Cardiovascular disorders (CVD) are the key cause of mortality worldwide. One in three male premature deaths and one in five female premature deaths are thought to be attributable to Cardiovascular disorders. Early prediction of CVDs may help to attenuate the disease, potentially lowering death rates. The existence of cardiac disease can be predicted using machine learning approaches; however, the effectiveness of the classifiers may be enhanced by applying PCA, relief feature selection, and data transformation techniques. The objective of employing data transformation, PCA, and relief feature selection approaches is to enhance classifier performance and increase the interpretability and ability of classifiers to predict heart disease. Heart disease anticipating is a challenging problem in the field of healthcare. This uses popular supervised machine learning (ML) algorithms including k-NN, LR, DT, RF, SVM, and ANN to help healthcare practitioners and specialists easily identify the prevalence of heart-related illnesses in patients. In these trials, data transformation is achieved using PCA, normalized features, and relief techniques, and RF surpasses all other classifiers with a prediction accuracy of 90%, followed by ANN and DT with AUCs of 87% and 86%, respectively. SVM and Naive Bayes classifiers were shown to be lesser effective at predicting heart disease.
{"title":"Improving Heart Disease Prediction of Classifiers with Data Transformation using PCA and Relief Feature Selection","authors":"Guggulla Varshini, Ananthaneni Ramya, Chitrakavi Lakshmi Sravya, Vinod Kumar, Brajesh K. Shukla","doi":"10.1109/ICEARS56392.2023.10085401","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085401","url":null,"abstract":"Cardiovascular disorders (CVD) are the key cause of mortality worldwide. One in three male premature deaths and one in five female premature deaths are thought to be attributable to Cardiovascular disorders. Early prediction of CVDs may help to attenuate the disease, potentially lowering death rates. The existence of cardiac disease can be predicted using machine learning approaches; however, the effectiveness of the classifiers may be enhanced by applying PCA, relief feature selection, and data transformation techniques. The objective of employing data transformation, PCA, and relief feature selection approaches is to enhance classifier performance and increase the interpretability and ability of classifiers to predict heart disease. Heart disease anticipating is a challenging problem in the field of healthcare. This uses popular supervised machine learning (ML) algorithms including k-NN, LR, DT, RF, SVM, and ANN to help healthcare practitioners and specialists easily identify the prevalence of heart-related illnesses in patients. In these trials, data transformation is achieved using PCA, normalized features, and relief techniques, and RF surpasses all other classifiers with a prediction accuracy of 90%, followed by ANN and DT with AUCs of 87% and 86%, respectively. SVM and Naive Bayes classifiers were shown to be lesser effective at predicting heart disease.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123390164","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085553
B. Lakshmi, M. Robinson Joel
The adoption of wearable technology will increase and its integration into daily life will improve, particularly in the healthcare sector. The emergence of mobile medicine, the development of new technologies like smart sensing, and the adoption of customised health ideas have all contributed to the rapid growth of smart wearable technology in recent years. The study was primarily focused on the use of wearable technology in office situations with the goal of daily health and safety monitoring of employees. In order to perform data classification and data labeling, a machine learning model is constructed. This research work has proposed a novel framework for processing data with text-related properties using machine learning techniques. Further a data analysis process has been carried out by using a Machine Learning (ML) framework. In the proposed study, machine learning classifiers are used. This study has analyzed the outcomes by considering accuracy as a performance indicator after applying the algorithms to the datasets. After analyzing the accuracy, it is evident that the machine learning algorithms like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are effective on processing the text datasets.
{"title":"IoT based Illness Prediction System using Machine Learning","authors":"B. Lakshmi, M. Robinson Joel","doi":"10.1109/ICEARS56392.2023.10085553","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085553","url":null,"abstract":"The adoption of wearable technology will increase and its integration into daily life will improve, particularly in the healthcare sector. The emergence of mobile medicine, the development of new technologies like smart sensing, and the adoption of customised health ideas have all contributed to the rapid growth of smart wearable technology in recent years. The study was primarily focused on the use of wearable technology in office situations with the goal of daily health and safety monitoring of employees. In order to perform data classification and data labeling, a machine learning model is constructed. This research work has proposed a novel framework for processing data with text-related properties using machine learning techniques. Further a data analysis process has been carried out by using a Machine Learning (ML) framework. In the proposed study, machine learning classifiers are used. This study has analyzed the outcomes by considering accuracy as a performance indicator after applying the algorithms to the datasets. After analyzing the accuracy, it is evident that the machine learning algorithms like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are effective on processing the text datasets.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123591495","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085327
Analene Montesines Nagayo, S. Sangeetha, Mahmood Zayid K. Al Ajmi, Abdullah Yousuf M. Al Bulushi, Mohammed Said A. Al Hinaai, Loay Yahia T. Al Hamadani
This article discusses about the design and deployment of a smart robotic system on university campuses for monitoring the indoor environment, health protocols, and sanitation. The designed VEX autonomous robotic system performed the following tasks: (a) moving around the university classrooms and scanning the body temperature of students and staff, as well as tracking environmental parameters in classrooms; (b) executing sanitation function by disinfecting objects in classrooms; and (c) performing security function by sending an alert signal to health and safety officer if a student or staff with fever enters the classroom, or if staff or student is not wearing face mask indoors. Particle Photon microcontrollers linked to sensors and actuators were used to detect and manage indoor environmental conditions as well as track individuals' body temperatures from a distance, with the data being stored in the ThingSpeak and Particle cloud platforms and displayed on smartphone apps. Transfer learning through MIT App Inventor's Personal Image Classifier was used to detect health protocol violations with 93.33% accuracy. The maximum distance traversed by the robot prototype was 38 meters, with an average time of 220 seconds and an average speed of 0.17 meters per second. The robot had an 88.89% success rate in following the black-lined course. This intelligent robotic system can limit staff and student exposure to infectious diseases and implement "new normal" health and safety practices on campus as post-COVID-19 precautions.
{"title":"Indoor Environment and Health Protocol Monitoring and Control System Integrated into a Smart Robot to Promote Safety on University Campuses","authors":"Analene Montesines Nagayo, S. Sangeetha, Mahmood Zayid K. Al Ajmi, Abdullah Yousuf M. Al Bulushi, Mohammed Said A. Al Hinaai, Loay Yahia T. Al Hamadani","doi":"10.1109/ICEARS56392.2023.10085327","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085327","url":null,"abstract":"This article discusses about the design and deployment of a smart robotic system on university campuses for monitoring the indoor environment, health protocols, and sanitation. The designed VEX autonomous robotic system performed the following tasks: (a) moving around the university classrooms and scanning the body temperature of students and staff, as well as tracking environmental parameters in classrooms; (b) executing sanitation function by disinfecting objects in classrooms; and (c) performing security function by sending an alert signal to health and safety officer if a student or staff with fever enters the classroom, or if staff or student is not wearing face mask indoors. Particle Photon microcontrollers linked to sensors and actuators were used to detect and manage indoor environmental conditions as well as track individuals' body temperatures from a distance, with the data being stored in the ThingSpeak and Particle cloud platforms and displayed on smartphone apps. Transfer learning through MIT App Inventor's Personal Image Classifier was used to detect health protocol violations with 93.33% accuracy. The maximum distance traversed by the robot prototype was 38 meters, with an average time of 220 seconds and an average speed of 0.17 meters per second. The robot had an 88.89% success rate in following the black-lined course. This intelligent robotic system can limit staff and student exposure to infectious diseases and implement \"new normal\" health and safety practices on campus as post-COVID-19 precautions.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"35 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839739","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085648
V. K. Neeli, M. Raghavendra, Sk. Chan Basha, K. Chowdary, N. Sameera
In this current work, one of the maiden approaches was made frequency regulation in a Standalone AC Microgrid (μG) by considering Spotted Hyena optimizer based Cascaded PI-PD controller. These μGrid can be formed by merging some the isolated such as renewable energy resources, wind power and also solar electricity irradiations. Discrepancy any these-sources will influence system frequency and hence-frequency control-theme in MG was challenging issue for all-the researchers. Inspite of these struggling this current paper consider a-Cascaded PI-PD-controllers as secondary frequency controller for the-Standalone μGrid, and a novel Spotted Hyena Optimizer (SHO) is used to tuning and obtaining the controller parameters. The proposed cascaded controllers inspected on a μGrid test system, and robustness is assessed considering -dissimilar variations in load. In order to manifest the effectiveness of the Cascaded PI-PD controller, it-is being compared to some more conventional controller as Proportional Integral (PI), and Proportional Integral and-Derivative (PID) controllers and also to verify the-potency of the-Spotted Hyena optimizer, the-results obtained-by the SHO are been-compared with other intelligence swarm-techniques.
{"title":"Spotted Hyena Optimized PI-PD Controller for Frequency control of Standalone μ-Grid Incorporating Electric Vehicles","authors":"V. K. Neeli, M. Raghavendra, Sk. Chan Basha, K. Chowdary, N. Sameera","doi":"10.1109/ICEARS56392.2023.10085648","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085648","url":null,"abstract":"In this current work, one of the maiden approaches was made frequency regulation in a Standalone AC Microgrid (μG) by considering Spotted Hyena optimizer based Cascaded PI-PD controller. These μGrid can be formed by merging some the isolated such as renewable energy resources, wind power and also solar electricity irradiations. Discrepancy any these-sources will influence system frequency and hence-frequency control-theme in MG was challenging issue for all-the researchers. Inspite of these struggling this current paper consider a-Cascaded PI-PD-controllers as secondary frequency controller for the-Standalone μGrid, and a novel Spotted Hyena Optimizer (SHO) is used to tuning and obtaining the controller parameters. The proposed cascaded controllers inspected on a μGrid test system, and robustness is assessed considering -dissimilar variations in load. In order to manifest the effectiveness of the Cascaded PI-PD controller, it-is being compared to some more conventional controller as Proportional Integral (PI), and Proportional Integral and-Derivative (PID) controllers and also to verify the-potency of the-Spotted Hyena optimizer, the-results obtained-by the SHO are been-compared with other intelligence swarm-techniques.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125800306","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085042
Anil Manohar Dogra, Monika Singh
In the last few years, surveillance cameras have gained much popularity mainly due to their convenience, flexibility, and portability. As crimes are increasing, so there is a need to enhance the existing video surveillance system. There are several flaws and Challenges in the existing system regarding malfunctioning of systems, system failure, backup procedures, and self-adaptation mechanisms, and so on. This study has developed an algorithm to make the existing system intelligent to take its own decision i.e., to execute the backup procedure, generate a warning message, and execute a self-orientation mechanism in case of any emergency.
{"title":"Self-Adaptive Multimedia Networked System for Effective Real-Time Feedback","authors":"Anil Manohar Dogra, Monika Singh","doi":"10.1109/ICEARS56392.2023.10085042","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085042","url":null,"abstract":"In the last few years, surveillance cameras have gained much popularity mainly due to their convenience, flexibility, and portability. As crimes are increasing, so there is a need to enhance the existing video surveillance system. There are several flaws and Challenges in the existing system regarding malfunctioning of systems, system failure, backup procedures, and self-adaptation mechanisms, and so on. This study has developed an algorithm to make the existing system intelligent to take its own decision i.e., to execute the backup procedure, generate a warning message, and execute a self-orientation mechanism in case of any emergency.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128465616","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085091
P. Balasubramani, S. Suresh, S. Kirubashankar, S. Kowsika, S. Guhan
This design describes the transmission of aquatic images over an OFDM system. Different modulation schemes are used to transmit images over wireless technology. Due to channel fading, only a subset of carriers can be used for successful data transmission in an OFDM system. Channel state information can be used at the transmitter to best match predictive decisions to reject image frames if they are DWT-compressed in use. Compressed data is uploaded to the OFDM system. Next, examine descriptions to the correct subcarriers and to make the individual shard channel status data available at the transmitter. This indicates that sub-channels are good or bad for ocean metamorphism via OFDM channels. The descriptors assigned to currently active channels are in descending order of priority based on the sender's 1-bit channel state information. Allocations to the problematic subchannels described are omitted in the transmitter to reduce system power consumption. Through analysis accompanying Demonstration of effectiveness of proposed method by MATLAB simulation best signal-to- noise ratio and in terms of saving system performance without sacrificing quality
{"title":"Efficient Image Transmission in Underwater Communication using OFDM Modulation","authors":"P. Balasubramani, S. Suresh, S. Kirubashankar, S. Kowsika, S. Guhan","doi":"10.1109/ICEARS56392.2023.10085091","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085091","url":null,"abstract":"This design describes the transmission of aquatic images over an OFDM system. Different modulation schemes are used to transmit images over wireless technology. Due to channel fading, only a subset of carriers can be used for successful data transmission in an OFDM system. Channel state information can be used at the transmitter to best match predictive decisions to reject image frames if they are DWT-compressed in use. Compressed data is uploaded to the OFDM system. Next, examine descriptions to the correct subcarriers and to make the individual shard channel status data available at the transmitter. This indicates that sub-channels are good or bad for ocean metamorphism via OFDM channels. The descriptors assigned to currently active channels are in descending order of priority based on the sender's 1-bit channel state information. Allocations to the problematic subchannels described are omitted in the transmitter to reduce system power consumption. Through analysis accompanying Demonstration of effectiveness of proposed method by MATLAB simulation best signal-to- noise ratio and in terms of saving system performance without sacrificing quality","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129978275","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10084960
R. Josphineleela, Kotla Venkata Siva Reddy, M. S. Reddy, R. S. Rawat
Most people using the Pathway currently find sprinkler watering systems to be inconvenient. In this situation, it is important to monitor sprinklers fixed in the walking area to reduce human foots to water. A practical survey has been done to collect the actual data. They are addressing the applications of the current devices for spraying. A solution to the problem has been presented. The monitoring system could be automated by turning it on and off when a human interruption is nearby. Functioning sprinkler To ensure sustainability, the meteorological and soil factors are also tracked. To measure the heat, humidity and water content, the sensing devices are employed. An IoT-enabled autonomous device activates a sprinkler mechanism. The mechanical pump delivers water to the sprinklers. Although this equipment looks like highly functional, the system requires a effective design to reduce the water supply and energy loss when operated remotely. Two probes on the sensor can be used to calculate the quantity of water. IoT moisture measuring is employed to find moisture content. In this device, sprinkler system to turned on or off, as well as a calibrate, to determine the amount of. The moisture sensor is linked straight into the internet module to present the data in real-time. The user's field's moisture content is displayed on the connected device for them.
{"title":"Design and Development of a Smart Sprinkler Device for IoT-Integrated Plants Irrigation","authors":"R. Josphineleela, Kotla Venkata Siva Reddy, M. S. Reddy, R. S. Rawat","doi":"10.1109/ICEARS56392.2023.10084960","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10084960","url":null,"abstract":"Most people using the Pathway currently find sprinkler watering systems to be inconvenient. In this situation, it is important to monitor sprinklers fixed in the walking area to reduce human foots to water. A practical survey has been done to collect the actual data. They are addressing the applications of the current devices for spraying. A solution to the problem has been presented. The monitoring system could be automated by turning it on and off when a human interruption is nearby. Functioning sprinkler To ensure sustainability, the meteorological and soil factors are also tracked. To measure the heat, humidity and water content, the sensing devices are employed. An IoT-enabled autonomous device activates a sprinkler mechanism. The mechanical pump delivers water to the sprinklers. Although this equipment looks like highly functional, the system requires a effective design to reduce the water supply and energy loss when operated remotely. Two probes on the sensor can be used to calculate the quantity of water. IoT moisture measuring is employed to find moisture content. In this device, sprinkler system to turned on or off, as well as a calibrate, to determine the amount of. The moisture sensor is linked straight into the internet module to present the data in real-time. The user's field's moisture content is displayed on the connected device for them.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130614797","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085315
G. Swathi, M. Shwetha, Pandarinath Potluri, Kommisetti Murthy Raju, Yogesh Kumar, K. Rajchandar
For intelligent phishing site recognition, this proposal introduces particle swarm optimization-based feature weights in order to improve phishing site detection. Particle Swarm Optimization (PSO) is used to identify phishing sites more accurately by checking multiple website properties. PSO-based recommended site feature weighting is used to rank web elements according to their importance in distinguishing real websites from phishing sites. Based on the test results, the PSO-based feature weighting significantly improved the classification accuracy, the true positive and negative rates, and the false negative and false positive rates. Phishing is the collection of personal information through fake websites, including passwords, account numbers, and credit card details. Attackers lure fake visitors by using attractive URLs. Recently, the Unified Resource Locator phishing was successfully detected using machine learning-based detection. K-nearest neighbors, decision trees, and random forests are just some of the machine learning classifiers used to determine if a site is real or not. This classification may make it easier to identify fake sites. A genetic algorithm, however, has been shown to improve the accuracy of feature selection and thus increase the detection efficiency.
{"title":"Smart Cities Hybridized to Prevent Phishing URL Attacks","authors":"G. Swathi, M. Shwetha, Pandarinath Potluri, Kommisetti Murthy Raju, Yogesh Kumar, K. Rajchandar","doi":"10.1109/ICEARS56392.2023.10085315","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085315","url":null,"abstract":"For intelligent phishing site recognition, this proposal introduces particle swarm optimization-based feature weights in order to improve phishing site detection. Particle Swarm Optimization (PSO) is used to identify phishing sites more accurately by checking multiple website properties. PSO-based recommended site feature weighting is used to rank web elements according to their importance in distinguishing real websites from phishing sites. Based on the test results, the PSO-based feature weighting significantly improved the classification accuracy, the true positive and negative rates, and the false negative and false positive rates. Phishing is the collection of personal information through fake websites, including passwords, account numbers, and credit card details. Attackers lure fake visitors by using attractive URLs. Recently, the Unified Resource Locator phishing was successfully detected using machine learning-based detection. K-nearest neighbors, decision trees, and random forests are just some of the machine learning classifiers used to determine if a site is real or not. This classification may make it easier to identify fake sites. A genetic algorithm, however, has been shown to improve the accuracy of feature selection and thus increase the detection efficiency.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130677934","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085388
V. Rukkumani, T. Anitha, P. A. Evangilin, P. Booja Aniruti, P. Deepthiga
Electric Vehicles (EVs) are getting more and more popular in the modern world as petrol costs climb. Due to this situation, a lot of automakers are exploring gas substitutes for other energy sources. By lowering pollutants, using electrical energy sources might be good for the environment. In addition, EVs offer noteworthy advantages in terms of energy savings and environmental protection. Rechargeable lithium-ion batteries will be used in a greater number of electric vehicles. It's considerably smaller than lead acid. It actually has a life cycle that is 6 to 10 times longer than a lead acid battery and provides consistent power. In the recommended method, an electric car battery's performance is tracked via the Internet of Things. It is clear that a battery is the only source of electricity for an electric vehicle. Performance, however, degrades when energy input to the vehicle drops. This poses a severe problem for the battery business. The idea of employing IoT technology to directly monitor the performance of the vehicle is put out in this article. The suggested IoT-based battery monitoring system includes monitoring tools and a user interface. According to the findings of the experiments, the system may be able to recognize declining battery efficiency and send alert notifications directing the user's next move.
{"title":"IoT-based Battery Health Monitoring System for Electric Vehicle","authors":"V. Rukkumani, T. Anitha, P. A. Evangilin, P. Booja Aniruti, P. Deepthiga","doi":"10.1109/ICEARS56392.2023.10085388","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085388","url":null,"abstract":"Electric Vehicles (EVs) are getting more and more popular in the modern world as petrol costs climb. Due to this situation, a lot of automakers are exploring gas substitutes for other energy sources. By lowering pollutants, using electrical energy sources might be good for the environment. In addition, EVs offer noteworthy advantages in terms of energy savings and environmental protection. Rechargeable lithium-ion batteries will be used in a greater number of electric vehicles. It's considerably smaller than lead acid. It actually has a life cycle that is 6 to 10 times longer than a lead acid battery and provides consistent power. In the recommended method, an electric car battery's performance is tracked via the Internet of Things. It is clear that a battery is the only source of electricity for an electric vehicle. Performance, however, degrades when energy input to the vehicle drops. This poses a severe problem for the battery business. The idea of employing IoT technology to directly monitor the performance of the vehicle is put out in this article. The suggested IoT-based battery monitoring system includes monitoring tools and a user interface. According to the findings of the experiments, the system may be able to recognize declining battery efficiency and send alert notifications directing the user's next move.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130845765","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085123
Jose Triny K, G. J, Padmaja S
Deep learning techniques have been increasingly used technique in prediction and analysis. Analyzing the temporal patterns in the crime data and extracting relevant features from the demographic information is a big task. Machine learning involves using algorithms to learn patterns present in data and make predictions. It can be used to identify crime hotspots, predict criminal behavior, and forecast the likelihood of theft in specific areas. Deep learning, on the other hand, involves using artificial neural networks with multiple layers to model complex relationships in data. It is well-suited to large datasets and can be used to analyze images, audio, and text data in addition to numerical data. Deep learning can be used for theft crime prediction by identifying patterns in criminal behavior and helping to detect crime before it happens. Algorithms including Random Forest, Naive Bayes, XGBoost, and other models were used for prediction but all the mentioned models have drawbacks including low accuracy, low performance, etc. Overall, our study shows the potential of deep learning for crime prediction, emphasizing the value of using both demographic data and historical crime data in the modeling process and the shortcomings.
{"title":"A Survey on Prediction of Risk Related to Theft Activities in Municipal Areas using Deep Learning","authors":"Jose Triny K, G. J, Padmaja S","doi":"10.1109/ICEARS56392.2023.10085123","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085123","url":null,"abstract":"Deep learning techniques have been increasingly used technique in prediction and analysis. Analyzing the temporal patterns in the crime data and extracting relevant features from the demographic information is a big task. Machine learning involves using algorithms to learn patterns present in data and make predictions. It can be used to identify crime hotspots, predict criminal behavior, and forecast the likelihood of theft in specific areas. Deep learning, on the other hand, involves using artificial neural networks with multiple layers to model complex relationships in data. It is well-suited to large datasets and can be used to analyze images, audio, and text data in addition to numerical data. Deep learning can be used for theft crime prediction by identifying patterns in criminal behavior and helping to detect crime before it happens. Algorithms including Random Forest, Naive Bayes, XGBoost, and other models were used for prediction but all the mentioned models have drawbacks including low accuracy, low performance, etc. Overall, our study shows the potential of deep learning for crime prediction, emphasizing the value of using both demographic data and historical crime data in the modeling process and the shortcomings.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128789503","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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