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Articles 3391 - 3420 of 11267
Full-Text Articles in Computer Sciences
Flipper Control Method For Tracked Robot Based On Deep Reinforcement Learning, Hainan Pan, Bailiang Chen, Kaihong Huang, Junkai Ren, Chuang Cheng, Huimin Lu, Hui Zhang
Flipper Control Method For Tracked Robot Based On Deep Reinforcement Learning, Hainan Pan, Bailiang Chen, Kaihong Huang, Junkai Ren, Chuang Cheng, Huimin Lu, Hui Zhang
Journal of System Simulation
Abstract: Tracked robots with flippers have certain terrain adaptation capabilities. To improve the intelligent operation level of robots in complex environments, it is significant to realize the flipper autonomously control. Combining the expert experience in obstacle crossing and optimization indicators, Markov decision process(MDP) modeling of the robot's flipper control problem is carried out and a simulation training environment based on physics simulation engine Pymunk is built. A deep reinforcement learning control algorithm based on dueling double DQN(D3QN) network is proposed for controlling the flippers. With terrain information and robot state as the input and the four flippers' angle as the …
Short-Term Bus Passenger Flow Prediction Based On Convolutional Long-Short-Term Memory Network, Jing Chen, Zhaochong Zhang, Linkai Wang, Mai An, Wei Wang
Short-Term Bus Passenger Flow Prediction Based On Convolutional Long-Short-Term Memory Network, Jing Chen, Zhaochong Zhang, Linkai Wang, Mai An, Wei Wang
Journal of System Simulation
Abstract: To address the problem that the traditional short-time passenger flow prediction method does not consider the temporal characteristics similarity between the inter-temporal passenger flows, a shorttime passenger flow prediction model k-CNN-LSTM is proposed by combining the improved k-means clustering algorithm with the CNN and the LSTM. The k-means is used to cluster the intertemporal timeseries data, the k-value is determined by using the gap-statistic, and a traffic flow matrix model is constructed. A CNN-LSTM network is used to process the short-time passenger flows with spatial and temporal characteristics. The model is tested and parameter tuned by the real dataset. …
Ground Target Recognition And Damage Assessment Of Patrol Missiles Based On Multi-Source Information Fusion, Yibo Xu, Qinghua Yu, Yanjuan Wang, Ce Guo, Shiru Feng, Huimin Lu
Ground Target Recognition And Damage Assessment Of Patrol Missiles Based On Multi-Source Information Fusion, Yibo Xu, Qinghua Yu, Yanjuan Wang, Ce Guo, Shiru Feng, Huimin Lu
Journal of System Simulation
Abstract: For the multiple patrol missiles to attack the high defense capacity targets, a mobile ground target detection and damage assessment method based on multi-source information fusion is proposed. The multi-source information fusion of infrared images and RGB images is carried out by using IoU determination. A novel two-stage tightly coupled damage assessment method based on YOLO-VGGNet of patrol missiles to mobile ground targets is proposed. This method can fully use the advantage of deep semantic information extraction of CNNs and introduce the infrared damaging information simultaneously to achieve the online and real-time damage assessment of mobile ground targets. The …
Research Advances On Electric Vehicle Routing Problem Models And Algorithms, Helin Zhuang, Xiaoyun Xia, Kangshun Li, Zefeng Chen, Xianchao Zhang
Research Advances On Electric Vehicle Routing Problem Models And Algorithms, Helin Zhuang, Xiaoyun Xia, Kangshun Li, Zefeng Chen, Xianchao Zhang
Journal of System Simulation
Abstract: The development of electric vehicle provides an alternative to conventional fuel vehicles for logistics companies. Using electric vehicles has the merits of less pollution and low noise, but the characteristics of limited cruising range and limited number of charging stations are new challenges. Electric vehicle routing problems(EVRPs) have been widely used in transportation, logistics and other fields, and have received much attention. A comprehensive survey of EVRP and its many variants are presented and the respective backgrounds and applicable conditions are analyzed. The solving approaches of EVRPs are categorized, the strengths and weaknesses of each algorithm are analyzed, and …
Optimal Dispatch Of Microgrid Clusters Considering Energy Storage Life And Communication Failures, Jianfang Jiao, Anjie Wang, Guang Wang, Jiale Xie
Optimal Dispatch Of Microgrid Clusters Considering Energy Storage Life And Communication Failures, Jianfang Jiao, Anjie Wang, Guang Wang, Jiale Xie
Journal of System Simulation
Abstract: To ensure the economy and stability of microgrid operation, the power fluctuations of renewable energy source (RES) and the lifetime characteristics of battery energy storage system (BESS) should be considered. The influence of charging and discharging depth and rate on the lifetime of BESS is researched, a model of battery energy storage system for real-time optimal scheduling is established, and the alternating direction method of multipliers is adopted for the distributed optimal scheduling of microgrid clusters. The distributed optimization method does not require any global information and can protect the privacy of microgrid in the maximum extent. Simulation results …
Dynamic Spatio-Temporal Anomaly-Aware Correlation Filtering Object Tracking Algorithm, Yunfei Qiu, Xiangrui Bu, Boqiang Zhang
Dynamic Spatio-Temporal Anomaly-Aware Correlation Filtering Object Tracking Algorithm, Yunfei Qiu, Xiangrui Bu, Boqiang Zhang
Journal of System Simulation
Abstract: In view of the fact that the background perception algorithm does not establish a relationship with the spatio-temporal domain characteristics of the target, and cannot accurately deal with the occlusion, deformation and other abnormal tracking, a object tracking algorithm which can adaptively perceive the spatio-temporal anomalies is proposed. In the training stage of correlation filter, the adaptive spatial regularization term is introduced to establish a relationship with the spatio-temporal characteristics of sample. The abnormal perception method is proposed according to the peak value of response map. Taking advantage of the different confidence of historical filter and the continuity of …
Fault Detection Based On Sliding Window And Multiblock Convolutional Autoencoders, Jianpeng Mou, Weili Xiong
Fault Detection Based On Sliding Window And Multiblock Convolutional Autoencoders, Jianpeng Mou, Weili Xiong
Journal of System Simulation
Abstract: In order to further improve the fault detection performance and fully mine the timing and hidden feature information, a fault detection method based on convolutional auto encoder is proposed. On the basis of modeling the original information set, the modeling of cumulative information and rate of change information is added to enhance the mining of implicit information; The three reconstructed information sets are sampled by sliding windows, and time series feature extraction and modeling are performed based on convolutional auto encoders. Bayesian fusion of the decision results of the convolutional auto encoder is performed to obtain the statistics, and …
Improved Multi-Objective Swarm Algorithm To Optimize Wash-Out Motion And Its Simulation Experiment, Hui Wang, Le Peng
Improved Multi-Objective Swarm Algorithm To Optimize Wash-Out Motion And Its Simulation Experiment, Hui Wang, Le Peng
Journal of System Simulation
Abstract: Addressing the issues such as signal loss, distraction, and bad wash-out effect caused by improper parameter selection in classic wash-out algorithms, an improved multi-objective artificial bee colony algorithm is proposed to optimize the filter parameters of the classical wash-out algorithm to improve the effect. For the problems in the initialization and local optimization of traditional swarm algorithm, Circle mapping and Pareto local optimization algorithm are introduced. The human perception error model, acceleration difference model, and displacement model are established, and the model function is used as the objective function, the parameters of the classical wash-out algorithm is optimized by …
Bus Traffic Strategy Based On Immune Theory In Network Environment, Cao Li, Rui Zheng, Xiaolu Ma, Ziqiong Ding, Junyi Zhong, Sheng Zhang, Jingjing Qi
Bus Traffic Strategy Based On Immune Theory In Network Environment, Cao Li, Rui Zheng, Xiaolu Ma, Ziqiong Ding, Junyi Zhong, Sheng Zhang, Jingjing Qi
Journal of System Simulation
Abstract: In V2X network environment, the bus system can obtain dynamic global information and the bus traffic strategy is carried out based on the road scene between adjacent bus stops. The mathematical model of bus rapid traffic is constructed with the difference of green time ratio as the main parameter. A hybrid genetic operator is proposed on the basis of the combination of genetic algorithm and immune theory, the design of affinity, the selection of excellent antibodies. A bus traffic strategy based on immune theory is proposed on the basis of the improvement of adaptive crossover and mutation probability. The …
Research On Vehicle Detection Method Based On Improved Yolox-S, Xiliu Zhang, Xiaoling Zhang, Minjun He
Research On Vehicle Detection Method Based On Improved Yolox-S, Xiliu Zhang, Xiaoling Zhang, Minjun He
Journal of System Simulation
Abstract: A improved vehicle detection model based on multi-scale feature fusion of YOLOX network is proposed to solve the problem of missing and false detection of small vehicle targets. Ghost-cross stage partial(CSP) based on the depth separable convolution is designed to replace part of cross stage partial in network to speed up the speed of detection. The max pooling mode of model is improved to Softpool mode, and coordinate attention mechanism is introduced to enhance the feature expression of target to be detected and to optimize the problem of target missing detection. Focal Loss is selected as the confidence loss …
Design And Application Of Hardware-In-The-Loop Simulation System For Infrared Imaging Guide Missile Test And Evaluation, Jianbin Dou, Xiaobing Wang, Hongjian Yang, Yulong Gao
Design And Application Of Hardware-In-The-Loop Simulation System For Infrared Imaging Guide Missile Test And Evaluation, Jianbin Dou, Xiaobing Wang, Hongjian Yang, Yulong Gao
Journal of System Simulation
Abstract: The characteristics of the army's conventional infrared imaging guide missile hardware-in-theloop simulation system used for test and evaluation is analyzed, and a system that can meet the test and evaluation requirement of infrared imaging guide missile is designed. The key technologies such as universal system design, rapid iterative development design of heterogeneous communication data, high radiation infrared interference dual channel coupling simulation, high-precision timing and synchronization, generation of complex battlefield environment are solved. The application of the system is verified on the typical anti-tank missile and helicopter borne missile tests. The system can be used for infrared imaging guide …
Design Of 3d Visualization Monitoring System For Oil Field Pumping Unit Based On Unity3d, Liqiang Liu, Wenlei Sun, Yi Wang, Bingkai Wang
Design Of 3d Visualization Monitoring System For Oil Field Pumping Unit Based On Unity3d, Liqiang Liu, Wenlei Sun, Yi Wang, Bingkai Wang
Journal of System Simulation
Abstract: Aiming at the defects of single monitoring form, low degree of 3D visualization and poor linkage of pumping unit, the information-physical real-time mapping is introduced to develop the 3D visualization monitoring system for oil field pumping unit. The digital space of pumping unit are designed and the design architecture of virtual-real interaction layer is built. The composition and relation framework of 3D visualization system are built. Combined with the inverse kinematics, the mathematical model is built, and the overall architecture of the pumping unit 3D visualization system is designed based on the five-dimensional model and Unity3D platform. The beam …
Using Natural Language Processing To Identify Mental Health Indicators In Aviation Voluntary Safety Reports, Michael Sawyer, Katherine Berry, Amelia Kinsella, R Jordan Hinson, Edward Bynum
Using Natural Language Processing To Identify Mental Health Indicators In Aviation Voluntary Safety Reports, Michael Sawyer, Katherine Berry, Amelia Kinsella, R Jordan Hinson, Edward Bynum
National Training Aircraft Symposium (NTAS)
Voluntary Safety Reporting Programs (VSRPs) are a critical tool in the aviation industry for monitoring safety issues observed by the frontline workforce. While VSRPs primarily focus on operational safety, report narratives often describe factors such as fatigue, workload, culture, staffing, and health, directly or indirectly impacting mental health. These reports can provide individual and organizational insights into aviation personnel's physical and psychological well-being. This poster introduces the AVIation Analytic Neural network for Safety events (AVIAN-S) model as a potential tool to extract and monitor these insights. AVIAN-S is a novel machine-learning model that leverages natural language processing (NLP) to analyze …
Sustainability Considerations Of Generative A.I., Thomas Pantazes
Sustainability Considerations Of Generative A.I., Thomas Pantazes
Sustainability Research & Practice Seminar Presentations
Dr. Thomas Pantazes of the WCU Teaching and Learning Center shares Sustainability Considerations of Generative A.I.
Adopting Ai In Faculty Teaching Methodologies, Karima Lanfranco, Kristen Migliano, Ellen Ramsey, David G. Wolf
Adopting Ai In Faculty Teaching Methodologies, Karima Lanfranco, Kristen Migliano, Ellen Ramsey, David G. Wolf
Faculty and Staff Publications & Presentations
No abstract provided.
Editorial Preview, Su-Cheng Haw
Editorial Preview, Su-Cheng Haw
Journal of Informatics and Web Engineering
This editorial highlights all 19 papers in the February issue that deal with the practical aspects of Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), the Internet of Things (IoT), and other topics in Computer Science. This issue also includes suggestions for several worthwhile works that deserve further research. With effective from this volume, we will be publishing triannually in our February, June and October issues.
Term Standardisation With Lda Model To Detect Service Disruption Events Using English And Manglish Tweets, Noraysha Yusuf, Maizatul Akmar Ismail, Tasnim M.A. Zayet, Kasturi Dewi Varathan, Rafidah Md Noor
Term Standardisation With Lda Model To Detect Service Disruption Events Using English And Manglish Tweets, Noraysha Yusuf, Maizatul Akmar Ismail, Tasnim M.A. Zayet, Kasturi Dewi Varathan, Rafidah Md Noor
Journal of Informatics and Web Engineering
Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its …
Modelling Of Virtual Campus Tour In Minecraft, Liyana Tan Lin, Han-Foon Neo
Modelling Of Virtual Campus Tour In Minecraft, Liyana Tan Lin, Han-Foon Neo
Journal of Informatics and Web Engineering
Virtual tours have revolutionized the way to explore and experience places from the comfort of our own home. Through advanced technology and immersive digital platforms, virtual tours offer a compelling alternative to tradition face-to-face visits. Whether a famous landmark, museum, real estate or natural wonders, virtual tours offer a unique opportunity to navigate and discover these places form a distance. Meanwhile, creating a virtual tour in Minecraft can provide a unique and immersive experience that sets the users apart from other virtual tour platforms. Minecraft is one of the most popular video games in the world and boasts a large …
A Lung Cancer Detection With Pre-Trained Cnn Models, Chai Chee Chiet, Khoh Wee How, Pang Ying Han, Yap Hui Yen
A Lung Cancer Detection With Pre-Trained Cnn Models, Chai Chee Chiet, Khoh Wee How, Pang Ying Han, Yap Hui Yen
Journal of Informatics and Web Engineering
Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT …
Optimizing Medical Iot Disaster Management With Data Compression, Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, Tse Kian Neo
Optimizing Medical Iot Disaster Management With Data Compression, Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, Tse Kian Neo
Journal of Informatics and Web Engineering
In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this …
Sentiment Analysis Using Support Vector Machine And Random Forest, Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Bin Mohd Su'ud
Sentiment Analysis Using Support Vector Machine And Random Forest, Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Bin Mohd Su'ud
Journal of Informatics and Web Engineering
Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into …
Implementation Of Grover’S Algorithm & Bernstein-Vazirani Algorithm With Ibm Qiskit, Yang-Che Liu, Mei-Feng Liu
Implementation Of Grover’S Algorithm & Bernstein-Vazirani Algorithm With Ibm Qiskit, Yang-Che Liu, Mei-Feng Liu
Journal of Informatics and Web Engineering
Quantum logic gates differ from classical logic gates as the former involves quantum operators. The conventional gates such as AND, OR, NOT etc., are generally classified as classical gates, however, some of the quantum gates are known as Pauli gates, Toffoli gates and Hadamard gates, respectively. Normally classical states only involve 0 and 1, whereas quantum states involve the superpositions of 0 and 1. Hence, underlying principles of algorithm implementation for classical logic gate and quantum logic gate are indeed different. In this paper, we introduce significant concepts of quantum computations, analyse the discrepancy between classical and quantum gates, compare …
A Campus-Based Chatbot System Using Natural Language Processing And Neural Network, Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh
A Campus-Based Chatbot System Using Natural Language Processing And Neural Network, Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh
Journal of Informatics and Web Engineering
A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science & Technology (FIST) of Multimedia University that facilitates the study life of FIST …
Personalized Healthcare: A Comprehensive Approach For Symptom Diagnosis And Hospital Recommendations Using Ai And Location Services, Seng-Keong Tan, Siew-Chin Chong, Kuok-Kwee Wee, Lee-Ying Chong
Personalized Healthcare: A Comprehensive Approach For Symptom Diagnosis And Hospital Recommendations Using Ai And Location Services, Seng-Keong Tan, Siew-Chin Chong, Kuok-Kwee Wee, Lee-Ying Chong
Journal of Informatics and Web Engineering
Utilizing digital advancements, an integrated Flask-based platform has been engineered to centralize personal health records and facilitate informed healthcare decisions. The platform utilizes a Random Forest model-based symptom checker and an OpenAI API-powered chatbot for preliminary disease diagnosis and integrates Google Maps API to recommend proximal hospitals based on user location. Additionally, it contains a comprehensive user profile encompassing general information, medical history, and allergies. The system includes a medicine reminder feature for medication adherence. This innovative amalgamation of technology and healthcare fosters a user-centric approach to personal health management.
Vision-Based Gait Analysis For Neurodegenerative Disorders Detection, Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Tee Connie, Michael Kah Ong Goh
Vision-Based Gait Analysis For Neurodegenerative Disorders Detection, Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Tee Connie, Michael Kah Ong Goh
Journal of Informatics and Web Engineering
Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A …
Plant Disease Detection And Classification Using Deep Learning Methods: A Comparison Study, Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy
Plant Disease Detection And Classification Using Deep Learning Methods: A Comparison Study, Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy
Journal of Informatics and Web Engineering
The presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. Plant diseases have a detrimental effect on agricultural yield, hence early detection is crucial to prevent food insecurity. To identify and categorise the indications of plant diseases, numerous developed or modified DL architectures are utilised. This paper aims to observe the performance of the YOLOv8 model, which has better performance than its predecessors, on a small-scale plant disease dataset. This …
Adaptive Gaussian Wiener Filter For Ct-Scan Images With Gaussian Noise Variance, Kai Liang Lew, Chung Yang Kew, Kok Swee Sim, Shing Chiang Tan
Adaptive Gaussian Wiener Filter For Ct-Scan Images With Gaussian Noise Variance, Kai Liang Lew, Chung Yang Kew, Kok Swee Sim, Shing Chiang Tan
Journal of Informatics and Web Engineering
Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise …
Comparison Of Machine Learning Methods For Calories Burn Prediction, Alfred Tan Jing Sheng, Zarina Che Embi, Noramiza Hashim
Comparison Of Machine Learning Methods For Calories Burn Prediction, Alfred Tan Jing Sheng, Zarina Che Embi, Noramiza Hashim
Journal of Informatics and Web Engineering
This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed …
Goholiday: Development Of An Improvised Mobile Application For Boutique Hotels And Resorts, Iftiaj Alom, Ismail Ahmed Al-Qasem Al-Hadi, Neesha Jothi, Sook Fern Yeo
Goholiday: Development Of An Improvised Mobile Application For Boutique Hotels And Resorts, Iftiaj Alom, Ismail Ahmed Al-Qasem Al-Hadi, Neesha Jothi, Sook Fern Yeo
Journal of Informatics and Web Engineering
One of the main challenges boutique hotels and resorts face is the direct outreach to tourists and customers. As a result, these independent hotels often resort to online platforms such as Agoda and Airbnb to expand their customer base. However, this approach comes at the cost of losing revenue to Online Travel Agencies (OTAs) that solely focus on room sales, hindering the establishment of a strong brand image for boutique hotels and resorts. Considering the heavy reliance on OTAs, this paper focuses on the development of GoHoliday, a cross-platform mobile app prototype that aims to bridge the gap between boutique …
Weather-Based Arthritis Tracking: A Mobile Mechanism For Preventive Strategies, Jin-Lun Goh, Sin-Ban Ho, Chuie-Hong Tan
Weather-Based Arthritis Tracking: A Mobile Mechanism For Preventive Strategies, Jin-Lun Goh, Sin-Ban Ho, Chuie-Hong Tan
Journal of Informatics and Web Engineering
Arthritis is a common joint disorder characterised by symptoms such as swelling, pain, stiffness, and limited joint movement. It primarily affects older individuals, women, and athletes. The advent of information technology has created opportunities for patients to manage their health conditions more effectively. Research indicates that weather can affect arthritis symptoms, with many patients experiencing severe discomfort during rainy weather due to the expansion of already inflamed tissues. However, there is currently no mobile application mechanism available that combines weather forecasting with health recommendations for arthritis patients, which means that patients may not have access to important information that could …