2023 International Conference on Inventive Computation Technologies (ICICT)最新文献_第9页

Alopecia Pattern Detection in Males using Classical Machine Learning 基于经典机器学习的男性脱发模式检测

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134212

Jyoti Madke, Mrunal Sondur, S. Bhatlawande

引用次数: 0

Design and Analysis of Low Power Energy Efficient Spin-based MCML 低功耗节能自旋MCML的设计与分析

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134010

M. Gayathiri, S. Santhi

引用次数: 0

Development and Improving Energy Management System in Smart Grid through Integration with Renewable Energy System with AI and Internet of Things 通过可再生能源系统与人工智能和物联网的融合，开发和完善智能电网能源管理系统

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134409

R. Bhavya, P. Manjuladevi, E. Sri Santhoshini, M. Ramya, R. Dhanapal

引用次数: 0

Detecting and Analyzing Depression: A Comprehensive Survey of Assessment Tools and Techniques 抑郁症的检测和分析:评估工具和技术的综合调查

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134165

Mohamed Rahul, Deena S, Shylesh R, L. B

{"title":"Detecting and Analyzing Depression: A Comprehensive Survey of Assessment Tools and Techniques","authors":"Mohamed Rahul, Deena S, Shylesh R, L. B","doi":"10.1109/ICICT57646.2023.10134165","DOIUrl":"https://doi.org/10.1109/ICICT57646.2023.10134165","url":null,"abstract":"Nowadays, clinical depression is a prevalent yet severe mood disorder that occurs with aging. Since sadness has an influence on the mind, it can be hard for the patient to tell the doctor about their situation. Typically utilized diagnostic tools include questionnaires or interview-style evaluations of the symptoms, and also using laboratory tests to see if the depressive symptoms coexist with other severe diseases. In recent years, a variety of approaches have been created to aid the diagnosis of depression, thanks to the development using convolutional neural networks with machine learning. Being a multifactorial condition, depression should be diagnosed using a multimodal approach for an efficient examination. In order to analyze depression using emotion recognition, a number have been created for both unimodal and multimodal approaches. This study reviews these approaches. When compared to multimodal approaches, which combine one or more features, the unimodal approach takes into account just one attribute from the range of facial expressions, voice, etc. for depression identification. This study also discusses many techniques for detecting depression in speech, including spectral, acoustic, and fisher vector algorithms, as well as approaches for extracting face characteristics from speech. The survey includes the current research on emotion recognition that uses auditory and visual information to identify depression. The survey demonstrates that multimodal methods and deep learning techniques outperform unimodal approaches in the study of depression for depression detection.","PeriodicalId":126489,"journal":{"name":"2023 International Conference on Inventive Computation Technologies (ICICT)","volume":"39 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116655686","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

Image-based Black Gram Crop Disease Detection 基于图像的黑革兰作物病害检测

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134027

S. Harika, G. Sandhyarani, D. Sagar, G. Reddy

{"title":"Image-based Black Gram Crop Disease Detection","authors":"S. Harika, G. Sandhyarani, D. Sagar, G. Reddy","doi":"10.1109/ICICT57646.2023.10134027","DOIUrl":"https://doi.org/10.1109/ICICT57646.2023.10134027","url":null,"abstract":"The productivity of agriculture is mostly influenced by the Indian economy. Because of the fore mentioned factor, plant diseases are more prevalent in agricultural fields and are easier to identify. Vigilance for the detection of plant diseases has risen due to current agricultural monitoring in numerous and diverse locations. This study presents an image-based method for the Detection of Black gram Crop Disease (DBCD). The Black gram plant is often referred to as “urad” in India and is officially recognized as “Vigna mungo”. This work considers four diseases anthracnose, leaf crinkle, powdery mildew, and yellow mosaic diseases, which have a considerable negative influence on the production of black gram. The black gram crop diseases were classified in this study using the BPLD dataset. For a comparati ve classification analysis, three machine learning algorithms and two deep learning techniques were considered. This classification study for the diagnosis of Black gram crop disease makes use of the artificial neural network and convolutional neural network of deep learning, as well as the decision tree, random forest, and k-nearest neighbor algorithms of machine learning. Here, the accuracy, precision and recall are measured in order to compare various classification models. As per the analysis, CNN outperforms in every aspect when compared to other classifications with 89% accuracy.","PeriodicalId":126489,"journal":{"name":"2023 International Conference on Inventive Computation Technologies (ICICT)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114286300","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: 3

Rice Leaf Disease Prediction: A Survey 水稻叶病预测研究综述

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134267

Gursewak Singh, Ranjit Singh

引用次数: 0

Electric Power Steering System for Commercial Vehicles using Computational Intelligent Technique 基于计算智能技术的商用车电动助力转向系统

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134327

Elango M K, K. P

引用次数: 0

Real-Time Distributed Information Sharing Platform for Novel Activities based on Cloud Collaborative Software 基于云协同软件的新颖活动实时分布式信息共享平台

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134049

Jinwen Chen, Meishan Liang

引用次数: 0

An Efficient Image Regeneration Framework for Metal Artifact Impacts 一种高效的金属伪影冲击图像再生框架

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134012

Gophika T, S. Sudha, Akash C, Akash Rv

{"title":"An Efficient Image Regeneration Framework for Metal Artifact Impacts","authors":"Gophika T, S. Sudha, Akash C, Akash Rv","doi":"10.1109/ICICT57646.2023.10134012","DOIUrl":"https://doi.org/10.1109/ICICT57646.2023.10134012","url":null,"abstract":"Image reconstruction is required in most of the medical analysis procedures to cross co-ordinate various diagnostic steps. Filtered back propagation is commonly used for image reconstruction techniques in medical screening systems such as x-ray computer tomography, which produces high-impact addresses in many cases. The presence of hard materials such as metals can directly attenuate the complete x-ray signals and create artifacts during the back propagation reconstruction technique. The metal artifacts need to be identified unrestricted during the Diagnostic procedures. The proposed system is focused on creating a robot architecture that detects the reflections happening in the screening images and enhances the image quality by removing the artifact reflections. The problem of artifact generation through metal are thoroughly analysed and removed to provide a high-quality imaging system. The proposed system considered an independent component analysis technique to remove the reflected pixel intensity interrupting image quality. The results of the system are evaluated by measuring the Power signal to noise ratio (PSNR), Mean square error (MSE), and structural similarity index (SSIM). The proposed system is compared with the existing state of art approaches regarding performance statistics.","PeriodicalId":126489,"journal":{"name":"2023 International Conference on Inventive Computation Technologies (ICICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122028125","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

Two-Fold Spoiled Onion Detection using Soft Computing and IoT 使用软计算和物联网的双重坏洋葱检测

2023 International Conference on Inventive Computation Technologies (ICICT) Pub Date : 2023-04-26 DOI: 10.1109/ICICT57646.2023.10134248

K. Shah, Muddam Usha Sri, Buyya Vinod Goud, Kiran Mannem

{"title":"Two-Fold Spoiled Onion Detection using Soft Computing and IoT","authors":"K. Shah, Muddam Usha Sri, Buyya Vinod Goud, Kiran Mannem","doi":"10.1109/ICICT57646.2023.10134248","DOIUrl":"https://doi.org/10.1109/ICICT57646.2023.10134248","url":null,"abstract":"With the advancement of technology and the dependency of people on phones, it is important to come up with solutions involving technology. Using traditional storage methods, farmers can inhibit the spoilage of onions. But, in some situations, people may fail to notice the spoiled onions and in such a scenario they can depend on technology involving some deep learning algorithms and sensors. In the existing techniques, the system which consists of IoT framework alone faced many challenges because sometimes it may predict the data wrongly due to environmental conditions and leading it to an inefficient technique to detect onion spoilage. To overcome this kind of challenge, it is a must that technology like image processing should be included. This paper discusses the model that was developed using Google Colab IDE, which is based on image processing. Combining the segmentation and object extraction process has improved the image features, as it discards the background and other unnecessary things around the main object in our application. CNN model has got 87% accuracy, this shows a good result after evaluation. After this image processing segment, the work continues with the IoT framework that senses the parameters of onions using esp8266 & sensors, and it displays the stages of spoilage on LCD. Through this system, farmers and retail sellers can get early information about the spoilage of onions by accessing the real-time values through the web page.","PeriodicalId":126489,"journal":{"name":"2023 International Conference on Inventive Computation Technologies (ICICT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128170715","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