Pub Date : 2023-08-20DOI: 10.21817/indjcse/2023/v14i4/231404005
Benila S, Karan Kumar R
With the rapid development of the economy, vehicles have become the primary mode of transportation in people's daily lives. Among the various types of car accidents, rear-end collisions are quite common. Installing a rear-facing camera on the back of a vehicle can provide valuable assistance to drivers, including collision warning systems. By incorporating rear-end detection, drivers no longer need to look behind them. This system can detect objects on the road when the car is traveling at speeds over 80 km/h on a highway. Once activated, the system pre-processes the camera image to identify objects within it. If another vehicle is less than ten feet away and traveling in the same lane, a beep will sound. This is achieved by determining the lane the vehicle is in, estimating the object's distance from the camera, and utilizing the YOLOv5 object detection algorithm. To address the issue of the YOLOv5 vehicle detection algorithm missing detections for small and dense objects in complicated situations, the YOLOv5 vehicle detection method has been developed. The third-order B-spline curve model and the canny edge detection method were employed to fit the lane lines. This method has strong flexibility and resilience, and can describe lane lines of various shapes. The distance can be approximated by considering the labeled region found in the video. An alarm will sound to alert the driver if the distance is less than 3 meters. This technology will eliminate the vehicle's rear blind spot, ensuring the driver's safety.
{"title":"REAR END OBJECT DETECTION AND ALARM SYSTEM FOR INTELLIGENT TRANSPORTATION","authors":"Benila S, Karan Kumar R","doi":"10.21817/indjcse/2023/v14i4/231404005","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i4/231404005","url":null,"abstract":"With the rapid development of the economy, vehicles have become the primary mode of transportation in people's daily lives. Among the various types of car accidents, rear-end collisions are quite common. Installing a rear-facing camera on the back of a vehicle can provide valuable assistance to drivers, including collision warning systems. By incorporating rear-end detection, drivers no longer need to look behind them. This system can detect objects on the road when the car is traveling at speeds over 80 km/h on a highway. Once activated, the system pre-processes the camera image to identify objects within it. If another vehicle is less than ten feet away and traveling in the same lane, a beep will sound. This is achieved by determining the lane the vehicle is in, estimating the object's distance from the camera, and utilizing the YOLOv5 object detection algorithm. To address the issue of the YOLOv5 vehicle detection algorithm missing detections for small and dense objects in complicated situations, the YOLOv5 vehicle detection method has been developed. The third-order B-spline curve model and the canny edge detection method were employed to fit the lane lines. This method has strong flexibility and resilience, and can describe lane lines of various shapes. The distance can be approximated by considering the labeled region found in the video. An alarm will sound to alert the driver if the distance is less than 3 meters. This technology will eliminate the vehicle's rear blind spot, ensuring the driver's safety.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47750098","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 : 2023-07-06DOI: 10.17010/ijcs/2023/v8/i3/172863
Rohan Vanmali, Tejas Kashid, W. Rodrigues, Asher Rodrigues, Sonali Suryawanshi
{"title":"Prediction of Osteoporosis Risk Level Using Machine Learning Techniques","authors":"Rohan Vanmali, Tejas Kashid, W. Rodrigues, Asher Rodrigues, Sonali Suryawanshi","doi":"10.17010/ijcs/2023/v8/i3/172863","DOIUrl":"https://doi.org/10.17010/ijcs/2023/v8/i3/172863","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90925122","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 : 2023-07-06DOI: 10.17010/ijcs/2023/v8/i3/172866
Nandu Sam Jose
{"title":"Navigating the Legal Landscape: Evaluating the Case for Artificial Intelligence as Juristic Persons","authors":"Nandu Sam Jose","doi":"10.17010/ijcs/2023/v8/i3/172866","DOIUrl":"https://doi.org/10.17010/ijcs/2023/v8/i3/172866","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77805999","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 : 2023-07-06DOI: 10.17010/ijcs/2023/v8/i3/172862
S. Garg, V. Subrahmanyam
{"title":"A Research Paper on Negation Handling: Sentiment Analysis Using Super Ensemble Method in Deep Learning","authors":"S. Garg, V. Subrahmanyam","doi":"10.17010/ijcs/2023/v8/i3/172862","DOIUrl":"https://doi.org/10.17010/ijcs/2023/v8/i3/172862","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80625199","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403136
D. N, G. N
Social Networking platforms like Facebook, Twitter, Reddit, Weibo, Instagram and many more are the most popular and very easy to use medium for social connectivity, staying up to date with current events and news, relaxing in spare time, sharing opinions on many occurring events etc., Usage of these platforms have tremendously increased year over year say 9 to 12%. As of 2021 half of the percentage of people are using social media out of entire population [1]. With this much usage, if the entire information available in the Network is real and informative then it is really appreciated, but there is clear evidence that there are high chances of dissemination of malicious information for variety of reasons which creates negative impact on the society. So, detecting this type of content in the Social Network is very important research area. From past many years researchers have come up with different ideas to identify this type of information with Data Mining, Machine Learning, Deep Learning techniques. In this paper we propose a hybrid approach HCSTCM (Hybrid Cluster derived Sentiment based Topic Classification Model) to identify malicious content in the Social Network by deriving clusters, sentiments and topic information of the documents, later on using these features for supervised learning. Main aim of the paper is to identify the most dependent features which effect the malicious content without redundancy and improve the classification accuracy. The proposed method is validated with three social platform data.
{"title":"MALICIOUS CONTENT DETECTION IN SOCIAL NETWORKS USING HYBRID MACHINE LEARNING MODEL","authors":"D. N, G. N","doi":"10.21817/indjcse/2023/v14i3/231403136","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403136","url":null,"abstract":"Social Networking platforms like Facebook, Twitter, Reddit, Weibo, Instagram and many more are the most popular and very easy to use medium for social connectivity, staying up to date with current events and news, relaxing in spare time, sharing opinions on many occurring events etc., Usage of these platforms have tremendously increased year over year say 9 to 12%. As of 2021 half of the percentage of people are using social media out of entire population [1]. With this much usage, if the entire information available in the Network is real and informative then it is really appreciated, but there is clear evidence that there are high chances of dissemination of malicious information for variety of reasons which creates negative impact on the society. So, detecting this type of content in the Social Network is very important research area. From past many years researchers have come up with different ideas to identify this type of information with Data Mining, Machine Learning, Deep Learning techniques. In this paper we propose a hybrid approach HCSTCM (Hybrid Cluster derived Sentiment based Topic Classification Model) to identify malicious content in the Social Network by deriving clusters, sentiments and topic information of the documents, later on using these features for supervised learning. Main aim of the paper is to identify the most dependent features which effect the malicious content without redundancy and improve the classification accuracy. The proposed method is validated with three social platform data.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45233704","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403023
Mrs. Nikita R. Hatwar, Dr. Ujwalla G. Gawande, Ms. Chetana B. Thaokar
{"title":"DETECTION OF HUMAN PSYCHOLOGICAL STRESS WITH DEEP CONVOLUTIONAL NEURAL NETWORK USING DIFFERENT CRITERIAS FOR FEATURE SELECTION ON BASIS OF CONFIDENCE VALUE OF PAIRED t-TEST","authors":"Mrs. Nikita R. Hatwar, Dr. Ujwalla G. Gawande, Ms. Chetana B. Thaokar","doi":"10.21817/indjcse/2023/v14i3/231403023","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403023","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45841286","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403009
Md. Mijanur Rahman, Zannatul Ferdousi, Puja Saha, R. Mayuri
Breast cancer is a prevalent disease, with the second highest incidence rate among all types of cancer. The risk of death from breast cancer is increasing due to rapid population growth, and a dependable and quick diagnostic system can assist medical professionals in disease diagnosis and lower the mortality rate. In this study, various machine-learning algorithms are examined for predicting the stages of breast cancer, and most especially in the medical field, where those methods are widely used in diagnosis and analysis for decision-making. We focused on boosting classification models and evaluated the performance of XGBoost, AdaBoost, and Gradient Boosting. Our goal is to achieve higher accuracy by using boosting classifiers with hyperparameter tuning for the prediction of breast cancer stages, precisely the distinction between "Benign" and "Malignant" types of breast cancer. The Wisconsin breast cancer dataset is employed from the UCI machine learning database. The performance of our model was evaluated using metrics such as accuracy, sensitivity, precision, specificity, AUC, and ROC curves for various strategies. After implementing the model, this study achieved the best model accuracy, and 98.60% was achieved on AdaBoost.
{"title":"A Machine Learning Approach to Predict Breast Cancer Using Boosting Classifiers","authors":"Md. Mijanur Rahman, Zannatul Ferdousi, Puja Saha, R. Mayuri","doi":"10.21817/indjcse/2023/v14i3/231403009","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403009","url":null,"abstract":"Breast cancer is a prevalent disease, with the second highest incidence rate among all types of cancer. The risk of death from breast cancer is increasing due to rapid population growth, and a dependable and quick diagnostic system can assist medical professionals in disease diagnosis and lower the mortality rate. In this study, various machine-learning algorithms are examined for predicting the stages of breast cancer, and most especially in the medical field, where those methods are widely used in diagnosis and analysis for decision-making. We focused on boosting classification models and evaluated the performance of XGBoost, AdaBoost, and Gradient Boosting. Our goal is to achieve higher accuracy by using boosting classifiers with hyperparameter tuning for the prediction of breast cancer stages, precisely the distinction between \"Benign\" and \"Malignant\" types of breast cancer. The Wisconsin breast cancer dataset is employed from the UCI machine learning database. The performance of our model was evaluated using metrics such as accuracy, sensitivity, precision, specificity, AUC, and ROC curves for various strategies. After implementing the model, this study achieved the best model accuracy, and 98.60% was achieved on AdaBoost.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42024092","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403071
Thanh-Nghi Doan
This article studies machine learning techniques and factors that affect tourism demand to develop a predictive model for tourism demand in the coming years. The model was developed using the back-propagation neural network approach and expert knowledge for analyzing factors affecting tourist satisfaction. The data used in the study were collected over a ten-year period and comprised information on the local economic and social situation, as well as specialized tourism data. In addition, survey results evaluating tourism in An Giang province in 2019 were included. The study results demonstrate that the developed model has successfully captured the underlying patterns in the An Giang tourism data, enabling the prediction of the necessary tourism indicators for the future. The model achieved a high level of accuracy with an RSME of 0.04. Furthermore, our approach showed several advantages when compared to other classical statistical methods. Based on our research findings, we proposed policies to support businesses, planning, and management units in forecasting and investing in the development of tourism in each specific locality more effectively.
{"title":"A BACK-PROPAGATION NEURAL NETWORK WITH DELAY AND SHIFT WINDOW FOR TOURISM DEMAND FORECASTING","authors":"Thanh-Nghi Doan","doi":"10.21817/indjcse/2023/v14i3/231403071","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403071","url":null,"abstract":"This article studies machine learning techniques and factors that affect tourism demand to develop a predictive model for tourism demand in the coming years. The model was developed using the back-propagation neural network approach and expert knowledge for analyzing factors affecting tourist satisfaction. The data used in the study were collected over a ten-year period and comprised information on the local economic and social situation, as well as specialized tourism data. In addition, survey results evaluating tourism in An Giang province in 2019 were included. The study results demonstrate that the developed model has successfully captured the underlying patterns in the An Giang tourism data, enabling the prediction of the necessary tourism indicators for the future. The model achieved a high level of accuracy with an RSME of 0.04. Furthermore, our approach showed several advantages when compared to other classical statistical methods. Based on our research findings, we proposed policies to support businesses, planning, and management units in forecasting and investing in the development of tourism in each specific locality more effectively.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47446261","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403068
Thanh-Nghi Doan
The healthcare industry is advancing rapidly in both technology and services. One recent development is remote health monitoring, which has become increasingly important in a world where the aging population is facing more health complications. Initially, this technology was limited to monitoring patients within hospital rooms. However, advancements in communication and sensor technologies have made it possible to monitor patients while they go about their daily activities at home. One popular device being used for this purpose is the smartwatch, due to its efficiency and ease of use in transmitting health data quickly and conveniently via smartphones. This study proposes an end-to-end remote monitoring framework for predicting and managing health risks using different types of personal health devices, smartphones, and smartwatches. Several machine learning methods were applied to a collected dataset, which underwent feature scaling, imputation, selection, and augmentation to predict health risks. The tenfold stratified cross-validation method achieved an accuracy of 99.5%, a recall of 99.5%, and an F1 of 99.5%, which is competitive with existing methods. Patients can utilize various personal health devices, such as smartphones and smartwatches, to monitor vital signs and manage the development of their health metrics, all while staying connected with medical experts. The proposed framework allows medical professionals to make informed decisions based on the latest health risk predictions and lifestyle insights while maintaining unobtrusiveness, reducing cost, and ensuring vendor interoperability. The cost of entire system is 328 USD.
{"title":"A NOVEL LOW-COST SYSTEM FOR REMOTE HEALTH MONITORING USING SMARTWATCHES","authors":"Thanh-Nghi Doan","doi":"10.21817/indjcse/2023/v14i3/231403068","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403068","url":null,"abstract":"The healthcare industry is advancing rapidly in both technology and services. One recent development is remote health monitoring, which has become increasingly important in a world where the aging population is facing more health complications. Initially, this technology was limited to monitoring patients within hospital rooms. However, advancements in communication and sensor technologies have made it possible to monitor patients while they go about their daily activities at home. One popular device being used for this purpose is the smartwatch, due to its efficiency and ease of use in transmitting health data quickly and conveniently via smartphones. This study proposes an end-to-end remote monitoring framework for predicting and managing health risks using different types of personal health devices, smartphones, and smartwatches. Several machine learning methods were applied to a collected dataset, which underwent feature scaling, imputation, selection, and augmentation to predict health risks. The tenfold stratified cross-validation method achieved an accuracy of 99.5%, a recall of 99.5%, and an F1 of 99.5%, which is competitive with existing methods. Patients can utilize various personal health devices, such as smartphones and smartwatches, to monitor vital signs and manage the development of their health metrics, all while staying connected with medical experts. The proposed framework allows medical professionals to make informed decisions based on the latest health risk predictions and lifestyle insights while maintaining unobtrusiveness, reducing cost, and ensuring vendor interoperability. The cost of entire system is 328 USD.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41707696","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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