpose of this work is to develop a smart method for the proper classification of paroxysmal AF arrhythmias. Long-Term AF Database (Itafdb) and MIT-BIH Fantasia Database (fantasia) have been chosen from Physio Bank ATM for carrying out this work.

Now a day digitalization is everywhere in the world, and there is a huge amount of data transfer takes place every second on the internet. In this digital world major contribution of data as Images. The entire data transfer will take place on the internet, i.e., a public network, so the security of these Image data is very important. Image encryption can be done through different methods, these are cryptographic algorithms and various transforms were used for Image encryption. In this paper, an innovative Image encryption algorithm is proposed using Image Pixel Scrambling and DFrFT. The Image pixel scrambling operation is performed using the sequence generated by the chaotic function. The scrambled Image is encrypted through DFrFT. The developed algorithm improves the information security and the robustness of the system. The effectiveness and the efficiency of the proposed algorithm are verified from the simulation results.

Internet of Vehicles (IoV) has gained immense popularity with increasing research on fully integrated smart cities. The live data collectively gathered by various types of sensors or cameras installed on running vehicles, traffic lights, etc., forms an integral part of smart city ecosystem. Various emergency situations require vehicles to divert from their respective default routes to some more feasible and optimal routes. In this work, we implement two well-known algorithms Ant Colony Optimization and Particle Swarm Optimization to allow a coordinated dynamic route customization among the vehicles for an overall optimal traffic management. A centralized decision-making module is implemented by applying ACO and PSO on the data that continuously gathered from the vehicles in the live environment. The experimental setup consists of two well-known simulator software tools SUMO and NS2 that are accompanied by TraNS to provide mutual interactions between them. The results confirm an increase in the overall performance of the system with application of the optimization approaches.

One of the major reasons for poor recognition rate in handwritten character recognition is the lack of unique features to represent handwritten characters. In this paper, an attempt is made to utilize the similarity already exist in different parts of the Gujarati characters. A novel feature extraction technique based on normalized cross correlation is proposed for handwritten Gujarati character recognition. An overall accuracy of 53.12%, 68.53%, and 66.43% is obtained using Naive Bayes classifier, linear and polynomial Support Vector Machine (SVM) classifiers, respectively, with the proposed feature extraction algorithm. Experimental results show significant contribution by proposed technique and improvement in recognition rate may be obtained by combining these features with some other significant features. One of the significant contributions of proposed work is the development of large and representative dataset of 20,500 isolated handwritten Gujarati characters.

The Internet of Things (IoTs) applications have garnered its interest to realize low-power memory circuit based on emerging nanoscale transistors for its data processing unit. Therefore, in this work, we focussed on tunneling mechanism-based tunnel field-effect transistor (TFET) which can be a suitable option beyond-CMOS devices for designing reliable and efficient memory circuits for its key sensing and data processing unit. However, this work is further extended toward low-power design strategy to meet the essential requirements of IoT applications. For this purpose, a junctionless (JL) TFET based on work-function engineering is reported in this work, where a high-k material (HfO$$_{2}$$) adjacent to the SiO$$_{2}$$ toward source side is considered underneath the gate region to improve the ON-current of the proposed device. The main benefits of junctionless architecture is that it reduces the fabrication complexity, high thermal budget, and is free from random dopant fluctuations (RDFs). The significant benefits in terms of hold, read, and write static noise margin (SNM) of JLTFET-based six-transistor (6T) memory cell enables its potential application for IoT memory unit.

The health Internet-of-Things (IoTs) applications have motivated to design nanoscale transistor based biosensors. Therefore, in this work, we mainly focus on how emerging transistors such as tunnel field-effect transistor (TFET) can be an alternative beyond-CMOS features for biosensor design and to further increase the low power design strategy to enable it for IoT applications. For this purpose, a charge plasma based dielectrically modulated TFET is proposed in this work, where source region is having SiGe material (with Si and Ge composition equal to 0.5) to improve the ON-state current. The charge plasma concept is employed to make the fabrication process simpler and to avoid the random dopant fluctuations (RDFs) and higher thermal budget. The significant improvement in the sensitivity of the proposed dielectrically modulated junctionless (DMJL) TFET biosensor with different dielectric constant and charge density, and a comparison with conventional MOSFET-based sensor enables its potential application for the Health-IoT applications.

Mining patterns from databases is like searching for precious gems which is a gruesome task but still a rewarding one. The frequent patterns are believed to be valuable assets for the researchers that provide them useful information. The frequent and rare pattern mining paradigm is broadly divided into Apriori and FP-Tree-based approaches. Experimental results and performance evaluation available in the literature have established the fact that FP-Tree-based approaches are superior to the Apriori ones on various grounds. This paper explores the various modifications of FP-Tree that were developed to tackle the major pattern mining research challenges. Through this paper, an attempt has been made to review the usefulness and applicability of the most eminent data structure in the domain of pattern mining, the FP-Tree.

In spite of lucrative features, customers are still afraid of deploying their business in the Cloud. The main hindrance in the acceptance of cloud computing is lack of trust on Cloud service providers by promising customers. This paper introduces a game-theoretic technique for selection of a trustworthy Cloud service provider in strategic and extensive-form games. Potential Cloud service providers as well as consumers are assumed as the two rational players, whose actions are measured in terms of ordinal payoffs. We then solved those games based on game theory solution concepts. The solutions are demonstrated in this paper through experiment.

In this paper, a full adder using two-phase clocked adiabatic static CMOS logic (2PASCL) has been presented. A six-transistor X-OR gate has been used with transmission gate multiplexer. The simulations of proposed and other designs have been performed in 0.18 µm CMOS technology. The proposed design shows improved power delay product (PDP) in the range of $$0.34\, \times \,10^{ - 21} \,{\text{J}}$$ to $$1.12\, \times \,10^{ - 21} \,{\text{J}}$$ as compared to $$4.53\, \times \,10^{ - 21} \,{\text{J}}$$ to 6.7 $$8\, \times \,10^{ - 21} {\text{J}}$$ (static energy recovery full adder), 3.4 $$1\, \times \,10^{ - 21} \,{\text{J}}$$ to $$7.36\, \times \,10^{ - 21} \,{\text{J}}$$ (10 transistor), $$6.40\, \times \,10^{ - 21} \,{\text{J}}$$ to $$19.17\, \times \,10^{ - 21} \,{\text{J}}$$ (transmission gate) with a supply voltage variation of 1.2 V–2.8 V respectively. The proposed design also performs better at varying temperature conditions as compared to other existing designs. Simulation results of proposed design have been compared with existing designs reported in the literature and proposed design shows better performance in terms of PDP.

In this work, the architecture and modeling of two different RSA encryption and decryption public key systems are presented, for a key size of 128 bits. The systems that require different levels of security can be utilized easily by changing the key size. Two different architectures are proposed with and without MMM42 multiplier to check the suitability for implementation in Wireless Sensor Nodes to utilize the same in Wireless Sensor Networks. Synthesis and simulation of VHDL code is performed using Xilinx-ISE for both the architectures. Architectures are related in terms of area and time. The RSA encryption and decryption algorithm implemented on FPGA with data and key size of 128 bits, without modified MMM42 multiplier gives good result with 50% less utilization of hardware. As device utilization is less in the second architecture, the key size can be increased to have more security with good speed for Wireless Sensor Networks.

Femtocells are required to connect the end users and also provide better services to access various applications such as web 2.0 services, mobile multimedia, etc. The major challenge for mobile network operators is, to provide such services with better coverage and capacity to its end users. This requires efficient load balancing of femtocells corresponding to number of users, such that, the cellular network can provide services to the end users in smooth manner. This paper presents a heuristic search technique to optimize the load on femtocells to provide better services to the end users.

Approximate algorithms have been well studied in order to solve combinatorial problems. This paper addresses cryptanalysis of the substitution cipher which is an interesting combinatorial problem. For this purpose, we utilize one of the latest approximate algorithms which is referred to as cuckoo search. Here, we point out that the proposed cuckoo search algorithm is not only an effective and efficient approach for solving the considered cryptanalysis problem, rather it can be a true and efficient choice for solving similar combinatorial problems.

This paper proposes a new WSN-based Landslide Prediction Algorithm, developed using Fuzzy Logic Inference System. Three factors conditioning landslides are considered, namely: slope angle, soil moisture and topographical elevation. These conditioning factors are sensed using WSN and analysed using proposed algorithm at sink node. A Mamdani-type Fuzzy Inference System (FIS) is used to develop the algorithm. Triangular membership functions are considered for all FIS parameters. A total of 45 rules have been developed in this FIS, which holds capability to generate a three-level alarm to warn residents of area about any impeding danger due to landslide. For results, surface plots are generated which show us the variation of landslide susceptibility with the change in three parameters considered.

Energy reduction is an important factor and a challenge in the design of the embedded system. In this work, we propose TEST, a process-aware partitioning scheme to study the impact of partitioning scheme based on process aware in instruction cache for multitasking embedded system. Process-aware partitioning will partition instruction cache which is based on the mapping between process and cache memory. This technique results in 70–80% reduction in dynamic energy and 50–70% in static leakage energy as compared to the base set-associative cache architecture. Results of the TEST are evaluated using the simple scalar 3.0 simulator using the Mi-bench-embedded benchmarks.

In bucolic area, the majority of the people become extinct owed to unacquainted of their health conditions and inadequacy of hospital facilities. In view of this, patient monitoring scheme plays a most imperative role. This article mainly includes remote monitoring progression using WBAN with ZigBee protocol, abet for tracking the human’s health care in and out of the hospital, which facilitates to monitor precisely. Patient’s details are stored in IoT cloud and data analytics technique is exploiting to analyze that details. All the time, the patient’s records can be stored and retrieved in IoT cloud. Any variation occurs in the patient’s wellbeing stipulation means, it afford a message to the specified doctor and the patient protectorate.

In the recent years, the improvement in the security is a challenging task in the Internet environment The Intrusion Detection System (IDS) is one of the significant tools used to detect the attacks. Various IDS techniques have been proposed to identify the attacks and alert the user or administrator about the attacks. However, they are unable to manage new attacks. This paper proposes an Intrusion Detection System based on the density maximization-based fuzzy c-means clustering (DM-FCC). In this approach, cluster efficiency is improved through a membership matrix generation (MMG) algorithm. Dissimilarity Distance Function (DDF) has been used to compute the distance metric while creating a cluster in proposing an IDS. The proposed enhanced fuzzy c-means algorithm has been tested upon ADFA Dataset and the model performs highly appreciable in terms of accuracy, precision, detection rates, and false alarms.

CPU/FPGA hybrid systems have emerged as a viable means to achieve high performance in the field of embedded applications and computing. High-Level Synthesis (HLS) tools facilitate software designers and programmers to utilize the underlying hardware in a hybrid system without requiring deep insights into hardware. HLS tools execute the program in sequential order by default. However, these tools provide mechanisms to parallelize the code wherein the user/programmer can apply constructs such as loop-unrolling, loop-flattening, and pipelining in the form of pragmas. Along with all these constructs in place, it is also important for programmers to understand the memory access pattern used in the program for efficiently utilizing the underlying capabilities of CPU/FPGA hybrid system. Memory access patterns in array references play a major role in deciding the latency and area required for a specific computation. Four typical memory access patterns with growing input sizes in array context were exercised in Vivado HLS with C code as an input and it was observed that change in the memory access pattern leads to a different area and timing requirements and change in the coding style may improve the performance of HLS tools.

Fuzzy clustering methods have been used extensively for image segmentation in the past decade. The most commonly used soft clustering algorithm is Fuzzy C-Means. An improvised version of FCM called Intuitionistic Fuzzy C-Means (IFCM) has also gained popularity in the recent past. In this paper, we propose a new hybrid algorithm which combines intuitionistic fuzzy c-means and firefly algorithm to propose Intuitionistic Fuzzy C-Means with Firefly Algorithm (IFCMFA). Experimental analysis confirms that IFCMFA is far more superior to both FCM and IFCM. Several measures like DB-index and D-index are used for this purpose. Also, different types of images like MRI scan, Rice, Lena and satellite image are used as inputs to establish our claim.

Passwords are normally used for authentication in systems. They have several drawbacks like passwords can be guessed easily, they can be copied. Since biometric authentication is excelling in every field whether it be banking sector, corporate sector, etc., they are considered quite secure and mostly preferred for authentication. But every system has some flaws; therefore biometric authentication can be attacked so as to obtain any confidential information. One of them is face authentication system. Face is a unique characteristic that can be used to authenticate a person. Face authentication systems can be easily spoofed by using Replay and Printed paper attacks. Spoofing means real person’s identity is copied and used for harming any type of data. In this review paper, mainly LBP (Local Binary Pattern) descriptor is used, which is considered especially for texture analysis. LBP descriptor divides the captured face into blocks and calculates histogram for each block. Thus each block histograms are concatenated and finally are combined together. The formed histogram of whole face is compared with other face histograms and the similarity between the faces is found out. Spoof faces will not have similar histograms like the real face. And this helps in detecting Spoof face. Different spoof face detection methods are discussed in this review paper. Detection of spoof face is done by considering Moiré patterns, image distortion analysis algorithm. This review paper aims at securing confidential information by providing face unlock mechanism wherein spoof faces are to be detected.

Rolling into modern processor technology, developers are increasing the number of transistors exponentially. NoC is a proficient on-chip communication platform for SoC architecture; partitioning a die into segments and stacking them in 3D fashion significantly reduce latency and energy consumption. A new Cube Network-on-Chip (NoC) based architecture is proposed, which takes the advantage of this exponential increase. In this model, the number of processing elements can be increased exponentially, while reducing the space complexity. In this paper, the thermal impact of the proposed cube NoC model is analyzed. Power and thermal aware hybrid routing method is employed in this model, to improve the reliability and performance. The experimental results reveal that the hybrid routing approach, offers better throughput and failsafe packet delivery, compared to other approaches in the literature.

The main objective of vehicular ad hoc networks (VANETs) is to improve driver safety and traffic efficiency. Most of VANET applications are based on periodic exchange of safety messages between nearby vehicles and between vehicles and nearby road side communication units (e.g., traffic lights, roadside lights etc.). This periodic communication generates huge amount of data that have typical storage, computation, and communication resources needs. In recent years, there have been huge developments in automotive industry, computing, and communication technologies. This has led Vehicular Cloud Computing (VCC) as a solution to satisfy the requirements of VANETs such as computing, storage, and networking resources. Fog computing is a standard that comprehends cloud computing and related services to the proximity of a network. Since VANET applications have special mobility, low latency, and location awareness requirement. Fog computing plays a significant role in VANET applications and services. In this paper, we present real-time applications of VANET that can be implemented using fog computing.

Control-flow discovery algorithms of Process Mining are capable of generating excellent process models until the process is structured (less number of activities and paths connecting between them). Otherwise, process model with Spaghetti structure will be generated. These models are unstructured, incomprehensible and cannot be used for operational support. This paper proposes the techniques for (1) converting Spaghetti (unstructured) process to Lasagna (structured) process, and (2) Identifying the frequent execution paths in the process under consideration.

To carry out accurate research for person’s identification, it is necessary to know about NIR and VW images, by some easy method especially for iris recognition. This work presents method to know diversity and noise in the NIR and VW iris images, on the bases of eight different statistical parameters. For this work four VW databases: UBIRIS.v2, UBIRIS.v1 (season1), UBIRIS.v1 (season2), UPOL and four NIR databases: IIT Delhi.v1, CASIA.v1, CASIA.v4-iris interval, and CASIA.v4-iris twin were chosen. In calculated points, it is found that VW iris databases UBIRIS.v2 and UBIRIS.v1 (season2) posses most diversity. VW images are noisy compared to NIR images. Out of which UPOL images is most noisy. Intensity of VW images are less as compared to the NIR images. Average standard deviation of both NIR and VW images is equal (approx. 13.5).

Demonetization is the process of stopping currency in the monetary standard. Mr. Narendra Modi shocked the whole country at live telecast on November 8, 2016 at 8:15 pm by announcing demonetization of 500 and 1000 rupee notes from November 9, 2016, to handle the threat of black money, terrorist funding, and fake currency. In this article, we study and analyzed what the people of India feel about this decision of the government on demonetization and analyzed the effect on the business community. The results show that 51.02% people are supported this while the 34.69% are against it, rest 14.28% are neutral.

Prediction of disease severity is highly essential for understanding the progression of disease and initiating an early diagnosis, which is priceless in treatment planning. A Modified Cascade Neural Network (ModCNN) is proposed for stratification of the patients who may need Endoscopic Retrograde Cholangiopancreatography (ERCP). In this study, gallstone disease (GSD) whose prevalence is increasing in India is considered. A retrospective analysis of 100 patients was conducted and their case history was recorded along with the routine investigations. Using ModCNN, the associated risk factors were extracted for the prediction of disease progression toward severe complication. The proposed model outperformed showing better accuracy with an area under receiver operating characteristic curve (area under ROC curve) of 0.9793, 0.9643, 0.9869, and 0.9768 for choledocholithiasis, pancreatitis, cholecystitis, and cholangitis, respectively, when compared with Artificial Neural Network (ANN) showing an accuracy of 0.884. Hence, the proposed technique can be used to conduct a nonlinear statistical analysis for the better prediction of disease progression and assist in better treatment planning, avoiding future complications.

Proteins are macromolecules which are virtually involved in all of the life processes. The study of protein structures is of utmost importance in the field of bioinformatics. With the advancement in the field of computational biology, there has been tremendous upsurge in the sequential and the structural data deposition. The structure of a protein depends upon the sequence of the amino acids present in it, although similarity in sequence does not guarantee a similarity in structure. Despite the fact that the three-dimensional structure of protein molecule is very important to predict its functionality, yet the backbone of the searching has been majorly dependent upon the sequences rather than the structures. The leading platforms for searching structural similarity in proteins make use of sequence-based searching or text-based searching but do not provide the desired results. In the current manuscript, a model has been proposed to perform “content-based searching” on protein images. Content-based searching takes into account the visual/structure-based similarity and the information contained in the data sets rather than the traditional sequence-based searching. Intelligent Vision Algorithm has been applied to extract the visual features from the protein images for determining the similarity between two proteins. The proposed search engine model will result in an efficient and fast retrieval of similar protein structures.

Smart collaborations among the smart grid, electric vehicles, and aggregators will provide range of benefits to stakeholders involved in an intelligent transportation system (ITS). The EVs, nowadays, are becoming the epicenter of smart power system research towards the electrification of transport. However, massive penetration of EVs will pose management threats to the supporting smart grid in the foreseeable future. This work proposes a risk averse optimization framework for smart charging management of electric vehicles. Adopting conditional value at risk (CVaR) for estimating the risks, the work attempts to propose an optimized bidding strategy for the smart charging stations (SCS) that act on behalf of aggregators for managing the financial risk caused by the uncertainties. Finally, a fuzzified translation model is discussed along with notable methodologies as a solution strategy to the risk averse cost optimization problem.

This paper presents a new and efficient way to track the movement of vehicles and counting them for the purpose of better and economical vehicle parking system. The numbers of the vehicles are growing very rapidly which produced the problem of the parking. There are diverse techniques that have been introduced by many researchers those are used according to the need and scale. This paper is addressed the problem of vehicle parking for small scale by using the surveillance camera. A background subtraction is used to detect moving vehicle. The moving vehicles are tracked using the Gaussian mixture model and a foreground mask is created. Dilation is applied to remove inconsistent and noise particles. The variable intensity distribution is plotted on a histogram and changes are identified to count vehicles. Each parking spot can accommodate only certain numbers of vehicle. If the count has reached the maximum limit then the display unit guides the driver to next available parking spot.

Technological innovation in communication and embedded systems has led to a new paradigm called Internet of Things (IoT). Context in association with the things in IoT plays a crucial role in effective data communication and processing. Contextual information if integrated with recommender systems (RS) can recommend more appropriate things to users. In this paper, context-aware recommender system (CARS) from an IoT perspective has been explored and its various aspects have been considered in detail. In addition, correlation between context and IoT as well as perquisite of making context-aware recommendations in IoT has been discussed. Moreover, the context flow depicting the movement of context in context-aware systems and an application scenario has been delineated. Finally, the paper concludes with highlighting various challenges and open issues in CARS.

Augmented Reality (AR) is a new and developing technology capable of unlimited future prospects. It has gained much attention in recent years and is in its initial phases of research and development. Slowly and steadily, AR is spreading into day-to-day lives through audiovisual media and in other fields such as sports, entertainment, business, designs, etc. This paper provides an overview of the concept of AR including its definition, architecture, and the different latency factors which affect the rotation of arm of machine and limitations of AR.

Question answering field has evolved alongside the Natural Language processing. There are several small-scale applications that use the linguistic, semantic, and syntactic interpretations of text and consume it in further processing. In case of a web search, knowing what the query is intended for can save hours of CPU processing and decrease response time tremendously. We have taken a small step in this direction by treating the query as a question and classifying it with the best suited classification algorithm. In this paper, we have tried to find out that when a perfect-informer of the question (knowing what is asked) is provided as an input for classification algorithms like SVN, Naïve Base, and decision tree, we want to observe their accuracy on the same data set of questions. In our experiment, we have used the concept of CRF to find question features that are relevant. CRF is a probabilistic model that treats features as observation sequence and emits all sequence labels with probability values.

Today, clustering for Sensor Node (SN) is a method in Wireless Sensor Networks (WSNs) to diminish the energy consumption of the SN by avoiding long distance communication between the SNs. This will protract the lifetime of sensor networks. However, a cluster head has to perform various tasks such as collection of data from member nodes, aggregation of the collected data, and send that data to the BS. Network load balance is a challenging issue in WSNs for the clustering schemes. Genetic algorithms (GA) with clustering schemes are implemented for better cluster formation. The GA run through again over a large no of iterations to find the optimal solution that leads to premature convergence. The chaotic GA (CGA) will solve this problem by avoiding local convergence, i.e., by choosing a chaotic map to generate the random values instead of traditional random function and improves the performance of the traditional GA. A chaotic GA (CGA) based clustering algorithm for WSNs has been proposed in the proposed work that has better convergence rate for cluster head selection and consequently improves the performance of sensor network.

Big data and Analytics (BDA) is one of the most talked about technology trend having a widespread impact on organizational value chain. The objective of the study is to explore and examine the key organizational factors that impact the big data adoption in service organizations. A research framework—grounded in organizational theories and IT adoption—examines the impact of four organizational variables on big data adoption and finds that three of them have a strong positive impact. The survey instrument is developed by employing rigorous measurement scales. The study targeted around 500 service organizations headquartered at Mumbai; of which 109 suitable responses are received. Structural equation modeling using the variance based, prediction-oriented PLS model estimation—SmartPLS is applied for testing. The precision estimation and standard errors are evaluated using bootstrapping with 109 cases and 300 samples (resamples).

Delivering better product value on each revision is the principal task for an organization’s existence in a competitive market. The customer’s base needs to be continuously increased to earn more revenue. Feedback is collected from customers to decide functionalities that need to be included in the future versions of the software. A software company gets flooded with feedback, which makes it difficult to analyze, process, and prioritize. Feedback may result in addition of new code in order to implement new functionality or to delete/modify existing one. Each task which alters the source code to implement a feedback requires modifying test suites which enhance the testing effort thereby impacting project schedules, budget, and plans. In this paper, we highlight a research framework that attempts to lower the testing effort by reducing the number of test cases by compounding the feedback which is similar to existing requirements of same or similar projects.

The quick extension of mobile big data by telecoms vendors, has presented a flexible worldwide platform that creates a user interface for many app data bases or app stores. Big data is a tremendously well-known idea, however what are we truly talking about? From a security point of view, there are two particular issues: securing the app stores or app databases with its source of information in big data context and utilizing big data procedures to break down, and even anticipate, security flaws. The main issue arise that many hackers or attackers are targeting mobile big data in the form of signaling big data, mobile traffic big data, location-based big data, and heterogeneous data in app store. In this paper, we are taking Android-based mobile operating system for experimental setup. This paper contains an extraction technique to extract the malware in different big data context and also analysis of these malware. We have worked with many Mallarme family (as approx 40 K malware) in mobile big data and result of the whole analysis is approx 90% to identify the current malware in mobile big data.

Medical education places emphasis on situational learning of real-life clinical contexts, while simultaneously focusing on human body three-dimensional (3D) visualization. However, classrooms and laboratories being the main learning environment in early years of medical education, there is limited exposure to real clinical environments to adequately meet such objectives. This study proposes that augmented reality (AR) applications can provide both affordances. Such applications fulfill many of the criteria for smart learning environments (SLE). A systematic review aiming to identify the affordances of AR applications, their design architectures, and impact evaluations was conducted. This review evaluated 25 studies and, with model case studies, analyzed how the different AR applications provided situational learning and visualization in medical education and how their design architecture provided such affordances toward contextualized, interactive, and personalized SLEs. It was found that AR affords facilitation of situational learning and visualization individually. Their integrated educational impact, however, needs to be evaluated further.

The paper presents development of a sole for protection of female using Arduino microcontroller which is named as ‘SAFE SOLE’. The research goal of safe sole is to develop a safety device/tool for females in the event they might face any danger. The advantages of automaticity of safe sole are hands-free user independence; it uses GPS and GSM to ping user’s location automatically. The device is programmed in such a way that it recognizes defined movements as tapping forcefully three times on ground or any abnormal/vigorous movement/gait of user instantaneously and pings the distress signal and the location to relevant authorities and saved contacts, and additionally taking countermeasures—triggering of siren. In view of this, aforementioned system is a source of developing a product for general population which helps in maintaining the safety of the user by eliminating the involvement of user to initiate any procedural activities against any situation which user might feel in danger.

Cloud computing is a reliable computing platform for large computationally intensive or data-intensive tasks. This has been accepted by many industrial giants of the software industry for their software solutions; companies like Microsoft, Accenture, Ericson, etc. have adopted cloud computing as their first choice for cheap and reliable computing. By increasing the number of clients adopting this, there is a requirement of much more cost-efficient and high-performance computing for more trust and reliability among the client, and the service providers to guarantee cheap and more efficient solutions. So the tasks in cloud need to be allocated in an efficient manner to provide high resource utilization and least execution time for high performance, and at the same time provide least computational execution cost. We have proposed a learning-based grey wolf optimization algorithm for task allocation to reduce the request time and scheduling time to improve QoS (Quality of Service). Proposed algorithm has been inspired from behaviour of wolfs hunting in real world with a unique technique which they have evolved from long evolution and learning cycle

A lot of energy is consumed in organizing the wireless sensor network. Several cluster-based protocols have been proposed to increase the lifespan of the network. The objective of WSN is the lifetime enhancement through energy efficiency. Clustering not only enhances the lifetime but also makes the network scalable and energy efficient. In this proposed work (SPEZ), we have divided the network into zones and randomly deployed the sensor nodes. Extraneous energy is supplied to some of the nodes resulting in heterogeneity. Energy-efficient approach is applied so as to increase the stability span which plays a vital role in many scenarios. Simulation and evaluation of performance validate the elongated lifetime in terms of the stable period in comparison with ZSEP, SEP, LEACH, and DEEC protocols.

Transmit beamforming in MIMO technology is employed to improve receiver SNR. It was added to 802.11n WLAN as an optional feature but not implemented. In 802.11ac, as this technique is highly simplified and also standardized, it is foreseen as a major contributor in improving performance and is expected to be extensively used in 802.11ac devices. In this paper, this feature is studied in depth through simulations in MATLAB and the performance improvement is measured (using parameters like received power, EVM, and constellation diagrams) comparing the results to another MIMO mechanism—Spatial Expansion.