Journal of Cognitive Human-Computer Interaction最新文献_第3页

A Decentralized and Cooperative Methodology For Organ Donation Management Based on Ethereum Blockchain 一种基于以太坊区块链的去中心化合作的器官捐赠管理方法

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.060101

P. Rani, Harini. M., N. N., Teena A. Naahz. G.

{"title":"A Decentralized and Cooperative Methodology For Organ Donation Management Based on Ethereum Blockchain","authors":"P. Rani, Harini. M., N. N., Teena A. Naahz. G.","doi":"10.54216/jchci.060101","DOIUrl":"https://doi.org/10.54216/jchci.060101","url":null,"abstract":"The digital world is a vast and ever- evolving ecosystem that encompasses a wide range of technologies, applications, and platforms. Blockchain has played a significant role in bringing the healthcare business forward. Blockchain may significantly enhance the traceability, efficiency, and safety of confidential data such as organ donation and transplantation, as well as the administration of electronic health data. This paper presents a secure and efficient web application for organ donation that uses private Ethereum blockchain technology to create a proof of authority (PoA) model for this consortium and also to automate a number of processes, including matching donors and recipients. The fairness of all the entities—patient, donor, hospital, or insurance company—involved in the system is guaranteed without the involvement of a third party. The security and privacy of the patient’s details are protected. The logic of the application is implemented using smart contracts and deployed in Ganache. It depicts various interactions and transactions among the participants, thus helping to automate these processes, promote transparency, improve efficiency, and minimise service time.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664356","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

Machine Learning Based Product Price Inference Using Price Elasticity of Demand Approach 基于需求价格弹性方法的机器学习产品价格推断

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.030101

K. K.Vengatesan, Raghvendra Vijay Naidu, Kunal Joshi, Chaitanya Tekane, Siddhant Ravindra Gore

引用次数: 0

Artificial Intelligence Based Accident Detection and Alert System 基于人工智能的事故检测与预警系统

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.020104

A. Admin, B. Buvaneswari, S. Sangeetha, Shalini Shalini, S. Sundareswari, V. Priya

引用次数: 0

ActivBench: Leveraging Human Activity Inference from Smartphone Sensors for Human Computer Interactions ActivBench:利用智能手机传感器的人类活动推断进行人机交互

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.050205

Basma K. Eldrandaly

{"title":"ActivBench: Leveraging Human Activity Inference from Smartphone Sensors for Human Computer Interactions","authors":"Basma K. Eldrandaly","doi":"10.54216/jchci.050205","DOIUrl":"https://doi.org/10.54216/jchci.050205","url":null,"abstract":"Human activity recognition (HAR) from smartphone sensors has gained significant attention due to its potential to enhance user experience (UX) and human computer interaction (HCI) in various domains, HAR can enable personalized, context-aware, and adaptive interfaces that improve accessibility and promote health and wellness in various applications such as healthcare, smart homes, fitness tracking, and context-aware systems. However, evaluating the performance of different machine learning (ML) algorithms on activity recognition tasks remains challenging, primarily due to the lack of standardized benchmark datasets and evaluation protocols. In this paper, we presented ActivBench, an end-to-end computational intelligence benchmark designed to facilitate the evaluation and comparison of ML algorithms for human activity inference from smartphone sensors. We addressed the challenges in benchmarking activity recognition systems by providing a unified evaluation protocol and standardized performance metrics. Through extensive experiments using various state-of-the-art algorithms, we demonstrated the effectiveness of ActivBench in assessing the strengths and limitations of different approaches. The benchmark results provide valuable insights into the strengths and limitations of different algorithms, facilitating the development of robust and accurate activity recognition systems that can enhance human computer interaction in various applications. ActivBench is serving as a valuable resource for researchers and practitioners in human activity recognition and human-computer interaction, enabling fair comparisons and fostering advancements in the field. It also serves as a catalyst for advancements in the field, enabling the exploration of novel algorithms, feature engineering techniques, and sensor modalities.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"107 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113970586","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

Personnel Monitoring System Using Mobile Application during the COVID 19 新型冠状病毒肺炎期间基于移动应用的人员监控系统

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.020201

A. Admin, Yuvashree Yuvashree, Althaaf .., Supraja. RSupraja. R

{"title":"Personnel Monitoring System Using Mobile Application during the COVID 19","authors":"A. Admin, Yuvashree Yuvashree, Althaaf .., Supraja. RSupraja. R","doi":"10.54216/jchci.020201","DOIUrl":"https://doi.org/10.54216/jchci.020201","url":null,"abstract":"Corona virus disease(COVID-19) is a disease caused by the new corona virus called severe acute respiratory syndrome corona virus (SARS CoV-2). This disease has infected almost the entire world with a total of 47.5 million sufferers and a death total of 1.2 million people, WHO categorizes it as a global pandemic. Proven efforts to reduce the spread of COVID-19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home. Government and private agencies have required employees to report their health conditions via web pages. Real-time and accurate mobile applications can help prevent the spread of COVID-19. This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology. The application will collect GPS-based location data and the user's body condition in the form of temperature and oxygen levels in the blood. User data is stored and processed in a real-time database in cloud computing which can be accessed through an application on the user's smart phone. The database also stores data on COVID-19 sufferers and where they live. Advice is given by the app when the recording of the body condition points to the early symptoms of COVID-19.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240871","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: 4

A Survey on Cyber Security Meets Artificial Intelligence: AI– Driven Cyber Security 网络安全与人工智能的结合:人工智能驱动的网络安全

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.020202

A. Admin, Dr.P Dr.P.Kavitha2, A. Akshaya, P. P.Shalin, R. R.Ramya

{"title":"A Survey on Cyber Security Meets Artificial Intelligence: AI– Driven Cyber Security","authors":"A. Admin, Dr.P Dr.P.Kavitha2, A. Akshaya, P. P.Shalin, R. R.Ramya","doi":"10.54216/jchci.020202","DOIUrl":"https://doi.org/10.54216/jchci.020202","url":null,"abstract":"The computerized version of human intelligence is Artificial Intelligence(AI). Artificial Intelligence systems combine large sets of data with intelligent and iterative processing algorithms in order to make predictions, based on patterns and features in the data that they analyse. With the booming technologies such as IOT and Cloud Computing, huge amounts of data are generated and collected that require cyber security protection today. There is a growing need for cyber security methods which are both robust and intelligent due to the ever-increasing complexity of cyber crimes. While data can be used to benefit business interests, it poses a number of challenges in terms of security and privacy protection. Artificial Intelligence (AI) based technologies, such as machine learning statistics, big data analysis, deep learning and so on, have been used to deal with cyber security threats. These technologies are used for intrusion detection systems, malicious software detection, and encrypted communications. In the rapidly growing field of AI driven security, scientists from multiple disciplines work together to combat cyber threats. AI models require unique cyber security defence and protection technologies. This survey provides various method, different datasets and methodologies that may be used for the proposed IA enabled cyber security technologies. This study aims to classify the AI-based cyber security solutions gathered and describe how they can help solve problems in the field of cyber security.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"11 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127921251","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

An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor 统计特征算法在脑肿瘤检测中的实现

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.010202

P. Kavitha, R. S. Shini, R. Priya

{"title":"An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor","authors":"P. Kavitha, R. S. Shini, R. Priya","doi":"10.54216/jchci.010202","DOIUrl":"https://doi.org/10.54216/jchci.010202","url":null,"abstract":"A member of a population who is at risk of becoming infected by disease is a susceptible individual. Finding disease susceptibility and generating an alert in advance, is valuable for an individual. The aim of the work presented a feature vector using different statistical texture analyses of brain tumors from an MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of brain tumor cell structure. For this paper, the brain tumor cell segmented using the strip method to implement hybrid Assured Convergence Particle Swarm Optimization (ACPSO) - Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o, and 135o have calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed on different types of images using past years. So, the algorithm proposed statistical texture features are calculated for iterative image segmentation. The algorithm FETC (Feature Extraction Tumor Cell) extracts statistical features of GLCM. These results show that MRI images can be implemented in a system of brain cancer detection.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123168078","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

Food Ordering Systems' Newness 订餐系统的新颖性

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.040102

R. Venkatesan, M. M.Sumithra, B. Buvaneswari, R. Selvalingeshwaran

{"title":"Food Ordering Systems' Newness","authors":"R. Venkatesan, M. M.Sumithra, B. Buvaneswari, R. Selvalingeshwaran","doi":"10.54216/jchci.040102","DOIUrl":"https://doi.org/10.54216/jchci.040102","url":null,"abstract":"The justification for the Web-based Food Requesting System is to repurpose the ongoing conventional structure with the aid of digital resources and unquestionable PC frameworks, accomplishing their fundamentals so their vital data and information can be effectively managed for an extended timeframe with concise admittance to and control of something almost identical. The typical programming and equipment are fairly accessible and simple to deal with. The Web-based Food Requesting Structure, as illustrated above, might generate a genuinely free, confidential, robust, and rapid organisational technique. It may help the customer focus on their complex projects rather than just the bookkeeping. Moreover, it will support the partnership with improved resource utilisation. The association may remain conscious of mechanised records without unnecessary portions. That says that one need not be connected with information that isn't important while having the option to show up at the data. The point is to streamline and automate its ongoing conventional architecture with the assistance of digital kinds of stuff as well as certain PC programming, satisfying their prerequisites so their significant data or information can be taken care of for a more extensive duration with the streamlined overseeing of something practically the same. Basically, the attempt demonstrates how to supervise for superior execution and greater client organisation.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"312-315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860970","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

Effective lung cancer detection using deep learning network 利用深度学习网络有效检测肺癌

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.050202

Vidyul Narayanan, N. P, S. M.

{"title":"Effective lung cancer detection using deep learning network","authors":"Vidyul Narayanan, N. P, S. M.","doi":"10.54216/jchci.050202","DOIUrl":"https://doi.org/10.54216/jchci.050202","url":null,"abstract":"The use of a computer-assisted diagnosis system was crucial to the results of the clinical study conducted to determine the nature of the human illness. When compared to other disorders, lung cancer requires extra caution during the examination process. This is because the mortality rate from lung cancer is higher because it affects both men and women. Poor image resolution has hampered previous lung cancer detection technologies, preventing them from achieving the requisite degree of dependability. Therefore, in this study, we provide a unique approach to lung cancer prognosis that makes use of improved machine learning and processing of images. Images of lung disease from CT scan databases created using quasi cells are used for diagnosis. Multilayer illumination was used to analyse the generated images, which improved the precision of the lungs' depiction by probing each and every one of their pixels while simultaneously decreasing the amount of background noise. Lung CT images are pre-processed to remove noise, and then a more advanced deep learning network is used to isolate the affected region. The territory is partitioned into subnetworks according to the number of existing networks, from which different features are subsequently extracted. Next, an ensemble classifier should be used to correctly diagnose lung diseases. Using MATLAB simulation, the authors examine how the provided technique improves the rate at which lung cancer could be diagnosed.","PeriodicalId":330535,"journal":{"name":"Journal of Cognitive Human-Computer Interaction","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121624072","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

Modern Medical Innovation on the Preferred Information about the Medicine using AI Technique 基于AI技术的医药优选信息的现代医学创新

Journal of Cognitive Human-Computer Interaction Pub Date : 1900-01-01 DOI: 10.54216/jchci.010102

A. .., A. A, Sindhu P, A. .., Rani V

引用次数: 0