Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037310
A. Waghumbare, Upasna Singh, Nihit Singhal
In recent years, Deep Convolutional Neural Networks (DCNNs) have demonstrated some promising results in classification of micro-Doppler (m-D) radar data in human activity recognition. Compared with camera-based, radar-based human activity recognition is robust to low light conditions, adverse weather conditions, long-range operations, through wall imaging etc. An indigenously developed “DIAT-J.1RADHAR” human activity recognition dataset comprising micro-Doppler signature images of six different activites like (i) person fight punching (boxing) during the one-to-one attack, (ii) person intruding for pre-attack surveillance (army marching), (iii) person training (army jogging), (iv) person shooting (or escaping) with a rifle (jumping with holding a gun), (v) stone/hand-grenade throwing for damage/blasting (stone-pelting/grenades-throwing), and (vi) person hidden translation for attack execution or escape (army crawling and compared performance of this data on various DCNN models. To reduce variations in data, we have cleaned data and make it suitable for DCNN model by using preprocessing methods such as re-scaling, rotation, width shift range, height shift range, sheer range, zoom range and horizontal flip etc. We used different DCNN pre-trained models such as VGG-16, VGG-19, and Inception V3. These models are fine-tuned and the resultant models are performing efficiently for human activity recognition in DIAT-μRadHAR human activity dataset.
{"title":"DCNN Based Human Activity Recognition Using Micro-Doppler Signatures","authors":"A. Waghumbare, Upasna Singh, Nihit Singhal","doi":"10.1109/IBSSC56953.2022.10037310","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037310","url":null,"abstract":"In recent years, Deep Convolutional Neural Networks (DCNNs) have demonstrated some promising results in classification of micro-Doppler (m-D) radar data in human activity recognition. Compared with camera-based, radar-based human activity recognition is robust to low light conditions, adverse weather conditions, long-range operations, through wall imaging etc. An indigenously developed “DIAT-J.1RADHAR” human activity recognition dataset comprising micro-Doppler signature images of six different activites like (i) person fight punching (boxing) during the one-to-one attack, (ii) person intruding for pre-attack surveillance (army marching), (iii) person training (army jogging), (iv) person shooting (or escaping) with a rifle (jumping with holding a gun), (v) stone/hand-grenade throwing for damage/blasting (stone-pelting/grenades-throwing), and (vi) person hidden translation for attack execution or escape (army crawling and compared performance of this data on various DCNN models. To reduce variations in data, we have cleaned data and make it suitable for DCNN model by using preprocessing methods such as re-scaling, rotation, width shift range, height shift range, sheer range, zoom range and horizontal flip etc. We used different DCNN pre-trained models such as VGG-16, VGG-19, and Inception V3. These models are fine-tuned and the resultant models are performing efficiently for human activity recognition in DIAT-μRadHAR human activity dataset.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128506502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037445
Subhash Mondal, Ranjan Maity, Yash Raj Singh, Soumadip Ghosh, A. Nag
Coronary-Heart-Disease (CHD) risk increases daily due to the uncontrolled lifestyle of today's adult age group. The early detection of the disease can prevent unfortunate death due to heart-related complications. The Machine Learning (ML) technique is essential for the early diagnosis of CHD and for identifying its many contributing factor variables. To build the prediction model, we have used the dataset consisting of 4240 instances and 15 related features to predict the possibility of future risk of CHD in the next ten years. Initially, thirteen ML models were deployed with 10-fold cross-validation, reflecting the highest test accuracy of 91.28% for the Random Forest (RF) classifier. The models were turned further, and the boosting algorithms showed the highest accuracy of 91 % and above; the Gradient Boost (GB) classifier performed better with an accuracy of 92.11 %. The voting ensemble approaches using the best-performing boosting models, namely GB, HGB, XGB, CB, and LGBM, have been considered for the final prediction. The prediction results reflected an accuracy of 92.26%, an F1 score of 91.25%, a ROC-AUC score of 0.917, and the number of False Negatives (FN) values is about 6.25% of the total test dataset.
{"title":"Early Prediction of Coronary Heart Disease using Boosting-based Voting Ensemble Learning","authors":"Subhash Mondal, Ranjan Maity, Yash Raj Singh, Soumadip Ghosh, A. Nag","doi":"10.1109/IBSSC56953.2022.10037445","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037445","url":null,"abstract":"Coronary-Heart-Disease (CHD) risk increases daily due to the uncontrolled lifestyle of today's adult age group. The early detection of the disease can prevent unfortunate death due to heart-related complications. The Machine Learning (ML) technique is essential for the early diagnosis of CHD and for identifying its many contributing factor variables. To build the prediction model, we have used the dataset consisting of 4240 instances and 15 related features to predict the possibility of future risk of CHD in the next ten years. Initially, thirteen ML models were deployed with 10-fold cross-validation, reflecting the highest test accuracy of 91.28% for the Random Forest (RF) classifier. The models were turned further, and the boosting algorithms showed the highest accuracy of 91 % and above; the Gradient Boost (GB) classifier performed better with an accuracy of 92.11 %. The voting ensemble approaches using the best-performing boosting models, namely GB, HGB, XGB, CB, and LGBM, have been considered for the final prediction. The prediction results reflected an accuracy of 92.26%, an F1 score of 91.25%, a ROC-AUC score of 0.917, and the number of False Negatives (FN) values is about 6.25% of the total test dataset.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127385264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037475
Asmi Choudhary, Avaneesh Kumar, R. Jain, Syed Abou Iltaf Hussain
Optimization is a group of mathematical strategies for resolving quantitative issues in a variety of fields. The industries are relentlessly working to optimize more than one objective which are often conflicting in nature. Hence researchers are shifting their focus towards the multi-objective optimization algorithm which computes a set of Non-dominated solutions (NDS) which predominates other solutions in the search space. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is one such multi-objective optimization algorithm but it fails to compute an accurate result when applied to rocky datasets. In order to overcome the difficulties, we have integrated the Artificial Neural Network (ANN) and TOPSIS with NSGA-II. The ANN algorithm creates the objective functions and the TOPSIS algorithm creates a trade-off between the NDS for better exploration. For testing the applicability of our approach we have applied it for computing the machining parameters for turning Aluminum alloy 6061-T6 using a high speed steel tool so that the objective performances namely machining time, material removal rate (MRR) and surface roughness (SR) are optimized. For validating the approach two experiments are conducted at the optimized parameters and the parameters obtained by the traditional NSGA-II approach. The computed the relative error (RAE) between the simulated and the first experimental values which is 1.87% for machining time, 4.2% for MRR and 4.3% for SR and the simulated and the second experimental values which is 14.8% for machining time, 12% for MRR and 11.2% for SR. The RAE value is very less and within the acceptable limit for the result computed by the proposed approach. The strength of our proposed algorithm is its practical applicability and ability to provide an accurate solution to an industry problem and hence our model is suitable for industrial applications.
优化是解决各种领域定量问题的一组数学策略。各个行业都在不懈地努力优化多个目标，而这些目标往往在本质上是相互冲突的。因此，研究人员将重点转向多目标优化算法，该算法计算一组在搜索空间中占主导地位的非支配解(NDS)。非支配排序遗传算法II (non - dominant Sorting Genetic Algorithm II, NSGA-II)就是其中的一种多目标优化算法，但应用于岩石数据集时无法计算出准确的结果。为了克服这些困难，我们将人工神经网络(ANN)和TOPSIS集成到NSGA-II中。ANN算法创建目标函数，TOPSIS算法在NDS之间进行权衡，以便更好地进行探索。为了验证该方法的适用性，将其应用于6061-T6铝合金高速刀具车削加工参数的计算，优化了加工时间、材料去除率(MRR)和表面粗糙度(SR)。为了验证该方法，在优化参数和传统NSGA-II方法得到的参数下进行了两次实验。计算出模拟值与第一次实验值的相对误差(RAE)，加工时间为1.87%，MRR为4.2%，SR为4.3%;模拟值与第二次实验值的相对误差(RAE)，加工时间为14.8%，MRR为12%，SR为11.2%，RAE值很小，在可接受的范围内。我们提出的算法的优势在于它的实用性和为工业问题提供准确解决方案的能力，因此我们的模型适合工业应用。
{"title":"A Hybrid ANN coupled NTOPSIS Approach: An Intelligent Multi-Objective Framework for solving Engineering Problems","authors":"Asmi Choudhary, Avaneesh Kumar, R. Jain, Syed Abou Iltaf Hussain","doi":"10.1109/IBSSC56953.2022.10037475","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037475","url":null,"abstract":"Optimization is a group of mathematical strategies for resolving quantitative issues in a variety of fields. The industries are relentlessly working to optimize more than one objective which are often conflicting in nature. Hence researchers are shifting their focus towards the multi-objective optimization algorithm which computes a set of Non-dominated solutions (NDS) which predominates other solutions in the search space. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is one such multi-objective optimization algorithm but it fails to compute an accurate result when applied to rocky datasets. In order to overcome the difficulties, we have integrated the Artificial Neural Network (ANN) and TOPSIS with NSGA-II. The ANN algorithm creates the objective functions and the TOPSIS algorithm creates a trade-off between the NDS for better exploration. For testing the applicability of our approach we have applied it for computing the machining parameters for turning Aluminum alloy 6061-T6 using a high speed steel tool so that the objective performances namely machining time, material removal rate (MRR) and surface roughness (SR) are optimized. For validating the approach two experiments are conducted at the optimized parameters and the parameters obtained by the traditional NSGA-II approach. The computed the relative error (RAE) between the simulated and the first experimental values which is 1.87% for machining time, 4.2% for MRR and 4.3% for SR and the simulated and the second experimental values which is 14.8% for machining time, 12% for MRR and 11.2% for SR. The RAE value is very less and within the acceptable limit for the result computed by the proposed approach. The strength of our proposed algorithm is its practical applicability and ability to provide an accurate solution to an industry problem and hence our model is suitable for industrial applications.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126584520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037571
Javesh Dafda, Mansi Subhedar
In software defined networking, load balancing is a crucial management operation for moving traffic packets from source to destination. Ant Colony Optimization (ACO) was employed with dynamic load balancing to enhance SDN performance in existing works. In order to improve the search for the ideal path, response time, span-time, and energy consumption, it is proposed in this article to employ energy-aware routing with a Genetic Algorithm (GA) and ACO load balancing. The goals are to minimize energy consumption while maintaining a quality of service for user flows and to achieve link load balancing. Simulation results demonstrate that the proposed scheme performs better in terms of response time and energy consumption.
{"title":"Dynamic Load balancing in SDN using Energy Aware Routing and Optimization Algorithm","authors":"Javesh Dafda, Mansi Subhedar","doi":"10.1109/IBSSC56953.2022.10037571","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037571","url":null,"abstract":"In software defined networking, load balancing is a crucial management operation for moving traffic packets from source to destination. Ant Colony Optimization (ACO) was employed with dynamic load balancing to enhance SDN performance in existing works. In order to improve the search for the ideal path, response time, span-time, and energy consumption, it is proposed in this article to employ energy-aware routing with a Genetic Algorithm (GA) and ACO load balancing. The goals are to minimize energy consumption while maintaining a quality of service for user flows and to achieve link load balancing. Simulation results demonstrate that the proposed scheme performs better in terms of response time and energy consumption.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121881207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037459
Anika Kapoor, Shivani Goel
Anxiety disorders have seen an elevating number since the Covid-19 pandemic. This paper aims at identifying more about the various anxiety disorders using machine learning Techniques. Further, symptoms of the types of anxiety disorders: Generalized Anxiety Disorder, Panic Disorder, Post-Traumatic Stress Disorder, Obsessive-Compulsive Disorder and Social Anxiety Disorder are also discussed. The datasets used in the paper are collected by researchers from hospitals/organizations/educational institutions mainly through questionnaires and surveys. Some of the many Machine Learning techniques used for prediction of these anxiety disorders include Random Forest, Linear Regression, Support Vector Machine among others. Lastly, the performance metric for the techniques is presented here and henceforth, the result is drawn from this available data followed by the conclusion.
{"title":"Prediction of Anxiety Disorders using Machine Learning Techniques","authors":"Anika Kapoor, Shivani Goel","doi":"10.1109/IBSSC56953.2022.10037459","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037459","url":null,"abstract":"Anxiety disorders have seen an elevating number since the Covid-19 pandemic. This paper aims at identifying more about the various anxiety disorders using machine learning Techniques. Further, symptoms of the types of anxiety disorders: Generalized Anxiety Disorder, Panic Disorder, Post-Traumatic Stress Disorder, Obsessive-Compulsive Disorder and Social Anxiety Disorder are also discussed. The datasets used in the paper are collected by researchers from hospitals/organizations/educational institutions mainly through questionnaires and surveys. Some of the many Machine Learning techniques used for prediction of these anxiety disorders include Random Forest, Linear Regression, Support Vector Machine among others. Lastly, the performance metric for the techniques is presented here and henceforth, the result is drawn from this available data followed by the conclusion.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128091164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037362
A. Manekar, Lochan Jolly
EEG signals convey information about a person's mental state, such as brain activity or degree of consciousness. Alcohol can also influence a person's degree of alertness. Long-term alcohol usage can cause certain patterns in EEG signals to emerge. Manual EEG signal analysis approach is difficult and time deterrent. As a result, neurologists make use of automated techniques to evaluate EEG data from their frequency sub-bands. The two separate brain states, alcoholism and normal, are identified in the current work utilizing Discrete Wavelet Transform technique for feature extraction from electroencephalogram (EEG) recordings. From the EEG signals under analysis, the sub-band coefficients using wavelet decomposition using Daubechies 7 basis wavelets are calculated. From the selected wavelet coefficients, statistical parameters including Minimum, Maximum, Average, Kurtosis, Mean square, and Standard-deviation are retrieved. In this research, this data is then sent to classifiers like Ensemble boosted trees, SVM, neural networks, and decision trees to distinguish between alcoholic and non-alcoholic EEG signals. While calculating accuracy ten-fold cross-validation is used to train the data. We discovered that the best results were provided by Ensemble boosted trees, with an Accuracy of 95.6 percent, Sensitivity of 91.3 percent, and FI score of 95.5 percent.
{"title":"Wavelet Decomposition based Automated Alcoholism Classification using EEG Signal","authors":"A. Manekar, Lochan Jolly","doi":"10.1109/IBSSC56953.2022.10037362","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037362","url":null,"abstract":"EEG signals convey information about a person's mental state, such as brain activity or degree of consciousness. Alcohol can also influence a person's degree of alertness. Long-term alcohol usage can cause certain patterns in EEG signals to emerge. Manual EEG signal analysis approach is difficult and time deterrent. As a result, neurologists make use of automated techniques to evaluate EEG data from their frequency sub-bands. The two separate brain states, alcoholism and normal, are identified in the current work utilizing Discrete Wavelet Transform technique for feature extraction from electroencephalogram (EEG) recordings. From the EEG signals under analysis, the sub-band coefficients using wavelet decomposition using Daubechies 7 basis wavelets are calculated. From the selected wavelet coefficients, statistical parameters including Minimum, Maximum, Average, Kurtosis, Mean square, and Standard-deviation are retrieved. In this research, this data is then sent to classifiers like Ensemble boosted trees, SVM, neural networks, and decision trees to distinguish between alcoholic and non-alcoholic EEG signals. While calculating accuracy ten-fold cross-validation is used to train the data. We discovered that the best results were provided by Ensemble boosted trees, with an Accuracy of 95.6 percent, Sensitivity of 91.3 percent, and FI score of 95.5 percent.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124438400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037438
Shivani Pandya, Swati Jain, J. P. Verma
Autism spectrum Disorder(ASD) is a complex neurobehavioral disorder that affects a person's ability to communicate and interact with others. It is also characterized by repetitive behaviors and restricted interests. There is no one-size-fits-all approach to autism, but early intervention and treatment can make a big difference in a person's life. Machine learning and deep learning are two promising areas of research that may help to improve our understanding of autism and lead for better treatments. Machine learning and Deep Learning approaches of artificial intelligence allows computers to learn from data without being explicitly programmed. These models could potentially be used to improve our ability to communicate with, and understand people with autism. Various machine-learning techniques are used to predict autism at an early stage. Support Vector Machine (SVM), Decision tree, Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbour are some of the machine learning techniques used in this research area. Various advancement in the field of machine learning and Artificial Intelligence (AI) has helped in the development of ASD Detection using Machine learning and Deep Learning. In this research work, the prediction of Autism Spectrum Disorder has been performed on a video dataset. The video dataset contains the video of Autistic and Non-Autistic kids performing four different actions. The video features have been extracted through Convolutional Neural Network(CNN) models such as Inception V3and Resnet50 and are trained through long Short Term Memory(LSTM) based models by using this we get 91 % accuracy.
{"title":"AI based Classification for Autism Spectrum Disorder Detection using Video Analysis","authors":"Shivani Pandya, Swati Jain, J. P. Verma","doi":"10.1109/IBSSC56953.2022.10037438","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037438","url":null,"abstract":"Autism spectrum Disorder(ASD) is a complex neurobehavioral disorder that affects a person's ability to communicate and interact with others. It is also characterized by repetitive behaviors and restricted interests. There is no one-size-fits-all approach to autism, but early intervention and treatment can make a big difference in a person's life. Machine learning and deep learning are two promising areas of research that may help to improve our understanding of autism and lead for better treatments. Machine learning and Deep Learning approaches of artificial intelligence allows computers to learn from data without being explicitly programmed. These models could potentially be used to improve our ability to communicate with, and understand people with autism. Various machine-learning techniques are used to predict autism at an early stage. Support Vector Machine (SVM), Decision tree, Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbour are some of the machine learning techniques used in this research area. Various advancement in the field of machine learning and Artificial Intelligence (AI) has helped in the development of ASD Detection using Machine learning and Deep Learning. In this research work, the prediction of Autism Spectrum Disorder has been performed on a video dataset. The video dataset contains the video of Autistic and Non-Autistic kids performing four different actions. The video features have been extracted through Convolutional Neural Network(CNN) models such as Inception V3and Resnet50 and are trained through long Short Term Memory(LSTM) based models by using this we get 91 % accuracy.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134548350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037377
Siddharth Padhiar, K. Mehta, Juhi Patel, S. Panda
As the outbreak of COVID-19 increased in various countries. India is also majorly affected with the COVID-19 by that education system is affected, and it has transferred the traditional face-to-face teaching to online education platform. Considering student's perspective on both online and offline learning mode in India, we conducted a survey to collect the data. In that survey questionnaire, focus was on the factors and situation which can affect the education system. Using that data, we used Kruskal Wallis test to collect the evidence for which learning mode is better and Naive Bayes Algorithm, we were able to conclude the results.
{"title":"Observation of Online vs Offline Learning Experience","authors":"Siddharth Padhiar, K. Mehta, Juhi Patel, S. Panda","doi":"10.1109/IBSSC56953.2022.10037377","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037377","url":null,"abstract":"As the outbreak of COVID-19 increased in various countries. India is also majorly affected with the COVID-19 by that education system is affected, and it has transferred the traditional face-to-face teaching to online education platform. Considering student's perspective on both online and offline learning mode in India, we conducted a survey to collect the data. In that survey questionnaire, focus was on the factors and situation which can affect the education system. Using that data, we used Kruskal Wallis test to collect the evidence for which learning mode is better and Naive Bayes Algorithm, we were able to conclude the results.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132315459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037311
Lokesh Ramesh, Crispin Marie Peter G, Gladwyn K, Sundeep R, T. A, Ramkumar
The AGV's are beginning to change the way of the industries, there are still rooms for development of those AGV's. The hybrid AGV's which can climb walls and move on land for various purposes. The magnetic adhesion plays a major role in deciding the payload of the robot. The distance between the magnet and the iron rail surface embedded in the wall. The analysis was done on the magnet and the metal surface with FEMM software to find the best position to place the magnet in the robot. The distance between the magnet and the iron rail was also analyzed to reduce the friction and avoid magnets sticking to the rail. As it was found that the magnets positioning does play an important role in the overall payload and to give the required data to design the AVG to increase its performance. The design of the AGV is an important factor to consider the payload and the balance of the robot while climbing the wall to make sure that it doesn't fail. The motor modelling has been done with the help of MATLAB and the results are been recorded and is used for further studies and to incorporate the same in the mechanical design and make the AGV work properly. In summarizing the work, the magnets along with a design can improve the overall ability to perform the operations is essential, also the Motor modelling and the analysis done in MATLAB with Simulink will provide the results and data to make the AGV move with more precision.
{"title":"Motor Modelling and Magnetic adhesion Simulation For Hybrid Wall Climbing AGV","authors":"Lokesh Ramesh, Crispin Marie Peter G, Gladwyn K, Sundeep R, T. A, Ramkumar","doi":"10.1109/IBSSC56953.2022.10037311","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037311","url":null,"abstract":"The AGV's are beginning to change the way of the industries, there are still rooms for development of those AGV's. The hybrid AGV's which can climb walls and move on land for various purposes. The magnetic adhesion plays a major role in deciding the payload of the robot. The distance between the magnet and the iron rail surface embedded in the wall. The analysis was done on the magnet and the metal surface with FEMM software to find the best position to place the magnet in the robot. The distance between the magnet and the iron rail was also analyzed to reduce the friction and avoid magnets sticking to the rail. As it was found that the magnets positioning does play an important role in the overall payload and to give the required data to design the AVG to increase its performance. The design of the AGV is an important factor to consider the payload and the balance of the robot while climbing the wall to make sure that it doesn't fail. The motor modelling has been done with the help of MATLAB and the results are been recorded and is used for further studies and to incorporate the same in the mechanical design and make the AGV work properly. In summarizing the work, the magnets along with a design can improve the overall ability to perform the operations is essential, also the Motor modelling and the analysis done in MATLAB with Simulink will provide the results and data to make the AGV move with more precision.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131778708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037324
Sannidhi Rao, S. Mehta, Shreya Kulkarni, Harshal Dalvi, Neha Katre, M. Narvekar
Autonomous disease prediction systems are the new normal in the health industry today. These systems are used for decision support for medical practitioners and work based on users' health details input. These systems are based on Machine Learning models for generating predictions but at the same time are not capable to explain the rationale behind their prediction as the data size grows exponentially, resulting in the lack of user trust and transparency in the decision-making abilities of these systems. Explainable AI (XAI) can help users understand and interpret such autonomous predictions helping to restore the users' trust as well as making the decision-making process of such systems transparent. The addition of the XAI layer on top of the Machine Learning models in an autonomous system can also work as a decision support system for medical practitioners to aid the diagnosis process. In this research paper, we have analyzed the two most popular model explainers Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) for their applicability in autonomous disease prediction.
{"title":"A Study of LIME and SHAP Model Explainers for Autonomous Disease Predictions","authors":"Sannidhi Rao, S. Mehta, Shreya Kulkarni, Harshal Dalvi, Neha Katre, M. Narvekar","doi":"10.1109/IBSSC56953.2022.10037324","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037324","url":null,"abstract":"Autonomous disease prediction systems are the new normal in the health industry today. These systems are used for decision support for medical practitioners and work based on users' health details input. These systems are based on Machine Learning models for generating predictions but at the same time are not capable to explain the rationale behind their prediction as the data size grows exponentially, resulting in the lack of user trust and transparency in the decision-making abilities of these systems. Explainable AI (XAI) can help users understand and interpret such autonomous predictions helping to restore the users' trust as well as making the decision-making process of such systems transparent. The addition of the XAI layer on top of the Machine Learning models in an autonomous system can also work as a decision support system for medical practitioners to aid the diagnosis process. In this research paper, we have analyzed the two most popular model explainers Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) for their applicability in autonomous disease prediction.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132910859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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