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Linear and non-linear analysis of solute-magneto convection in a couple stress fluid with porous medium under concentration modulation
The effect of concentration modulation and magnetic field in a couple stress fluid with porous medium and salted from above is studied using linear and non-linear analysis. Venezian approach based on perturbation method is used to obtain the expression for solute Rayleigh number and correction solute Rayleigh number. The expression for correction solute Rayleigh number is obtained as a function of couple stress parameter, Chandrashekar number, Darcy number and Schmidt number. The effect of parameters on symmetric and asymmetric concentration modulation are discussed in the paper. A non-autonomous Ginzburg-Landau equation with time periodic co-efficient is obtained to study the effect of parameters on mass transfer. It is found that onset of convection and mass transfer can be delayed or advanced by varying the parameters of the problem. Asymmetric modulation is found to be more stable than the symmetric modulation. 2019 Author(s). -
Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE. -
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s). -
Universal Electrical Motor Acoustic Noise Reduction based on Rotor Surface Modification
Electromagnetic noise is referred to the audible sound which is produced by materials vibrating due to electromagnetic force. In the present day circumstances, a greater attention is being given to the electromagnetic acoustic noise produced by electrical machines. It is found to annoy human beings and other living organisms due to its tonal sound. The current work aims at designing a rotor for a universal motor with the objective to decrease the acoustic noise by minimization of forced density harmonics. The design consists of some irregularities in the rotor surface to decrease the acoustic noise by internally modifying the air gap permeance. Simulation shall be carried out based on FEM. A lot of research is being carried out on the methods of reducing the noise from electrical machines. The results of the current work significantly help in reducing a lot of noise pollution. The change in the rotor surface will reduce the electromagnetic acoustic noise from the electrical machine. It will also affect the torque parameters positively as studied from earlier research work. 2019 IEEE. -
Impact of Variable Distributed Generation on Distribution System Voltage Stability
With advances in renewable energy (RE)technologies and the promotion of restructuring, distributed energy (DG)sources play a vital role in today's power sector. From the technical and economic point of view, DG sources provide a no of benefits such as lesser system losses, better system voltage profile and lower line congestion. The aim of this work is to determine the voltage stability of a distribution system at different levels of DG compensation determined as a percentage of the total load on the system. The objective function is formulated to minimize the real power loss. At first, the locations are chosen based on strategy using Loss Sensitivity Factors (LSF)and the optimal sizing of multiple units of DG sources is optimized using Particle Swarm Optimization (PSO)algorithm. The simulations are performed on standard IEEE 33-bus and 69-bus test systems and the results validate the importance of placing appropriately sized DG sources at suitable locations to achieve improved voltage stability and reduced distribution losses. 2019 IEEE. -
Recognition of Green Colour Vegetables' Images Using an Artificial Neural Network
Image processing is used in all the domains including agriculture. In this paper, we have introduced a computationally simple and small feature vector, as a tool for the recognition of green colour vegetable images. The RGB colour system is used and the feature set is computationally economic and performs well on locally available vegetable images. For recognition of vegetable images, an ANN-based classifier is deployed. The recognition percentage is in the scale of 74-100 for 15 vegetable types. This work finds application in the packing of vegetables, food processing, automatic vending. 2019 IEEE. -
LENN: Laplacian Probability Based Extended Nearest Neighbor Classification Algorithm for Web Page Retrieval
Web page prediction is the area of interest that enables to tackle the problem of dealing with the massive collection of the web pages, mainly, in retrieving the highly relevant web pages. The hectic challenge of the web page prediction methods relied on time-effective and cost-effective management. The problem of dealing with the issue is tackled using the efficient web page retrieval algorithm. The paper proposes a new classifier called, Laplacian probability based Extended Nearest Neighbor (LENN)that is formed through the integration of the Laplacian probability with the Extended Nearest Neighbor (ENN)classifier. The proposed LENN classifier determines the nearest web pages of the query. Accordingly, the web page retrieval is done in three important steps, such as pre-processing, feature indexing and web page retrieval. The key words are stored in the database for performing the feature match such that the highly relevant web page is retrieved based on the maximum value of the score. The experimentation using five benchmarks prove that the proposed method is effective compared with the existing methods of web page retrieval. The maximum precision, recall, and F-measure of the proposed method is found to be 98%, 96.7%, and 97.3%, respectively. 2019 IEEE. -
Accident Detection Using Convolutional Neural Networks
Accidents have been a major cause of deaths in India. More than 80% of accident-related deaths occur not due to the accident itself but the lack of timely help reaching the accident victims. In highways where the traffic is really light and fast-paced an accident victim could be left unattended for a long time. The intent is to create a system which would detect an accident based on the live feed of video from a CCTV camera installed on a highway. The idea is to take each frame of a video and run it through a deep learning convolution neural network model which has been trained to classify frames of a video into accident or non-accident. Convolutional Neural Networks has proven to be a fast and accurate approach to classify images. CNN based image classifiers have given accuracy's of more than 95% for comparatively smaller datasets and require less preprocessing as compared to other image classifying algorithms. 2019 IEEE. -
Non invasive methods of blood glucose measurement: Survey, challenges, scope
Noninvasive body parameters monitoring and disease detection is one of the emerging research area now a days. In this paper a review on Non-invasive methods of blood glucose measurement has been made. A comparative study has been made which describes the methodology incorporated in the published literatures, research challenges and the used tools. This paper also describes about the factors which highly impacts the non-invasive measurement. Finally, a deep learning based noninvasive measurement method compatible with IOT is mentioned. This paper serves as a proper reference for future researchers working in non-invasive blood glucose measurement domain in selecting appropriate non-invasive method algorithm for glucose monitoring non-invasively. 2019 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
Inphase and outphase concentration modulation on the onset of magneto-convection and mass transfer in weak electrically conducting micropolar fluids
The paper analyses the effect of concentration modulation at the onset of solute magneto-convection and heat transfer in a weak electrically conducting fluid by carrying out a linear and non-linear analysis. The Venezian approach is assented encompassing the correction Solute Rayleigh number and wave numbers for meagre amplitude concentration modulation. A multiscale method is applied to convert the analytically untraceable Lorenz model to an analytically traceable Ginzburg-Landau equation which is solved to quantify mass transfer through Sherwood number. It is observed that concentration modulation results in sub-critical motion however out-of-phase concentration modulation is more stable compare to others. 2019 Author(s). -
Comparative study of Breakdown Phenomena and Viscosity in Liquid Dielectrics
Liquid dielectrics are extensively used in electrical apparatus which are operating in distribution and transmission systems. The function of electrical equipment strongly depends on the conditions of liquid dielectric. Liquid dielectrics used are the most expensive components in power system apparatus like transformers and circuit breakers. A failure of these equipment would causes a heavy loss to the electrical industry and also utilities. Insulation failures are the leading cause of transformer failures and thus the liquid dielectrics plays a major role in the safe operation of transformers. One of the main causes for the failure of transformers is due to the presence of moisture. In this work, the life of insulating medium is estimated by comparing the Breakdown strength and Viscosity of different pure oils with that of the contaminated oils (which contains moisture) and also finding the alternative for mineral oil. vegetable oils which are reliable, cost-effective and environmental friendly even when they are contaminated. 2019 IEEE. -
ChIPSeq Analysis with Bayesian Machine Learning
ChIP-sequencing, otherwise called ChIP-seq, is a technique used to identify protein co-operations with DNA. A crucial advancement to the field of bio-informatics, ChIP sequencing is conducted in research labs around the world to get a better understanding of the way transcription factors and other associated proteins influence the gene in many biological processes and in tackling disease states. ChIP-seq is predominantly a field under the domain of biotechnology, however recent advancements and development of tools to process ChIP data have turned the study into one involving bio-informatics, allowing computer scientists and lab technicians to work on an otherwise scholarly field of biochemistry, molecular biology, microbiology and biomedicine. This report illustrates the predominant work-flow undertaken to sequence chromatin from a cell and to gain insights on the gene/protein of interest. Another aspect added is to use Machine Learning with Bayesian statistical techniques for Peak Calling. The different stages enumerated in this paper have been completed either with the R language or on a Web Server titled Galaxy.org. 2019 IEEE. -
IoT Based Water Management Using HC-12 and Django
Water is one of the important needs for a human being. Life on Earth is possible due to the presence of water on its surface. Even though 71% of Earth's surface is covered with water, the availability of water in certain areas is very less. So, the people in these areas must reserve water for ensuring a steady availability. These problems can be rectified with the help of Internet of Things (IOT). IOT is a global infrastructure with certain standards and communication protocols by which virtual and physical things can interact and exchange data by connecting to each other. In this paper, we propose a system for monitoring the availability of water, based on the water level in the storage system. Water level is measured with the help of a waterproof ultrasonic sensor and when the level reaches a threshold value, a notification is sent to the user or to the vendor to take the necessary action. The live feed data is sent to a relational database for storing and analyzing the data to predict when the water will run out, and to make sure that the water storage system gets refilled before that point of time. After processing the raw data from the sensors, the system can generate a fusion chart that can show or indicate the amount of water inside each storage system. With this, the user can have an idea of how much water is left in each of the storage system. The main aim of the proposed system is to showcase the functionalities and uses of different sensors and modules used in an IOT based system with the application of Wireless Sensor Network (WSN). In this present scenario, the world is filled with data both relevant and irrelevant, wherein the data for predicting a water crisis is less. So, through the proposed system we are generating a dataset for the prediction of a water crisis in an organization or a community. 2019 IEEE. -
Sentiment Analysis on Indian Government Schemes Using Twitter data
People use social media for entertainment, fetching information, news, business, communication and many more. Few of such social media applications are Facebook, Twitter, WhatsApp, Snapchat and so on. Twitter is one among the micro blogging websites. We are using Twitter mainly because it has gained a lot of media attention. The text written is referred to as tweets, where a common man can tweet or can write their hearts out. We would be fetching the direct responses from the public and hence the data is more real-time. First step is to fetch the tweets on a particular scheme using python language code followed by the cleaning process then comes the creation of bag of words. Later these bags of words are given as an input to the algorithms. Finally, after training the algorithms, we will be getting the sentiment of the public on that scheme. 2019 IEEE. -
Inter Frame Statistical Feature Fusion for Human Gait Recognition
Researches showed that gait is unique for individuals and human gait recognition gained much attention nowadays. The sequence of gait silhouettes extracted from the video sequences has its own significance for gait recognition performance. In this paper, a novel inter frame feature discriminating the individual gait characteristics is proposed. Consecutive frames within a gait cycle are divided into equal number of blocks and corresponding block differences are calculated. It can preserve the minute temporal variations of the different body parts within each block and the cumulative difference provide a unique feature capable of discriminating individuals. To avoid synchronization problems, secondary statistical features are extracted from the primary inter frame variations. Finally, feature level fusion schemes are applied on these statistical features with existing features extracted from CEI representation. The efficiency of the proposed feature is evaluated on widely adopted CASIA gait dataset B using subspace discriminant analysis. The experimental results show that our proposed feature has better recognition accuracy in comparison with existing features. 2019 IEEE. -
Enhancements in anomaly detection in body sensor networks
Anomaly detection in Body Sensor Networks (BSNs), have recently received much attention from the healthcare community. This is partly due to the development of sensor based real-time tracking and monitoring networks. These networks have been responsible not only for ensuring critical medical treatment at times of emergency, but have also made it easier for health-care personnel to administer critical treatment. In this paper we consider improvements to existing machine learning methods that detect anomalous sensor measurements. The improved methods are a step in the right direction in ensuring unduly overheads due to faulty sensors don't interfere while administering life-critical treatment in a limited resources scenario. 2019 IEEE. -
Power Efficient e-Bike with Terrain Adaptive Intelligence
Electric bicycles or e-bikes are gaining momentum in the market as they are offering a smooth, noiseless and pollution free option for individual transportation in cities as well as in countryside. E-bikes are usually with a battery powered electric motor drive with an additional option for pedaling. In this work a low cost e-bike was designed and developed with a brushless DC hub motor with controllers. For smart control, smartphone was used a console and the e-bike can be controlled using a mobile application which was connected to the e-bike through Bluetooth. The controller will pick the gradient of the terrain and will control the power of the motor, which results in energy saving. Predicted range of the e-bike, speed, acceleration and total distance covered were displayed in the console along with the geographical position on the map and throttle control options. The bike with the proposed control tested and the results were giving a reduction in current drawn from the battery. 2019 IEEE. -
A Proposal of smart hospital management using hybrid Cloud, IoT, ML, and AI
There has been a rapid shift in the medical industry from the service point of view. More importance is being given to patient care and customer satisfaction than ever before. The need to keep the customers happy with the hospital's service has increased rapidly and one way they can improve a patient's experience, even more is if they integrate cloud, IoT, ML, and AI into their system. This would help the medical sector to achieve customization which would enable them to address the needs of their customers more efficiently and offering personalized solutions. In this paper, we are proposing a novel model which focuses on a smart hospital information management system that runs by using hybrid cloud, IoT, ML, and AI. This system would be beneficial not only from the hospitals perspective but also from the patient's side as well. Patients and doctors unique ID would make the entire process a lot more efficient and easier. The advances happening in the field of AI and ML due to cloud-based computing is extremely beneficial for the medical industry. By integrating these components along with IoT it is possible for multi-specialty hospitals and super specialty hospital to be able to set up a smart hospital information management system. 2019 IEEE. -
Proposal of smart home resource management for waste reduction and sustainability using AI and ML
A research indicated that electricity is obliterating extra non-renewable sources for its production. In that, as per Centre for Policy Research (CPR), about 25% of the total production is diverted to meet the daily consumption in an Indian household. Not only this but also, waste management has become an important issue to deal with. According to Municipal Solid Waste (MSW) of India, waste generation in Indian urban communities extends between 200 - 870 grams per day, contingent on the localities' standard of living and the area of the city. Therefore, in this paper we propose a concept that focuses on a sustainable solution using Artificial Intelligence and Machine Learning algorithms for waste and carbon footprint reduction in a home. This concept explains a solution availed with the help of a proposed model called Home Resource Management (HoReM) that is imbibed in a Smart home. 2019 IEEE.