Micro-Electronics and Telecommunication Engineering

This paper’s aim is to improve the operation of tracking and locating transmitters through leveraging of the emergence of cost-effective software-defined radios (SDRs) and the additional degrees of freedom of an unmanned aerial vehicle (UAV) platform. Different radio direction-finding (RDF) systems and techniques are investigated to find a suitable solution that fits within the constraints of a suitable UAV platform.

Wines are being produced since thousands of years. But, it is a complex process to determine the relation between the subjective quality of a wine and its chemical composition. Industries use Product Quality Certification to promote their products and become concern for every individual who consumes any product. It is not possible to ensure quality with experts with such a huge demand of product as it will increase the cost. Wine-makers need a permanent solution to optimize the quality of their wine. This paper explores the space to easy out and make the whole process cost-effective and more trustworthy using machine learning. It allows to build a model with user interface which predicts the wine quality by selecting the important parameters of wine which play a significant role in determining the wines quality. Random forest algorithm is used in determining wines’ quality whose correctness would further be escalated using KNN which makes our model dynamic. Output of this proposed model is used to determine the wines’ quality on a scale of Good, Average or Bad. This proposed model can further be applied to several other products which need quality certification. Our prediction model provides ideal solution for the analysis of wine, which makes this whole process more efficient and cheaper with less human interaction.

Growth of Internet resulted in increased number of Internet users along with wide use of Internet. Besides its advantages, the disadvantage of this exponential rise is excess data flooding on the network. To ensure good quality of service, to maintain the speed of Internet, to secure the data flowing on the network, it has become essential to monitor and control the data traffic. Analysis of dataflow involves categorizing it into different types and further filtering it. On the basis of port numbers, payload information, source and destination IP address or statistical information, the data packets are categorized. This paper discusses classification of Internet traffic into different transaction protocols categories, on the basis of statistical parameters such as inter-packet arrival time, time to live, duration of packets and number of packets on the network. Categorizing using statistical parameters prevents invasion of packet data and preserves data privacy. Use of machine learning reduces human intervention in monitoring the Internet traffic. Classification of Internet traffic in the UNSW NB 15 data set is done using five machine learning algorithms, which are K-nearest neighbours, Naïve Bayes, artificial neural network, decision tree and random forest. The aim is to achieve maximum accuracy in minimum execution time. Amongst all algorithms, random forest algorithm gives best result with classification accuracy of 85%. Decision tree requires least execution time and gives accuracy almost equal to random forest algorithm.

Complexity management and system performance optimization are achieved using machine learning in high level of digitized automation systems. In this paper, we worked on enhancement of the link state heuristic performance in terms of reliability, scalability, power consumption and capture effect with outcomes that have demonstrated the usefulness, flexibility and configurability with security. Handover in heterogeneous ad hoc network plays an important role in the performance of the network. As technology advances, massive IoT devices communicate with each other with different technologies and transmit the data to the desired device through cloud or fog by using different technologies. Cross-domain, cross-platform, cross-network with cross-layering can enhance the operation of smart projects. QoS is desired for video and heavy data traffic. In this paper, we presented the handover within HetNet with its performance. Observation shows that link state heuristic for medium and dense area performs better. The impact of queue size, distance and packet size is represented in the graph.

Being the major influential aspect within the lives of the people, health has become an increasing cause of consciousness and concern today. In this paper, a healthcare system has been set up and designed which enables patients to collect daily, the vital parameters at home and sending them over the cloud with the use of IoT i.e. Internet of things. Identification of a set of five parameters—weight, temperature, heart rate, pulse oximetery, and attentiveness of alcohol in breathe, is done by using biomedical sensors which are interfaced with Arduino Uno microcontroller that transmits the recorded data through ESP8266 Wi-Fi module on IoT platform i.e. ThingSpeak. The vital parameters can be visualized and monitored on devices that include desktops, laptops, or smart phones which are connected under similar networks.

Nowadays, electrical energy is playing vital role in human life. The economic operation of the power system is always desirable and this can be achieved by economic load dispatch. The economic load dispatch means allocating the power to different generating units to minimize or reduce the total fuel cost satisfying the different power system constraints. The main purpose of economic load dispatch (ELD) is to allocate the total power at generating units to meet total load demand with minimum operational cost fulfilling all operational obstacles (constraints). For optimal dispatch of power, different evolutionary optimization techniques are being used like CSO, PSO, etc. In this paper, PSO has been implemented on MATLAB to minimize the total cost for 3-unit and 6-unit systems and the results are compared with CSO and other evolutionary optimization techniques for different power demands.

In order to avoid damage to mechanical structure, the vibration damping is necessary. The conventional methods of damping are less effective as well as tedious. This paper deals with passive techniques of vibration damping using piezoelectric material. Piezoelectric materials called as smart materials are bonded to structure and can be used as actuator as well as sensor. By rigorous variations of R and L values, a RLC circuit is designed, which nullifies the vibrations at the frequency of resonance. It results in 80% of vibration damping as compared to conventional methods.

In the present scenario, solar energy contributes maximum to the pool of renewable energy. The rate of consumption of energy is increasing day by day; therefore, the demand of energy is also increasing. To satisfy this increase in demand of energy, building integrated photovoltaic module (BIPV) system is one of the ways to satisfy the consumer demand by providing the energy according to the consumption, since the BIPV system is mainly affected by the outermost conditions the most, and therefore, in the present work, its performance is analyzed under the effect of external conditions. In this paper, the observations under different external conditions are taken which follow the effect of coal, dust, and shading as well as for the normal conditions. The results are then compared and are observed that the performance of BIPV solar panel is low under the effect of external conditions as compared to normal conditions.

In the course of recent years, the size of individuals with diabetes mellitus has been dramatically increased than before. There is a need for screening and interventions which could prevent the individuals from the serious diabetic complications. Prediabetes may be a forerunner of type two diabetes mellitus, as well as a risk factor for heart illness. The body temperature is an essential parameter used for indicating the abnormal activity of human tissues. The thermal imaging primarily uses the infrared radiation emitted from the body naturally. The aim of this study was to evaluate the potential of thermography in screening the prediabetes. Sixty subjects were recruited for this study. Group I: HbA1c is <5.7%, Group II: HbA1c is 5.7–6.4%, Group III: HbA1c is >6.5%. The plantar thermograms were captured, and the temperature was measured at toe, metatarsal 1, metatarsal 3, metatarsal 5, instep and heel, respectively. The HbA1c was measured using the standard biochemical method. Three groups were categorized based on the accuracy rate obtained by five different machine learning algorithms (support vector machine, random forest, Naïve Bayes, multilayer perceptron and k-nearest neighbour). In prediabetes group, HbA1c exhibited positive correlation with measured temperature at toe region (r = 0.917, p < 0.01) and the negative relationship with measured temperature at metatarsal 1 (r = −0.474, p < 0.05), metatarsal 3 and heel regions (r = −0.895, −0.901, p < 0.01). The support vector machine has outperformed the other classifiers with good accuracy rate as 81.6%. The findings from this preliminary study indicate that measured temperature from plantar thermograms may be useful in screening the population for prediabetes.

Nowadays, our day-to-day life starts with smart devices those are fall under the Internet of thing gadgets and these gadgets are becoming a part of our lifestyle of which the health care becomes the most crucial area. Researchers provide a hint of technology, by making health care it through Fog computing in order to make the information transfer through IoT devices simpler. Interoperability and security are particularly major issues raised by such impediments. In this paper, talk about the present issues, including advantages and challenges, ways to deal with, avoid the issues by utilizing, and incorporating gadgets in medicinal services frameworks. Here, present the talk with regards to the Fog networking venture, which focuses on an answer for home consideration, monitoring the patients with ceaseless ailments.

Rheumatoid arthritis (RA) is a systemic life-threatening disorder and affects the body’s self immune system and invades the smaller and larger joints. There are different modern imaging techniques used such as magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT) and colour Doppler ultrasound were used to evaluate the inflammatory conditions in RA. Still radiography (X-ray) was investigated as the best method in diagnosis of RA. X-ray imaging has been the easiest and most used method of diagnosis of any abnormalities in the hard tissues of the human body. The study aimed as follows (i) to perform a manual and semi-automated segmentation of feet region in the total population studied. (ii) To compute the features extracted from the joint space width, joint space narrowing and erosion in the feet. The analysis of X-ray images was performed using manual method (DICOM viewer) and semi-automated method using Mimics software. The SPSS statistical analysis software was used to calculate mean standard deviation and standard deviation error and further plotted accordingly. There was a significant differences found between the manual and semi-automated methods. Furthermore, it was inferred that the semi-automated method showed more precise readings of the joint space narrowing in RA and normal feet and should be used more extensively for further study.

The Brahmi script is widely used in ancient time for writing the various types of religious books, information about the rules and regulation of the kingdom of king Ashok and Pali language is used to read the Brahmi script. Pali to English dictionary is available to understand the Pali language. However, any dictionary or translation system related to Brahmi script to Pali script is not available. In order to understand the content in Brahmi script, an automated system for facilitating machine translation of Brahmi text to Pali language is suggested in this study. Detail research on Brahmi script to Pali language has been accomplished, and necessary rules are analyzed. Based on the research and analysis, this study illustrates one-to-one and many-to-one mapping system for Brahmi text to Pali language translation.

Publicly available dataset is important for character, word or document recognition. The use of a standardized dataset will provide a fair or reliable comparison between the performances of the underlying recognition algorithms. Research on Brahmi words recognition had achieved encouraging results. However, there is no publicly available standardized Brahmi dataset. In this paper, the steps in producing a publicly available Brahmi dataset are presented. These steps include data collection, segmentation, storage, labeling, and statistical distribution. A total of 7,011 images of Brahmi characters were collected. The collected dataset is divided into three classes: vowel, consonants, and compound characters. In total, there are 170 classes with 4 of these classes belong to vowels, 27 classes of consonants, and 139 classes of compound characters. The 170 classes of characters are further divided into training and testing sets; 6,475 images in the training set while 536 images in the testing set.

In the present scenario, the requirement of wideband antenna is increasing day by day. Keeping the view, a symmetrical wideband fractal boundary antenna is proposed in this paper. This proposed antenna with DGS structure has wideband with a compact size of 25 mm × 25 mm. Proposed antenna was simulated using IE3d antenna simulator software and got good result from 8.5 to 11.7 GHz. Presented antenna is resonating at 10 GHz, achieved wide bandwidth of 3.2 GHz, which is quite high. To validate the simulated result, the proposed antenna was fabricated, tested, and very good result has been achieved. Simulated and measured result has been compared, and there is a little deviation in the results due to minor changes in the physical dimension. This antenna can be used for X-band application.

This paper presents a deep neural network model for performing spam detection. Unlike conventional machine learning models like naïve Bayes, support vector machines, a deep neural network is immune to various fluctuating environments. This paper also proposes the application of CountVectorizer in order to perform feature extraction on the text-based data. In order to increase the accuracy score of the proposed model, hyperparameter tuning has also been done. This paper also compares the accuracy of the proposed deep neural network to various machine learning classifiers like logistic regression, support vector machine, k-nearest neighbor, Bayes, etc. Experimental results of this paper show that the proposed deep neural network model is able to outclass all other machine learning models in terms of achieved accuracy score, and naïve Bayes classifier is the most efficient model with respect to its computation cost.

In the world of computer, the skill of sending and displaying secret data particularly in public places faced several challenges and had received more attention now. Protecting information on communication media is a vital need in information transmission technology. We can protect information through encryption process over the transmission media but due to advanced computing techniques, encrypted information can easily be detected and decrypted. So, for avoiding the unauthorized user from accessing information, we need some more advanced techniques. For this purpose, we use steganography which is a technique to conceal secret message over communication networks. In this paper, IWT-SVD scheme is proposed to conceal watermark image in video cover file. Concealing watermark in video cover file gives higher concealing capacity. It has been shown that concealing watermark in HH and LL sub-band results in good perceptual quality, more robustness, and less computational cost. Simulation results also show that this new scheme outperforms adaptive steganography based on IWT-SVD in term of PSNR, MSE, and concealing capacity.

Cross-domain recommendation Systems (CDRS) is a significant research area which has been target of many companies these days. From last few years, there is amount of publications in CDRS domain including recommendation systems which have been rising sharply alongside information retrieval and machine learning. Recommender systems help companies to specifically identify preferences of the user from the collected data from various sources. These data values are then used to identify user preferences also known as recommendations. In cross-domain, we use the data from one domain such as movies to recommend items in other domains, for instance, Books. There are numerous issues which recommender systems suffer together with sparse data, synonymy, data privacy, algorithm scalability, perspective awareness (context) and cold start problems. Data sparsity is a major issue in recommender systems, especially in the presence of novel users or items, or when user drift exists. This paper reviews recent efforts made for CDRS sparsity and user drift which are prevalent in most CDRSs such as user-based, item-based or knowledge transfer. This paper formalizes the CDRS illustrates sparsity related issues which are addressed in prior works and finally proposes for future research trail.

Internet of Things (IoT) applications employ several sensors for gathering data. These sensors are often placed at hard to reach locations since they need to be left unmonitored. Sensors collect data in real time and send it to a cloud server for further processing. Often the sensors generate a large volume of data which is redundant in nature. Transferring sensor data to a cloud server for processing leads to high bandwidth consumption, delay, and increase in operational cost and data security issues. Edge computing allows the sensor data to be stored and analyzed on an edge device, and only the data summary is sent to the cloud server. In this paper, an edge computing approach for managing and analyzing data in a weather monitoring application is proposed. The application has been built using a Raspberry Pi system. The computation has been performed by creating Microsoft Azure IoT Hub resource and Microsoft Azure IoT Edge solution. Results have been compared with AzureML cloud platform.

In the present era, technology plays a very significant role in human life. It makes human life easier and has become a crucial part of it. Like air, water and food, technology has become a necessity for human survival. Specifically, technologies related to the areas of computer science and the internet play a very wide role in day-to-day life. Everyday new internet technologies and computing devices are being launched into the market and they define the working style of human beings. With this ever-increasing demand and usage of technology; the amount of carbon footprint is also increasing as a byproduct of technology. The increased amount of carbon footprint accounts for considerable global warming. Energy requirement for the huge number of computing resources is also huge and so is the magnitude of heat produced resulting in global warming. Thus, there is an urgent need of a transition towards green computing. IT Companies/industries should integrate green agenda in their products. Green IT is not a technology itself but it is a transition from conventional computing to green computing. This transition may help to curb negative effects on the environment.

The work mainly focuses on the detection of a hazardous analyte like silicon carbide using TGTFT which has an ITO gate, a bio-receptor of silicon nitride and further compared with the same device with an additional analyte, i.e., silicon carbide added into it. Transfer characteristics and some more electrical properties have been simulated and compared. The drain current (Id) of the analyte TFT increased by 21.59% in contrast to the device which has air. A substantial increment of 17.34% in the electric field of the analyte-added TFT was observed in contrast to the air-filled TGTFT. Some changes were observed in valence band energy (VBE) and conduction band energy (CBE) of analyte device and without analyte device. The addition of analyte changes chemical composition of interface, i.e., changes the electron concentration at interface and therefore altering the effect of specific gate voltage as potential of the interface changes resulting in different results from the one with no analyte. Therefore, changes in the electrical properties of the device pave the way of hazardous analyte detection.

In this study, the main objective is to carry out the robustness analysis of an artificial intelligence (AI) approach, namely nonlinear autoregressive neural networks (NAR) using Monte Carlo simulations for predicting the high fluctuation rainfall. Various algorithms of the NAR including Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) were developed. Statistical criteria, namely coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), were used to quantify the impact of fluctuations on the prediction output. Results showed that SCG algorithm was not sufficiently robust, while LM and BR methods exposed a strong capability in forecasting daily rainfall. In addition, prediction using BR was slightly better than LM, especially in terms of standard deviation of R2, RMSE and MAE distributions over 500 Monte Carlo realizations.

In this paper, a prediction model using Nonlinear Autoregressive Neural Networks with external variables (NARX) was proposed in order to forecast daily rainfall at Hoa Binh city, Vietnam. For this aim, eight-year time series of meteorological data were first collected, involving temperature, wind speed, relative humidity, solar radiation as input variables and daily rainfall as output variable. NARX-based daily rainfall prediction model was then constructed and validated using various criteria such as coefficient of correlation (R), root mean squared error (RMSE) and mean absolute error (MAE). Results show a good statistical correlation between measured and predicted rainfall values, i.e., R = 0.8846, RMSE = 5.3793 mm, and MAE = 3.0218 mm. Therefore, it is reasonably stated that the developed model is promising for the forecast of daily rainfall.

This project describes, in detail, the different regression models used in the analysis of population on various parameters like HDI, GDP, energy consumption and Internet usage. Population analysis can be useful in providing demographics of an area, help in targeted marketing and also help government to identify the areas of weakness of a country. Here, we have taken major countries and their various parameters like HDI, GDP, etc., and applied the regression models on them to find the most suitable one.

Disclosure of sensitive data leads to identity theft and violation of privacy. The untrusted cloud service provider (CSP) may try to disclose/misuse the data. It is necessary to provide access control and security over the outsourced and shared data to hide it from the CSP and unauthorized users. Traditional access control schemes are prone to security threats in the cloud environment. Attribute-based access control schemes (ABAC) are well suited for the cloud environment. Attribute-based encryption (ABE) is a promising cryptographic solution to provide fine-grained access control over the shared data. It selectively shares the data among the users and hides data from the CSP and unauthorized users. It preserves the privacy of users and the security of data being shared. Users can decrypt the data only if their attributes are satisfied with the access policy associated in the ciphertext. This paper presents a comprehensive survey of the ABE schemes. Taxonomy, performance comparison, and applications of ABE schemes are dealt with. The taxonomy and performance comparison help the selection of the most suitable ABE scheme based on specific usage scenarios. Thus, the survey opens up very interesting avenues for further research in this area, which are also discussed.

In this work the performance of Junctionless Gaussian Doped Negative Capacitance Silicon-on-Insulator (JLGDNCSOI) transistor has been explored to examine the suitability of device for various analog and digital applications. The Negative Capacitance phenomenon of ferroelectric layer along with vertical Gaussian doped channel significantly enhances the performance of JL devices. To explore the electrical characteristics of JLGDNCSOI transistor TCAD models along with Landau-Khalatnikov equation which takes into account properties of Hafnium oxide based ferroelectric layer such as coercive field and remanent polarization have been used. It has been demonstrated that device exhibits substantially improved transfer characteristics, output characteristics, transconductance generation factor, output conductance and unity gain frequency.

The development of a down-converter is investigated that can be used for a software-defined radio (SDR) environment. The paper also compares down-conversion receiver architectures, and an alternative front-end receiver architecture is proposed. The proposed front-end receiver will extend the range of the typical SDR to the X band which will help reduce the complexity and cost of higher-frequency SDRs.

The Internet of Things (IoT) is not just a promising inquire about subject yet in addition a sprouting modern pattern. In spite of the fact that the essential thought is to bring things or articles into the Internet, there are different methodologies, in light of the fact that an IoT framework is exceptionally application situated. This paper displays a remote sensor network (WSN)-based IoT stage for wide territory and heterogeneous detecting applications. The stage, comprising of one or different WSNs, entryways, a Web server, and a database, gives a dependable association between sensors at fields and the database on the Web. The WSN is fabricated dependent on the IEEE 802.15.4e time opened channel jumping convention, since it has the advantages such as multi-jump transmission, impact-free transmission and high vitality proficiency. Notwithstanding the plan of at equipment for range expansion, synchronization conspires and a burst transmission highlight is likewise displayed to support the system limit and decrease the vitality squander. Accordingly, the proposed stage can satisfy the high throughput necessity for high rate applications and the prerequisite of long battery life for low-rate applications in the meantime. It has built up a testbed in our grounds to approve the proposed framework.

This paper presents the comparative analysis of average power dissipation for conventional CMOS and different adiabatic logic techniques like efficient charge recovery logic (ECRL) and positive feedback adiabatic logic (PFAL) based digital circuits like inverter, NAND, NOR, 2:1 MUX, EXOR, and full adder. These circuits are based on reversible logic that works on AC power supply which can be trapezoidal or sinusoidal voltage source. The analysis of average power and delay is carried out at 180, 90, and 45 nm technology files for different frequencies. The result shows the significant reduction in power dissipation up to 26, 36, 16, 59, 73, 99% for inverter, NAND gate, NOR gate, EXOR gate, 2:1 MUX, and full adder circuits, respectively with adiabatic logic comparatively CMOS within specified frequency range of 1 kHz to 1 MHz and also manifests till what extent the power can be reduced so as to avoid degradation in performance. The design and simulation are performed on cadence virtuoso EDA tool.

Universities/higher educational institutions are finding ways to increase the student-faculty interactions beyond the traditional classroom, helping institutions to gather the information to enhance the student learning experiences with the help of learning analytics. These interactions are captured using the virtual learning environment through which institutions learn from the student interactions and behavioral patterns within those systems. This helps the institutions for better retention rate, prediction of the results and focus on weak students. Many institutions have placed an early detection system for management and faculty to engage with the students and figure out the problems faced by the students and provide a remedy to improvise for the faculty members. Most of the institutions rely mainly on one system such as the learning management system to capture the student interactions thus creating a gap. The Internet gives an edge to its users for practicing, learning, by doing, this leads to the emergence of video-based learning technologies that are practiced and used in several ways, such as flipped classrooms. Student faces a doubt often in their phase of learning, to clear their doubts they refer to multiple sources to get the information and knowledge. These videos provide complete skill sets, students due to lack of skill set they use these sources for their specific problems. This paper discusses literature and background studies on the big data used in institutions of higher education. It establishes a framework based on the latest trends in this area that can help stakeholders to predict their business needs.

Interference from the nearby wireless communication bands may weaken the desire frequency signals. Hence, to overcome or minimize the problem of interference from nearby wireless communication bands, a dual stop band unit cell structure of FSS that realized from the pass band geometry of FSSs, is proposed. Complimentary structure of the FSS unit cell offers transmission characteristics which are complimentary of the original transmission characteristics. For example orignal FSS unit cell have single square shaped structure that imprinted on substrate offers stop band transmission characteristics. In addition complimentary of the square loop that is square shaped slot in substrate offers pass band transmission characteristics. Therefore, the stop band transmission characteristics from pass band can be realized by simply replacing the metal elements of FSS unit cell structure by slots/aperture of the same size and geometry (i.e. complimentary of the original FSS unit cell structure). But here, it is important to mention that the resultant frequency response of the complimentary structure will not be exactly opposite to that of frequency response of original structure. Therefore, the FSS unit cell is optimized to get the desired stop band response for S and X bands.

Cross-project defect prediction (CPDP) is a technique of detecting defects in software modules in which the training and the testing projects for the classification model are different. The effective prediction leads to a more reliable software. The merging of dataset from varying sources results to an imbalanced dataset. The complex structure and the imbalance data make it a challenge for an effective cross-project defect prediction. To overcome these issues, in this paper, we propose a cross-project defect prediction framework. In the first stage of this framework, PCA is applied for dimensionality reduction of the dataset into two components. In the second phase, SMOTE technique of data sampling is applied to handle the class imbalance problem. Then the ensemble classifiers random forest and XGBoost are applied for an effective defect-prediction model. We have conducted the experiments on eight open source software projects. The results are compared with few baseline techniques. The results indicate that the proposed framework gave comparable performance of cross-project defect prediction to some baseline methods.

This paper proposes the use of triangular cross-sectioned CNT (T-CNT) bundle interconnects for VLSI circuits. The geometry of T-CNT bundles has the advantage of offering least possible crosstalk between adjacent interconnects. The performance factors like propagation delay, power dissipated, crosstalk delay, crosstalk power, On/OFF time of the output waveforms and the output waveform swing are analyzed and compared with traditionally used square CNT (S-CNT) bundle interconnects. Results show that T-CNT bundles offer lesser power dissipation at longer lengths >1000 µm. Also the crosstalk delay is lesser for T-CNT bundles compared to S-CNT bundles. Both the types of interconnects are driven by GAA CNTFET based inverter circuits.

This paper reports a tunable sinusoidal oscillator using current-controlled differential difference current conveyor (CCDDCC) with grounded capacitor and resistor. The proposed oscillator circuit has the following advantages, viz. minimum number of active components, suitable for high-frequency operation and electronically tunable frequency of oscillation (FO) and condition of oscillation (CO) using bias current of CCDDCC. The workability test has been examined using PSpice with 0.18 μm CMOS technology.

The community detection in a given network is the idea to find a cluster in the structure. A community is the most densely populated part of the graph. The observed network is mostly sparse having multiple dense partitions in it, for example, a protein–protein interaction network where different proteins interact with each other. Here, communities can be detected by finding the cluster of proteins in the network to find different functional modules. Another example is of Facebook friendship network. Several authors try to find the structure and communities in this type of network. Multiple clusters in one network can also be detected which can overlap with each other. This paper covers the classification of different community detection techniques in dynamic networks and then compares them on the basis of different features, e.g., parallelization, network models, community instability, temporal smoothness, etc.

The proposed design is composed of simple ultra-wideband (UWB) monopole antenna. The structure consists of semi-rectangular ground plane and rectangular patch on opposite side of the FR4 substrate. In order to achieve ultra-wideband characteristics, half ring-shaped tapered microstrip feed is utilized at the patch. By introducing a square slit at lower edge of the rectangular patch, wide bandwidth can be easily realized. The bandwidth can be further enhanced by creating defect in ground structure. The most impressive feature of the proposed design is its compactness (17 × 25 mm2). Also, this antenna structure has reflection coefficient S11 < −10 dB and voltage standing wave ratio VSWR ≤ 2 in the whole realized band from 2.1 to 15.8 GHz. The overall antenna structure is simulated on substrate having dielectric constant 4.4. The bandwidth of proposed antenna is wide enough to be used in wireless communication applications such as Wi-MAX (3.3–3.8 GHz), WLAN (5.1–5.8 GHz), and X-band communication systems (7.25–7.75 GHz).

The system obtained in this paper is an advanced result for monitoring weather conditions in certain places and anywhere in the world. Prepared with physical equipments, cars, structures and sensors, electronics, software, and other network-related matters make these items in statistical collection and conversation. Sensor control through environmental monitoring information and sensor temperature, humidity, light intensity, rainfall, MQ-135 pollution sensor, and BMP180 air pressure sensor send information to web pages and then sensor data as graph data. The latest data design of the implemented system can enter the Internet anywhere in the world.

Whether it is common cold or something as big as a brain tumor, a timely detection of a disease goes a long way in curing of a disease or even the survival of the patient. There are just so much more options available at the initial stage than at the last. There are various ways to detect brain tumor such as neurological exams, computerized tomography (CT), positron emission tomography (PET), and magnetic resonance imaging which have various steps that if not applied carefully may or may not yield helpful results, and these steps are pre-processing which includes noise removal, image enhancement, filtering, edge detection, etc., segmentation, feature extraction such as thresholding and image subtraction, and finally, area estimation. For filtering, we are going to be using anisotropic diffusion filtering techniques which reduces the contrast with nearby neighboring pixels.

‘Disability’ is a state of body where it lacks any vital senses or organ functioning which limits a person’s usual activities or movements. Due to a disability, any individual had to suffer a lot, as a disabled person had to be dependent on some other individual. It is generally observed that disability not only affects their physical condition, but it also exacerbates their mental condition as they are treated differently than normal humans in society. Being treated differently by society also affects their psychological health which is generally seen in disabled children. Several times, it is quite difficult to find teachers who are prepared for students with special needs in every geographic area which overall affects their education. The current work proposes how modern hardware along with the implementation of appropriate technology can be made useful to disabled people up to a great extent. The present invention relates to devices operable by the deaf, mute or people having low vision. The present invention more particularly relates to a multi-use simple, low cost and efficient point of sale terminals operable by the deaf, mute or people having low vision. The device can also be implemented at several public as well as private platforms where there is limited interaction. The device has an enormous scope of implementation at a departmental store, shopping malls and movie theatres, where range to employ such PWD person at the job position like cashier, store inventory manager and at several other places using present invention. The device is customizable, easy to use, efficient, economical and robust.

The advent of the technological advancements has set up a stage for multifarious online businesses in India, therefore, changing the course of the ordinary shopping trends in the direction of online shopping. India has a total of 560 million end users of the Internet and has been emerged as the second largest applicant in this domain. As per a study in 2011, the retail market of India was estimated at 470 billion dollars. The E-commerce sites cater the innumerable needs of the shoppers. From such a big list of these sites, to pick one, it is termed as a multi-criteria decision-making (MCDM) problem which makes it hard for the online shoppers to come to a conclusion that which site is the best to go with. With the objective of exercising judgment upon the ranking of the E-commerce sites so as to free the shoppers from the tedious job of selection of a particular Web site, this paper uses AHP method used to assign the criteria weights which in turn assist in generating the ranking of the Web sites. The end result is substantiated by a dint of entropy method calculations.

Cholesterol is a blended mixture that presents in tissues of the human body. Cholesterol and by-products of cholesterol are essential constituents of cell mucosa. A sudden increase in the level of cholesterol leads to increased risk of coronary heart diseases. Cholesterol is not required to be consumed as a part of the daily diet; human body can exaggerate cholesterol on its own. Cholesterol supports the proper functioning of human metabolism. Cholesterol is essential to build healthy body cells. Sudden increase in the level of cholesterol does not have any notable indication. Increased cholesterol levels in blood can only be suspected through a blood test. Factors such as poor diet, obesity, lack of exercise, smoking, age, and diabetes cause poor cholesterol level in the human body. Poor cholesterol level can cause complications like chest pain, heart attack, and stroke. Human liver secrets about 80% of the human body’s cholesterol and the other comes from the dietary constituents such as fish, meat, eggs, and dairy products. The proposed work suggests the dietary patterns to recommend a diet for patients with poor cholesterol level. This diet will help in maintaining a good cholesterol level. Analytic hierarchy process is used as a technique to implement this proposed work. Estimation of ideal diet has been recommended to patients with poor cholesterol level. The final outcomes reveal the ideal diet plan for the patients with a bad cholesterol level. This recommends the diets to be consumed by the patients at breakfast, lunch, and dinner. Fuzzy methods are used to validate our work. The final results are in match with the results obtained by analytic hierarchy process.

Vehicular number plate recognition (VNPR) is an important issue which can be solved using image processing and computer vision. VNPR process, when taken into consideration, can be used to solve a variety of issues in the fields of road safety and security. The problems can range from parking concerns to traffic control and might also include situations related to tollbooth or speed limit issues. Vehicle number plate recognition has a lot of side complexities arising due to a lot of factors including variable light dissipation, camera quality, and also speed at which the vehicle is moving. The proposed system will be able to identify number plates of cars and bikes and help trace the owner of that vehicle. We use various machine learning techniques and image processing utilities along with computer vision to address few of the issues which can be solved using the same.

In this paper, a high-frequency differential ring dual-delay voltage-controlled oscillator (DR-VCO) is proposed. This 9 GHz DR-VCO is designed using 45 nm CNTFET technology. CNTFET is faster than MOSFET, and hence it provides a high tuning range. VCO with four-delay cells is designed in this work. This design consists of four-stage delay cells, and every delay cell is designed on dual-delay path topology. This topology results in high-output oscillation frequency which includes the range from 3 to 9 GHz. The observed phase noise at offset of 1 MHz frequency is −66 dBc/Hz.

Quadrotors are suitable aerial platform for carrying out agile flight maneuvers. Currently, quadrotors are handled by ignoring different aerodynamic effects such as rotor drag. Rotor drag is the main aerodynamic effect that causes trajectory tracking error in flight during high speed. Therefore, a control model considering the rotor drag effect is proposed in this research paper. Our proposed model exploits the differential flatness property of dynamic quadrotor model to reduce the trajectory tracking error during the flight of unmanned aerial vehicle (UAV) in the environment. Further, a geometric controller is used to stabilize the UAV in the midair. A trajectory publisher is also used to provide the stream of feed-forward control terms for the desired trajectory. The performance of proposed control method is checked with the benchmark controller by computing root-mean-square position error. The proposed solution is tested for predefined horizontal Gerono Lemniscate trajectory and circular trajectory. Further, the proposed model is also tested with change in angular velocity values to prove its robustness in the environment.

The people in the present scenario are leading a busy life which is filled with modern technology that changes rapidly. In such a rapid growth of the human race, technological developments were also increasing rapidly. At the beginning of the twenty-first century, technology turned its focus towards automation which leads to the development of new innovative technology called IoT. But it is a bit harder to implement because IoT is not just connecting hardware devices with the Internet, but it is the interconnection of devices with the Internet that should work with intelligence. To do that, we should require gateways and a cloud to store data. The main key element in the complete success of IoT is the gateway. A gateway is a device either a hardware device or a software program that connects the client and the server. The server or the cloud consists of data and this data from the cloud to server and vice versa can be done through these gateways. The functioning of a gateway is not only to relocate the information between the consumer and the server but also to maintain protocols and to ensure security to the continuous flow of data that is exchanged between the client and the server. In the above paper, we gave a complete gist regarding the IoT gateways and explain the working or functionalities of different gateways in different applications.

In digital signal processing (DSP), the power consumption is more so, to decrease power and latency without affecting the other parameters, and mostly, the filters are designed using finite state machine (FSM). This paper gives a view of the multiplier architectures and its design issues for the expected level of performance. Literature states that the FSM approach is also a good choice in designing the multiplier architectures. In this paper, various design approaches are also described with the HDL modeling language, like in Verilog HDL, in building efficient multipliers. High-speed multipliers like Vedic multipliers are good in terms of speed and are considered as fastest and low-power multipliers.

Data partitioning is a backbone of distributed systems that boost the performance of big data applications, especially in distributed systems. In past years, many data partitioning algorithms have been developed which had improved the big data management and its processing for the real-time applications of the big data stores. Furthermore, the feature of “elasticity” to the data partitioning has removed the need for human interaction while handling the big data applications on the distributed system during the high workloads and skews. In this survey, a taxonomy is proposed that characterizes and classifies various types of data partitioning algorithms, which will help to identify the current limitations in the state of the art and will extend the state of the art to improve the enhancements for the effective and efficient performance of the big data stores on distributed systems. The taxonomy not only highlights the design, the similarities, and the differences within state of the art for different types of data partitioning algorithms but also identifies the areas that need further research.

Voice control automation system is a system that enables the control of appliances with your voice through which an android application and an Arduino mega board is used to control the relay through which an appliance is switched on/off. The aim of the research was to develop a voice support platform, design an interface for a Bluetooth medium and a microcontroller board (Arduino board), activate communication between an android device and the Bluetooth-enabled microcontroller board wirelessly via Bluetooth communication, detect and transfer speech data via Bluetooth to the microcontroller board, prepare a software to run on the microcontroller that will perform the necessary control, and prepare the software to run on the android device in other to get speech data and transferring over Bluetooth. The methodology used in this project was waterfall model. The output design of the proposed system consists of the Bluetooth module, Arduino, and the relays. The Bluetooth module receives the input data and sends it to the Arduino, and then it processes the input command and sends an output signal to a particular relay required for switching. The complete application software was achieved successfully by using Android, C Language, Bluetooth module, microcontroller, and relay.

There are many systems used to find out the location of the object or vehicle. The most widely used location tracking system is GPS that is the global positioning system. To find out the location of the missile, the inertial navigation system (INS) is used. Inertial measurement unit (IMU) performs the main role in this system, which consists of microelectromechanical system (MEMS) sensors. Accelerometer and gyroscope are used to give linear acceleration and angular rotation. Integrate the rates obtain from accelerometer and gyroscope twice to get velocity and position. To obtain the exact position of the missile, it is necessary to reduce the Coriolis effect from the rates. This survey paper elaborates on the modeling and simulation of the INS (Brown in Test results of a GPS/inertial navigation system using a low cost MEMS IMU, 04 2004 [1]).

The watermarking technique is a very vital method to identify the originality of the object. This paper describes the dual watermarking method for the digital images which is invisible. In this technique, the watermark is added in host image, and specifically, the dual watermarks provide more security compared with the single one. The robust and fragile watermarks are used in this scheme for image authenticity and the copyright protection purpose. Firstly, the colour image is converted into YCbCr space separated into each space; the Y colour space is used as the embedding watermark section. The DWT is applied to the Y space from which the LL section is embedded with the watermark. The improved values of PSNR and MSE can define the quality of image.

The computers and hence computing have become an integral part of human lives in the present Information Age. The increased use of computers enables tedious tasks to be performed in a hassle-free and faster manner. But, this ever-increasing use of computing devices is taking its toll on the environment both in terms of resource utilization and its quality. Green computing is an environmental-friendly way and the current trend in the field of computing. Also, known as green IT, it paves the path to a greener version of computing. A slight transition from the conventional IT or non-green IT can make a huge difference and reduce carbon footprint to a great extent.

Gesture recognition technology has evolved greatly over the years. It is a field in language innovation and development with the aim of deciphering human motions by the use of certain algorithms. In this age of technology, we have fabricated boundless techniques and subsequently seen their drawbacks making us aware of our own limits in terms of speed and naturalness of the human body. The intellect and inventiveness of human beings have led to the development of many tools, gesture recognition technology being one of them. They help us extend the capabilities of our senses by combining natural gestures to operate technology, thereby reducing human efforts and going beyond human abilities. Gestures can be viewed as a route for PCs to start to comprehend human non-verbal communication, in this manner fabricating a more extravagant extension among machines and people than crude content UIs or GUIs, which still breaking point most of contribution to console and mouse and connect normally with no mechanical gadgets. Utilizing the idea of motion acknowledgment, it is conceivable to point a finger now will move as needs be. With the expeditious inventions of three-dimensional applications and the upcoming hype of virtual environments in systems, there is a need of new devices that can sustain these interactions. The advancement of user interface molds and develops the human-computer interaction (HCI). The paper aims to present a review of vision-based hand gesture recognition techniques for HCI and using it to tally finger count using the same.

In this paper, coplanar waveguide-based rectangular dielectric resonating antenna has been investigated by employing dielectric waveguide model (DWM) method. It is found that with CPW, feed line back radiation can be minimized and a strong capacitive matching can be obtained to achieve resonance in quad band. Proposed DRA provides efficiency as high as 90% with sufficient undesired band rejection and stable gain.

This paper presents design and performance analysis of circular microstrip patch array for S-band (2–4 GHz) wireless communications. This design exhibits improved gain with minimum return loss. Directivity of the antenna also improved. This design is prepared and simulated using HFSS software.

Idea behind the key agreement protocol is to enable the entities to communicate safely over insecure public networks. In this paper, we proposed a secure key agreement protocol using Diffie-Hellman key agreement. In 2005, Lee and Lee [1] proposed a key agreement protocol dependent on Diffie-Hellman and guarantee that their protocol beat the attacks like man-in-the-middle attack. However, we brought up that Lee and Lee are defenseless against man-in-the-middle attack, impersonate attack and replay attack. Further proposed an improved key agreement protocol and demonstrated that the protocol is secure against the attacks. To confirm the security properties, we have done formal verification called ProVerif tools. Finally, to demonstrate the efficient, we compared other related authentication key agreement schemes with the proposed scheme.

This paper proposes a secure steganographic scheme based on DNA computation and chaos based on random bit generation. This scheme uses a cross-coupled chaotic map to generate random DNA sequences. DNA computing is used for encoding the secret message, and the encoded secret is embedded onto a cover image using well-known LSB substitution technique. This scheme is equally effective for hiding image as well as text. The secret message is converted to a DNA sequence, and another random DNA sequence is generated using a cross-coupled chaotic system. Now, the two DNA sequences are added using DNA addition. Then, the LSBs of the cover image pixels are substituted by the secret DNA sequence. The results of the experiment and security analysis show that the algorithm devised by us is effective and also can resist statistical and differential attack.

In this paper, deep neural networks were proposed to find the crop disease for the normal image, brown spot, blast, sheath rot and bacterial blight. Dataset consists of 209 images. In the image preprocessing, RGB images are converted into HSV to remove the background portion using hue and saturation part. The image segmentation by k-means clustering, various colour and texture features are extracted. The classification is done with existing KNN algorithm. The accuracy obtained is 88% bacterial blight, 82% blast, 88% brown spot, 87% sheath rot and 86% normal images. To improve the accuracy our proposed DNN is implemented. The accuracy obtained for DNN is 93% bacterial blight, 89% blast, 93% brown spot, 92% sheath rot and 96% normal images.

Integration of fiber and wireless (Fi-Wi) technology in latest communication has brought massive improvements in terms of high data rate, low interference and attenuation. The frequency range of 30–300 GHz (millimeter wave) is capable of generating orders of magnitude greater bandwidth. Merging two concepts fiber communication has large bandwidth capacity and wireless coverage are combining both features also became an emerging platform for Internet providers for ever-increasing bandwidth hungry. Bandwidth and channel capacity can be improved by increasing frequency range and implementing wavelength division multiplexing (WDM). Direct and indirect modulated waves imposed with signal generator at transmitter side to generate millimeter (mm) wave. Phase modulator (PM) and Mach–Zehnder modulator (MZM) based techniques of generating mm-wave are investigated and results are shown for various input sine wave generators and different fiber wavelengths.

In this paper, the design and fabrication of a compact rectangular microstrip antenna (RMSA) are presented. The defected ground structure (DGS) along with T-shaped slots in patch is utilized to reduce the size of the antenna. The designed patch antenna with optimized dimensions is fabricated using FR4 substrate. Using the aforementioned methods, the antenna patch size is reduced by 25% when compared to a conventional rectangular microstrip patch antenna. The designed antenna is operating at mobile communication frequency band. The designed antenna is simulated and the simulation is performed using computer simulation technology microwave studio software. The presented antenna is compact in size and suitable for mobile devices.

Wireless communication systems require high gain antennas for many reasons such as for better quality of receiving signals and for low transmitting power. In this paper, a rectangular microstrip antenna (MSA) with enhanced gain for 2.4 GHz wireless local area network applications is presented. The gain of the MSA is increased by using defected ground structure (DGS) technique and a reflected ground plane. The simulation of the designed MSA is performed in CST microwave studio. Using CST microwave studio, the dimensions of the MSA are optimized. The designed MSA is fabricated and measured. Simulated MSA parameters along with and measured results are presented and discussed. The comparison between simulated and measured results shows reasonable agreement. The MSA provides a high directivity of 7.282 dBi and gain of 3.675 dB which is much higher than the MSA with normal ground plane and without reflected ground plane. The designed MSA is suitable for 2.4 GHz wireless local area networks (WLAN) applications.

This paper presents investigation about the drain current parameters of Surrounding Gate MOSFET (SG MOSFET) with junction and junctionless transistor. The junctionless SG MOSFET (JLSG MOSFET) exhibits more current available at low voltage but junction based SG MOSFET exhibits less current at same voltage but depends on parameters. The junction based devices are less costly. Device length also improved in JLSG MOSFET. General variable issues like oxide thickness, channel length and doping concentration are also discussed.

Video searching in search engines uses metadata information to find the relevant videos according to the search queries. Metadata information mainly comprises the title and description of the video. The major drawback of this approach is that it overlooks whether or not the content of the video is genuine or not. Since the metadata information is provided by the uploader, the person may provide false information about it. Therefore, there is a need of improving the results of video searched. The proposed work classifies the video in different categories and then compares the tag provided to each video with the tags that were extracted from the metadata of the video. The other factor like views count, likes and dislikes, comments is also considered for the ranking of the video searched. It improves the genuineness of the content of the video searched.

Hate speech is a type of written or a spoken statement that is used to demean or humiliate a person or a community. In this era of new age socialism, this type of speech is prevalent on social media platforms, where certain groups of people display offensive behaviour towards some people that may be distributed over gender, religion, nationality, etc. These kinds of activities must be avoided or suspended on social media platforms. Therefore, it is necessary to automate the detection of hateful content that gets circulated on the social media. The research work provides an enhanced technique as compared to the existing techniques with improved performance. The proposed model of IN-Gram compares the performance of detection of hateful content on social media with the traditional TF-IDF, N-Gram and PMI techniques. The proposed approach improves the hate speech detection rate by 10–12% for larger datasets as compared to existing approaches.

This article introduces a configurable analog block with wide functionality which is then used for designing a new sinusoidal signal generator with three outputs. The circuit uses three current feedback operational amplifiers and provides control over the frequency of oscillation independent of condition of oscillation. The new circuit is experimentally tested using AD844 ICs and also simulated using capture CIS tool. Both experimental and simulation results have good agreement. Possible applications are suggested, and one such application is demonstrated in generation of square and triangular waveforms.

In the present study, a detailed analysis has been done to investigate the device performance of Polarity Controllable–Ferroelectric–Field Effect Transistors (PC–FE–FET) under the influence of fixed trap charges. It has been observed that by virtue of ferroelectric layer, the proposed device shows improved device characteristics over the conventional device for both n- and p-modes of operation even in the presence of traps. Here, NTC (PTC) present in PC–FE–FET device operating in n- (p-) mode exhibits superior subthreshold characteristics over the conventional device. Moreover, due to higher on-state current obtained for PC–FE–FET, devices with PTC (NTC) in n- (p-) mode demonstrate increased values of transconductance, cut-off frequency and Ion/Ioff ratios.

The use of artificial neural network (ANN) in estimating runoff of a river is popular among hydrologists and scientist from a long time. The classical gradient descent algorithm (GD) is the most commonly used algorithm for training the ANN runoff models so far. The performance of GD algorithm, however, is affected by chances to get stuck at the local minimum. In this paper, one of the popular evolutionary optimization algorithms, known as particle swarm optimization (PSO), has been explored to train the ANN rainfall–runoff model. The superiority of the PSO over the GD method in training ANN rainfall–runoff model is illustrated using data from a real catchment. On the basis of various error statistics, it has been observed that particle swarm optimization can be very effective optimizer in developing ANN-based models for water resources applications, especially in modeling rainfall–runoff process.

The domain of natural language processing has lately achieved exceptional breakthroughs especially after the origination of the deep neural networks. This has enabled the machine learning engineers to develop such deep models that are capable of performing high-level automation, empowering computer systems to interact with the humans in a competent manner. With the usage of special types of deep neural networks known as recurrent neural networks, it is possible to accomplish various applications in the domain of natural language processing including sentiment analysis, part-of-speech tagging, machine translation, and even text generation. This paper presents a deep, stacked long short-term memory network, an advanced form of recurrent neural network model which can generate text from a random input seed. This paper discusses the shortcomings of a conventional recurrent neural network hence bringing forward the concept of long short-term memory networks along with its architecture and methodologies being adopted.

The purpose of this paper is to develop a socio-medic drone which can act as a life savior for accident victims in remote areas such as highways or maybe for a person who is dealing with heart issues. During a fatal accident or a cardiac arrest, the first few minutes after the accident or cardiac arrest are the most important which decides between life and death of the individual. This socio-medic drone will be equipped with a first aid kit and also an integrated defibrillator which can act as a great life support to the individual till the ambulance actually arrives through the busy traffic to the ground zero. Real-time parameters can also be checked by the drone such as temperature, heart rate and heartbeat. The values of these parameters can be transmitted to the monitoring hospital database which will initiate the recovery process accordingly.

Many sources of data yield non-structured data like the Internet of things (IoT), geospatial data, E-commerce, social media, and scientific research that is not appropriate in to traditional, structured warehouses. Nowadays, sophisticated analytical techniques allow companies to obtain perspicacity from data with earlier unachievable levels of accuracy and speed. Real-time analytics for big data is the capability to achieve the most suitable decisions and get significant actions at the best time. First, we present a survey of processing the big data (BD) in real time (RT) and focus on its challenges. Then, we propose an algorithm to handle BD by integration with machine learning operations in multi-user environment optimization operations, reduce maintenance costs and better speed of fault detector and provide common operations necessary to process unstructured information. There are important conditions that have been taken into a concern to guarantee the quality of services (QoS) and transmission velocity and ensure the system’s physical time synchronization and the correctness of the data processing.

So far, various approaches have been proposed for implementing chatbots with business intelligence tools, yet most of these approaches have limited adoption in practice. The objective of this paper is to offer an easy to understand approach by the business users that could be quickly adopted for integrating the chatbot with a business intelligence tool. This communication describes a process of applying artificial intelligence technologies; in particular, natural language processing (NLP) for conversation using chatbot. The chatbot implementation and integration with the BI tool has given encouraging results.

Quantum dot cellular automata is a well known technology which is a prospective paradigm for quantum computing. It is evident that QCA is going to be an alternative for CMOS technology for future circuits due to its property of low power, high speed and high density. Numerous digital circuits employ exclusive OR functions for executing arithmetic, error detecting or correcting operations. Thus, design of XOR gate is crucial with regard to cost efficiency and modular design competency. In QCA circuits, improved cost efficiency can be attained by minimizing one of the important parameters i.e. area of the QCA layout. Multilayer topology works successfully to reduce area and enhance the density of large circuits. This paper targets the design of multilayer XOR gate with improved cost function. Four possible QCA structures of XOR gate are proposed here, using multilayer topology. Multilayer topology assures area efficient structures. Moreover, the multiplexer and half adder circuits are presented here, using the most efficient proposed XOR design.

In this paper, a new design of digital differentiator of the second order using genetic algorithm is presented. By the use of genetic algorithm, transfer function differentiator of second order is derived. Then compare the results with the ideal second-order differentiator. Result is also compared with digital IIR differentiator second order using backward difference formula which is already exist.

Here, we report the facial synthesis of oxide (ZnO) and sulfide (ZnS) of zinc in nanorods and nanotubular structures by simple hydrothermal reduction method. The structural and phase analysis of the prepared samples has been done through X-ray diffraction, whereas morphological investigations have been done through transmission electron microscopy studies. The optical band gaps of the synthesized materials have been examined through UV-Vis spectroscopy studies. Photoconductivity measurements have been performed of both the ZnO nanorods and ZnS nanotubes samples in UV-illumination (λ ≈ 365 nm) and at an illumination intensity of ~3.3 mW/cm2. The prepared ZnO nanorods are found to have greater photoresponse of ~30% as compared to ZnS nanotubes ~25%. The adhesion and removal of oxygen molecules on the prepared samples’ surface are considered to be the mechanism of photodetection.

The research area of social media platform for e.g. Twitter is being regarded as most popular social media platform not only to share and disseminate the information but also raising complaints/grievances from citizens. In fact, high velocity, veracity and variety of real time data, it is very hard to analyze the data related to citizen’s complaints and problems through manual processing (Agarwal et al. in CoDS-COMAD ‘18 Proceedings of the ACM India joint international conference on data science and management of data. ACM, pp 67–77, 2018 [1]). So there is a need to filter relevant data with automation which requires some actions by concerned authority as a part of Smart Governance. Smart Governance demands distinguishing the appreciations, praises, issues, problems and grievances posted at social platform by civilians to inform government authorities. It also includes facilitating public agencies to respond to these problems, issues, complaints so that citizen’s services can be improved without delay. Thus this paper proposes intelligent techniques to mining public citizens’ complaints and grievances from user-generated contents. Since Twitter considered as most popular social media platform which increases the chances of immediate action and fast processing of their request and grievances by the relevant government department or authorities excellent.

In the original version of the book, in Chapter 73, Figure 1 is replaced with a revised figure as given below and the heading of Section 2.2 “Micro Post Enrichment Algorithm” is now replaced with the heading “Extraction of Tweets”. The chapter and book have been updated with the change.