2022 IEEE Bombay Section Signature Conference (IBSSC)最新文献_第2页

DCNN Based Human Activity Recognition Using Micro-Doppler Signatures 基于DCNN的微多普勒特征人体活动识别

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037310

A. Waghumbare, Upasna Singh, Nihit Singhal

{"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}

引用次数: 3

Early Prediction of Coronary Heart Disease using Boosting-based Voting Ensemble Learning 基于boosting的投票集合学习的冠心病早期预测

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037445

Subhash Mondal, Ranjan Maity, Yash Raj Singh, Soumadip Ghosh, A. Nag

{"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}

引用次数: 1

A Hybrid ANN coupled NTOPSIS Approach: An Intelligent Multi-Objective Framework for solving Engineering Problems 混合人工神经网络耦合NTOPSIS方法:解决工程问题的智能多目标框架

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037475

Asmi Choudhary, Avaneesh Kumar, R. Jain, Syed Abou Iltaf Hussain

{"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}

引用次数: 0

Dynamic Load balancing in SDN using Energy Aware Routing and Optimization Algorithm 基于能量感知路由和优化算法的SDN动态负载均衡

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037571

Javesh Dafda, Mansi Subhedar

引用次数: 1

Prediction of Anxiety Disorders using Machine Learning Techniques 使用机器学习技术预测焦虑症

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037459

Anika Kapoor, Shivani Goel

引用次数: 1

Wavelet Decomposition based Automated Alcoholism Classification using EEG Signal 基于小波分解的脑电信号酒精中毒自动分类

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037362

A. Manekar, Lochan Jolly

{"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}

引用次数: 1

AI based Classification for Autism Spectrum Disorder Detection using Video Analysis 基于视频分析的自闭症谱系障碍检测AI分类

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037438

Shivani Pandya, Swati Jain, J. P. Verma

{"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}

引用次数: 1

Observation of Online vs Offline Learning Experience 线上与线下学习体验的观察

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037377

Siddharth Padhiar, K. Mehta, Juhi Patel, S. Panda

引用次数: 0

Motor Modelling and Magnetic adhesion Simulation For Hybrid Wall Climbing AGV 混合爬壁AGV电机建模与磁附着仿真

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037311

Lokesh Ramesh, Crispin Marie Peter G, Gladwyn K, Sundeep R, T. A, Ramkumar

{"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}

引用次数: 0

A Study of LIME and SHAP Model Explainers for Autonomous Disease Predictions 自主疾病预测的LIME和SHAP模型解释器研究

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037324

Sannidhi Rao, S. Mehta, Shreya Kulkarni, Harshal Dalvi, Neha Katre, M. Narvekar

{"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}

引用次数: 1