International Journal of Advanced Computer Science and Applications最新文献

{"title":"Patient Health Monitoring System Development using ESP8266 and Arduino with IoT Platform","authors":"J. Alsayaydeh, Mohd Faizal bin Yusof, Muhammad Zulhakim Bin Abdul Halim, M. N. S. Zainudin, S. Herawan","doi":"10.14569/ijacsa.2023.0140467","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140467","url":null,"abstract":"The Internet of Things (IoT) has emerged as a transformative technology that has revolutionized the field of healthcare. One of the most promising applications of Internet of Things (IoT) in healthcare is patient health monitoring, which allows healthcare providers to remotely monitor patients' health and provide prompt medical attention when needed. This research work focuses on developing an Internet of Things (IoT)based patient health monitoring system aimed at providing a solution for patients, particularly the elderly, who face the risk of unexpected death due to the lack of medical attention. The proposed system utilizes a heartbeat sensor and an Infrared IR temperature sensor connected to Arduino UNO and Nodemcu, respectively, to monitor the patient's vital signs. The sensors collect the data, which is then sent to an Internet of Things (IoT) web platform via a Wi-Fi connection. The Internet of Things (IoT) platform displays the real-time data of the patient's health status, including the temperature and heartbeat rate, which can be monitored by doctors and nurses. The system is designed to send alerts to healthcare providers in the event of any medical emergency, ensuring that prompt medical attention can be provided to the patient. The significance of this research work lies in its potential to revolutionize the healthcare industry by providing a more efficient and effective means of patient health monitoring. The system can be used to monitor a large number of patients simultaneously, which is particularly beneficial in hospitals with a large patient load. Moreover, it can reduce the workload of healthcare providers, allowing them to focus on other critical tasks. This innovative system has the potential to improve the overall quality of healthcare services and lead to better health outcomes for the society. Keywords—Patient health monitoring; Internet of Things (IoT); Arduino UNO; Nodemcu ESP8266; thingspeak; wearable device; temperature value; heartbeat value; remotely","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"100 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81401751","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}

{"title":"Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review","authors":"Muhammad Irfan Hashfi, Teguh Raharjo","doi":"10.14569/ijacsa.2023.0140940","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140940","url":null,"abstract":"This paper presents a systematic literature review (SLR) investigating the challenges and impacts of implementing artificial intelligence (AI) in project management, specifically mapping them into the process groups defined in the Project Management Body of Knowledge (PMBOK). The study aims to contribute to the understanding of integrating AI in project management and provides insights into the challenges and impacts within each process group. The SLR methodology was applied, and a total of 34 scientific articles were analyzed. The results and analysis reveal the specific challenges and impacts within each process group. In the Initiating Process Group, AI tools and analysis techniques address challenges in risk assessment, cost prediction, and decision-making. The Planning process group benefits from various tools and methodologies that improve risk assessment, project selection, cost estimation, resource allocation, and decision-making. The Execution process group emphasizes the importance of advanced tools and techniques in enhancing productivity, resource utilization, cost reduction, and decision-making. The Monitoring and Controlling process group demonstrates the potential of advanced tools in achieving efficiency, cost reduction, improved quality, and informed decision-making. Lastly, the Closing process group emphasizes the importance of utilizing advanced tools to minimize waste, optimize resource utilization, reduce costs, improve quality, and project closure success. Overall, this research provides valuable insights and strategies for organizations seeking to implement AI in project management, thereby enhancing the potential for success within the PMBOK Process Group.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135956982","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}

{"title":"A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities","authors":"Bakhytzhan Omarov, Nurlan Nurmash, Bauyrzhan Doskarayev, Nagashbek Zhilisbaev, Maxat Dairabayev, Shamurat Orazov, Nurlan Omarov","doi":"10.14569/ijacsa.2023.0140977","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140977","url":null,"abstract":"In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136002810","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}

{"title":"A Survey of Evolving Performance Analysis Technologies, Algorithms and Models for Sports","authors":"Shamala Subramaniam, Manoj Ravi Shankar, Azyyati Adiah Zazali, Hong Siaw Swin, Zarina Muhamed, Sivakumar Rajagopal, Mohamad Zamri Napiah, Faisal Embung","doi":"10.14569/ijacsa.2023.0140916","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140916","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136004360","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}

{"title":"Implementation of a Smarter Herbal Medication Delivery System Employing an AI-Powered Chatbot","authors":"M. Vera, T. Palaoag","doi":"10.14569/ijacsa.2023.0140358","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140358","url":null,"abstract":"—Medicinal plants are a practical and cost-effective alternative for treating common ailments, especially in areas with limited access to public healthcare systems. This paper introduces a prototype of an intelligent interactive system that merges chatbot technology with artificial intelligence (AI) to address inquiries related to treatment alternatives and the application of different medicinal plants for prevalent health conditions which promote and advance alternative healing practices in the locality. The platform is a hybrid online chat service that prioritizes consumer health and encourages the responsible use of medicinal plants. This study used a survey questionnaire to gather information from traditional healers and users and concerned government agencies about how well the system prototype performed. The system's performance was assessed in terms of effectiveness, efficiency, and customer satisfaction, with respondents providing an aggregate rating of \"Strongly Agree\". Significantly, this study lays the groundwork for education on the use of local medicinal plants to cure illnesses and highlights the importance of providing users with accurate and reliable information on the safe use of medicinal plants. This approach empowers users to make informed decisions about the plants they use, reducing the likelihood of harmful effects and optimizing the potential benefits of medicinal plants. By supporting this effort, this study contributes to the achievement of the third Sustainable Development Goal of the UN, which aims to promote health and well-being by offering the local populace a low-cost option as a first line of defense for improving their health and wellness.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"14 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73509702","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}

{"title":"The Application of Virtual Technology Based on Posture Recognition in Art Design Teaching","authors":"Qinyan Gao","doi":"10.14569/ijacsa.2023.0140589","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140589","url":null,"abstract":"—With the development of virtual technology, posture recognition technology has been integrated into virtual technology. This new technology allows users to further understand and observe the activities carried out in life scenes based on their original observation of the external world. And it enables them to make intelligent decisions. Existing posture recognition cannot meet the requirements of precise positioning in virtual environments. Therefore, a two-stage three-dimensional pose recognition model is proposed. The experiment illustrates that the three-dimensional gesture recognition performance is excellent. In addition, under the ablation experiment, the error accuracy of the research model decreased by more than 5 mm, and the overall error accuracy decreased by 10%. In the P-R curve, the accuracy rate of the model reaches 0.741, and the recall rate reaches 0.65. When conducting empirical analysis, the virtual posture disassembly action is complete; the disassembly error is less than 5%, and the disassembly error accuracy is good. The fit degree of the leg bending amplitude reaches over 96%, and the fit degree of the arm bending amplitude reaches over 95%. When the model is applied to actual teaching, the overall satisfaction score of teachers and students reaches 94.6 points. This has effectively improved the teaching effect of art design and is of great significance to the development of education in China.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"90 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72593355","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}

{"title":"Machine Learning Techniques in Keratoconus Classification: A Systematic Review","authors":"Aatila Mustapha, Lachgar Mohamed, Hrimech Hamid, Kartit Ali","doi":"10.14569/ijacsa.2023.0140569","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140569","url":null,"abstract":"—Machine learning (ML) algorithms are being integrated into several disciplines. Ophthalmology is one field of health sector that has benefited from the advantages and capacities of ML in processing of different types of data. In a large number of studies, the detection and classification of various diseases, such as keratoconus, was carried out by analyzing corneal characteristics, in different data types (images, measurements, etc.), using ML tools. The main objective of this study was to conduct a rigorous systematic review of the use of ML techniques in the detection and classification of keratoconus. Papers considered in this study were selected carefully from Scopus and Web of Science digital databases, according to their content and to the adoption of ML methods in the classification of keratoconus. The selected studies were reviewed to identify different ML techniques implemented and the data types handled in the diagnosis of keratoconus. A total of 38 articles, published between 2005 and 2022, were retained for review and discussion of their content.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72795760","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}

{"title":"Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter","authors":"Giat Karyono, Asmala Ahmad, S. A. Asmai","doi":"10.14569/ijacsa.2023.0140356","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140356","url":null,"abstract":"—Removing as much noise as possible in an image while preserving its fine details is a complex and challenging task. We propose a wavelet-based and non-local means (NLM) denoising method to overcome the problem. Two well-known wavelets: dual-tree complex wavelet transform (DT-CWT) and discrete wavelet transform (DWT), have been used to change the noise image into several wavelet coefficients sequentially. NLM filtering and universal hard thresholding with cycle spinning have been used for thresholding on its approximation and detail coefficients, respectively. The inverse two-dimensional DWT was applied to the modified wavelet coefficients to obtain the denoised image. We conducted experiments with twelve test images on the set12 data set, adding the additive Gaussian white noise with variances of 10 to 90 in increments of 10. Three evaluation metrics, such as peak signal noise to rate (PSNR), structural similarity index metric (SSIM), and mean square error (MSE), have been used to evaluate the effectiveness of the proposed denoising method. From these measurement results, the proposed denoising method outperforms DT-CWT, DWT, and NLM almost in all noise levels except for the noise level of 10. At that noise level, the proposed denoising method is lower than NLM but better than DT-CWT and DWT.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74449077","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}

{"title":"Improved Speaker Recognition for Degraded Human Voice using Modified-MFCC and LPC with CNN","authors":"Amit Moondra, P. Chahal","doi":"10.14569/ijacsa.2023.0140416","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140416","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"191 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74458282","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}

{"title":"Heart Disease Classification and Recommendation by Optimized Features and Adaptive Boost Learning","authors":"Pardeep Kumar, Ankit Kumar","doi":"10.14569/ijacsa.2023.01403103","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01403103","url":null,"abstract":"—In recent decades, cardiovascular diseases have eclipsed all others as the main reason for death in both low and middle income countries. Early identification and continuous clinical monitoring can reduce the death rate associated with heart disorders. Neither service is yet accessible, as it requires more intellect, time, and skill to effectively detect cardiac disorders in all circumstances and to advise a patient for 24 hours. In this study, researchers suggested a Machine Learning-based approach to forecast the development of cardiac disease. For precise identification of cardiac disease, an efficient ML technique is required. The proposed method works on five classes, one normal and four diseases. In the research, all classes were assigned a primary task, and recommendations were made based on that. The proposed method optimises feature weighting and selects efficient features. Following feature optimization, adaptive boost learning using tree and KNN bases is used. In the trial, sensitivity improved by 3-4%, specificity by 4-5%, and accuracy by 3-4% compared to the previous approach.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"37 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74665844","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}