Pub Date : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193402
R. Sathya, V. Bharathi, S. Ananthi, K. Vaidehi, S. Sangeetha
The creation of an automated security system aims to protect residences and workplaces by automating visitor entrance and enabling more flexibility in visitor record maintenance. Among all biometric authentications, face recognition is very secure because of unique facial features. There are two phases in authentication, face mask detection and face recognition. In first phase, Grassmann algorithm is used for face mask detection. If any mask is discovered, an alarm will sound for the user to remove the mask and in second phase face recognition is done through CNN. The CNN method is utilized to compare facial traits, and if an outsider is found, a warning message is then displayed to the user. Real time datasets are collected for training and testing the CNN model. The executed result gives 98.02% higher accuracy compared to existing method.
{"title":"Intelligent Home Surveillance System using Convolution Neural Network Algorithms","authors":"R. Sathya, V. Bharathi, S. Ananthi, K. Vaidehi, S. Sangeetha","doi":"10.1109/ICESC57686.2023.10193402","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193402","url":null,"abstract":"The creation of an automated security system aims to protect residences and workplaces by automating visitor entrance and enabling more flexibility in visitor record maintenance. Among all biometric authentications, face recognition is very secure because of unique facial features. There are two phases in authentication, face mask detection and face recognition. In first phase, Grassmann algorithm is used for face mask detection. If any mask is discovered, an alarm will sound for the user to remove the mask and in second phase face recognition is done through CNN. The CNN method is utilized to compare facial traits, and if an outsider is found, a warning message is then displayed to the user. Real time datasets are collected for training and testing the CNN model. The executed result gives 98.02% higher accuracy compared to existing method.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"116886125","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: 10.1109/ICESC57686.2023.10193264
Mr. S. Vimalkumar, Dr.R. Latha
Maize is a main global food crop and is the most productive grain crop. It is also an optimum feed for the progress of animal husbandry and crucial raw material for the chemical industry, light industry, health medicine, and. Diseases are the significant factor limiting the high and stable yield of maize. For classifying diseases based on that damages the plants, the leaves of affected plants can be studied utilizing pixel-wise approaches. The Convolutional Neural Network (CNN) is the most effectual Deep Learning (DL) algorithm utilized in classification of an image to correctly diagnose plant ailments. Therefore, this study introduces an automated Maize Leaf Disease Detection using Biogeography-based Optimization with Deep Learning (MLDDBBODL) algorithm. The presented MLDD-BBODL method aims to identify and classify the occurrence of maize disease accurately. To achieve this, the presented MLDD-BBODL method employs contrast enhancement as an initial preprocessing stage. Besides, the SqueezeNet model is exploited for the derivation of feature vectors. Meanwhile, a Backpropagation Neural Network (BPNN) classifier is utilized for the recognition of maize leaf ailments. Furthermore, the BBO technique is implemented for the parameter tuning of the BPNN model which in turn enhances the classification results. The performance evaluation of the MLDD-BBODL technique is carried out on the leaf disease dataset. An extensive comparison study stated that the MLDD-BBODL technique reaches outperformed results over other recent approaches in terms of different measures.
{"title":"Heuristic Optimization with Deep Learning based Maize Leaf Disease Detection Model","authors":"Mr. S. Vimalkumar, Dr.R. Latha","doi":"10.1109/ICESC57686.2023.10193264","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193264","url":null,"abstract":"Maize is a main global food crop and is the most productive grain crop. It is also an optimum feed for the progress of animal husbandry and crucial raw material for the chemical industry, light industry, health medicine, and. Diseases are the significant factor limiting the high and stable yield of maize. For classifying diseases based on that damages the plants, the leaves of affected plants can be studied utilizing pixel-wise approaches. The Convolutional Neural Network (CNN) is the most effectual Deep Learning (DL) algorithm utilized in classification of an image to correctly diagnose plant ailments. Therefore, this study introduces an automated Maize Leaf Disease Detection using Biogeography-based Optimization with Deep Learning (MLDDBBODL) algorithm. The presented MLDD-BBODL method aims to identify and classify the occurrence of maize disease accurately. To achieve this, the presented MLDD-BBODL method employs contrast enhancement as an initial preprocessing stage. Besides, the SqueezeNet model is exploited for the derivation of feature vectors. Meanwhile, a Backpropagation Neural Network (BPNN) classifier is utilized for the recognition of maize leaf ailments. Furthermore, the BBO technique is implemented for the parameter tuning of the BPNN model which in turn enhances the classification results. The performance evaluation of the MLDD-BBODL technique is carried out on the leaf disease dataset. An extensive comparison study stated that the MLDD-BBODL technique reaches outperformed results over other recent approaches in terms of different measures.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"131577207","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: 10.1109/ICESC57686.2023.10193559
Mahesh, Ananth, Dheepthi
It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. The training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. This study has implemented a parallel data processing pipeline so that handle the large dataset. This pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users.
{"title":"Using Machine Learning to Detect and Classify URLs: A Phishing Detection Approach","authors":"Mahesh, Ananth, Dheepthi","doi":"10.1109/ICESC57686.2023.10193559","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193559","url":null,"abstract":"It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. The training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. This study has implemented a parallel data processing pipeline so that handle the large dataset. This pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"132367386","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: 10.1109/ICESC57686.2023.10193199
Dr.S.L. Jany Shabu, Dr. J. Refonaa, Chintala Janaardhan, Kodhanda Bhaskar, Students, Dr.S. Dhamodaran, Dr.A. Viji, Amutha Mary
Music streaming services now make it simple to listen to a wide variety of music. Consumers are increasingly relying on recommendation systems to help them choose appropriate music at all times. However, there is certain chances for improvement in terms of customization and emotion-based suggestions. Furthermore, music tastes will change depending on the user’s current mood. If these issues are not solved, these online services will fail to meet user expectations. This research study shows how to create a personalized music recommendation system based on listener thoughts, emotions, and facial expressions. A recommendation system is created using a combination of artificial intelligence technology and generalized music therapy approaches to help people choose music for different life situations while maintaining their mental and physical health.
{"title":"Music Recommendation System based on Facial Expression","authors":"Dr.S.L. Jany Shabu, Dr. J. Refonaa, Chintala Janaardhan, Kodhanda Bhaskar, Students, Dr.S. Dhamodaran, Dr.A. Viji, Amutha Mary","doi":"10.1109/ICESC57686.2023.10193199","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193199","url":null,"abstract":"Music streaming services now make it simple to listen to a wide variety of music. Consumers are increasingly relying on recommendation systems to help them choose appropriate music at all times. However, there is certain chances for improvement in terms of customization and emotion-based suggestions. Furthermore, music tastes will change depending on the user’s current mood. If these issues are not solved, these online services will fail to meet user expectations. This research study shows how to create a personalized music recommendation system based on listener thoughts, emotions, and facial expressions. A recommendation system is created using a combination of artificial intelligence technology and generalized music therapy approaches to help people choose music for different life situations while maintaining their mental and physical health.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"132720149","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: 10.1109/ICESC57686.2023.10193491
R. Devi, S. Deepthi Shree, K. S. Harita, L. Keerthika
Cardiac arrest claims the lives of many people among us. Recent years have seen an increase in cases of cardiac arrest while driving. This is a result of their diet, advanced age, lack of exercise, and numerous other factors. Cardiovascular arrest is the main cause of death in today’s world. A serious medical emergency like cardiac arrest needs to be attended to right away. Cardiac arrest is difficult to recognize, and male and female cardiac arrest symptoms differ. This study develops a novel system to combat and defend our society against heart diseases and attacks. Utilizing this system requires riding a motorbike. It tracks the user’s heart rate using a heart rate sensor, and in the event of a cardiac arrest, it alerts the user’s family and emergency contacts. Additionally, it averts potential tragedies. As the cause is identified earlier by this system, the victim may also be spared from a potentially fatal accident.
{"title":"Sensor based Cardiac Arrest Monitoring using Internet of Things (IoT)","authors":"R. Devi, S. Deepthi Shree, K. S. Harita, L. Keerthika","doi":"10.1109/ICESC57686.2023.10193491","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193491","url":null,"abstract":"Cardiac arrest claims the lives of many people among us. Recent years have seen an increase in cases of cardiac arrest while driving. This is a result of their diet, advanced age, lack of exercise, and numerous other factors. Cardiovascular arrest is the main cause of death in today’s world. A serious medical emergency like cardiac arrest needs to be attended to right away. Cardiac arrest is difficult to recognize, and male and female cardiac arrest symptoms differ. This study develops a novel system to combat and defend our society against heart diseases and attacks. Utilizing this system requires riding a motorbike. It tracks the user’s heart rate using a heart rate sensor, and in the event of a cardiac arrest, it alerts the user’s family and emergency contacts. Additionally, it averts potential tragedies. As the cause is identified earlier by this system, the victim may also be spared from a potentially fatal accident.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"131857901","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: 10.1109/ICESC57686.2023.10193060
P. Nagaraj, V. Muneeswaran, B. Karthik Goud, K. Arjun, G. Vigneshwar Reddy, P. Girish Kumar Reddy
The main causes of death in India and around the world are chronic illnesses like heart disease, diabetes, and Parkinson’s disease. There is a need for potential treatments for chronic diseases because of its higher mortality rate than other diseases. The increase of medical data in healthcare domain and its accurate analysis are beneficial for early disease identification, patient treatment, and community services. Incorrect diagnosis increases the fatality. Thus, precise diagnosis tools for chronic diseases are required due to the high risk of diagnosis. Hence, to provide a promising solution with high accuracy, this study offers a unique diagnosis method based on machine learning. Several machine learning methods are being used in this study, and the algorithm for the prediction is chosen based on the model’s accuracy. The proposed model performs disease prediction with an accuracy of 87.66%.
{"title":"Identifying Multiple Diseases in the Human Body using Machine Learning","authors":"P. Nagaraj, V. Muneeswaran, B. Karthik Goud, K. Arjun, G. Vigneshwar Reddy, P. Girish Kumar Reddy","doi":"10.1109/ICESC57686.2023.10193060","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193060","url":null,"abstract":"The main causes of death in India and around the world are chronic illnesses like heart disease, diabetes, and Parkinson’s disease. There is a need for potential treatments for chronic diseases because of its higher mortality rate than other diseases. The increase of medical data in healthcare domain and its accurate analysis are beneficial for early disease identification, patient treatment, and community services. Incorrect diagnosis increases the fatality. Thus, precise diagnosis tools for chronic diseases are required due to the high risk of diagnosis. Hence, to provide a promising solution with high accuracy, this study offers a unique diagnosis method based on machine learning. Several machine learning methods are being used in this study, and the algorithm for the prediction is chosen based on the model’s accuracy. The proposed model performs disease prediction with an accuracy of 87.66%.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"134579875","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: 10.1109/ICESC57686.2023.10193272
E. Indhuja, J. Angelina, S. Subhashini, B.Ajay Kumar, L. Amulya, G. Gopi
This hospital management system aims to develop a user-friendly and efficient system using PHP as the front-end interface and MySQL as the database. The system enables the management of patient information, doctor information, prescription details, and appointment details. The system provides a centralized platform for the management of these aspects, enabling healthcare providers to access and manage the data in real-time from anywhere. The system allows authorized users to add or remove doctor details, manage patient appointments and claims securely. The system has been designed to ensure the protection of personal data to speed up data processing. The system provides various features such as appointment scheduling, patient record management, doctor record management, prescription management, and billing management. Overall, the hospital management system is a reliable and efficient solution that streamlines the management of healthcare facilities.
{"title":"E-Health Records Stored Over the Cloud with Automated Medication Reminders for Enhanced Patient Care","authors":"E. Indhuja, J. Angelina, S. Subhashini, B.Ajay Kumar, L. Amulya, G. Gopi","doi":"10.1109/ICESC57686.2023.10193272","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193272","url":null,"abstract":"This hospital management system aims to develop a user-friendly and efficient system using PHP as the front-end interface and MySQL as the database. The system enables the management of patient information, doctor information, prescription details, and appointment details. The system provides a centralized platform for the management of these aspects, enabling healthcare providers to access and manage the data in real-time from anywhere. The system allows authorized users to add or remove doctor details, manage patient appointments and claims securely. The system has been designed to ensure the protection of personal data to speed up data processing. The system provides various features such as appointment scheduling, patient record management, doctor record management, prescription management, and billing management. Overall, the hospital management system is a reliable and efficient solution that streamlines the management of healthcare facilities.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"133808479","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: 10.1109/ICESC57686.2023.10193045
Swayon Bhunia, Dr. T. Abirami
According to WHO, Dengue is a viral infection transmitted to humans through the bite of infected mosquitoes i.e., Aedes aegypti mosquitoes. There is currently no known cure for dengue or severe dengue. Artificial Intelligence (AI) in the form of Machine Learning (ML) allows software programs to predict outcomes more correctly without explicit instructions. Machine learning algorithms use historical data as input to forecast new output values. The aim of this study is to identify, evaluate and interpret suitable hybrid algorithms/approaches relevant to the application of machine learning in limiting the spread of deadly disease outbreaks. It focuses on finding a way of predicting the next dengue fever local epidemic by comparing the bench mark approaches available until now. For this the study proposes the use of XGBoost coupled with Moving Average Rolling Features in order to learn the long-term temporal relations in the features to get accurate predictions. The dataset used for evaluating the proposed approach contains number of cases in the two locations: San Juan and Iquitos and it includes information on temperature, precipitation, humidity, vegetation, and what time of the year the data was obtained. A correlation analysis-based feature selection along with Moving Average Rolling Features has been used for getting more precise data implemented with ML approach resulting in MS E 11.37 in San Juan and MSE 6.37 in Iquitos.
{"title":"Correlation based Feature Selection and Hybrid Machine Learning Approach for Forecasting Disease Outbreaks","authors":"Swayon Bhunia, Dr. T. Abirami","doi":"10.1109/ICESC57686.2023.10193045","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193045","url":null,"abstract":"According to WHO, Dengue is a viral infection transmitted to humans through the bite of infected mosquitoes i.e., Aedes aegypti mosquitoes. There is currently no known cure for dengue or severe dengue. Artificial Intelligence (AI) in the form of Machine Learning (ML) allows software programs to predict outcomes more correctly without explicit instructions. Machine learning algorithms use historical data as input to forecast new output values. The aim of this study is to identify, evaluate and interpret suitable hybrid algorithms/approaches relevant to the application of machine learning in limiting the spread of deadly disease outbreaks. It focuses on finding a way of predicting the next dengue fever local epidemic by comparing the bench mark approaches available until now. For this the study proposes the use of XGBoost coupled with Moving Average Rolling Features in order to learn the long-term temporal relations in the features to get accurate predictions. The dataset used for evaluating the proposed approach contains number of cases in the two locations: San Juan and Iquitos and it includes information on temperature, precipitation, humidity, vegetation, and what time of the year the data was obtained. A correlation analysis-based feature selection along with Moving Average Rolling Features has been used for getting more precise data implemented with ML approach resulting in MS E 11.37 in San Juan and MSE 6.37 in Iquitos.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"113996825","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}
Drones have emerged as a promising solution to deliver medicines and healthcare supplies to remote and inaccessible areas. This research study focuses on the use of drones to supply medicines to remote areas. The paper discusses the benefits of using drones, including their ability to reach areas with poor road infrastructure, reduce delivery times, and improve healthcare access for underserved communities. Also, this study analyses the challenges in implementing drone delivery systems, such as regulatory barriers, technical limitations, and public perception. Finally, case studies of successful drone delivery programs for medical supplies are presented and the potential for scaling up these initiatives in the future are discussed. Overall, this study argues that drones have the potential to revolutionize the delivery of medicines and healthcare supplies to remote areas and that further research and investment in this area are necessary to fully realize their potential.
{"title":"Biomedical Engineering Impacting Community Service with Embedded Systems","authors":"Mandala Bhuvana Reddy, Rajashekar Reddy, Varagani Ramu, Bochu Vardhan, V. Gunturu","doi":"10.1109/ICESC57686.2023.10193671","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193671","url":null,"abstract":"Drones have emerged as a promising solution to deliver medicines and healthcare supplies to remote and inaccessible areas. This research study focuses on the use of drones to supply medicines to remote areas. The paper discusses the benefits of using drones, including their ability to reach areas with poor road infrastructure, reduce delivery times, and improve healthcare access for underserved communities. Also, this study analyses the challenges in implementing drone delivery systems, such as regulatory barriers, technical limitations, and public perception. Finally, case studies of successful drone delivery programs for medical supplies are presented and the potential for scaling up these initiatives in the future are discussed. Overall, this study argues that drones have the potential to revolutionize the delivery of medicines and healthcare supplies to remote areas and that further research and investment in this area are necessary to fully realize their potential.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"128971162","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: 10.1109/ICESC57686.2023.10193398
Ketan Rathor, S. Vidya, M. Jeeva, M. Karthivel, Shubhangi N. Ghate, V. Malathy
ATMs are vulnerable to a wide variety of assaults and fraud because of the money and personal information available on it. In response, today’s ATMs feature enhanced hardware security systems are capable of identifying specific forms of fraud and manipulation. However, there is no defense in place for future attacks that can’t be anticipated during design. It shows how automated teller machines (ATMs) can be secured against theft without the need for extra hardware. The goal is to employ automatic techniques of model generation to learn normal behavior patterns from the status information of the standard de vices that make up an ATM, with a significant divergence from the taught behavior indicating a fraud attempt. Preprocessing, feature selection, and model training are all parts of the proposed method. Cleaning, integrating, and deduplicating data are all parts of data preprocessing. BOA is employed in feature selection and C-LSTM is used for model training. In C-LSTM, a LSTM recurrent neural network is used to obtain the sentence representation after CNN is used to extract a sequence of higher-level phrase representations. C-LSTM can learn the global and temporal sentence semantics in addition to the local aspects of phrases. When compared to LSTM and CNN, the proposed method fares very well.
{"title":"Intelligent System for ATM Fraud Detection System using C-LSTM Approach","authors":"Ketan Rathor, S. Vidya, M. Jeeva, M. Karthivel, Shubhangi N. Ghate, V. Malathy","doi":"10.1109/ICESC57686.2023.10193398","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193398","url":null,"abstract":"ATMs are vulnerable to a wide variety of assaults and fraud because of the money and personal information available on it. In response, today’s ATMs feature enhanced hardware security systems are capable of identifying specific forms of fraud and manipulation. However, there is no defense in place for future attacks that can’t be anticipated during design. It shows how automated teller machines (ATMs) can be secured against theft without the need for extra hardware. The goal is to employ automatic techniques of model generation to learn normal behavior patterns from the status information of the standard de vices that make up an ATM, with a significant divergence from the taught behavior indicating a fraud attempt. Preprocessing, feature selection, and model training are all parts of the proposed method. Cleaning, integrating, and deduplicating data are all parts of data preprocessing. BOA is employed in feature selection and C-LSTM is used for model training. In C-LSTM, a LSTM recurrent neural network is used to obtain the sentence representation after CNN is used to extract a sequence of higher-level phrase representations. C-LSTM can learn the global and temporal sentence semantics in addition to the local aspects of phrases. When compared to LSTM and CNN, the proposed method fares very well.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","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":"128440073","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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