2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第6页

Graph Signal Estimation Based on Maximum Correntropy Criterion (The Work of A. Chandrasekar Was Supported by the MATRICS, SERB, India, Under Grant MTR/2021/000405) 基于最大相关熵准则的图信号估计(A. Chandrasekar的工作得到了materials, SERB, India的支持，Under Grant MTR/2021/000405)

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084250

A. Chandrasekar, S. Radhika

引用次数: 0

Sugarcane Yield and Price Prediction Using Forecasting Models 利用预测模型预测甘蔗产量和价格

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084094

V. Sneha, V. Bhavana

引用次数: 0

Analysis of Breast Cancer Recognition in Histopathological Images using Convolutional Neural Network 基于卷积神经网络的乳腺癌组织病理图像识别分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084065

S. G, Ramkumar G

{"title":"Analysis of Breast Cancer Recognition in Histopathological Images using Convolutional Neural Network","authors":"S. G, Ramkumar G","doi":"10.1109/ICECONF57129.2023.10084065","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084065","url":null,"abstract":"The majority of women around the world will be diagnosed with breast cancer in their lifetime, making it the second leading cause of mortality among females. On the other hand, it is feasible to be cured of cancer if it is diagnosed at an early stage and given the appropriate treatment. By enabling patients to obtain timely therapeutic treatment, early breast cancer identification has the potential to significantly enhance both the prognosis and the odds of survival for those who are diagnosed with the disease. In addition, accurate categorization of benign tumors might assist patients in avoiding therapy that is not required. The advent of personalized medicine has resulted in a significant rise in the amount of work that must be done by pathologists as well as an increase in the complexity of digital pathology in cancer detection. Diagnostic protocols must now place equal emphasis on both efficiency and accuracy. Histopathology evaluations have been found to benefit from improvements in efficiency, accuracy, and consistency brought about by the application of computerized image processing technologies, which can also give decision support to assure diagnostic consistency. We demonstrate that convolutional neural networks, often known as CNN, can be an efficient method for identifying breast cancer histopathology images, and we test CNN's effectiveness as a binary predictor in the field of breast cancer diagnosis by using whole slide imaging. The model is trained using the data that can be found in the Kaggle archive. The suggested method is contrasted with other approaches already in use by employing a wide variety of achievement evaluation indicators.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114713492","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

Efficient Search Strategies in Selecting the Best Cluster Heads with Gray Wolf Optimization Based Clustering Technique in WSN 基于灰狼优化的WSN聚类技术中最佳簇头选择的高效搜索策略

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084007

V. Ramkumar, P. Jyothi, K. Karthikeyan, V. Senthilkumar, Ektha Sudhakar Reddy, R. Prabu

{"title":"Efficient Search Strategies in Selecting the Best Cluster Heads with Gray Wolf Optimization Based Clustering Technique in WSN","authors":"V. Ramkumar, P. Jyothi, K. Karthikeyan, V. Senthilkumar, Ektha Sudhakar Reddy, R. Prabu","doi":"10.1109/ICECONF57129.2023.10084007","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084007","url":null,"abstract":"Distributed, autonomous sensors are what make up a Wireless Sensor Network (WSN). Nevertheless, the sensor nodes in WSNs run on batteries, therefore power consumption is a major concern. Improving WSNs' durability is largely dependent on the clustering method. This method groups sensors together and chooses leaders from among them (CHs). Cluster Heads collect data from other cluster nodes and relay it to the hub (BS). Selecting the best CHs to maximize network longevity remains the toughest challenge. Here, we present a strategy for choosing group leaders that takes the finest parts of hybrid opposition-based training and the grey wolf efficiency technique and mixes them. The best CHs are chosen using a hybrid approach that dynamically shifts between exploitation and exploration search tactics. The four distinct measures of energy use, minimum distance, centrality, and degree are also used. By choosing the most effective CHs, the suggested selection method improves the effectiveness of the network as a whole. Additionally, the suggested approach is tested in MATLAB and confirmed by a variety of performance measures factors such battery life, network activity, BS location, and packet service efficiency. The suggested approach yields superior results in addition of the quantity of living nodes each round, the maximum amount of packets sent to the BS, the improvement of residual energy, and the enhancement of the lifespan of the network.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128903399","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

Conspectus of Techniques to Monitor and Control Diabetic Foot Ulcers 糖尿病足溃疡监测与控制技术综述

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083968

A. S, B. S, K. S

{"title":"Conspectus of Techniques to Monitor and Control Diabetic Foot Ulcers","authors":"A. S, B. S, K. S","doi":"10.1109/ICECONF57129.2023.10083968","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083968","url":null,"abstract":"Diabetic foot ulcers (DFUs) are severe impediments among diabetic people; whose glycemic levels are not regulated efficiently. A person with diabetes remains a chief therapeutic task for the management of foot ulcers throughout the world. They are usually caused by peripheral neuropathy, uncontrolled glycemic levels, vascular anomalies, and immunosuppression. Severe instances may entail amputations of the foot. For the purpose of preventing foot ulcerations and amputations, early recognition of high-risk feet and enlightening diabetic patients are crucial. An enhanced diagnosis is guaranteed through accurate evaluation and management. Revascularization methodologies, wound debridement, infection treatment, and ulcer offloading are practiced regularly in the management of the disease. The tending to the wound and types of dressing used are customized based on the severity of the wound. The very motive of this review study is to peruse diabetic foot ulcers, commencing with the assessment of the foot for susceptible regions, foot ulcer management, and progressing to different treatment methods.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130902155","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

Efficient Method for Detecting Abnormal Growth of Blood Vessels Using Convolutional Neural Network 基于卷积神经网络的血管异常生长检测方法

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083691

A. D. Kumar, T. Sasipraba

{"title":"Efficient Method for Detecting Abnormal Growth of Blood Vessels Using Convolutional Neural Network","authors":"A. D. Kumar, T. Sasipraba","doi":"10.1109/ICECONF57129.2023.10083691","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083691","url":null,"abstract":"Diabetic Retinopathy is the main problem in human life because of high sugar levels present in the blood. Various organs get affected due to this reason. The eye is the one of the parts of the human eye which goes to vision problems sometimes blindness. The early stages of detection need to protect the human eye. Existing model uses various methods which do not solve the problem completely. Machine learning based approach introduced for detection of the affected area of eye. The Blood Vessels of the eye get affected as a result bleeding in the eye and excess growth in the eye. Traditional algorithms are not suitable for detecting this growth rate due to the less resolution images. The CNN based model with integrated data sets are used to classify and detect the blood vessel. The high resolution images are used for detecting the location and exact difference in normal vessels. Various algorithms are used with different data sets for making multidimensional analysis. The objective of this method is to identify the heterogeneous vessels from the normal vessel with a high level of accuracy.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124770146","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

Particle Swarm Bacterial Foraging Optimization method for Enhanced digital image watermarking system for data security comparison with Genetic algorithm 基于粒子群细菌觅食优化的增强型数字图像水印系统与遗传算法的数据安全性比较

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083811

D. Pula, R. Puviarasi

{"title":"Particle Swarm Bacterial Foraging Optimization method for Enhanced digital image watermarking system for data security comparison with Genetic algorithm","authors":"D. Pula, R. Puviarasi","doi":"10.1109/ICECONF57129.2023.10083811","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083811","url":null,"abstract":"The primary objective of this study is to enhance the data security of digital image watermarking systems through the application of Bacterial Foraging with Particle Swarm Optimization (BF-PSO) and compare Peak Signal Noise Ratio (PSNR) with a Genetic algorithm (GA). The dataset in this paper utilizes the publicly available Kaggle database. The sample size for analysing the data security in a digital image watermarking system with enhanced PSNR was 20 (Group 1 = 10 and Group 2 = 10), and calculations were conducted using G-power 0.8, alpha and beta values of 0.05 and 0.2, and a 95% confidence interval. Bacterial foraging with particle swarm optimization (BF-PSO) and while number of samples (N=10) and Genetic algorithm (GA), where number of samples (N=10) are taken into consideration are used to analyze the digital image watermarking system with improved PSNR. The novel Bacterial Foraging with Particle Swarm Optimization (BF-PSO) has 58.30 higher PSNR when compared to the PSNR of Genetic algorithm (GA) is 40.55. The significance level of the study is p<0.05, or p=0.037. Bacterial Foraging with Particle Swarm Optimization (BF-PSO) in comparison to Genetic algorithm (GA) yields superior results in Peak Signal Noise Ratio (PSNR) when it comes to improving digital image watermarking systems and ensuring the safety of the data.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129920823","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

An Efficient Image Based Mammogram Classification Framework Using Depth Wise Convolutional Neural Network 基于深度智能卷积神经网络的高效图像分类框架

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083528

T. D. Subha, Dhanesshree. S, Galiveeti Sai Charan, Divya Dharshini. E, Princy. I, Chittagong Charisma Reddy

{"title":"An Efficient Image Based Mammogram Classification Framework Using Depth Wise Convolutional Neural Network","authors":"T. D. Subha, Dhanesshree. S, Galiveeti Sai Charan, Divya Dharshini. E, Princy. I, Chittagong Charisma Reddy","doi":"10.1109/ICECONF57129.2023.10083528","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083528","url":null,"abstract":"Architectural Distortion is the third most concerning sign of abnormal areas on a mammogram. It's challenging to diagnose architectural distortion (AD) using mammograms because of the condition's delicacy, fluctuating imbalance on mammary mass, and small size. The use of computer algorithms for the early identification of aberrant ADs areas in mammography might aid radiologists and clinicians. Classification performance is negatively impacted due to star-shaped structural defects in ROI recognition, noise reduction, and object localization. This method uses computer vision to automatically filter out background noise and pinpoint the precise placement of items inside complex patterns. This study used computer vision techniques to investigate the potential for identifying mammography with geometric deformation inside ROIs. The researcher proposed a computer-aided diagnostic approach that utilizes machine training to analyze architectural deformation in digital mammography for the purpose of identifying breast cancer. Image preprocessing, enhancement, and pixel-by-pixel segmentation are only some of the four components of the proposed mammography classification system. Architecture-based distorted region-of-interest (ROI) identification, deep learning and machine learning network training for malignant/benign ROI classification in Alzheimer's disease.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129059981","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

Novel Method for classification of Hepatitis C Using Support Vector Machine Classifier 基于支持向量机分类器的丙型肝炎分类新方法

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083597

D. Sravanthi, J. D

{"title":"Novel Method for classification of Hepatitis C Using Support Vector Machine Classifier","authors":"D. Sravanthi, J. D","doi":"10.1109/ICECONF57129.2023.10083597","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083597","url":null,"abstract":"Aim: The aim of this study is to figure out the presence of Novel Hepatitis C Detection using modern classifiers, and comparing the accuracy, sensitivity, specificity between SVM (Support Vector Machine) and K-NN (K-Nearest Neighbour) Classifiers. Materials and Methods: In this study, data was gathered via the kaggle website. According to clinicalc.com, samples were taken into account as $boldsymbol{(mathrm{N}=22)}$ for SVM and $boldsymbol{(mathrm{N}=22)}$ for K-NN, with the total sample size being determined using the following parameters: enrollment ratio of 0.1, 95% confidence interval, G power of 80%, and alpha error-threshold value of 0.05. With a standard data set, the accuracy, sensitivity, and specificity were calculated using MATLAB. Results: Independent sample t test SPSS software compares accuracy, sensitivity, and specificity. Between the K-Nearest Neighbor Classifier and Support Vector Machine Classifier, there is a statistically significant difference. In comparison to SVM, the K-NN performed better with $boldsymbol{mathrm{p}=0.026}$, p<0.05 accuracy (0.42%), $mathbf{p=0.021}$, p<0.05 sensitivity (0.43%), and $boldsymbol{mathrm{p}=0.001, mathrm{p} < 0.05}$ specificity (0.43%). Conclusion: K-NN showed better accuracy, sensitivity, specificity than SVM to predict Novel Hepatitis C Detection in a faster way.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122496195","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

Hiring and Recruitment Process Using Machine Learning 使用机器学习的招聘流程

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084133

Dahlia Sam, M. Ganesan, S. Ilavarasan, T. Victor

{"title":"Hiring and Recruitment Process Using Machine Learning","authors":"Dahlia Sam, M. Ganesan, S. Ilavarasan, T. Victor","doi":"10.1109/ICECONF57129.2023.10084133","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084133","url":null,"abstract":"In today's competitive world, it is a very complicated process to hire candidates with manual verification of resumes. This work is an experimental method for ranking of hiring resumes because manually ranking is quite a complicated job for the hiring team, as it takes more time to go through each of the candidates resumes. If the resumes are high in number then man power will also increase for the same task. To rectify these problems a new solution has been proposed. In order to make this whole hiring process more effective, an application for processing the resumes using machine learning is proposed. This work uses methods such as optimizing the candidates' performance in the preferred skill mentioned in the resume and also ranking method to display the selected candidates based on their overall performance according to the skill requirement of the company's required job position. In order to verify whether the information given by the user it will check the course completion certificate for the preferred skills given by the user. To check the details in resume, optimizing the user skills and ranking the candidates, machine learning algorithm is used. The whole idea is implemented using python language and the results are sure to make the recruitment process efficient.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119290","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