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

Citation Count Prediction Using Different Time Series Analysis Models 利用不同时间序列分析模型预测引文数量

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037553

Priyam Porwal, M. Devare

{"title":"Citation Count Prediction Using Different Time Series Analysis Models","authors":"Priyam Porwal, M. Devare","doi":"10.1109/IBSSC56953.2022.10037553","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037553","url":null,"abstract":"The paper helps to predict the future citation value of a fresh dataset of research papers by considering the past values of the citation count of paper using univariate time series analysis models and evaluating their performance through various evaluation metrics. It is important to predict future citation count as it helps to assess researcher's achievements, promotions, fund allocation, etc. This research is in addition to past research where for prediction, different parameters like content of paper, author details, venue impact etc. were considered. The real and original data for the dataset was extracted from the Google Scholar profile of top ranked authors. Three models of time series, Autoregressive Integrated moving average(ARIMA), Simple exponential smoothing (SES), and Holt winter's exponential Smoothing (HWES) are applied to observe the result variations. The models obtained error metric values for the complete dataset. All four-evaluation metrics were calculated. The best results for the predictions for citation count were obtained from the Simple exponential smoothing and Holt winter's exponential Smoothing models, whose values were almost the same for all evaluation metrics because of almost no change in formula. Among all fourerror metrics mentioned in the design, MASE gave sensible results, with almost all values being less than 1. The results showed similar graphs for both Simple exponential smoothing and Holt winter's exponential smoothing models for actual and predicted values of citation count as there is negligible difference in formula.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"13 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":"114389496","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

Gradient Boosting Approach for Sentiment Analysis for Job Recommendation and Candidate Profiling 面向职位推荐和候选人分析的梯度增强情感分析方法

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037443

Swapnil Singh, D. Krishnan, Pranit Sehgal, Harshit Sharma, Tarun Surani, Jay P. Singh

引用次数: 0

Variables identification for Students Performance Prediction 学生成绩预测的变量识别

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037560

Vandana Bharadi, Satya Prakash Awasthi

引用次数: 0

Fuzzy Expert System for Acidification and Deacidification Process in Red Wine Grape Juice 红酒葡萄汁酸化与脱酸过程模糊专家系统

2022 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2022-12-08 DOI: 10.1109/IBSSC56953.2022.10037276

Pratik. B. Kamble, B. Jadhav

{"title":"Fuzzy Expert System for Acidification and Deacidification Process in Red Wine Grape Juice","authors":"Pratik. B. Kamble, B. Jadhav","doi":"10.1109/IBSSC56953.2022.10037276","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037276","url":null,"abstract":"The fermentation process is one of the most important processes in wine making. A certain amount of ratio of chemical compounds in red wine grape juice provides good quality wine. Acidifying and deacidifying grapes juice process is very complicated and non-linear, and ambiguous. Before starting the fermentation process the optimum balance of acid and pH is necessary. The purpose of this study is to develop a fuzzy expert system, this system can easily manipulate how much amount of acid or carbonates are required in red wine grape juice which saves time and gives good quality to the wine. A fuzzy interference system is used, if the acid level is low i.e. below 5 g/L then the acidification process will be carried out if the acid level is high i.e. above 8 g/L deacidification process will be carried out. A fuzzy rule base system handles uncertainty and gives a decision on acidifying and deacidifying processes. Domain expert takes trials of tartaric acid and pH values to get the optimum required amount of tartaric acid and carbonates value which is a time-consuming task. According to results, this system can easily manipulate how much amount of acid or carbonates are required in red wine grape juice which saves time and gives good quality to the wine.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"2014 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":"127436191","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

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