2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)最新文献_第10页

Intelligent Home Surveillance System using Convolution Neural Network Algorithms 基于卷积神经网络算法的智能家庭监控系统

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193402

R. Sathya, V. Bharathi, S. Ananthi, K. Vaidehi, S. Sangeetha

引用次数: 0

Heuristic Optimization with Deep Learning based Maize Leaf Disease Detection Model 基于深度学习的启发式优化玉米叶片病害检测模型

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193264

Mr. S. Vimalkumar, Dr.R. Latha

{"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}

引用次数: 0

Using Machine Learning to Detect and Classify URLs: A Phishing Detection Approach 使用机器学习检测和分类url:一种网络钓鱼检测方法

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193559

Mahesh, Ananth, Dheepthi

{"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}

引用次数: 1

Music Recommendation System based on Facial Expression 基于面部表情的音乐推荐系统

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 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

引用次数: 0

Sensor based Cardiac Arrest Monitoring using Internet of Things (IoT) 基于传感器的物联网(IoT)心脏骤停监测

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193491

R. Devi, S. Deepthi Shree, K. S. Harita, L. Keerthika

引用次数: 0

Identifying Multiple Diseases in the Human Body using Machine Learning 使用机器学习识别人体多种疾病

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193060

P. Nagaraj, V. Muneeswaran, B. Karthik Goud, K. Arjun, G. Vigneshwar Reddy, P. Girish Kumar Reddy

引用次数: 0

E-Health Records Stored Over the Cloud with Automated Medication Reminders for Enhanced Patient Care 通过云存储的电子健康记录，具有自动药物提醒功能，可增强患者护理

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193272

E. Indhuja, J. Angelina, S. Subhashini, B.Ajay Kumar, L. Amulya, G. Gopi

引用次数: 0

Correlation based Feature Selection and Hybrid Machine Learning Approach for Forecasting Disease Outbreaks 基于相关性特征选择和混合机器学习的疾病爆发预测方法

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193045

Swayon Bhunia, Dr. T. Abirami

{"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}

引用次数: 0

Biomedical Engineering Impacting Community Service with Embedded Systems 生物医学工程与嵌入式系统影响社区服务

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193671

Mandala Bhuvana Reddy, Rajashekar Reddy, Varagani Ramu, Bochu Vardhan, V. Gunturu

引用次数: 0

Intelligent System for ATM Fraud Detection System using C-LSTM Approach 使用 C-LSTM 方法的 ATM 欺诈检测智能系统

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI: 10.1109/ICESC57686.2023.10193398

Ketan Rathor, S. Vidya, M. Jeeva, M. Karthivel, Shubhangi N. Ghate, V. Malathy

{"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}

引用次数: 0