Agriculture is the backbone of any thriving civilization. The recent technological innovations are revolutionizing agriculture in all possible ways. The proposed solution allows farmers to rent equipment based on their present needs, reducing waste and improving resource allocation. The platform includes multilingual presentations, a transaction database, and a feedback/rating system. Additionally, the website offers models for pest and disease prediction, weather prediction, crop recommendation, and crop price prediction. Transportation and loan options are also available on the platform. This solution provides a minimalistic approach to address the issue of idle equipment, reducing consumption and waste. The smart features integrated into the website provide a comprehensive and user-friendly platform for farmers to access a range of agricultural services. The proposed solution has the potential to improve resource utilisation and foster sustainability in agriculture, promoting efficient and effective use of resources.
{"title":"Revolutionizing Farming with Innovative Equipment Rental System","authors":"Anujatai Patil, Neelanjana Gupta, Prasanna Sridharan, Siddhant Krantikumar Patil, Vinita Mishra","doi":"10.1109/ICECAA58104.2023.10212293","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212293","url":null,"abstract":"Agriculture is the backbone of any thriving civilization. The recent technological innovations are revolutionizing agriculture in all possible ways. The proposed solution allows farmers to rent equipment based on their present needs, reducing waste and improving resource allocation. The platform includes multilingual presentations, a transaction database, and a feedback/rating system. Additionally, the website offers models for pest and disease prediction, weather prediction, crop recommendation, and crop price prediction. Transportation and loan options are also available on the platform. This solution provides a minimalistic approach to address the issue of idle equipment, reducing consumption and waste. The smart features integrated into the website provide a comprehensive and user-friendly platform for farmers to access a range of agricultural services. The proposed solution has the potential to improve resource utilisation and foster sustainability in agriculture, promoting efficient and effective use of resources.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114373269","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212206
G. G, N. P, S. Subhashini
A thorough understanding of lung cancer and tumor development pathways has made significant advancements in lung cancer treatment. Lung cancer diagnosis and treatment in its early stages still require new techniques. Recent advancements in genetics, computational biology, and other technologies provide an opportunity to better understand the immunological landscape associated with early-stage lung carcinogenesis and the mechanism of lung cancer evolution. This review focuses on immunoediting and discusses new research on immunological alterations and biomarkers in pulmonary premalignancy and early-stage non-small cell lung cancer. By concentrating on developing innovative techniques for intercepting cancer before it advances to later stages, researchers have the potential to revolutionize lung cancer therapy and significantly improve clinical outcomes. The use of an Ensemble of Classifiers with a Convolution Neural Network could further enhance this approach.
{"title":"A Review on Detection of Lung Cancer Using Ensemble of Classifiers with CNN","authors":"G. G, N. P, S. Subhashini","doi":"10.1109/ICECAA58104.2023.10212206","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212206","url":null,"abstract":"A thorough understanding of lung cancer and tumor development pathways has made significant advancements in lung cancer treatment. Lung cancer diagnosis and treatment in its early stages still require new techniques. Recent advancements in genetics, computational biology, and other technologies provide an opportunity to better understand the immunological landscape associated with early-stage lung carcinogenesis and the mechanism of lung cancer evolution. This review focuses on immunoediting and discusses new research on immunological alterations and biomarkers in pulmonary premalignancy and early-stage non-small cell lung cancer. By concentrating on developing innovative techniques for intercepting cancer before it advances to later stages, researchers have the potential to revolutionize lung cancer therapy and significantly improve clinical outcomes. The use of an Ensemble of Classifiers with a Convolution Neural Network could further enhance this approach.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114928190","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212258
Mahendran N, Muthuvel P, A. T, P. M, Bridget Nirmala J, Kottaimalai R
Brain tumour delineation is a challenging task from raw magnetic resonance images. To accurately delineate the different parts of tumours is the main aim of dissection process. Among the most common types of cerebral tumour, glioma that arises from glial cells. According to the World Health Organisation (WHO), tumour behaviours and microscopic images can be used to classify gliomas into four different levels. The popular imaging techniques used prior to and following surgical treatment is magnetic resonance imaging (MRI), which aims to provide vital details for the therapeutic plan. For effective tumour delineation from brain MRI, a novel combination of K-means and Salp Swarm Optimization (SSO) Algorithm is proposed. K-means clustering method groups the most similar pixels in to a single cluster. Salp Swarm Optimization Algorithm is one of the nature-inspired metaheuristic optimization algorithms based on the social and foraging behaviour of salps. In biomedical signal processing and control systems, SSO is used to tackle large-scale optimization problems. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The attained average computational time, MSE, PSNR, TC and DS are 16.9 Sec, 0.3787, 52.47 dB, 74.86 % and 83.44 %, respectively.
{"title":"Precise Identification and Segmentation of Brain Tumour in MR Brain Images Using Salp Swarm Optimized K-Means Clustering Technique","authors":"Mahendran N, Muthuvel P, A. T, P. M, Bridget Nirmala J, Kottaimalai R","doi":"10.1109/ICECAA58104.2023.10212258","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212258","url":null,"abstract":"Brain tumour delineation is a challenging task from raw magnetic resonance images. To accurately delineate the different parts of tumours is the main aim of dissection process. Among the most common types of cerebral tumour, glioma that arises from glial cells. According to the World Health Organisation (WHO), tumour behaviours and microscopic images can be used to classify gliomas into four different levels. The popular imaging techniques used prior to and following surgical treatment is magnetic resonance imaging (MRI), which aims to provide vital details for the therapeutic plan. For effective tumour delineation from brain MRI, a novel combination of K-means and Salp Swarm Optimization (SSO) Algorithm is proposed. K-means clustering method groups the most similar pixels in to a single cluster. Salp Swarm Optimization Algorithm is one of the nature-inspired metaheuristic optimization algorithms based on the social and foraging behaviour of salps. In biomedical signal processing and control systems, SSO is used to tackle large-scale optimization problems. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The attained average computational time, MSE, PSNR, TC and DS are 16.9 Sec, 0.3787, 52.47 dB, 74.86 % and 83.44 %, respectively.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122179831","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212347
V. Kukreja, Rishabh Sharma, Satvik Vats
The textile industry is one of the largest contributors to environmental degradation; nevertheless, the implementation of recycling practices for textile waste has the potential to significantly reduce the severity of this impact. The current study addresses the challenge of multi-classification in fabric recycling by presenting a unique strategy that blends a convolutional neural network (CNN) with a long short-term memory (LSTM) network. This approach was developed as part of this research. Following the collection of a dataset that included 10,000 photographs of different types of cloth, the data was then sorted into four unique recycling categories, namely mechanical recycling, chemical recycling, upcycling, and downcycling. An overall accuracy of 92.63 percent was achieved by the hybrid model that was recommended. The category that displayed the best accuracy was the mechanical recycling category, while the upcycling category demonstrated the highest recall. On the other side, the downcycling category received the maximum possible score in the F1 competition. According to the data, the model that was presented demonstrates a high degree of efficacy in the categorization of waste textiles into various recycling groups. This is the case. Because of its ability to maximise the classification and reutilization of textile waste, the application of this strategy has the potential to make it easier to develop a textile industry that is environmentally responsible.
{"title":"Sustainable Fabric Recycling using Hybrid CNN-LSTM Multi-Classification Model","authors":"V. Kukreja, Rishabh Sharma, Satvik Vats","doi":"10.1109/ICECAA58104.2023.10212347","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212347","url":null,"abstract":"The textile industry is one of the largest contributors to environmental degradation; nevertheless, the implementation of recycling practices for textile waste has the potential to significantly reduce the severity of this impact. The current study addresses the challenge of multi-classification in fabric recycling by presenting a unique strategy that blends a convolutional neural network (CNN) with a long short-term memory (LSTM) network. This approach was developed as part of this research. Following the collection of a dataset that included 10,000 photographs of different types of cloth, the data was then sorted into four unique recycling categories, namely mechanical recycling, chemical recycling, upcycling, and downcycling. An overall accuracy of 92.63 percent was achieved by the hybrid model that was recommended. The category that displayed the best accuracy was the mechanical recycling category, while the upcycling category demonstrated the highest recall. On the other side, the downcycling category received the maximum possible score in the F1 competition. According to the data, the model that was presented demonstrates a high degree of efficacy in the categorization of waste textiles into various recycling groups. This is the case. Because of its ability to maximise the classification and reutilization of textile waste, the application of this strategy has the potential to make it easier to develop a textile industry that is environmentally responsible.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124755686","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212250
Sandeep Joshi, M. Manu, Amit Mittal
Within this literary document, a panoramic insight is presented on the progression and practical uses of Convolutional Neural Networks (CNNs), an influential technique in deep learning that serves as a major element within computer vision research alongside other areas. Through a comprehensive analysis of the literature, this research study presents thehistorical development of CNNs from early work on perceptrons to current state-of-the-art architectures like VGGNet, ResNet, and EfficientNet. The review highlights the key contributions of CNNs in various fields, such as image and video recognition, natural language processing, and audio analysis. Furthermore, it discusses the potential for further research and development of CNNs, including the challenges in training and optimizing CNNs and the future directions of CNNs. Overall, this review underscores the importance of CNNs in enabling breakthroughsin diverse fields and their potential for continued impact on the scientific community.
{"title":"A Review of the Evolution and Applications of Convolutional Neural Network (CNN)","authors":"Sandeep Joshi, M. Manu, Amit Mittal","doi":"10.1109/ICECAA58104.2023.10212250","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212250","url":null,"abstract":"Within this literary document, a panoramic insight is presented on the progression and practical uses of Convolutional Neural Networks (CNNs), an influential technique in deep learning that serves as a major element within computer vision research alongside other areas. Through a comprehensive analysis of the literature, this research study presents thehistorical development of CNNs from early work on perceptrons to current state-of-the-art architectures like VGGNet, ResNet, and EfficientNet. The review highlights the key contributions of CNNs in various fields, such as image and video recognition, natural language processing, and audio analysis. Furthermore, it discusses the potential for further research and development of CNNs, including the challenges in training and optimizing CNNs and the future directions of CNNs. Overall, this review underscores the importance of CNNs in enabling breakthroughsin diverse fields and their potential for continued impact on the scientific community.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129708288","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212396
Mr. S. Murali, Mr. S. Shiva Rakesh, Mr. R.V.J. Dharwin, Mr. M. Gajendrapandi, Mr. R. Praveen Kumar
The Human body is made of tissues, fluids, hormones, organs and organ structures. To sustain a healthy life, people need to be in conscious of their health to prevent diseases. Various medical devices are used for tracking the health of people. Blood glucose level is essential need to track a patient's metabolism. In regular ways, the amount of glucose in the blood is measured by diagnosis of blood samples in a medical laboratory. Also there are products that measure blood glucose by pricking the fingers to draw drop of blood for glucose estimation. Both these ways are medically used for health care, which involves a invasive, painful way to measure blood glucose. This paper involves the design and implementation of a non-invasive technology based prototype for the measurement of blood glucose level along with Haemoglobin measurement. The prototype makes use of PhotoPlethysmoGraphy to achieve non-invasiveness, thereby overcoming the problems of prevailing medical devices. This prototype is implemented with low-cost sensors for providing economic viability. A Pilot study on volunteers to obtain results from the prototype. The results obtained from the prototype is analysed with the results from existing invasive product. Thus the paper defines the modelling a low-cost photoplethysmogrpahy based glucometer along with haemoglobin concentration measurement.
{"title":"PhotoPlethysmoGraphy based Low-Cost Glucometer with Haemoglobin Measurement","authors":"Mr. S. Murali, Mr. S. Shiva Rakesh, Mr. R.V.J. Dharwin, Mr. M. Gajendrapandi, Mr. R. Praveen Kumar","doi":"10.1109/ICECAA58104.2023.10212396","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212396","url":null,"abstract":"The Human body is made of tissues, fluids, hormones, organs and organ structures. To sustain a healthy life, people need to be in conscious of their health to prevent diseases. Various medical devices are used for tracking the health of people. Blood glucose level is essential need to track a patient's metabolism. In regular ways, the amount of glucose in the blood is measured by diagnosis of blood samples in a medical laboratory. Also there are products that measure blood glucose by pricking the fingers to draw drop of blood for glucose estimation. Both these ways are medically used for health care, which involves a invasive, painful way to measure blood glucose. This paper involves the design and implementation of a non-invasive technology based prototype for the measurement of blood glucose level along with Haemoglobin measurement. The prototype makes use of PhotoPlethysmoGraphy to achieve non-invasiveness, thereby overcoming the problems of prevailing medical devices. This prototype is implemented with low-cost sensors for providing economic viability. A Pilot study on volunteers to obtain results from the prototype. The results obtained from the prototype is analysed with the results from existing invasive product. Thus the paper defines the modelling a low-cost photoplethysmogrpahy based glucometer along with haemoglobin concentration measurement.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129902242","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212308
M. Nandhini, Dr. V. Sumalatha
The development of cloud computing technology and big data comprises more users to store their data in the cloud server. The increases in the data volume and storage are subjected to the increased risk of data access with unauthorized users. Traditionally, to improve data authorization the cloud data is encrypted before uploading to the server. To improve cloud authentication Single Factor Authentication (SFA) techniques are evolved. However, conventional SFA is not efficient for sensitive information that is able to be accessed by third parties. To overcome this limitation, this research proposes a Single-factor Samoa Substring Escrow Cryptography scheme (SSS-EC). The proposed SSS-EC model uses fingerprint biometric data for authentication in cloud data. Initially, Samoa Substring is implemented with the validation of the client single-factor i.e fingerprint data. The validated information is stored in the cloud escrow. The validated data is encrypted using homomorphic encryption. The encrypted data is accessed with the attribute structure those need to query and decrypt the data in the Samoa Substring. Upon the verification of the attribute i.e., fingerprint, cipher text based on Samoa Sub-String is shared between the owner and user without any keyword. The verification with the cipher text is performed with Elliptical Curve Cryptography (ECC). The implementation of the SSS-EC scheme improves authentication in the cloud. Finally, the Machine Learning (ML) method is implemented for the classification of the different attacks in the cloud server using CICIDS dataset. The simulation analysis of the proposed SSS-EC model with the existing authentication techniques such as Ring Learning with Errors (R-LWE) and Identity Concealed Authentication Scheme (ICAS) based on two factors is performed. The proposed SSS-EC exhibits higher authentication accuracy and reduced computational cost for the different users and cloud servers. The experimental results confirmed that the proposed SSS-EC scheme improves authentication with state-of-the-art techniques.
{"title":"SSS-EC: Cryptographic based Single-Factor Authentication for Fingerprint Data with Machine Learning Technique","authors":"M. Nandhini, Dr. V. Sumalatha","doi":"10.1109/ICECAA58104.2023.10212308","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212308","url":null,"abstract":"The development of cloud computing technology and big data comprises more users to store their data in the cloud server. The increases in the data volume and storage are subjected to the increased risk of data access with unauthorized users. Traditionally, to improve data authorization the cloud data is encrypted before uploading to the server. To improve cloud authentication Single Factor Authentication (SFA) techniques are evolved. However, conventional SFA is not efficient for sensitive information that is able to be accessed by third parties. To overcome this limitation, this research proposes a Single-factor Samoa Substring Escrow Cryptography scheme (SSS-EC). The proposed SSS-EC model uses fingerprint biometric data for authentication in cloud data. Initially, Samoa Substring is implemented with the validation of the client single-factor i.e fingerprint data. The validated information is stored in the cloud escrow. The validated data is encrypted using homomorphic encryption. The encrypted data is accessed with the attribute structure those need to query and decrypt the data in the Samoa Substring. Upon the verification of the attribute i.e., fingerprint, cipher text based on Samoa Sub-String is shared between the owner and user without any keyword. The verification with the cipher text is performed with Elliptical Curve Cryptography (ECC). The implementation of the SSS-EC scheme improves authentication in the cloud. Finally, the Machine Learning (ML) method is implemented for the classification of the different attacks in the cloud server using CICIDS dataset. The simulation analysis of the proposed SSS-EC model with the existing authentication techniques such as Ring Learning with Errors (R-LWE) and Identity Concealed Authentication Scheme (ICAS) based on two factors is performed. The proposed SSS-EC exhibits higher authentication accuracy and reduced computational cost for the different users and cloud servers. The experimental results confirmed that the proposed SSS-EC scheme improves authentication with state-of-the-art techniques.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128362518","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212209
Rahul Mishra
The difficulty of ensuring cyber-security is steadily growing as a result of the alarming development in computer connectivity and the sizeable number of applications associated to computers in recent years. The system also requires robust defines against the growing number of cyber threats. As a result, a possible role for cyber-security might be performed by developing Intrusion Detection Systems (IDS) to detect inconsistencies and threats in computer networks. An effective data-driven intrusion detection system has been created with the use of Artificial Intelligence, particularly Machine Learning techniques. This research proposes a novel Binary Grasshopper Optimized Twin Support Vector Machine (BGOTSVM) based security model which first considers the security features ranking according to their relevance before developing an IDS model based on the significant features that have been selected. By lowering the feature dimensions, this approach not only improves predictive performance for unidentified tests but also lowers the model's computational expense. Trials are conducted using four common ML techniques to compare the results to those of the current approaches (Decision Tree, Random Decision Forest, Random Tree, and Artificial Neural Network). The experimental findings of this study confirm that the suggested methods may be used as learning-based models for network intrusion detection and demonstrate that, when used in the real world, they outperform conventional ML techniques.
{"title":"Cyber Security Threat Detection Model Using Artificial Intelligence Technology","authors":"Rahul Mishra","doi":"10.1109/ICECAA58104.2023.10212209","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212209","url":null,"abstract":"The difficulty of ensuring cyber-security is steadily growing as a result of the alarming development in computer connectivity and the sizeable number of applications associated to computers in recent years. The system also requires robust defines against the growing number of cyber threats. As a result, a possible role for cyber-security might be performed by developing Intrusion Detection Systems (IDS) to detect inconsistencies and threats in computer networks. An effective data-driven intrusion detection system has been created with the use of Artificial Intelligence, particularly Machine Learning techniques. This research proposes a novel Binary Grasshopper Optimized Twin Support Vector Machine (BGOTSVM) based security model which first considers the security features ranking according to their relevance before developing an IDS model based on the significant features that have been selected. By lowering the feature dimensions, this approach not only improves predictive performance for unidentified tests but also lowers the model's computational expense. Trials are conducted using four common ML techniques to compare the results to those of the current approaches (Decision Tree, Random Decision Forest, Random Tree, and Artificial Neural Network). The experimental findings of this study confirm that the suggested methods may be used as learning-based models for network intrusion detection and demonstrate that, when used in the real world, they outperform conventional ML techniques.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128158357","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212391
E. Jyothi, M. Kranthi, Dankan Gowda V, R. Tanguturi
Due to political and financial considerations, large hospitals are also less likely to share their patient information with outside healthcare providers. To get around the barriers that prevent an efficient process of exchanging medical data. The integrated computerized clinical information system is part of the Hospital Information System (HIS), which aims to improve hospital operations and clinical management. Furthermore, the patient has access to an accurate electronic medical record that has been stored. For research and statistical applications, such records can be utilized in a data warehouse. The architecture of a centralized information system, on which HIS was established intended for the rapid transmission of both operational and administrative information. It would be difficult and It requires a lot of money and resources to set up an independent information management system for a small village hospital. The hospital information system in use presently, information is only shared within the same hospital. The theory of cloud computing serves as the proposal's basis. The “cloud” makes it possible for greater analysis, sharing, and exchange of medical data from images. Doctors may be able to get the data they need due to cloud-based medical image storage, patient will be able to get treatment across hospital departments automating the management of hospital information and computational resources. Hence, this system develops of intelligent medical integrity authentication and it is more effective for hospital administration to use secure information on public clouds, low-cost and time saving.
{"title":"Design of Intelligent Medical Integrity Authentication and Secure Information for Public Cloud in Hospital Administration","authors":"E. Jyothi, M. Kranthi, Dankan Gowda V, R. Tanguturi","doi":"10.1109/ICECAA58104.2023.10212391","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212391","url":null,"abstract":"Due to political and financial considerations, large hospitals are also less likely to share their patient information with outside healthcare providers. To get around the barriers that prevent an efficient process of exchanging medical data. The integrated computerized clinical information system is part of the Hospital Information System (HIS), which aims to improve hospital operations and clinical management. Furthermore, the patient has access to an accurate electronic medical record that has been stored. For research and statistical applications, such records can be utilized in a data warehouse. The architecture of a centralized information system, on which HIS was established intended for the rapid transmission of both operational and administrative information. It would be difficult and It requires a lot of money and resources to set up an independent information management system for a small village hospital. The hospital information system in use presently, information is only shared within the same hospital. The theory of cloud computing serves as the proposal's basis. The “cloud” makes it possible for greater analysis, sharing, and exchange of medical data from images. Doctors may be able to get the data they need due to cloud-based medical image storage, patient will be able to get treatment across hospital departments automating the management of hospital information and computational resources. Hence, this system develops of intelligent medical integrity authentication and it is more effective for hospital administration to use secure information on public clouds, low-cost and time saving.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130062820","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 : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212248
Jadda Midhun, A. S. Arun Raj, Manaswini Beereddy, Shalem Preetham Gandu, Gajula Parimala Sudha, Blessy Harshitha Gandu
A leading cause of death globally is cardiovascular disease (CVD). Early CVD detection is essential for successful treatment and complication prevention. Convolutional neural network (CNN), Recurrent neural networks (RNN), bidirectional recurrent neural networks (BiRNN), deep neural networks (DNN), and an ensemble model has all been used in this study's deep learning-based approach for CVD prediction. With a test size of 20%, suggested models were trained and assessed on a dataset of 303 patients. The models were assessed using a variety of criteria, including recall, sensitivity, specificity, F1-score, accuracy, and precision. The ensemble model achieved best performance, with 99% accuracy, 100% precision, 100% recall, 0.97 F1-score, 1.0 sensitivity, and 0.99 specificity. The training and validation loss vs. epoch graph for each model was also analysed to assess the model's performance. Findings from this research suggest that the proposed machine learning-based approach can effectively predict CVD, with the ensemble model outperforming individual models. The use of such models can aid in the early detection and prevention of CVD, improving patient outcomes. Future work can focus on evaluating the proposed models on a larger dataset and incorporating additional clinical variables.
{"title":"Ensemble Deep Learning Models for Accurate Prediction of Cardiovascular Disease Risk: A Comparative Analysis","authors":"Jadda Midhun, A. S. Arun Raj, Manaswini Beereddy, Shalem Preetham Gandu, Gajula Parimala Sudha, Blessy Harshitha Gandu","doi":"10.1109/ICECAA58104.2023.10212248","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212248","url":null,"abstract":"A leading cause of death globally is cardiovascular disease (CVD). Early CVD detection is essential for successful treatment and complication prevention. Convolutional neural network (CNN), Recurrent neural networks (RNN), bidirectional recurrent neural networks (BiRNN), deep neural networks (DNN), and an ensemble model has all been used in this study's deep learning-based approach for CVD prediction. With a test size of 20%, suggested models were trained and assessed on a dataset of 303 patients. The models were assessed using a variety of criteria, including recall, sensitivity, specificity, F1-score, accuracy, and precision. The ensemble model achieved best performance, with 99% accuracy, 100% precision, 100% recall, 0.97 F1-score, 1.0 sensitivity, and 0.99 specificity. The training and validation loss vs. epoch graph for each model was also analysed to assess the model's performance. Findings from this research suggest that the proposed machine learning-based approach can effectively predict CVD, with the ensemble model outperforming individual models. The use of such models can aid in the early detection and prevention of CVD, improving patient outcomes. Future work can focus on evaluating the proposed models on a larger dataset and incorporating additional clinical variables.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133006630","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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