This page has only limited features, please log in for full access.
Edge computing and fog computing have emerged as effective and efficient technologies for IoT-related issues. In the recent times, fellow researchers have proposed numerous researches under the area of edge and fog computing. Still, edge and fog computing have remained open research problems. In the proposed research work, we have proposed a MQTT-based broker security mechanism to protect the IoT-based system from a selection of security penetrations such as man-in-the-middle attacks, DDoS, DoS and many more. In general, MQTT broker architecture acts as an intermediator to establish connection between a publisher and subscriber. For secure communication between both the ends, it is essential to establish a novel security protocol which secure communication channel between subscribers and publishers. In the presented research work, we have proposed a novel authentication mechanism which makes the use of MQTT broker in achieving data privacy, authentication and data integrity. Furthermore, a detailed and rigorous analysis of a variety of security attacks has been analyzed and discussed in the end. At last but not the least, we have also presented multicast-MQTT secure messaging protocol and compared it with state-of-the-art methodologies such as RSA and advanced encryption standard.
Sharnil Pandya; Mayur Mistry; Ketan Kotecha; Anirban Sur; Pramit Parikh; Kashish Shah; Rutvij Dave. A Novel Multicast Secure MQTT Messaging Protocol Framework for IoT-Related Issues. Proceedings of International Conference on Big Data, Machine Learning and Applications 2021, 339 -359.
AMA StyleSharnil Pandya, Mayur Mistry, Ketan Kotecha, Anirban Sur, Pramit Parikh, Kashish Shah, Rutvij Dave. A Novel Multicast Secure MQTT Messaging Protocol Framework for IoT-Related Issues. Proceedings of International Conference on Big Data, Machine Learning and Applications. 2021; ():339-359.
Chicago/Turabian StyleSharnil Pandya; Mayur Mistry; Ketan Kotecha; Anirban Sur; Pramit Parikh; Kashish Shah; Rutvij Dave. 2021. "A Novel Multicast Secure MQTT Messaging Protocol Framework for IoT-Related Issues." Proceedings of International Conference on Big Data, Machine Learning and Applications , no. : 339-359.
IoT-enabled modern agricultural methodologies can change the current agriculture practices by automating the entire process of agriculture from crop management, water irrigation to making better decisions based on real-time monitoring of environmental conditions, soil conditions and landscape conditions. In the recent times, technology-enabled precision agriculture solutions have enabled a paradigm shift from static and manual agriculture methodologies to automated precision-oriented agricultural methodologies using the latest technologies such as Internet of agricultural things, AI-based agricultural analytics, cloud computing and WSN-enabled crop monitoring and control. In the proposed review work, a rigorous and detailed assessment has been conducted to identify the research gaps and analyze the latest technology-enabled PA methodologies and applications. Furthermore, in the proposed review work, we have presented IoAT-based PA model, which is comprised of five layers. The first layer depicts the physical layer devices, second layer describes security protocols, third layer highlights efficient data management practices, fourth layer provides effective irrigation models, and the final layer discusses technology-enabled water management services. In the end, along with future directions, we have represented categorical analysis of the conducted review work in the form of graphical results along with gained experiences and learnt lessons.
Sharnil Pandya; Mayur Mistry; Pramit Parikh; Kashish Shah; GauravSingh Gaharwar; Ketan Kotecha; Anirban Sur. Precision Agriculture: Methodologies, Practices and Applications. Proceedings of International Conference on Big Data, Machine Learning and Applications 2021, 163 -181.
AMA StyleSharnil Pandya, Mayur Mistry, Pramit Parikh, Kashish Shah, GauravSingh Gaharwar, Ketan Kotecha, Anirban Sur. Precision Agriculture: Methodologies, Practices and Applications. Proceedings of International Conference on Big Data, Machine Learning and Applications. 2021; ():163-181.
Chicago/Turabian StyleSharnil Pandya; Mayur Mistry; Pramit Parikh; Kashish Shah; GauravSingh Gaharwar; Ketan Kotecha; Anirban Sur. 2021. "Precision Agriculture: Methodologies, Practices and Applications." Proceedings of International Conference on Big Data, Machine Learning and Applications , no. : 163-181.
In the growing automation of existing world, activity modeling is being used in the field of technology to serve various purposes. One such field, which will be majorly benefited from daily activity modeling and life- living activities analysis, is monitoring of seasonal behavior pattern of elderly people, which can be further utilized in their remote health analysis and monitoring. Today’s demand is to develop a system with minimum human interaction and automatic anomaly detection and alert system. The proposed research work emphasizes to diagnose elderly persons daily behavioral patterns by observing their day-to-day routine activities with respect to time, location and context. To grow the accurateness of the structure, numerous sensing as well as actuator units have been deployed in elderly homes. Popular this research paper, we have recommended a unique sensing fusion technique to monitor seasonal, social, weather related and wellness observations of routine tasks. A novel daily activity learning model has been proposed which can record contextual data observations of various locations of a smart home and alert caretakers in the case of anomaly detection. We have analyzed monthly data of two old-aged smart homes with more than 5000 test samples. Results acquired from the investigation validate the accuracy and the efficiency of the proposed system which are recorded for 20 activities.
Sharnil Pandya; Mayur Mistry; Ketan Kotecha; Anirban Sur; Asif Ghanchi; Vedant Patadiya; Kuldeep Limbachiya; Anand Shivam. Smart Aging Wellness Sensor Networks: A Near Real-Time Daily Activity Health Monitoring, Anomaly Detection and Alert System. Proceedings of International Conference on Big Data, Machine Learning and Applications 2021, 3 -21.
AMA StyleSharnil Pandya, Mayur Mistry, Ketan Kotecha, Anirban Sur, Asif Ghanchi, Vedant Patadiya, Kuldeep Limbachiya, Anand Shivam. Smart Aging Wellness Sensor Networks: A Near Real-Time Daily Activity Health Monitoring, Anomaly Detection and Alert System. Proceedings of International Conference on Big Data, Machine Learning and Applications. 2021; ():3-21.
Chicago/Turabian StyleSharnil Pandya; Mayur Mistry; Ketan Kotecha; Anirban Sur; Asif Ghanchi; Vedant Patadiya; Kuldeep Limbachiya; Anand Shivam. 2021. "Smart Aging Wellness Sensor Networks: A Near Real-Time Daily Activity Health Monitoring, Anomaly Detection and Alert System." Proceedings of International Conference on Big Data, Machine Learning and Applications , no. : 3-21.
Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.
Nivedita Mishra; Sharnil Pandya. Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review. IEEE Access 2021, 9, 59353 -59377.
AMA StyleNivedita Mishra, Sharnil Pandya. Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review. IEEE Access. 2021; 9 ():59353-59377.
Chicago/Turabian StyleNivedita Mishra; Sharnil Pandya. 2021. "Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review." IEEE Access 9, no. : 59353-59377.
Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.
Pooja Mehta; Sharnil Pandya; Ketan Kotecha. Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science 2021, 7, e476 .
AMA StylePooja Mehta, Sharnil Pandya, Ketan Kotecha. Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science. 2021; 7 ():e476.
Chicago/Turabian StylePooja Mehta; Sharnil Pandya; Ketan Kotecha. 2021. "Harvesting social media sentiment analysis to enhance stock market prediction using deep learning." PeerJ Computer Science 7, no. : e476.
In this work, a multiple user deep neural network-based non-orthogonal multiple access (NOMA) receiver is investigated considering channel estimation error. The decoding of the symbol in the case of the NOMA system follows the sequential order and decoding accuracy depends on the detection of the previous user. Without estimating the throughput, a deep neural network-based NOMA orthogonal frequency division multiplexing (OFDM) system is proposed to decode the symbols from the users. Firstly, the deep neural network is trained. Secondly, the data are trained and lastly, the data are tested for various users. In this work, for various values of signal to noise ratio, the performance of the deep neural network is investigated, and the bit error rate (BER) is calculated on a per subcarrier basis. The simulation results show that the deep neural network is more robust to symbol distortion due to inter-symbol information and will obtain knowledge of the channel state information using data testing.
Sharnil Pandya; Manoj Ashok Wakchaure; Ravi Shankar; Jagadeeswara Rao Annam. Analysis of NOMA-OFDM 5G wireless system using deep neural network. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 2021, 1 .
AMA StyleSharnil Pandya, Manoj Ashok Wakchaure, Ravi Shankar, Jagadeeswara Rao Annam. Analysis of NOMA-OFDM 5G wireless system using deep neural network. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology. 2021; ():1.
Chicago/Turabian StyleSharnil Pandya; Manoj Ashok Wakchaure; Ravi Shankar; Jagadeeswara Rao Annam. 2021. "Analysis of NOMA-OFDM 5G wireless system using deep neural network." The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology , no. : 1.
Human movement is a significant factor in extensive spatial-transmission models of contagious viruses. The proposed COUNTERACT system recognizes infectious sites by retrieving location data from a mobile phone device linked with a particular infected subject. The proposed approach is computing an incubation phase for the subject's infection, backpropagation through the subjects’ location data to investigate a location where the subject has been during the incubation period. Classifying to each such site as a contagious site, informing exposed suspects who have been to the contagious location, and seeking near real-time or real-time feedback from suspects to affirm, discard, or improve the recognition of the infectious site. This technique is based on the contraption to gather confirmed infected subject and possibly carrier suspect area location, correlating location for the incubation days. Security and privacy are a specific thing in the present research, and the system is used only through authentication and authorization. The proposed approach is for healthcare officials primarily. It is different from other existing systems where all the subjects have to install the application. The cell phone associated with the global positioning system (GPS) location data is collected from the COVID-19 subjects.
Hemant Ghayvat; Muhammad Awais; Prosanta Gope; Sharnil Pandya; Shubhankar Majumdar. ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence. Sustainable Cities and Society 2021, 69, 102798 .
AMA StyleHemant Ghayvat, Muhammad Awais, Prosanta Gope, Sharnil Pandya, Shubhankar Majumdar. ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence. Sustainable Cities and Society. 2021; 69 ():102798.
Chicago/Turabian StyleHemant Ghayvat; Muhammad Awais; Prosanta Gope; Sharnil Pandya; Shubhankar Majumdar. 2021. "ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence." Sustainable Cities and Society 69, no. : 102798.
In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.
Arpan Srivastava; Sonakshi Jain; Ryan Miranda; Shruti Patil; Sharnil Pandya; Ketan Kotecha. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Computer Science 2021, 7, e369 .
AMA StyleArpan Srivastava, Sonakshi Jain, Ryan Miranda, Shruti Patil, Sharnil Pandya, Ketan Kotecha. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Computer Science. 2021; 7 ():e369.
Chicago/Turabian StyleArpan Srivastava; Sonakshi Jain; Ryan Miranda; Shruti Patil; Sharnil Pandya; Ketan Kotecha. 2021. "Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease." PeerJ Computer Science 7, no. : e369.
In recent times, Ambient Assisted Living has emerged as Smart Living. Smart living is a subset of ambient intelligence, which uses the latest technologies, intellectual processes, and ambient intelligent methodologies to enable house residents to live independently with a virtual companion 24 × 7. Typically, these residents are highly engrossed in the daily routine activities that they tend to ignore certain acoustic events attributing them to the white noise caused due to tap water leakage, flush water leakage, the acoustics of door opening/closing, cupboard opening/closing, curtain opening/closing, television, shower, radio, chair and many more. These unattended events lead to a waste of critical energy resources such as electricity, water, and gas and may cause accidents in some cases. For the conducted experiments, a customized dataset termed as “unknown-2000” and ESC-50 has been used, which has more than 2000 audio sound classification samples. The customized dataset is used for the conducted experiments, consisting of various length acoustic events ranging from 2 s to 10 s. In the proposed review, we have identified, analyzed, and evaluated resident acoustic events using Librosa machine learning libraries, texture analysis using LBP methodology, LSTM-CNN, SVM, KNN, LSTM, Bi-LSTM, and Decision Tree-based classification approaches. Furthermore, in the proposed approach, based on the conducted rigorous and detailed analysis, we are also envisioning the prospective ways to enhance smart living concepts by proposing a novel Acoustic Event Detection and Classification System. The investigation results validate the success of the proposed approach. The obtained results indicate that the customized version of the LSTM-CNN based classification approach used in the conducted experiment has outperformed all the other customized classification approaches, such as SVM, KNN-based classification, C4.5 decision tree-based classification, LSTM, and Bi-LSTM based classification. The LSTM-CNN based classification model has achieved an average value of approximately 0.77 and a standard deviation of 0.2295. Furthermore, the obtained experiential results show that the proposed approach has produced a good performance in various noisy conditions such as SNR0, SNR3, SNR6, SNR9, SNR12, and SNR15. The system classification accuracy has been enhanced to 77% for various acoustic events of a residence. In the end, a detailed comparison of LBP and without LBP approaches has been carried out, which proves that the combination of LBP and LSTM-CNN classification approach provides better results than without the LBP classification approach. The proposed Ambient Acoustic Event Assistive Framework is a cost-effective alternative due to the use of low-cost microphone sensors in the conducted experiments.
Sharnil Pandya; Hemant Ghayvat. Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence. Advanced Engineering Informatics 2021, 47, 101238 .
AMA StyleSharnil Pandya, Hemant Ghayvat. Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence. Advanced Engineering Informatics. 2021; 47 ():101238.
Chicago/Turabian StyleSharnil Pandya; Hemant Ghayvat. 2021. "Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence." Advanced Engineering Informatics 47, no. : 101238.
Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.
Chirag I. Patel; Dileep Labana; Sharnil Pandya; Kirit Modi; Hemant Ghayvat; Muhammad Awais. Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences. Sensors 2020, 20, 7299 .
AMA StyleChirag I. Patel, Dileep Labana, Sharnil Pandya, Kirit Modi, Hemant Ghayvat, Muhammad Awais. Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences. Sensors. 2020; 20 (24):7299.
Chicago/Turabian StyleChirag I. Patel; Dileep Labana; Sharnil Pandya; Kirit Modi; Hemant Ghayvat; Muhammad Awais. 2020. "Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences." Sensors 20, no. 24: 7299.
Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
Muhammad Awais; Hemant Ghayvat; Anitha Krishnan Pandarathodiyil; Wan Maria Nabillah Ghani; Anand Ramanathan; Sharnil Pandya; Nicolas Walter; Mohamad Naufal Saad; Rosnah Binti Zain; Ibrahima Faye. Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging. Sensors 2020, 20, 5780 .
AMA StyleMuhammad Awais, Hemant Ghayvat, Anitha Krishnan Pandarathodiyil, Wan Maria Nabillah Ghani, Anand Ramanathan, Sharnil Pandya, Nicolas Walter, Mohamad Naufal Saad, Rosnah Binti Zain, Ibrahima Faye. Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging. Sensors. 2020; 20 (20):5780.
Chicago/Turabian StyleMuhammad Awais; Hemant Ghayvat; Anitha Krishnan Pandarathodiyil; Wan Maria Nabillah Ghani; Anand Ramanathan; Sharnil Pandya; Nicolas Walter; Mohamad Naufal Saad; Rosnah Binti Zain; Ibrahima Faye. 2020. "Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging." Sensors 20, no. 20: 5780.
Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
Sharnil Pandya; Hemant Ghayvat; Anirban Sur; Muhammad Awais; Ketan Kotecha; Santosh Saxena; Nandita Jassal; Gayatri Pingale. Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living. Sensors 2020, 20, 5448 .
AMA StyleSharnil Pandya, Hemant Ghayvat, Anirban Sur, Muhammad Awais, Ketan Kotecha, Santosh Saxena, Nandita Jassal, Gayatri Pingale. Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living. Sensors. 2020; 20 (18):5448.
Chicago/Turabian StyleSharnil Pandya; Hemant Ghayvat; Anirban Sur; Muhammad Awais; Ketan Kotecha; Santosh Saxena; Nandita Jassal; Gayatri Pingale. 2020. "Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living." Sensors 20, no. 18: 5448.
This paper presents a thermodynamic model for predicting the cooling performance of a single-bed single-stage silica gel/methanol adsorption refrigeration system. Solar heat was collected through flat plate collectors and then stored in a hot water tank. Desorber bed was heated by the hot water from the hot water tank. The temperature of the desorber bed was varied from 65 °C to 85 °C, and its effect on system performance was observed. A numerical model was developed on the basis of mass and energy balance equations, adsorption equilibrium, and kinetic equations (Dubinin–Astakhov equation) for predicting the performance of the adsorption refrigeration system under the said conditions for four major parts (adsorber, desorber, condenser, and evaporator). A programing code was written in FORTRAN for solving these equations under pre-defined material properties, and the simulation result was observed. A refrigeration effect of 577 kJ with a coefficient of performance (COP) of 0.38 could be produced for a maximum bed temperature of 90 °C, which was restricted up to 90 °C because after this temperature, the input increases more than the refrigeration effect, and COP values reduce due to a reduction in desorption mass.
Anirban Sur; Sharnil Pandya; Ramesh P. Sah; Ketan Kotecha; Swapnil Narkhede. Influence of bed temperature on performance of silica gel/methanol adsorption refrigeration system at adsorption equilibrium. Particulate Science and Technology 2020, 39, 624 -631.
AMA StyleAnirban Sur, Sharnil Pandya, Ramesh P. Sah, Ketan Kotecha, Swapnil Narkhede. Influence of bed temperature on performance of silica gel/methanol adsorption refrigeration system at adsorption equilibrium. Particulate Science and Technology. 2020; 39 (5):624-631.
Chicago/Turabian StyleAnirban Sur; Sharnil Pandya; Ramesh P. Sah; Ketan Kotecha; Swapnil Narkhede. 2020. "Influence of bed temperature on performance of silica gel/methanol adsorption refrigeration system at adsorption equilibrium." Particulate Science and Technology 39, no. 5: 624-631.
As there is a time gap between milking and storage, milk spoilage is more in remote areas in India, hence, immediate pasteurization and storing facility is required. For pasteurization heating is compulsory. In India most villages face scarcity of electricity, hence, solar and biomass heat (easily available) is a good option for milk pasteurization. After the pasteurization process for storing (150Ltrs of milk) in low temperature till its distribution, adsorption refrigeration is proposed here as it is run by solar/ waste heat or biogas (easily available from farm waste). During milk heating and storing at low temperature, materials used for containers and piping have chosen stainless steel (SS316), so that materials should not react with milk. For pasteurization of milk, one parabolic collector has been designed and developed. The result shows that at 5LPM flow rate, milk can be easily heated at 75 °C for 30 min. For storing 150ltrs of milk at 15–20 °C for 10–12 hrs, a solar power (flat plate collectors 8 m2) adsorption refrigeration has been designed and discussed. For adsorption refrigeration, selection of adsorption pair is very important. After reviewing different literatures, here activated carbon and methanol have chosen and justified. The experimental result shows that for 400LPH hot water supplied at 90 °C (desorber bed temperature at 80 °C), 35 °C condenser temperature and evaporator temperature 5 °C system’s specific cooling power varies between 5.7 kW/kg to 5.4 kW/kg (variation due to uncertainty analysis) which is sufficient for storing 150ltrs of milk.
Anirban Sur; Ramesh P. Sah; Sharnil Pandya. Milk storage system for remote areas using solar thermal energy and adsorption cooling. Materials Today: Proceedings 2020, 28, 1764 -1770.
AMA StyleAnirban Sur, Ramesh P. Sah, Sharnil Pandya. Milk storage system for remote areas using solar thermal energy and adsorption cooling. Materials Today: Proceedings. 2020; 28 ():1764-1770.
Chicago/Turabian StyleAnirban Sur; Ramesh P. Sah; Sharnil Pandya. 2020. "Milk storage system for remote areas using solar thermal energy and adsorption cooling." Materials Today: Proceedings 28, no. : 1764-1770.
Air pollution has emerged as a major concern of the current century. In recent times, fellow researchers have conducted numerous researches in the area of air quality monitoring. Still, air quality monitoring remains an open research area due to various challenges such as sophisticated topology design, privacy and security, power backup, large memory requirements and deployment of such systems at resource-constrained sites. The proposed research work is an attempt to address the issues of communication topology design, assessment of the Quality of Service (QoS) levels against accuracy, sensing throughput and power consumption optimization. In the undertaken work, the proposed IoT based Air Quality Monitoring system has been deployed at indoor and outdoor sites to measure air quality parameters such as PM10, PM2.5, carbon monoxide, temperature and humidity. The proposed system is also tested at variety of quality of service levels at the indoor and outdoor sites. The conducted experiments have also recorded accuracy in terms of reliable delivery of the messages under employed protocol.
Virendra Barot; Viral Kapadia; Sharnil Pandya. QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization. Cybernetics and Information Technologies 2020, 20, 122 -140.
AMA StyleVirendra Barot, Viral Kapadia, Sharnil Pandya. QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization. Cybernetics and Information Technologies. 2020; 20 (2):122-140.
Chicago/Turabian StyleVirendra Barot; Viral Kapadia; Sharnil Pandya. 2020. "QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization." Cybernetics and Information Technologies 20, no. 2: 122-140.
Falls are a noteworthy reason for grievances and deaths in elderlies. Notwithstanding when no damage happens, about majority of elderlies are identity unfit to get up without help. The expanded time of lying on the floor frequently prompts restorative complications, including muscle impairment, lack of hydration, unease, and trepidation of falling. Here, a fall sensing unit is accounted that is affixed to a subjects’ midsection and incorporates a 3-axis accelerometer, 3-axis gyroscope, a multiplexer, a filter, and a microcontroller. Moreover, the fall detection system also used IMU data on the mobile phone. Change in angular velocity, noise cancelation, and the ADC transformation was achieved by the hardware. The handled flag is conveyed to a PC through ZigBee and processed through the dedicated programming. Fall sensing approach comprised feature selection, mining and a machine learning calculation for characterizing the parameters. In this paper, we propose a fall discovery calculation which is shaped by feature selection, discovery, mining and handling. An aggregate of six highlights was ascertained in feature selection. Four of them are identified with the gravity vector which is extricated from accelerometer information by utilizing the low-pass filter. As falling generally happens in a vertical course, the gravity-related characteristics are helpful. The system also uses one of the ambient sensing units, which is a movement sensing unit. The PIR sensor-based movement sensing unit is used to enhance the accuracy of fall detection activity. The feature from the movement sensing unit substantially reduced the false alarms.
Hemant Ghayvat; Sharnil Pandya; Ashish Patel. Proposal and Preliminary Fall-related Activities Recognition in Indoor Environment. 2019 IEEE 19th International Conference on Communication Technology (ICCT) 2019, 362 -366.
AMA StyleHemant Ghayvat, Sharnil Pandya, Ashish Patel. Proposal and Preliminary Fall-related Activities Recognition in Indoor Environment. 2019 IEEE 19th International Conference on Communication Technology (ICCT). 2019; ():362-366.
Chicago/Turabian StyleHemant Ghayvat; Sharnil Pandya; Ashish Patel. 2019. "Proposal and Preliminary Fall-related Activities Recognition in Indoor Environment." 2019 IEEE 19th International Conference on Communication Technology (ICCT) , no. : 362-366.
Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).
Hemant Ghayvat; Muhammad Awais; Sharnil Pandya; Hao Ren; Saeed Akbarzadeh; Subhas Chandra Mukhopadhyay; Chen Chen; Prosanta Gope; Arpita Chouhan; Wei Chen. Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors 2019, 19, 766 .
AMA StyleHemant Ghayvat, Muhammad Awais, Sharnil Pandya, Hao Ren, Saeed Akbarzadeh, Subhas Chandra Mukhopadhyay, Chen Chen, Prosanta Gope, Arpita Chouhan, Wei Chen. Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors. 2019; 19 (4):766.
Chicago/Turabian StyleHemant Ghayvat; Muhammad Awais; Sharnil Pandya; Hao Ren; Saeed Akbarzadeh; Subhas Chandra Mukhopadhyay; Chen Chen; Prosanta Gope; Arpita Chouhan; Wei Chen. 2019. "Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection." Sensors 19, no. 4: 766.
The implementation of the smart home system being motivated by technology push rather than demand pull. This impractical approach has disappointed many users especially in term of feasibility and affordability in conjunction with many other issues. The present had developed a practical smart home solution, named wellness protocol. Present research is to extend the work on wellness protocol's smart home system, to implement in the context of economical dense sensing, targeting to be practically used by an individual and to understand human lifestyle for activity pattern. The system uses large sensory data for training and testing, the oldest dataset of this system comes from the year 2013. In smart aging to generate the behavioral pattern, more the datasets better the accuracy of behavioral pattern recognition and forecasting.
Hemant Ghayvat; Sharnil Pandya. Wellness Sensor Network for modeling Activity of Daily Livings – Proposal and Off-Line Preliminary Analysis. 2018 4th International Conference on Computing Communication and Automation (ICCCA) 2018, 1 -5.
AMA StyleHemant Ghayvat, Sharnil Pandya. Wellness Sensor Network for modeling Activity of Daily Livings – Proposal and Off-Line Preliminary Analysis. 2018 4th International Conference on Computing Communication and Automation (ICCCA). 2018; ():1-5.
Chicago/Turabian StyleHemant Ghayvat; Sharnil Pandya. 2018. "Wellness Sensor Network for modeling Activity of Daily Livings – Proposal and Off-Line Preliminary Analysis." 2018 4th International Conference on Computing Communication and Automation (ICCCA) , no. : 1-5.
The challenge for deployment of low-cost and high-speed ubiquitous Smart Health services has prompted us to propose new framework design for providing excellent healthcare to humankind. So, there exists a very high demand for developing an Internet of Medical Things (IoMT) based Ubiquitous Real-Time LoRa (Long Range) Healthcare System using Convolutional Neural Networks (CNN) to agree if a sequence of frames contains a person falling. To model the video motion and make the system scenario sovereign, in this research, we use optical flow images as input to the networks. Right now hospital and home falls are a noteworthy medical services concern overall on account of the aging populace. Current observational information, vital signs and falls history give the necessary data identified with the patient's physiology, and movement information give an additional utensil in falls risk evaluation. The proposed framework utilizes Real-Time Vital signs monitoring and emergency alert message to caregivers or doctors. In this context, we introduce "LoRaWAN based Next Generation Ubiquitous Healthcare System (NXTGeUH), an intelligent middleware platform. In addition, this proposed method is evaluated with different public hospital datasets achieving the state-of-the-art outcomes in all aspects.
Warish Patel; Sharnil Pandya; Baki Koyuncu; Bhupendra Ramani; Sourabh Bhaskar; Hemant Ghayvat. NXTGeUH: LoRaWAN based NEXT Generation Ubiquitous Healthcare System for Vital Signs Monitoring & Falls Detection. 2018 IEEE Punecon 2018, 1 -8.
AMA StyleWarish Patel, Sharnil Pandya, Baki Koyuncu, Bhupendra Ramani, Sourabh Bhaskar, Hemant Ghayvat. NXTGeUH: LoRaWAN based NEXT Generation Ubiquitous Healthcare System for Vital Signs Monitoring & Falls Detection. 2018 IEEE Punecon. 2018; ():1-8.
Chicago/Turabian StyleWarish Patel; Sharnil Pandya; Baki Koyuncu; Bhupendra Ramani; Sourabh Bhaskar; Hemant Ghayvat. 2018. "NXTGeUH: LoRaWAN based NEXT Generation Ubiquitous Healthcare System for Vital Signs Monitoring & Falls Detection." 2018 IEEE Punecon , no. : 1-8.
The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.
Sharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Mohammed Awais; Saeed Akbarzadeh; Prosanta Gope; Subhas Chandra Mukhopadhyay; Wei Chen. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol. Applied System Innovation 2018, 1, 42 .
AMA StyleSharnil Pandya, Hemant Ghayvat, Ketan Kotecha, Mohammed Awais, Saeed Akbarzadeh, Prosanta Gope, Subhas Chandra Mukhopadhyay, Wei Chen. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol. Applied System Innovation. 2018; 1 (4):42.
Chicago/Turabian StyleSharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Mohammed Awais; Saeed Akbarzadeh; Prosanta Gope; Subhas Chandra Mukhopadhyay; Wei Chen. 2018. "Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol." Applied System Innovation 1, no. 4: 42.