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Articles 91 - 120 of 228
Full-Text Articles in Other Computer Sciences
Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm, Hikmal Muhammad, Fauzi Adi Rafrastara, Kristoforus Adrian Setiadi, Arley Japardi
Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm, Hikmal Muhammad, Fauzi Adi Rafrastara, Kristoforus Adrian Setiadi, Arley Japardi
Journal of Informatics and Web Engineering
Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and …
Editorial Preview For June 2025 Issue, Su-Cheng Haw
Editorial Preview For June 2025 Issue, Su-Cheng Haw
Journal of Informatics and Web Engineering
Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This June 2025 release contains 28 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Augmented Intelligence, Artificial Intelligence (AI), Generative AI, Data Mining (DM), Software Engineering, Sentiment Analysis, Recommender Systems, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 6 papers curated by our Thematic Editor, Assoc. Prof. Dr. Heru Agus Santoso, under the theme "Augmented Intelligence". In his editorial, Assoc. Prof. Dr. Heru …
Diabetes Risk Prediction Using Shapley Additive Explanations For Feature Engineering, Chinwe Miracle Chituru, Sin-Ban Ho, Ian Chai
Diabetes Risk Prediction Using Shapley Additive Explanations For Feature Engineering, Chinwe Miracle Chituru, Sin-Ban Ho, Ian Chai
Journal of Informatics and Web Engineering
Diabetes is prevalent globally, expected to increase in the next few years. This includes people with different types of diabetes including type 1 diabetes and type 2 diabetes. There are several causes for the increase: dietary decisions and lack of exercise as the main ones. This global health challenge calls for effective prediction and early management of the disease. This research focuses on the decision tree algorithm utilization to predict the risk of diabetes and model interpretability with the integration of SHapley Additive exPlanations (SHAP) for feature engineering. Random forest and gradient boosting models were developed to identify the risk …
Image-Based Detection And Classification Of Poultry Diseases From Chicken Droppings In Open House Poultry Farms, Md Najmul Hasan, Zu Jun Khow, Vik Tor Goh, Sarina Mansor, Yi-Fei Tan
Image-Based Detection And Classification Of Poultry Diseases From Chicken Droppings In Open House Poultry Farms, Md Najmul Hasan, Zu Jun Khow, Vik Tor Goh, Sarina Mansor, Yi-Fei Tan
Journal of Informatics and Web Engineering
Monitoring chicken health is essential for maintaining the production efficiency of poultry farms and meeting the demand for poultry products. Previous studies have explored various methods, including utilizing sound, behaviour, and the shape of the chickens, as well as the conditions of their droppings, to assess chicken health. In this research, we monitor chicken droppings as a reliable indicator of chicken health. We develop an automated system for detecting chicken droppings and identifying health conditions, specifically in open house poultry farms in Malaysia. Open poultry houses are the most common design in Malaysia due to their lower construction and maintenance …
Analysis Of Forensic Disk Imaging Tools For Data Acquisition And Preservation, Michelle Chee Ern Lim, Brandon Chen Hong Chow, Le Ying Lim, Tarini Shanbagamaran, Darren Yong Jun Lim, Ngu War Hlaing, Ahmad Sahban Rafsanjani
Analysis Of Forensic Disk Imaging Tools For Data Acquisition And Preservation, Michelle Chee Ern Lim, Brandon Chen Hong Chow, Le Ying Lim, Tarini Shanbagamaran, Darren Yong Jun Lim, Ngu War Hlaing, Ahmad Sahban Rafsanjani
Journal of Informatics and Web Engineering
The identification, preservation, analysis, and presentation of electronic evidence to support legal or organizational inquiries constitute the discipline of digital forensics, which is crucial to contemporary investigations. A crucial component of forensic inquiry, disk imaging guarantees precision, dependability, and legal defensibility. To preserve the original evidence, disk imaging makes an identical, bit-by-bit duplicate of a digital storage device, capturing hidden data, deleted material, and active files. Given the critical role of disk imaging in forensic investigations, selecting the right tool is crucial for accuracy, efficiency, and compliance with forensic standards. This study assesses widely used tools, including AccessData FTK Imager, …
Performance Benchmarking: Pre-Trained Models And Custom Convolutional Neural Networks In Deep Learning, Sheemona Joseph C., S. Ganesh, S. Kannadhasan, K. Selvipriya
Performance Benchmarking: Pre-Trained Models And Custom Convolutional Neural Networks In Deep Learning, Sheemona Joseph C., S. Ganesh, S. Kannadhasan, K. Selvipriya
Journal of Informatics and Web Engineering
Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, …
Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz
Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz
Journal of Informatics and Web Engineering
The rapid proliferation of digital devices has generated vast amounts of data, presenting significant challenges in collection, processing, and analysis that traditional systems struggle to overcome. This study investigates big data management approaches, explicitly focusing on technologies capable of efficiently handling real-time data at scale. Within the context of Air Operations, we propose a Hadoop-based architecture designed to support the Observe-Orient-Decide-Act (OODA) loop and enhance air traffic management. By leveraging a distributed system deployed on a cloud-based platform, we demonstrate a cost-effective solution for optimised data processing and improved decision-making capabilities. Our analysis highlights the advantages of using Hadoop's distributed …
Editorial: Augmented Intelligence For Enabling Knowledge-Driven Decision Making, Heru Agus Santoso
Editorial: Augmented Intelligence For Enabling Knowledge-Driven Decision Making, Heru Agus Santoso
Journal of Informatics and Web Engineering
More flexible and cooperative decision-making processes are required as a result of society's digital transformation. The role of augmented intelligence, that is the synergistic fusion of artificial intelligence and human judgment in facilitating knowledge-driven strategies across various domains is examined in this thematic issue. The integration of business intelligence and software engineering, which forms the foundation for creating intelligent, scalable, and explicable systems, is essential to this investigation. The six chosen papers in this issue show how machine learning techniques can be used to mine and model both structured (such as health records indicators) and unstructured (such as product reviews, …
K-Means Clustering Optimization Of Toddler Malnutrition Status Using Elbow Method, Femmi Widyawati, Ahmad Yahya Dawod, Heru Agus Santoso
K-Means Clustering Optimization Of Toddler Malnutrition Status Using Elbow Method, Femmi Widyawati, Ahmad Yahya Dawod, Heru Agus Santoso
Journal of Informatics and Web Engineering
The problem of nutritional status is still a major challenge in the health sector in developing countries, including Indonesia. Malnutrition in toddlers can have serious long-term impacts on children's growth and development, including increased risk of disease, impaired cognitive function, and low productivity in the future. To overcome this problem, an in-depth analysis is needed to determine the distribution of nutritional status of toddlers in one of the provincial capitals in Indonesia, which can be used as a basis for planning more effective interventions. This study uses the K-Means algorithm to classify areas based on the prevalence of malnutrition in …
Exploring The Relationship Between Academic Achievement And Chatgpt Usage: A Survey Of Higher Education Students In Malaysia, Mawar Madiah, A'Tiqah Rashidah Abu Samah
Exploring The Relationship Between Academic Achievement And Chatgpt Usage: A Survey Of Higher Education Students In Malaysia, Mawar Madiah, A'Tiqah Rashidah Abu Samah
Journal of Informatics and Web Engineering
Students' academic achievement is caused by several factors which include cognitive capability, study habits, instructor effectiveness, family support, socio-economic status, and access to modern learning technologies like ChatGPT. Since its introduction, studies have suggested that ChatGPT has significantly impacted students’ academic performance. This study aims to explore whether students who perform well academically in higher education are more likely to use ChatGPT to enhance their studies and to gather their opinions on this learning tool. A survey was conducted with forty-one students with excellent academic performance. The results of this study show that most students with high academic achievement view …
Improving Culinary Informatics Through Meaningful Social Web Engineering, Suraya Nurain Kalid, Fahmi Mikail Fahrid, Sin-Ban Ho
Improving Culinary Informatics Through Meaningful Social Web Engineering, Suraya Nurain Kalid, Fahmi Mikail Fahrid, Sin-Ban Ho
Journal of Informatics and Web Engineering
This paper gives a good reflection about social web engineering aspirations to ardently see the truth to enlighten culinary concentration and wisdom. With the compassionate path to discover food and engage customers to share content with loving kindness spread to every corner. There are so many features realized, which include direct interfaces through intuitive interfaces to lead the daily food streamlining. May one engaged with constructive creation to grow compassions in every culinary living sharing. Contentment would develop in our heart involved in different phases put forward in this paper. The initial phase begins with honesty in planning and design. …
Performance Evaluation On Covid-19 Prediction Using Machine Learning Models, Obai Ali Abderlahman, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal
Performance Evaluation On Covid-19 Prediction Using Machine Learning Models, Obai Ali Abderlahman, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal
Journal of Informatics and Web Engineering
The COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawbacks, especially in determining the long-term consequences of the pandemic and understanding its broad reach across various locations and populations. This project proposes an evaluation of machine learning (ML) models with the aim of improving predictions, particularly the accuracy in long-term forecasting, of subsequent trends of the COVID-19 pandemic. A systematic review highlights previous forecasting attempts as a reference for the approach. This project emphasizes extensive data collection, model formulation and testing …
Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities, Sellappan Palaniappan, Rajasvaran Logeswaran, Yoke Leng Yong
Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities, Sellappan Palaniappan, Rajasvaran Logeswaran, Yoke Leng Yong
Journal of Informatics and Web Engineering
This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength …
A Conceptual Approach To Predicting Seismic Events And Flood Risks Using Convolutional Neural Networks, Mahmoud Rehan, Wan-Noorshahida Mohd-Isa, Noramiza Hashim
A Conceptual Approach To Predicting Seismic Events And Flood Risks Using Convolutional Neural Networks, Mahmoud Rehan, Wan-Noorshahida Mohd-Isa, Noramiza Hashim
Journal of Informatics and Web Engineering
This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historical data limits. Traditional risk assessment methods often struggle to estimate such extremes, highlighting the need for more advanced predictive models capable of handling rare but high-impact events. This research enhances CNN architecture to improve accuracy in high quantile predictions by integrating multi-source spatiotemporal data, addressing a critical research gap. The methodology involves incorporating diverse datasets, including geospatial, meteorological, and historical seismic or flood records, into CNN models to augment …
Detecting Black Hole Attack Using Support Vector Machine With Xgboosting In Mobile Ad-Hoc Networks, Anhar Al Madani, Saima Anwar Lashari, Sana Salah Uddin, Abdullah Khan, Muhammad Nouman Atta, Dzati Athiar Ramli
Detecting Black Hole Attack Using Support Vector Machine With Xgboosting In Mobile Ad-Hoc Networks, Anhar Al Madani, Saima Anwar Lashari, Sana Salah Uddin, Abdullah Khan, Muhammad Nouman Atta, Dzati Athiar Ramli
Journal of Informatics and Web Engineering
Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this …
The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya
The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya
Journal of Informatics and Web Engineering
The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features …
Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia, Kai En Lim, Ying Han Pang, Shih Yin Ooi, Wan Xuan Kow, Tang Xing Cheang, Mao Wei Tan
Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia, Kai En Lim, Ying Han Pang, Shih Yin Ooi, Wan Xuan Kow, Tang Xing Cheang, Mao Wei Tan
Journal of Informatics and Web Engineering
Mobile banking has become increasingly popular in Malaysia due to its convenience and efficiency. However, persistent security concerns such as vulnerabilities to cyber attacks, malware and phishing affect user trust and impede adoption. This study aims to explore how users perceive and experience the security features of mobile banking applications. In spite of the implementation of security controls, understanding user perceptions and experiences remains essential, as these are core factors which influence mobile adoption. Thus, this study investigates the user experience of mobile banking security features in Malaysia. Specifically, we focus on the users’ perceptions, satisfaction levels and concerns. We …
Classification Of Smartphone Product Reviews On E-Commerce Using The Recurrent Neural Network (Rnn) Method, Rajibul Anam, Fernanda Tata Pradhana, Imam Abu Yasin, Junta Zeniarja
Classification Of Smartphone Product Reviews On E-Commerce Using The Recurrent Neural Network (Rnn) Method, Rajibul Anam, Fernanda Tata Pradhana, Imam Abu Yasin, Junta Zeniarja
Journal of Informatics and Web Engineering
Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sentiment analysis, which looks at customer reviews to find out what buyers really think and feel about products. However, analysing these reviews is not always straightforward. Many people use informal language, slang, or mixed languages, which makes it hard for computers to interpret their opinions accurately. On top of that, there is often an imbalance in the types of data available, particularly in developing countries, where some opinions might …
Editorial Preview For February 2025 Issue, Su-Cheng Haw
Editorial Preview For February 2025 Issue, Su-Cheng Haw
Journal of Informatics and Web Engineering
Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This change would thus ensure steady and timely publication of research articles in the fast-changing domains of informatics and web engineering. This issue contains a diverse collection of 24 papers that demonstrate the recent developments and innovative applications in various fields such as Information Systems (IS), Web Technologies, Artificial Intelligence (AI), Machine Learning (ML), Data Mining (DM), Blockchain, IoT, Cybersecurity, Healthcare and Software Engineering that persist in moulding the digital landscape.
Comprehensive Insights Into Smart Contracts: Architecture, Sectoral Applications, Security Analysis, And Legal Frameworks, Farah Mazlan, Nur Faizah Omar, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin
Comprehensive Insights Into Smart Contracts: Architecture, Sectoral Applications, Security Analysis, And Legal Frameworks, Farah Mazlan, Nur Faizah Omar, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin
Journal of Informatics and Web Engineering
Most conventional contract systems have issues with middlemen, drawn-out implementation procedures, fraud risk, and human error. Considering this, the project uses smart contract technology to provide a decentralized, automated, and safe solution in an effort to address such inefficiencies and the trust issues they raise. Smart contracts enable self-execution of contracts whose conditions are expressed explicitly in lines of code by presenting solutions using blockchain technology. The concept behind a smart contract is that each party may carry out their portion of the duties without depending on a third party and the contract will automatically execute in the meantime. This …
Predicting Short-Range Weather In Tropical Regions Using Random Forest Classifier, Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung
Predicting Short-Range Weather In Tropical Regions Using Random Forest Classifier, Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung
Journal of Informatics and Web Engineering
In this paper, we present a Random Forest classifier machine learning model for predicting short-range weather in in tropical regions like Malaysia. Our model uses environmental factors such as temperature, humidity, wind speed, and cloud cover to predict weather conditions like clear skies, rain, and thunderstorms. Tropical weather, influenced by high humidity, fluctuating temperatures, and frequent rainfall, present unique challenges for forecasting accurately. To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. Our model achieved an accuracy of 98.66% in predicting short-term weather conditions, identifying …
A Comparative Study Of Oracle Erp Netsuite And Microsoft Dynamics 365 Contributions To Contemporary Business Development In India, Padmanabhan Subramanian, Ponmalar S
A Comparative Study Of Oracle Erp Netsuite And Microsoft Dynamics 365 Contributions To Contemporary Business Development In India, Padmanabhan Subramanian, Ponmalar S
Journal of Informatics and Web Engineering
ERP systems centrally provide support to a company's business operations and are capable of providing solutions in the area of resource management, process streamlining, and making data-driven decisions. Acceptance of ERP systems is growing in India as companies begin to become more efficient and competitive. Among the most prominent players are Oracle ERP Netsuite and Microsoft Dynamics 365, both holding unique attributes which pose a challenge for businesses when choosing the right system. The aim of this paper of study is to compare between Oracle ERP Netsuite and Microsoft Dynamics 365 by exploring their contribution to business development in India. …
Improved Accuracy For Heart Disease Diagnosis Using Machine Learning Techniques, Neeraja Joshi, Tejal Dave
Improved Accuracy For Heart Disease Diagnosis Using Machine Learning Techniques, Neeraja Joshi, Tejal Dave
Journal of Informatics and Web Engineering
This work primarily focuses on diagnosis of heart disease before explicit visit to the expert doctor. Machine learning based systems have been found useful in medical diagnosis applications because of their ability to learn human like expertise and to utilize acquired knowledge for diagnosis. This work is performs classification of heart disease utilizing subject’s vital parameters. Pathological laboratory results available after testing are not understood by common people and patients have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression to various machine learning based systems including back propagation neural network, support …
Enhancing Financial Literacy: A Progressive Web Application Approach For Malaysian Youth, Jun-Xuan Kok, Sin-Ban Ho, Chuie-Hong Tan
Enhancing Financial Literacy: A Progressive Web Application Approach For Malaysian Youth, Jun-Xuan Kok, Sin-Ban Ho, Chuie-Hong Tan
Journal of Informatics and Web Engineering
Financial management is a crucial skill that individuals of all age ranges should acquire and master. It offers a transparent view of our financial status, enabling us to comprehend where our expenses are directed and manage every facet of our finances. Studies have indicated that Malaysian youth lack understanding in financial management. Nowadays, with so many people using the internet, we have the opportunity to share this expertise with a larger audience. Providing easily accessible materials for learning about and managing personal finances is essential to comprehending people's individual financial circumstances. In light of this, the purpose of this article …
Social Engineering Threat Analysis Using Large-Scale Synthetic Data, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Pulasthi Gunawardhana
Social Engineering Threat Analysis Using Large-Scale Synthetic Data, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Pulasthi Gunawardhana
Journal of Informatics and Web Engineering
We frequently hear news about compromised systems, virus attacks, spam emails, stolen bank account numbers, and loss of money. Safeguarding and protecting digital assets against these and other cyber-attacks are extremely important in our digital connected world today. Many organizations spend substantial amounts of money to protect their digital assets. One type of cyber threat that is rampant these days is social engineering attacks that work on human psychology. These attacks typically persuade, convince, trick and threaten naïve and innocent individuals to divulge sensitive information to the attackers. Consequently, traditional approaches have not been effective or successful in preventing these …
Hyperledger Fabric Blockchain For Securing The Edge Internet Of Things: A Review, Muhammad Haziq Zulhazmi Hairul Nizam, Muhammad Afiq Ahmad Nizam, Muhammad Hadi Husaini Jummadi, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin
Hyperledger Fabric Blockchain For Securing The Edge Internet Of Things: A Review, Muhammad Haziq Zulhazmi Hairul Nizam, Muhammad Afiq Ahmad Nizam, Muhammad Hadi Husaini Jummadi, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin
Journal of Informatics and Web Engineering
Life has become more convenient, efficient, and productive in aspects like homes, healthcare, and other businesses’ due to applied IoT. Nonetheless, the proliferation of IoT has led to enormous data production, which has presented daunting tasks of providing sound protective security solutions. It is crucial to address the above challenges in order to protect the data assets in IoT systems. This work deals with the concern on how to extend Hyperledger Fabric to IoT, this being a very crucial aspect in allowing for secure techniques in the collection, storage and sharing of data. Hyperledger Fabric offers advanced capabilities of smart …
Machine Learning Approaches For Detecting Vine Diseases: A Comparative Analysis, Waheed Ahmad, Eshill Azhar, Maham Anwar, Sarah Ahmed, Tayyaba Noor
Machine Learning Approaches For Detecting Vine Diseases: A Comparative Analysis, Waheed Ahmad, Eshill Azhar, Maham Anwar, Sarah Ahmed, Tayyaba Noor
Journal of Informatics and Web Engineering
This study investigates the classification of vine leaf diseases using convolutional neural networks (CNNs), focusing on three major diseases: powdery mildew, caused by fungus Uncinula necator, Red Blotches associated with pathogens such as Phomopsis viticola, Grapevine Leafroll Disease and leafroll associated Grape -linked virus (GLRaV). Accurate diagnosis of these high-risk diseases is critical to vine health and yields. We evaluated the performance of three CNN algorithms—MobileNetV2, ResNet50, and VGG16 —by comparing their training and validation accuracies, as well as loss over ten seasons. MobileNetV2 emerged as the most robust model, exhibiting high accuracy and low loss, indicating strong generalizability. ResNet50 …
Implementing Identity-Based Signature Schemes For Secure Data Transfer In Cloud Computing Environments, Paul Osinuga, Ji-Jian Chin, Terry Shue Chien Lau
Implementing Identity-Based Signature Schemes For Secure Data Transfer In Cloud Computing Environments, Paul Osinuga, Ji-Jian Chin, Terry Shue Chien Lau
Journal of Informatics and Web Engineering
In this paper, we present the implementation of the Cha-Cheon Identity-Based Signature (IBS) scheme to enhance secure data transfer in cloud computing environments. Cloud computing rely on traditional Public Key Infrastructure (PKI) systems, which is burdened by certificate management infrastructure. The primary focus of this research to simplify key and certificate management by leveraging identity-based elliptic curve cryptography (ECC) within the Cha-Cheon IBS framework. We show that the proposed IBS solution integrates seamlessly with Amazon Web Services (AWS), utilizing services like S3 for secure data storage and KMS for key management. By applying ECC, the Cha-Cheon scheme achieves efficient cryptographic …
Iot-Based Nerve Stimulator For Women’S Safety, K Revathi, W Gracy Theresa
Iot-Based Nerve Stimulator For Women’S Safety, K Revathi, W Gracy Theresa
Journal of Informatics and Web Engineering
Women deserve a right to live free from intimidation, mistreatment, unfair treatment, and eliminating hurdles from a hazardous workplace can help them reach their maximum potential both personally and as contributions to economies, societies, and the workplace. Physical, emotional, and environmental safety have a variety of effects on wellbeing, including stress management, emotional stability, and physical health. The Women Safety System with Nerve Stimulator is a comprehensive system that integrates essential components for women's safety using an Arduino Uno microcontroller. It has an SOS button for emergency activation, a temperature sensor for environmental monitoring, a pulse oximeter for tracking health, …
Lung Tumor Segmentation In Medical Imaging Using U-Net, J Jayapradha, Su-Cheng Haw, Naveen Palanichamy, Senthil Kumar Thillaigovindhan, Mutaz Al-Tarawneh
Lung Tumor Segmentation In Medical Imaging Using U-Net, J Jayapradha, Su-Cheng Haw, Naveen Palanichamy, Senthil Kumar Thillaigovindhan, Mutaz Al-Tarawneh
Journal of Informatics and Web Engineering
Tumors are a deadly condition often triggered by a range of abnormal modifications and genetic abnormalities. Early tumor diagnosis is essential due to the highly concerned nature of the disease. Early detection and treatment of tumors can significantly reduce mortality rates. This paper presents a model for tumor segmentation in medical imaging that uses the U-NET architecture to increase precision. The model’s encoding and decoding processes have been applied with skip connections to boost performance while simplifying model training. Images were cropped around the lower abdominal regions, and all images used in the study were then resized to 256*256 pixels …