2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第8页

Detection of COVID-19 Patients using Speech Recognition with Support Vector Machine” and Comparing with “K Nearest Neighbour Algorithm” “支持向量机语音识别”对COVID-19患者的检测及与“K近邻算法”的比较

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083960

Rallapalli Jhansi, G. Uganya

引用次数: 1

A Hybrid Model for Pupil Detection Using FPGA and CPU by Iterative Circle Fitting 基于FPGA和CPU迭代圆拟合的瞳孔检测混合模型

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084084

Kishore Kumar.S, V. S, Bhuvanesh. S

引用次数: 0

Accurate Human Palm Recognition System in Cybercrime Analysis using Naive Bayes in comparison with Decision Tree 基于朴素贝叶斯与决策树的准确手掌识别系统在网络犯罪分析中的应用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083899

Aigi Saisundar, D. T

{"title":"Accurate Human Palm Recognition System in Cybercrime Analysis using Naive Bayes in comparison with Decision Tree","authors":"Aigi Saisundar, D. T","doi":"10.1109/ICECONF57129.2023.10083899","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083899","url":null,"abstract":"Aim: Main purpose for research work accurately recognizing human palm in cybercrime analysis using Naive Bayes (NB) and Decision Tree (DT) and palm recognition helps to identify a person easily. Materials and Methods: The proposed algorithm is Naive Bayes and the compared algorithm is Decision Tree. Both the algorithms work on human palm recognition for accuracy. Accuracy is analysed for human palm recognition. Naive Bayes is an act of processing technique based on Bayes' theorem. Decision Tree place with the group of guided learning calculations. Dissimilar with machine learning calculations, calculations related to decision trees take care of relapse and grouping issues. Palm recognition is performed by a Naive Bayes with size of sample $(mathrm{N}=23)$ as well as Decision Tree of sample size $(mathrm{N}=23)$, G-power takes 80%. Result: Naive Bayes (NB) accuracy is 94.173% along with Decision Tree (DT) of 91.739%. There is a significant contrast among two groups whose significance value 0.215 $(mathrm{p} > 0.05)$. Conclusion: Naive Bayes (NB) generate better accuracy compared with Decision Tree (DT) in accuracy of human palm recognition in cybercrime analysis.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"28 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":"123578702","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

Diagnosis of Depressive Disorder Based on Facial and Text-Based Features Using Effective Techniques 基于面部特征和文本特征的抑郁症诊断

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083832

K. Pathak, Prisha Gupta, K. Nimala

{"title":"Diagnosis of Depressive Disorder Based on Facial and Text-Based Features Using Effective Techniques","authors":"K. Pathak, Prisha Gupta, K. Nimala","doi":"10.1109/ICECONF57129.2023.10083832","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083832","url":null,"abstract":"Depression, which is one of the most common mental diseases in the world, has a significant and detrimental impact on society as a whole. Early detection of depression via the use of automated methods and assessments is very necessary in order to improve one's own physical state. Analysis of people's feelings is quickly becoming one of the most useful techniques we have for spotting signs of depression. Textual and natural language processing methods are used in sentiment analysis, with the end goal of extracting views and feelings hidden within the data. In this research, we examine the use of computers and methodologies for sentiment analysis, which will provide an effective way for diagnosing and monitoring mental illnesses like depression. Specifically, we focus on how these two areas may work together. This project shows and explores prospective techniques for emotional technologies that combine sentiment analysis with the capability of detecting and measuring depression. Additionally, a concept design for an integrated multimodal system for the diagnosis of sadness is given. This system makes use of sentiment analysis and affective computing approaches. This project had the goal of developing an application that could run on several platforms, be hosted in the cloud, and maintain high confidentiality while being independent of its operator. As our training data, we made use of word classifications in conjunction with specified characteristics of human facial motions. The combination of these two strategies results in a performance that is more effective overall. The correctness of the models that were detailed in the step before this one is tested using a variety of different algorithms, and the results of these tests will give us with a rough sketch of the right technique. Additional optimization may bring to a significant increase in accuracy.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"8 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":"125621812","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

Deep Learning based Lung Segmentation Prior for Robust COVID-19 Classification 基于深度学习的肺分割先验鲁棒性COVID-19分类

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083646

T. Suvathi, A. Chandrasekar, Palani Thanaraj Krishnan

引用次数: 0

New Crow Search Algorithm for Economic Load Dispatch Resolution vs. the Time-Proven BAT Algorithm 经济负荷调度的新乌鸦搜索算法与久经考验的BAT算法

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083563

K. Sumanth, M. V. Priya

引用次数: 0

Detecting Parkinson's disease from Speech signals using Boosting Ensemble Techniques 利用增强集成技术从语音信号中检测帕金森病

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083634

P. Deepa, Rashmita Khilar

引用次数: 0

Performance Analysis of Mammogram Tumor Classification using Deep Belief Network 基于深度信念网络的乳房x线影像肿瘤分类性能分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083516

M. Karthik, N. Bhavani

引用次数: 0

A Framework for Secure Cooperative Spectrum Sensing based with Blockchain and Deep Learning model in Cognitive Radio 认知无线电中基于区块链和深度学习模型的安全协同频谱感知框架

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083887

Neelam Dewangan, Arunima S Kumar, R. N. Patel

{"title":"A Framework for Secure Cooperative Spectrum Sensing based with Blockchain and Deep Learning model in Cognitive Radio","authors":"Neelam Dewangan, Arunima S Kumar, R. N. Patel","doi":"10.1109/ICECONF57129.2023.10083887","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083887","url":null,"abstract":"Today we live in era where not only humans interact but machines interact too. Internet of Things has disrupted the communication with an enormous growth in number of connected devices worldwide. This resulted in big challenges to meet the spectrum requirement of these devices such as seamless connectivity, scalability and accessibility. Cognitive Radio (CR) is designed to meet the requirement since it uses spectrum holes in the licensed bands. Security issues put at risk spectrum sensing, a crucial part of the Cognitive Radio Network (CRN).A malicious user (MU) reduces the accuracy of spectrum sensing, particularly in the situation of cooperative spectrum sensing where MU transmits fabricated data to the fusion centre. The performance of cognitive radios may suffer from the presence of such MU in the system that create erroneous sensing data. As a result, this paper proposes a Blockchain-based method for MU detection in networks. This strategy makes it simple to distinguish between a trustworthy user and a MU using cryptographic keys. The effectiveness of the suggested technique is examined using python tool. The proposed method detects Malicious user with 100 % efficiency in very less sensing time of 0.6ms. The results were also compared with adaptive threshold, FOF and TTA algorithms.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"22 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":"132167981","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

Comprehensive Analysis on Drowsiness Detection of Drivers using Facial Analysis 基于面部分析的驾驶员睡意检测综合分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083687

Dhandapani Samiappan, Pavai Vendhan Ganesan, Rithick Subramanian, Yuvaraj Rajasekar

{"title":"Comprehensive Analysis on Drowsiness Detection of Drivers using Facial Analysis","authors":"Dhandapani Samiappan, Pavai Vendhan Ganesan, Rithick Subramanian, Yuvaraj Rajasekar","doi":"10.1109/ICECONF57129.2023.10083687","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083687","url":null,"abstract":"Accidents on the world's roads are rising at a ratethat is proportional to the explosion inthe number of cars on the planet, which is occurring everyday. In today'sworld, accidents are an everyday occurrence, and they often end infatalities. It is possible that tiredness on the part of the driver is one of the primary contributors to accidents. Therefore, a monitoring system that is both useful and effective should bedesigned in order to check the degree of observant of the driveras well as to inform him to avoid an accident. Several different approaches have been suggested as potential means of identifying sleepy drivers and so reducing the risk of collisions. One of the methods includes detecting the driver's eyes, whilethe other approach takes into account the driver's eyes, mouth, and head tilt. Both approaches include the system monitoringthe driver's attentiveness and then sounding an alarm to bringthe driver' sattentiontothesituation. Theotherappr oachconsiders the tilt of the head in addition to the mouth and theeyes. If the system detects that the driver's eyes are closed, hismouth is wide, suggesting that he is yawning, or his head istilted, then the system will inform the driver with a projectedtext and alert him with an alarm, with an accuracy rate of 92percent. It is appropriate for motorists who need the use of corrective lenses.","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":"129318319","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