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Journal of Artificial Intelligence and Capsule Networks最新文献

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AI-Integrated Proctoring System for Online Exams 在线考试人工智能集成监考系统
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-07-20 DOI: 10.36548/jaicn.2022.2.006
Arjun Sridhar, J. S. Rajshekhar
{"title":"AI-Integrated Proctoring System for Online Exams","authors":"Arjun Sridhar, J. S. Rajshekhar","doi":"10.36548/jaicn.2022.2.006","DOIUrl":"https://doi.org/10.36548/jaicn.2022.2.006","url":null,"abstract":"Lately, remote education has been more popular. However, there hasn't been a legitimate outcome for academic exams. Several institutions have mandated remote proctoring, where an invigilator proctor continuously monitors student performance, while others have collected assignments that students can copy and paste from the internet, where an invigilator proctor keeps watching pupil conditioning. There must be a solution if the way we live is to become the new norm. We have proposed a methodology in this research to formulate a comprehensive system that is AI-based and can help avoid exam cheating. The system monitors for fraudulent activity and records the proof. This technology will be both affordable and secure.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124337155","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: 8
Leather Defect Segmentation Using Semantic Segmentation Algorithms 基于语义分割算法的皮革缺陷分割
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-07-20 DOI: 10.36548/jaicn.2022.2.005
Aashish Ghimire, Aman Mahaseth, Ramesh Thapa, Suraj Ale Magar Ale Magar, Sudheer Kumar Singh, Salik Ram Khanal
{"title":"Leather Defect Segmentation Using Semantic Segmentation Algorithms","authors":"Aashish Ghimire, Aman Mahaseth, Ramesh Thapa, Suraj Ale Magar Ale Magar, Sudheer Kumar Singh, Salik Ram Khanal","doi":"10.36548/jaicn.2022.2.005","DOIUrl":"https://doi.org/10.36548/jaicn.2022.2.005","url":null,"abstract":"Leather is one of the essential materials in our life. It can be used widely to make different industrial products. Products made from leather are strong, expensive and durable which lasts for decades. So, It is very important for the industry to make a defect free product for their maximum profit and good customer feedback. Quality inspection is one of the important processes in the textile industry. It is done manually in most of the industry which is time taking, expensive, less accurate and requires lots of people. The main aim of our research work is to replace the manual process with automatic leather defect detection techniques which can save both time and money and increase the rate of production in the company. In this article, we proposed a deep learning-based semantic segmentation model that detects defects in leather images and highlights the defect with proper defect type. The experiments were carried out using the MVTEC leather dataset. The input images are changed into 256*256 pixels and then converted to gray-scale image and finally a semantic segmentation algorithm is applied to detect the leather defects. The experimental results are evaluated and compared using various semantic segmentation algorithms. We obtained the satisfactory result with evaluation metrics of 72.1% Intersection of Union (IOU) with 82.59% F1 Score on one of the semantic segmentation architectures Mobilenet_unet.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128453234","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 models on Heart Disease Estimation - A review 心脏病评估中的深度学习模型综述
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-07-18 DOI: 10.36548/jaicn.2022.2.004
T. M. A. Monisha Sharean, G. Johncy
{"title":"Deep learning models on Heart Disease Estimation - A review","authors":"T. M. A. Monisha Sharean, G. Johncy","doi":"10.36548/jaicn.2022.2.004","DOIUrl":"https://doi.org/10.36548/jaicn.2022.2.004","url":null,"abstract":"Heart disease, also known as cardiovascular disease (CVD), is the foremost among all widespread diseases in the people community. Any disorder that affects the heart's function is typically called heart disease. Narrowing or blockage of the coronary arteries, which supply blood to the heart, is the most common cause of heart failure. Coronary Artery Disease (CAD) is a common form of heart disease and the leading cause of heart attacks. Nowadays, there is no age limit for people to get affected by this disease. There are so many diagnosis methods available where most are costly, the risk involved, and technical experts are needed to perform the disease diagnosis. Clinical research has pointed out different factors that increase the risk of CAD and heart attack. These factors can be categorized into two types, i.e., risk factors that cannot be changed and those that can be changed. Sex, age and family history are those factors that cannot be altered. In contrast, factors related to a subject's lifestyle, e.g., smoking, high cholesterol, high blood pressure and physical inactivity, can be changed. This paper reviews various deep learning techniques involving heart disease prognostic and their accuracy in predicting that they can be treated in advance to prevent fatalities.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114075735","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: 9
Comparative analysis on Emotion Recognition by Multi-Channel CapsNet Learning Framework 基于多通道CapsNet学习框架的情绪识别比较分析
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-06-14 DOI: 10.36548/jaicn.2022.2.003
D. Vinod Kumar
{"title":"Comparative analysis on Emotion Recognition by Multi-Channel CapsNet Learning Framework","authors":"D. Vinod Kumar","doi":"10.36548/jaicn.2022.2.003","DOIUrl":"https://doi.org/10.36548/jaicn.2022.2.003","url":null,"abstract":"This study uses electroencephalography (EEG) data to construct an emotion identification system utilizing a deep learning model. Modeling numerous data inputs from many sources, such as physiological signals, environmental data and video clips has become more important in the field of emotion detection. A variety of classic machine learning methods have been used to capture the richness of multimodal data at the sensor and feature levels for the categorization of human emotion. The proposed framework is constructed by combining the multi-channel EEG signals' frequency domain, spatial properties, and frequency band parameters. The CapsNet model is then used to identify emotional states based on the input given in the first stage of the proposed work. It has been shown that the suggested technique outperforms the most commonly used models in the DEAP dataset for the analysis of emotion through output of EEG signal, functional and visual inputs. The model's efficiency is determined by looking at its performance indicators.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127564881","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
Leaf Disease Detection using Deep Learning 基于深度学习的叶片病害检测
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-05-26 DOI: 10.36548/jaicn.2022.2.002
R. Anitha, A. Bazila Banu
{"title":"Leaf Disease Detection using Deep Learning","authors":"R. Anitha, A. Bazila Banu","doi":"10.36548/jaicn.2022.2.002","DOIUrl":"https://doi.org/10.36548/jaicn.2022.2.002","url":null,"abstract":"Agriculture plays an important role in determining India's economy. So, the detection of disease that affects the plants is most important as it affects productivity. The proposed system is designed to detect the diseases that degrade the health of the leaves. The diseases may be of bacterial, viral and late blight. The diseases can be detected with the help of Convolutional Neural Network (CNN). It is composed of several layers that help in the prediction of diseases. The designed CNN classifies the disease into three major categories. An input leaf image is provided to test whether the leaf is healthy or not. The system has been trained with different input leaves. Once it is trained the new input leaves are given to the classifier, then the classifier identifies the label of the affected leaves. Based on the disease identified, the necessary remedies can be taken for curing the disease.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126531147","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
https://irojournals.com/aicn/AllVolumes.html https://irojournals.com/aicn/AllVolumes.html
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-05-18 DOI: 10.36548/jaicn.2022.2.001
Li Yang-yang, Wang Ya-jun, Zhang Mi-yuan
{"title":"https://irojournals.com/aicn/AllVolumes.html","authors":"Li Yang-yang, Wang Ya-jun, Zhang Mi-yuan","doi":"10.36548/jaicn.2022.2.001","DOIUrl":"https://doi.org/10.36548/jaicn.2022.2.001","url":null,"abstract":"Most of the traditional recommendation algorithm models are recommended based on the user's own historical preferences, although it can recommend POI for users to a certain extent. But in real life, people are more willing to ask their friends what they think when they have a difficult decision. Therefore, a word2vec-based social relationship point of interest recommendation model (W-SimTru) is proposed, which combines the similarity of friends based on cosine similarity with the friend trust recommendation algorithm based on TF-IDF to improve the model recommendation effect. In addition, before modeling the similarity of users, word2vec is used to process the user's historical check-in behavior to solve the problem of inaccurate recommendation due to sparse check-in data. Finally, experiments are carried out on three datasets of Los Angeles, Washington and NYC in Gowalla, and the experimental results show that the proposed W-SimTru recommendation algorithm outperforms the algorithms of the three comparative experiments.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"9 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123298946","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
March 2022 2022年3月
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2022-03-01 DOI: 10.36548/jaicn.2022.1
A. H. Moffitt
{"title":"March 2022","authors":"A. H. Moffitt","doi":"10.36548/jaicn.2022.1","DOIUrl":"https://doi.org/10.36548/jaicn.2022.1","url":null,"abstract":"","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129281890","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: 18
June 2021 2021年6月
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2021-06-01 DOI: 10.36548/jaicn.2021.2
{"title":"June 2021","authors":"","doi":"10.36548/jaicn.2021.2","DOIUrl":"https://doi.org/10.36548/jaicn.2021.2","url":null,"abstract":"","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624975","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 SECURITY FRAMEWORK FOR DATA MIGRATION IN A CLOUD COMPUTING ENVIRONMENT 云计算环境下数据迁移的高效安全框架
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2019-09-28 DOI: 10.36548/jaicn.2019.1.006
S. Shakya
{"title":"AN EFFICIENT SECURITY FRAMEWORK FOR DATA MIGRATION IN A CLOUD COMPUTING ENVIRONMENT","authors":"S. Shakya","doi":"10.36548/jaicn.2019.1.006","DOIUrl":"https://doi.org/10.36548/jaicn.2019.1.006","url":null,"abstract":"Cloud computing is advantageous in several applications. Data migration is constantly carried out to hybrid or public cloud. Certain large enterprises will not move their business-critical data and applications to the cloud. This is due to the concerns regarding data security and privacy protection. In this paper, we provide a data security analysis and solution for privacy protection framework during data migration. A Secure Socket Layer (SSL) is established and migration tickets with minimum privilege is introduced. Further, data encryption is done using Prediction Based Encryption (PBE). This system will be of use for healthcare systems and e-commerce systems that can store data regarding credit card details. We provide a strict separation between sensitive and non-sensitive data and provide encryption for the sensitive data.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":" 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951662","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: 60
IDENTIFICATION AND CLASSIFICATION OF CANCER CELLS USING CAPSULE NETWORK WITH PATHOLOGICAL IMAGES 利用病理图像的胶囊网络识别和分类癌细胞
Journal of Artificial Intelligence and Capsule Networks Pub Date : 2019-09-18 DOI: 10.36548/jaicn.2019.1.005
Pasumpon Pandian Dr
{"title":"IDENTIFICATION AND CLASSIFICATION OF CANCER CELLS USING CAPSULE NETWORK WITH PATHOLOGICAL IMAGES","authors":"Pasumpon Pandian Dr","doi":"10.36548/jaicn.2019.1.005","DOIUrl":"https://doi.org/10.36548/jaicn.2019.1.005","url":null,"abstract":"Cancer is a deadly disease that is costing the lives of many people. Over 9.6 million death is reported in 2018 due to cancer. We propose an ideal methodology to identify and classify cancer cells using pathological images with the help of capsule network. Capsule network’s capability to learn patterns based on previous iterations can be exploited for this purpose. This can help in identification of cancer at early stages and work at the root cause of the disease and walk towards completely shutting down the disease. Image processing is done along with fuzzification and further, it is handled with capsule network classifier and analysed.","PeriodicalId":399652,"journal":{"name":"Journal of Artificial Intelligence and Capsule Networks","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132409099","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: 45
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