Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01406139
Yu Wang
Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level. Keywords—Deep learning; calligraphy teaching; BPNN; intelligent evaluation; sparrow search algorithm
{"title":"The Application of Intelligent Evaluation Method with Deep Learning in Calligraphy Teaching","authors":"Yu Wang","doi":"10.14569/ijacsa.2023.01406139","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01406139","url":null,"abstract":"Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level. Keywords—Deep learning; calligraphy teaching; BPNN; intelligent evaluation; sparrow search algorithm","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":"89786521","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140555
S. Fuada, T. Adiono, Prasetiyo -, Harthian Widhanto, Shorful Islam, Tri Chandra Pamungkas
—This study aims to design and develop Wi-Fi tracker system that utilizes RSSI-based distance parameters for crowd-monitoring applications in indoor settings. The system consists of three main components, namely 1) an embedded node that runs on Raspberry-pi Zero W, 2) a real-time localization algorithm, and 3) a server system with an online dashboard. The embedded node scans and collects relevant information from Wi-Fi-connected smartphones, such as MAC data, RSSI, timestamps, etc. These data are then transmitted to the server system, where the localization algorithm passively determines the location of devices as long as Wi-Fi is enabled. The mentioned devices are smartphones, tablets, laptops, while the algorithm used is a Non-Linear System with Lavenberg–Marquart and Unscented Kalman Filter (UKF). The server and online dashboard (web-based application) have three functions, including displaying and recording device localization results, setting parameters, and visualizing analyzed data. The node hardware was designed for minimum size and portability, resulting in a consumer electronics product outlook. The system demonstration in this study was conducted to validate its functionality and performance.
-本研究旨在设计和开发基于rssi距离参数的Wi-Fi跟踪系统，用于室内环境下的人群监控应用。该系统由三个主要部分组成，即1)一个运行在Raspberry-pi Zero W上的嵌入式节点，2)一个实时定位算法，以及3)一个带有在线仪表板的服务器系统。嵌入式节点扫描并收集wi - fi连接的智能手机的相关信息，如MAC数据、RSSI、时间戳等。然后将这些数据传输到服务器系统，只要启用Wi-Fi，定位算法就会被动地确定设备的位置。提到的设备是智能手机，平板电脑，笔记本电脑，而使用的算法是一个非线性系统与拉文伯格-马夸特和Unscented卡尔曼滤波器(UKF)。服务器和在线仪表板(基于web的应用程序)具有显示和记录设备本地化结果、设置参数和可视化分析数据三个功能。节点硬件的设计是为了最小的尺寸和便携性，导致消费电子产品的前景。本研究进行了系统演示，以验证其功能和性能。
{"title":"A Consumer Product of Wi-Fi Tracker System using RSSI-based Distance for Indoor Crowd Monitoring","authors":"S. Fuada, T. Adiono, Prasetiyo -, Harthian Widhanto, Shorful Islam, Tri Chandra Pamungkas","doi":"10.14569/ijacsa.2023.0140555","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140555","url":null,"abstract":"—This study aims to design and develop Wi-Fi tracker system that utilizes RSSI-based distance parameters for crowd-monitoring applications in indoor settings. The system consists of three main components, namely 1) an embedded node that runs on Raspberry-pi Zero W, 2) a real-time localization algorithm, and 3) a server system with an online dashboard. The embedded node scans and collects relevant information from Wi-Fi-connected smartphones, such as MAC data, RSSI, timestamps, etc. These data are then transmitted to the server system, where the localization algorithm passively determines the location of devices as long as Wi-Fi is enabled. The mentioned devices are smartphones, tablets, laptops, while the algorithm used is a Non-Linear System with Lavenberg–Marquart and Unscented Kalman Filter (UKF). The server and online dashboard (web-based application) have three functions, including displaying and recording device localization results, setting parameters, and visualizing analyzed data. The node hardware was designed for minimum size and portability, resulting in a consumer electronics product outlook. The system demonstration in this study was conducted to validate its functionality and performance.","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":"90132610","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140829
J. Pontón, Verónica Ojeda, Víctor Asanza, L. L. Lorente-Leyva, D. H. Peluffo-Ordóñez
—The shrimp farming industry in Ecuador, renowned for its shrimp breeding and exportation, faces challenges due to diseases related to variations in abiotic factors during the maturation stage. This is partly attributed to the traditional methods employed in shrimp farms. Consequently, a prototype has been developed for monitoring and controlling abiotic factors using IoT technology. The proposed system consists of three nodes communicating through the LoRa interface. For control purposes, a fuzzy logic system has been implemented that evaluates temperature and dissolved oxygen abiotic factors to determine the state of the aerator, updating the information in the ThingSpeak application. A detailed analysis of equipment energy consumption and the maximum communication range for message transmission and reception was conducted. Subsequently, the monitoring and control system underwent comprehensive testing, including communication with the visualization platform. The results demonstrated significant improvements in system performance. By modifying parameters in the microcontroller, a 2.55-fold increase in battery durability was achieved. The implemented fuzzy logic system enabled effective on/off control of the aerators, showing a corrective trend in response to variations in the analyzed abiotic parameters. The robustness of the LoRa communication interface was evident in urban environments, achieving a distance of up to 1 km without line of sight.
{"title":"Design and Implementation of an IoT Control and Monitoring System for the Optimization of Shrimp Pools using LoRa Technology","authors":"J. Pontón, Verónica Ojeda, Víctor Asanza, L. L. Lorente-Leyva, D. H. Peluffo-Ordóñez","doi":"10.14569/ijacsa.2023.0140829","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140829","url":null,"abstract":"—The shrimp farming industry in Ecuador, renowned for its shrimp breeding and exportation, faces challenges due to diseases related to variations in abiotic factors during the maturation stage. This is partly attributed to the traditional methods employed in shrimp farms. Consequently, a prototype has been developed for monitoring and controlling abiotic factors using IoT technology. The proposed system consists of three nodes communicating through the LoRa interface. For control purposes, a fuzzy logic system has been implemented that evaluates temperature and dissolved oxygen abiotic factors to determine the state of the aerator, updating the information in the ThingSpeak application. A detailed analysis of equipment energy consumption and the maximum communication range for message transmission and reception was conducted. Subsequently, the monitoring and control system underwent comprehensive testing, including communication with the visualization platform. The results demonstrated significant improvements in system performance. By modifying parameters in the microcontroller, a 2.55-fold increase in battery durability was achieved. The implemented fuzzy logic system enabled effective on/off control of the aerators, showing a corrective trend in response to variations in the analyzed abiotic parameters. The robustness of the LoRa communication interface was evident in urban environments, achieving a distance of up to 1 km without line of sight.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"35 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90538665","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140354
Shelena Soosay Nathan, N. Hashim, A. Hussain, A. Sivaji, M. A. A. Pozin
—Usability is an important element that enables the identification of the efficiency for application or product. However, many applications have been developed for general users’ needs and are unable to provide adequate applications usage for disabled people. This study focuses on the development of usability evaluation model and the validation process on the proposed model through experts. The developed model later evaluated by group of experts through focus group method. Focus group method enables to identify the 13 variables derived to develop the model are appropriately placed and useful in the evaluation process. The results shows that the selected variables are appropriate to identify usability of mobile application for the hearing impairment through three variables tested namely, gain satisfaction with the model, satisfaction with the model presentation, and support for tasks. Conclusively, the developed model can identify usability of mobile applications for hearing impairment and enable in identifying useful criteria to be included during application development process in real life process. As future study, the model can be tested among the hearing impairment people and practitioner to establish the results obtained which contributes to usability practitioners and application developers for the disabled.
{"title":"Validating the Usability Evaluation Model for Hearing Impaired Mobile Application","authors":"Shelena Soosay Nathan, N. Hashim, A. Hussain, A. Sivaji, M. A. A. Pozin","doi":"10.14569/ijacsa.2023.0140354","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140354","url":null,"abstract":"—Usability is an important element that enables the identification of the efficiency for application or product. However, many applications have been developed for general users’ needs and are unable to provide adequate applications usage for disabled people. This study focuses on the development of usability evaluation model and the validation process on the proposed model through experts. The developed model later evaluated by group of experts through focus group method. Focus group method enables to identify the 13 variables derived to develop the model are appropriately placed and useful in the evaluation process. The results shows that the selected variables are appropriate to identify usability of mobile application for the hearing impairment through three variables tested namely, gain satisfaction with the model, satisfaction with the model presentation, and support for tasks. Conclusively, the developed model can identify usability of mobile applications for hearing impairment and enable in identifying useful criteria to be included during application development process in real life process. As future study, the model can be tested among the hearing impairment people and practitioner to establish the results obtained which contributes to usability practitioners and application developers for the disabled.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"134 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90621794","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140243
Asep Id Hadiana, S. K. Baharin, Zahriah Othman
—Indoor navigation is crucial, particularly during indoor disasters such as fires. However, current spatial subdivision models struggle to adapt to the dynamic changes that occur in such situations, making it difficult to identify the appropriate navigation space, and thus reducing the accuracy and efficiency of indoor navigation. This study presents a new framework for indoor navigation that is specifically designed for first responders, with a focus on improving their response time and safety during rescue operations in buildings. The framework is an extension of previous research and incorporates the combustibility factor as a critical variable to consider during fire disasters, along with definitions of safe and unsafe areas for first responders. An algorithm was developed to accommodate the framework and was evaluated using Pyrosim and Pathfinder software. The framework calculates walking speed factors that affect the path and walking speed of first responders, enhancing their chances of successful evacuation. The framework captures dynamic changes, such as smoke levels, that may impact the navigation path and walking speed of first responders, which were not accounted for in previous studies. The experimental results demonstrate that the framework can identify suitable navigation paths and safe areas for first responders, leading to successful evacuation in as little as 148 to 239 seconds. The proposed framework represents a significant improvement over previous studies and has the potential to enhance the safety and effectiveness of first responders during emergency situations.
{"title":"First Responders Space Subdivision Framework for Indoor Navigation","authors":"Asep Id Hadiana, S. K. Baharin, Zahriah Othman","doi":"10.14569/ijacsa.2023.0140243","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140243","url":null,"abstract":"—Indoor navigation is crucial, particularly during indoor disasters such as fires. However, current spatial subdivision models struggle to adapt to the dynamic changes that occur in such situations, making it difficult to identify the appropriate navigation space, and thus reducing the accuracy and efficiency of indoor navigation. This study presents a new framework for indoor navigation that is specifically designed for first responders, with a focus on improving their response time and safety during rescue operations in buildings. The framework is an extension of previous research and incorporates the combustibility factor as a critical variable to consider during fire disasters, along with definitions of safe and unsafe areas for first responders. An algorithm was developed to accommodate the framework and was evaluated using Pyrosim and Pathfinder software. The framework calculates walking speed factors that affect the path and walking speed of first responders, enhancing their chances of successful evacuation. The framework captures dynamic changes, such as smoke levels, that may impact the navigation path and walking speed of first responders, which were not accounted for in previous studies. The experimental results demonstrate that the framework can identify suitable navigation paths and safe areas for first responders, leading to successful evacuation in as little as 148 to 239 seconds. The proposed framework represents a significant improvement over previous studies and has the potential to enhance the safety and effectiveness of first responders during emergency situations.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"7 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86744294","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140512
T. Nguyen, Niansong Tu, T. Ha
www.ijacsa.thesai.org
www.ijacsa.thesai.org
{"title":"Input Value Chain Affect Vietnamese Rice Yield: An Analytical Model Based on a Machine Learning Algorithm","authors":"T. Nguyen, Niansong Tu, T. Ha","doi":"10.14569/ijacsa.2023.0140512","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140512","url":null,"abstract":"www.ijacsa.thesai.org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"20 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91274185","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140638
El Mehdi Ben Laoula, M. Midaoui, M. Youssfi, O. Bouattane
The rising number of automobiles has led to an increased demand for a reliable license plate identification system that can perform effectively in diverse conditions. This applies to local authorities, public organizations, and private companies in Morocco, as well as worldwide. To meet this need, a strong License Plate Recognition (LPR) system is required, taking into account local plate specifications and fonts used by plate manufacturers. This paper presents an intelligent LPR system based on the YOLOv5 framework, trained on a customized dataset encompassing multiple fonts and circumstances such as illumination, climate, and lighting. The system incorporates an intelligent region segmentation level that adapts to the plate's type, improving recognition accuracy and addressing separator issues. Remarkably, the model achieves an impressive precision rate of 99.16% on problematic plates with specific illumination, separators, and degradations. This research represents a significant advancement in the field of license plate recognition, providing a reliable solution for accurate identification and paving the way for broader applications in Morocco and beyond. Keywords—License plate recognition; YOLOv5; intelligent region segmentation; customized dataset; Moroccan license plate issues; fonts-based data
{"title":"Intelligent Moroccan License Plate Recognition System Based on YOLOv5 Build with Customized Dataset","authors":"El Mehdi Ben Laoula, M. Midaoui, M. Youssfi, O. Bouattane","doi":"10.14569/ijacsa.2023.0140638","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140638","url":null,"abstract":"The rising number of automobiles has led to an increased demand for a reliable license plate identification system that can perform effectively in diverse conditions. This applies to local authorities, public organizations, and private companies in Morocco, as well as worldwide. To meet this need, a strong License Plate Recognition (LPR) system is required, taking into account local plate specifications and fonts used by plate manufacturers. This paper presents an intelligent LPR system based on the YOLOv5 framework, trained on a customized dataset encompassing multiple fonts and circumstances such as illumination, climate, and lighting. The system incorporates an intelligent region segmentation level that adapts to the plate's type, improving recognition accuracy and addressing separator issues. Remarkably, the model achieves an impressive precision rate of 99.16% on problematic plates with specific illumination, separators, and degradations. This research represents a significant advancement in the field of license plate recognition, providing a reliable solution for accurate identification and paving the way for broader applications in Morocco and beyond. Keywords—License plate recognition; YOLOv5; intelligent region segmentation; customized dataset; Moroccan license plate issues; fonts-based data","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"18 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73240481","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140798
Asmae Yassine, M. E. Riffi
—Genome assembly plays a crucial role in the field of bioinformatics, as current sequencing technologies are unable to sequence an entire genome at once where the need for fragmenting into short sequences and reassembling them. The genomes often contain repetitive sequences and duplicated regions, which can lead to ambiguities during assembly. Thus, the process of reconstructing a complete genome from a set of reads necessitates the use of efficient assembly programs. Over time, as genome sequencing technology has advanced, the methods for genome assembly have also evolved, resulting in the utilization of various genome assemblers. Many artificial intelligence techniques such as machine learning and nature-inspired algorithms have been applied in genome assembly in recent years. These technologies have the potential to significantly enhance the accuracy of genome assembly, leading to functionally correct genome reconstructions. This review paper aims to provide an overview of the genome assembly, highlighting the significance of different methods used in machine learning techniques and nature-inspiring algorithms in achieving accurate and efficient genome assembly. By examining the advancements and possibilities brought about by different machine learning and metaheuristics approaches, this review paper offers insights into the future directions of genome assembly.
{"title":"A Review on Machine-Learning and Nature-Inspired Algorithms for Genome Assembly","authors":"Asmae Yassine, M. E. Riffi","doi":"10.14569/ijacsa.2023.0140798","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140798","url":null,"abstract":"—Genome assembly plays a crucial role in the field of bioinformatics, as current sequencing technologies are unable to sequence an entire genome at once where the need for fragmenting into short sequences and reassembling them. The genomes often contain repetitive sequences and duplicated regions, which can lead to ambiguities during assembly. Thus, the process of reconstructing a complete genome from a set of reads necessitates the use of efficient assembly programs. Over time, as genome sequencing technology has advanced, the methods for genome assembly have also evolved, resulting in the utilization of various genome assemblers. Many artificial intelligence techniques such as machine learning and nature-inspired algorithms have been applied in genome assembly in recent years. These technologies have the potential to significantly enhance the accuracy of genome assembly, leading to functionally correct genome reconstructions. This review paper aims to provide an overview of the genome assembly, highlighting the significance of different methods used in machine learning techniques and nature-inspiring algorithms in achieving accurate and efficient genome assembly. By examining the advancements and possibilities brought about by different machine learning and metaheuristics approaches, this review paper offers insights into the future directions of genome assembly.","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":"74954145","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140467
J. Alsayaydeh, Mohd Faizal bin Yusof, Muhammad Zulhakim Bin Abdul Halim, M. N. S. Zainudin, S. Herawan
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
{"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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140940
Muhammad Irfan Hashfi, Teguh Raharjo
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.
{"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}
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