Journal of Informatics and Web Engineering最新文献_第5页

Enhancing Migraine Management System through Weather Forecasting for a Better Daily Life 透过天气预报改善偏头痛管理系统，改善日常生活

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.15

Wen-Xuan Ong, Sin-Ban Ho, Chuie-Hong Tan

{"title":"Enhancing Migraine Management System through Weather Forecasting for a Better Daily Life","authors":"Wen-Xuan Ong, Sin-Ban Ho, Chuie-Hong Tan","doi":"10.33093/jiwe.2023.2.2.15","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.15","url":null,"abstract":"A migraine is a severe, throbbing, or pulsing headache that typically affects one side of the head. A migraine attack can be so painful that it interferes with daily activities and can last for hours or even days. Migraine is a common health issue that affects approximately 1 in every 5 women and 1 in every 15 men. Additionally, millions of people worldwide suffer from migraine attacks due to the inability to anticipate or adapt to their environment. In today's globalized world, mobile phones have become a necessity for the general public, enabling communication, internet shopping, food purchases, and even health applications. Therefore, the purpose of this research is to develop a mobile application that serves as an online migraine management system that is responsive to meteorological conditions. The app was created using development tools such as Android Studio, Visual Code Studio, Flutter framework, and Firebase Firestore, which act as databases. This report also includes essential information on migraines and a comparison of similar or existing applications. In addition, the research is designed to provide a reliable and user-friendly interface for collecting migraine data for robust evidence, processing relevant demographic features such as medical history, and generating reports. While researching topics relevant to the application, we found a scarcity of data on weather-based migraines. As a result, the system will predict the impact and risk of migraine based on Headache Impact Test (HIT-6) data provided by migraine patients as well as weather forecasts. With the features of this research, migraine patients can hopefully better prepare themselves for their daily routine and manage their symptoms more effectively.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989839","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

Multi-Label Classification with Deep Learning for Retail Recommendation 基于深度学习的多标签分类零售推荐

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.16

Zhi Yuan Poo, Choo Yee Ting, Yuen Peng Loh, Khairil Imran Ghauth

{"title":"Multi-Label Classification with Deep Learning for Retail Recommendation","authors":"Zhi Yuan Poo, Choo Yee Ting, Yuen Peng Loh, Khairil Imran Ghauth","doi":"10.33093/jiwe.2023.2.2.16","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.16","url":null,"abstract":"Selecting the right retail business for a location is crucial for the success of a business because it determines the likelihood of favourable return on investment. One common approach used in retail recommendation is multi-class classification, where retail businesses are categorized into different classes or categories based on various features or attributes. Existing research in the field of retail recommendation has extensively proposed and evaluated different algorithms, techniques, and approaches for multi-class classification in the context of retail recommendation, however, limited work has been focusing on formulating retail recommendation as a multi-label problem. This is because in retail recommendation, one location can fit multiple retail businesses so that it can provide more options to recommend the most suitable business for the location. Therefore, multi-label classification will be attempted in this study. An analytical dataset will be constructed that provides comprehensive insights into the characteristics of the business area, and subsequently employ deep learning technique for multi-label classification. The analytical dataset is constructed based on the list of sites of interest data from YellowPages, population data from Humanitarian Data Exchange (HDX) and property data sourced from brickz.my. This work will be focusing on implement deep learning technique which is 1D convolutional neural network (CNN) model. The findings showed that the proposed model achieved 61.22% in terms of accuracy.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989653","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

Impact of green building certification on the rent of commercial properties: A review 绿色建筑认证对商业物业租金的影响:综述

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.2

Thebuwena Arachchige Chandana Hemantha Jayakody, Anthony Vaz

{"title":"Impact of green building certification on the rent of commercial properties: A review","authors":"Thebuwena Arachchige Chandana Hemantha Jayakody, Anthony Vaz","doi":"10.33093/jiwe.2023.2.2.2","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.2","url":null,"abstract":"The world is currently facing two major problems, namely, increasing energy costs and global warming. As a result, it is crucial to take proactive measures to effectively address and mitigate the detrimental impacts arising from elevated energy costs, the pressing issue of global warming, and various types of environmental degradation. As a reaction, international organizations are advocating for the development of eco-friendly, sustainable, or green buildings as a strategy to reduce the harmful effects of the construction sector on the environment. While green development may entail higher costs for developers, it is imperative to evaluate the return on investment from their perspective. As such, this study initially delves into the global landscape of green building certifications and their associated institutions by thoroughly examining relevant literature published between 2003 and 2021. Following that, the study scrutinizes the advancement of research on green building practices across diverse office building sectors and over the course of time. Following that, the study explores green incentives in property markets worldwide, with a particular emphasis on the United States and Europe, as well as other regions. Ultimately, the paper assesses the efficacy of green incentives in driving sustainable building practices in diverse real estate markets. The findings from the review suggest that green certification is associated with higher commercial building rents as opposed to buildings without certifications, with 77% of the research demonstrating a significant positive correlation between green certification and rental rates. Nevertheless, the study recommends that relative rent should be regarded as an endogenous variable in future research, as investment profitability is jointly influenced by both occupancy rate and rental value, underscoring the need for careful consideration in analytical analyses.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989834","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

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification 基于成本的双卷积-注意迁移学习心电心跳分类模型

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.7

Johnson Olanrewaju Victor, XinYing Chew, Khai Wah Khaw, Ming Ha Lee

{"title":"A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification","authors":"Johnson Olanrewaju Victor, XinYing Chew, Khai Wah Khaw, Ming Ha Lee","doi":"10.33093/jiwe.2023.2.2.7","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.7","url":null,"abstract":"The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989835","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

Predicting Travel Insurance Purchases in an Insurance Firm through Machine Learning Methods after COVID-19 COVID-19后通过机器学习方法预测保险公司的旅行保险购买情况

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.4

Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw, XinYing Chew

{"title":"Predicting Travel Insurance Purchases in an Insurance Firm through Machine Learning Methods after COVID-19","authors":"Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw, XinYing Chew","doi":"10.33093/jiwe.2023.2.2.4","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.4","url":null,"abstract":"Travel insurance serves as a crucial financial safeguard, offering coverage against unforeseen expenses and losses incurred during travel. With the advent of the proliferation of insurance types and the amplified demand for Covid-related coverage, insurance companies face the imperative task of accurately predicting customers’ likelihood to purchase insurance. This can assist the insurance providers in focusing on the most lucrative clients and boosting sales. By employing advanced machine learning techniques, this study aims to forecast the consumer segments most inclined to acquire travel insurance, allowing targeted strategies to be developed. A comprehensive analysis was carried out on a Kaggle dataset comprising prior clients of a travel insurance firm utilizing the K-Nearest Neighbors (KNN), Decision Tree Classifier (DT), Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models. Extensive data cleaning was done before model building. Performance evaluation was then based on accuracy, F1 score, and the Area Under Curve (AUC) with Receiver Operating Characteristics (ROC) curve. Inexplicably, KNN outperformed other models, achieving an accuracy of 0.81, precision of 0.82, recall of 0.82, F1 score of 0.80, and an AUC of 0.78. The findings of this study are a valuable guide for deploying machine learning algorithms in predicting travel insurance purchases, thus empowering insurance companies to target the most lucrative clientele and bolster revenue generation.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134990005","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

Building Cyber Resilience: Key Factors for Enhancing Organizational Cyber Security 构建网络弹性:增强组织网络安全的关键因素

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.5

Thavaselvi Munusamy, Touraj Khodadi

引用次数: 0

Traffic Impact Assessment System using Yolov5 and ByteTrack 使用Yolov5和ByteTrack的交通影响评估系统

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.13

Jin Jie Ng, Kah Ong Michael Goh, Connie Tee

{"title":"Traffic Impact Assessment System using Yolov5 and ByteTrack","authors":"Jin Jie Ng, Kah Ong Michael Goh, Connie Tee","doi":"10.33093/jiwe.2023.2.2.13","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.13","url":null,"abstract":"Monitoring software for traffic is not too much in this era of digital. Even cheaper is decent traffic monitoring software. You can gauge the quality of the software. It should be possible to assess the code's performance outside of a test environment. The most useful metrics are frequently those that support the program's ability to fulfil business requirements. Therefore, this project is planning to develop a traffic assessment system. The main purpose of development is to improve heavy traffic in this country – Malaysia. This system includes function vehicle detection using YOLOv5, vehicle counting with a different type (such as bus, car, truck), vehicle classification, vehicle idling time by each region, and vehicle counting for each junction. Users can draw regions and lines for each camera/video to count and record vehicles. After the traffic analysis, intelligent signal light systems that respond to loads and timing can be helpful in easing traffic congestion. Smart traffic lights may adapt to the patterns of bustle at junctions and other important road traffic places based on the number of cars, data from queue detectors, and images from cameras. Also, this report includes comparisons with StrongSORT, OC-SORT and ByteTrack and accuracy test for vehicle counting.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989840","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

GenReGait: Gender Recognition using Gait Features genregit:基于步态特征的性别识别

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.10

Yue Fong Ti, Tee Connie, Michael Kah Ong Goh

{"title":"GenReGait: Gender Recognition using Gait Features","authors":"Yue Fong Ti, Tee Connie, Michael Kah Ong Goh","doi":"10.33093/jiwe.2023.2.2.10","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.10","url":null,"abstract":"Gender recognition based on gait features has gained significant interest due to its wide range of applications in various fields. This paper proposes GenReGait, a robust method for gender recognition utilizing gait features. Gait, the unique walking pattern of individuals, contains distinct gender-specific characteristics, such as stride length, step frequency, and body posture, making it a promising modality for gender estimation. The proposed GenReGait method begins by extracting landmark positions on the human body using a human keypoint estimation technique. These landmarks serve as informative cues for estimating gender based on their spatial and temporal characteristics. However, environmental factors can impact gait patterns and introduce fluctuations in landmark points, affecting the accuracy of gender estimation. To overcome this challenge, GenReGait introduces a robust preprocessing technique known as Weighted Exponential Moving Average to smoothen the gait signals and reduce noise caused by environmental factors. The smoothed signals are then fed into a deep learning network trained to perform gender estimation based on the gait features extracted from the landmark positions. By leveraging deep learning algorithms, the proposed GenReGait method effectively captures complex patterns and relationships within the gait features, enhancing the accuracy and reliability of gender recognition. Experimental evaluations conducted on the Gait in the Wild dataset and a self-collected dataset validate the robustness and effectiveness of the proposed GenReGait approach.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989841","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

Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM 基于加权多特征和支持向量机的mooc大学生辍学预测模型

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.3

Zhang Yujiao, Ling Weay Ang, Shi Shaomin, Sellappan Palaniappan

{"title":"Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM","authors":"Zhang Yujiao, Ling Weay Ang, Shi Shaomin, Sellappan Palaniappan","doi":"10.33093/jiwe.2023.2.2.3","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.3","url":null,"abstract":"Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predicting MOOC dropout based on behavioral data assign equal weights to each behavioral characteristic, despite the fact that each behavioral characteristic has a different effect on predicting dropout. To address this problem, this paper proposes a dropout prediction model based on the fusion of behavioral data and Support Vector Machine (SVM). This innovative model assigns different weights to different behavior features based on Pearson principle and integrates them as data inputs to the model. Dropout prediction is essentially a binary problem, Support Vector Machine Classifier is then trained using the training dataset 1 and dataset 2. Experimental results on both datasets show that this predictive model outperforms previous models that assign the same weights to the behavior features.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989998","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

QR Food Ordering System with Data Analytics 二维码订餐系统与数据分析

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.18

Chee-Chun Wong, Lee-Ying Chong, Siew-Chin Chong, Check-Yee Law

{"title":"QR Food Ordering System with Data Analytics","authors":"Chee-Chun Wong, Lee-Ying Chong, Siew-Chin Chong, Check-Yee Law","doi":"10.33093/jiwe.2023.2.2.18","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.18","url":null,"abstract":"As the epidemic starts to slow down and Malaysians are more confident about containing the outbreak with the norm of vaccination, diners have been aching to return to dining rooms, with many restaurants functioning at full capacity, but staffing is an entirely different story. As restaurateurs try to keep their businesses running at full speed and solve limited staff issues, there is only one solution: process automation. This paper aims to design a food ordering system that covers the benefits of automating the ordering process using the QR code and provides visualised insightful information based on the business data. Customers place the food order by scanning the QR code on the restaurant table, and it is then brought to a digital version of the restaurant's menu and make orders. The proposed system automates customer bills after the order, and it helps reduce human error in calculating bills. On the other hand, the proposed system has an admin interface that enables restaurant owners to modify the restaurant's menu, generate QR codes for the new dining table, receive orders from customers, and get automated bills generated by customers' orders. Most importantly, the system allows restaurant owners to have an insightful view of their business data such as visualised charts on sales data, highlighted crucial data and so on to improve decision-making and forecasting future demand using data analysis techniques which are not populated in similar systems currently. Machine learning has become a huge trend nowadays, it is also included to in the proposed system to forecast more valuable data for the business.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989999","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