Pub Date : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193269
Harshil T. Kanakia, Suraj Nair
This research paper presents a website that leverages the power of the Image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using Image GPT’s natural language processing and machine learning capabilities. The paper details the architecture of the website, the Image GPT API integration, and the algorithms used for image generation. Additionally, we present the results and evaluate its quality and discuss potential applications of this technology in various industries such as advertising, art and design. We also discuss how the performance of image generation can be potentially improved. Overall, the website demonstrates the potential of combining natural language processing and machine learning for image generation and opens up new avenues for future research in this field.
{"title":"Designing a User-Friendly and Responsive AI based Image Generation Website and Performing Diversity Assessment of the Generated Images","authors":"Harshil T. Kanakia, Suraj Nair","doi":"10.1109/ICESC57686.2023.10193269","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193269","url":null,"abstract":"This research paper presents a website that leverages the power of the Image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using Image GPT’s natural language processing and machine learning capabilities. The paper details the architecture of the website, the Image GPT API integration, and the algorithms used for image generation. Additionally, we present the results and evaluate its quality and discuss potential applications of this technology in various industries such as advertising, art and design. We also discuss how the performance of image generation can be potentially improved. Overall, the website demonstrates the potential of combining natural language processing and machine learning for image generation and opens up new avenues for future research in this field.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126736056","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/ICESC57686.2023.10193557
Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana
Shoreline change is a constantly evolving phenomenon that threatens people and their livelihoods around the globe. India observes this phenomenon strongly at different locations being a tropical peninsular country with 6635kms of coastline. This study analyzes the effect of shoreline along the entire coast of Kerala state in India. Net changes in coastline positions are statistically calculated and observed using Linear Regression Rate (LRR) and validated using Artificial Neural Network. The study also employes a random forest regression to predict the ground water level changes with respect to shoreline change rate in the region. The shoreline change rate shows most of the region are undergoing erosion, only few accretions or land formation are observed which is formed artificially due to harbor building. The highest erosion rate in terms of LRR is 7m/year and highest accretion is 28m/year.
{"title":"Shore Line Change Detection using ANN and Ground Water Variability Along Kerala Coast Using Random Forest Regression","authors":"Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana","doi":"10.1109/ICESC57686.2023.10193557","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193557","url":null,"abstract":"Shoreline change is a constantly evolving phenomenon that threatens people and their livelihoods around the globe. India observes this phenomenon strongly at different locations being a tropical peninsular country with 6635kms of coastline. This study analyzes the effect of shoreline along the entire coast of Kerala state in India. Net changes in coastline positions are statistically calculated and observed using Linear Regression Rate (LRR) and validated using Artificial Neural Network. The study also employes a random forest regression to predict the ground water level changes with respect to shoreline change rate in the region. The shoreline change rate shows most of the region are undergoing erosion, only few accretions or land formation are observed which is formed artificially due to harbor building. The highest erosion rate in terms of LRR is 7m/year and highest accretion is 28m/year.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114120477","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/ICESC57686.2023.10193280
Aravind Karrothu, B. Brindavathi, Chunduru Anilkumar
Till date, all the fuzzy identity-based encryption (IBE) cryptosystems for generating public keys used biometrics namely finger print, iris biometric identification, voice-based identification, and other set of identification types. This work is a concept for combining both fuzzy IBE system with human odor thresholds as identities. Naturally human body develops a one-inch layer of odor on skin, which will be used as identity for public keys generation and by using sample-left algorithm the size of public keys is minimized.
{"title":"New Fuzzy IBE System using Odor Detection","authors":"Aravind Karrothu, B. Brindavathi, Chunduru Anilkumar","doi":"10.1109/ICESC57686.2023.10193280","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193280","url":null,"abstract":"Till date, all the fuzzy identity-based encryption (IBE) cryptosystems for generating public keys used biometrics namely finger print, iris biometric identification, voice-based identification, and other set of identification types. This work is a concept for combining both fuzzy IBE system with human odor thresholds as identities. Naturally human body develops a one-inch layer of odor on skin, which will be used as identity for public keys generation and by using sample-left algorithm the size of public keys is minimized.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122717373","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/ICESC57686.2023.10193514
Dipanshu Kumar Mishra, Deepak Kumar
Facial recognition is the technique used to identify the face of a person which is already detected and shows the results whether it is known or an unknown face. Face recognition is followed by the process of face detection. Both the processes are difficult tasks at their level. There are several methods or techniques to develop the system of face recognition, viz., Eigenface and Fisherface. The challenge for this system is that face images are with different backgrounds, different lighting, different facial expressions and occlusions. This system starts when an image is processed to train it. It is continued on the test image, the face is being identified, then the trained faces are compared and ultimately categorized it using classifiers of OpenCV. This study discusses the comparative study of different algorithms and come up with the most effective and convenient technique for the mentioned system.
{"title":"Face Recognition System using Artificial Intelligence: Comparison of Classifiers","authors":"Dipanshu Kumar Mishra, Deepak Kumar","doi":"10.1109/ICESC57686.2023.10193514","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193514","url":null,"abstract":"Facial recognition is the technique used to identify the face of a person which is already detected and shows the results whether it is known or an unknown face. Face recognition is followed by the process of face detection. Both the processes are difficult tasks at their level. There are several methods or techniques to develop the system of face recognition, viz., Eigenface and Fisherface. The challenge for this system is that face images are with different backgrounds, different lighting, different facial expressions and occlusions. This system starts when an image is processed to train it. It is continued on the test image, the face is being identified, then the trained faces are compared and ultimately categorized it using classifiers of OpenCV. This study discusses the comparative study of different algorithms and come up with the most effective and convenient technique for the mentioned system.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122968950","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/ICESC57686.2023.10193473
D. Banerjee, V. Kukreja, Satvik Vats, Vishal Jain, Bhawna Goyal
This research utilizes a novel Convolutional Neural Network (CNN) and Support Vector Machine (SVM) based model to predict the sunflower diseases. For training the proposed model, three convolutional layers, three max-pooling layers, and two fully connected layers were used, with the second fully connected layer includes SVM. The proposed model is trained with a dataset of different diseases that affect sunflowers. The results of the proposed research study have resulted in a F1 score of 83.45 and a total accuracy of 83.59%. For classifying each disease, accuracy value has been obtained in the range of 80.65% to 85.37%. According to the meta-analysis of the layer parameters, the second fully connected layer highly influences the model’s accuracy. The results indicate that combining CNN and SVM could be an efficient strategy for predicting diseases in sunflowers and would also assist the process of disease management and crop yield.
{"title":"AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines","authors":"D. Banerjee, V. Kukreja, Satvik Vats, Vishal Jain, Bhawna Goyal","doi":"10.1109/ICESC57686.2023.10193473","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193473","url":null,"abstract":"This research utilizes a novel Convolutional Neural Network (CNN) and Support Vector Machine (SVM) based model to predict the sunflower diseases. For training the proposed model, three convolutional layers, three max-pooling layers, and two fully connected layers were used, with the second fully connected layer includes SVM. The proposed model is trained with a dataset of different diseases that affect sunflowers. The results of the proposed research study have resulted in a F1 score of 83.45 and a total accuracy of 83.59%. For classifying each disease, accuracy value has been obtained in the range of 80.65% to 85.37%. According to the meta-analysis of the layer parameters, the second fully connected layer highly influences the model’s accuracy. The results indicate that combining CNN and SVM could be an efficient strategy for predicting diseases in sunflowers and would also assist the process of disease management and crop yield.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476966","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/ICESC57686.2023.10192582
A.Naveen Kumaar, J. Akilandeswari, P. R. Mathangi, P. Kavya, S. Dhanush Prabhu, V. Ashwin Kumar
Computers are now considered as the daily necessities for both mankind and medical science. A doctor examines a patient, with the physical interaction and then with all the reports like scans, X-rays, blood reports, and so on. In case of Radiologist, they can’t frequently touch the screen or buttons while browsing the radiology report images, this may lead to radioactive contamination. A gesture-based browsing method is developed to overcome this issue by making the radiologist to browse the images without any close interactions with the device. An interface is provided for the surgeon where their hand-gestures are used for safe browsing of radiology report images using recent hand-gesture recognition methodologies. Further the accuracy of the system is increased by the proposed modified Convolutional Neural Network technique which uses De-noising Auto Encoder based CNN (DAECNN) to identify the hand-gesture made by the radiologist. A detailed study is made on the recent hand-gesture recognition methodologies used on secure browsing of radiology images based on accuracy. The proposed technique is compared with the existing deep learning methodologies such as CNN, Adaline (Adaptive Linear Neuron), DAE (Denoising Autoencoder) and the performances are examined. The findings of the research show that the DAECNN methodology outperforms the currently used classification techniques.
{"title":"Secure radiology image browsing tool improvised using Denoising Autoencoder with Convolutional Neural Network (DAECNN)","authors":"A.Naveen Kumaar, J. Akilandeswari, P. R. Mathangi, P. Kavya, S. Dhanush Prabhu, V. Ashwin Kumar","doi":"10.1109/ICESC57686.2023.10192582","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10192582","url":null,"abstract":"Computers are now considered as the daily necessities for both mankind and medical science. A doctor examines a patient, with the physical interaction and then with all the reports like scans, X-rays, blood reports, and so on. In case of Radiologist, they can’t frequently touch the screen or buttons while browsing the radiology report images, this may lead to radioactive contamination. A gesture-based browsing method is developed to overcome this issue by making the radiologist to browse the images without any close interactions with the device. An interface is provided for the surgeon where their hand-gestures are used for safe browsing of radiology report images using recent hand-gesture recognition methodologies. Further the accuracy of the system is increased by the proposed modified Convolutional Neural Network technique which uses De-noising Auto Encoder based CNN (DAECNN) to identify the hand-gesture made by the radiologist. A detailed study is made on the recent hand-gesture recognition methodologies used on secure browsing of radiology images based on accuracy. The proposed technique is compared with the existing deep learning methodologies such as CNN, Adaline (Adaptive Linear Neuron), DAE (Denoising Autoencoder) and the performances are examined. The findings of the research show that the DAECNN methodology outperforms the currently used classification techniques.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126695754","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":"119 1","pages":"0"},"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/ICESC57686.2023.10193045
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":"93 4","pages":"0"},"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":"3 1","pages":"0"},"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":"1 1","pages":"0"},"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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