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Articles 121 - 150 of 228
Full-Text Articles in Other Computer Sciences
A Hybrid Deep Learning Vgg-16 Based Svm Model For Vehicle Type Classification, Muhammad Imran, Jafar Usman, Muntazir Khan, Abdullah Khan
A Hybrid Deep Learning Vgg-16 Based Svm Model For Vehicle Type Classification, Muhammad Imran, Jafar Usman, Muntazir Khan, Abdullah Khan
Journal of Informatics and Web Engineering
Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies have not been widely utilized, resulting in low accuracy levels. The goal of this paper is to create a machine learning system that is especially made to categories models of two Pakistan's top automakers, Toyota, and Honda. Ten Toyota models such as Avalon, Land Cruiser, Camry, Corolla, C-HR, Highlander, Prius, Tundra, RAV4, and Yaris and a dataset of Honda automobiles, which also includes 10 models (Accord, Civic, CR-V, Fit, HR-V, …
Early Identification Of Parkinson's Disease Using Time Frequency Analysis On Eeg Signals, Tanvir Hasib, V Vijayakumar, Ramakrishnan Kannan
Early Identification Of Parkinson's Disease Using Time Frequency Analysis On Eeg Signals, Tanvir Hasib, V Vijayakumar, Ramakrishnan Kannan
Journal of Informatics and Web Engineering
Parkinson's Disease (PD) is a progressive neurological disorder. It affects movement and can significantly impact quality of life. Early and accurate diagnosis is crucial for effective management and intervention. Traditional diagnostic methods can be time-consuming and less effective in the early stages of the disease. This study aims to develop an automated approach for identifying PD using time-frequency image analysis of electroencephalogram (EEG) signals. The goal is to enhance diagnostic accuracy and efficiency, facilitating early detection. EEG signals, often contaminated with artifacts such as eye blinks and muscle movements etc., were first cleaned. Time-frequency images were then plotted from the …
Debugprograde: Improving Automated Assessment Of Coding Assignments With A Focus On Debugging, Amit Patel, Hardik Joshi
Debugprograde: Improving Automated Assessment Of Coding Assignments With A Focus On Debugging, Amit Patel, Hardik Joshi
Journal of Informatics and Web Engineering
In education, the evaluation of programming assignments is challenging, especially to do with the debugging aspect. Self-grading technologies are unable to capture the level of understanding of students and context-bound responses. In light of these, we created DebugProGrade to take what we normally know about grading and improve it with semantic analysis and keyword extraction. DebugProGrade identified 1000 first-year BCA students who in Google Forms provided their answers to evaluate error detection and solution proposals for a basic C programming assignment. For the explanations’ specificity and for the context-level evaluation, the system employs the SBERT embedding, namely, the sentence-transformer bidirectional …
Optimising Phishing Detection: A Comparative Analysis Of Machine Learning Methods With Feature Selection, Mohamad Asraf Daniel, Siew-Chin Chong, Lee-Ying Chong, Kuok-Kwee Wee
Optimising Phishing Detection: A Comparative Analysis Of Machine Learning Methods With Feature Selection, Mohamad Asraf Daniel, Siew-Chin Chong, Lee-Ying Chong, Kuok-Kwee Wee
Journal of Informatics and Web Engineering
Phishing is an act of cybersecurity attack that tricks people into sharing sensitive data. Due to the inefficiency of the current security technologies, researchers have been paying much attention to employing machine learning methods for phishing detection lately. In our proposed solution, the effectiveness of machine learning techniques with feature selection techniques for phishing detection is investigated. To be specific, Random Forest (RF) and Artificial Neural Network (ANN) are integrated with feature selection techniques, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The goal was to identify and classify the model with the highest accuracy. The experiments were evaluated …
Integrating Moral Values In Ai: Addressing Ethical Challenges For Fair And Responsible Technology, Khushboo Shah, Hiren Joshi, Hardik Joshi
Integrating Moral Values In Ai: Addressing Ethical Challenges For Fair And Responsible Technology, Khushboo Shah, Hiren Joshi, Hardik Joshi
Journal of Informatics and Web Engineering
Today, Artificial Intelligence (AI) has become an integral part of our day-to-day life. From personal task to professional life everywhere we need AI. All the sectors like - transport, education, healthcare, agriculture, we find AI everywhere. Each coin has two side, similarly AI has its own pros and cons. Main challenge with AI is ethics. There is no doubt in the work efficiency of AI but ultimately, it’s a machine without emotions hence whatever the decision it takes it purely without ethical values. Sometimes, this type of decision may lead to disasters, especially in the industry where human life is …
Creating An Android-Based Calisthenics Application To Assist Students In Improving Their Physical Fitness, Restyandito, Narendra Poetra Wisnoewardhana, Danny Sebastian
Creating An Android-Based Calisthenics Application To Assist Students In Improving Their Physical Fitness, Restyandito, Narendra Poetra Wisnoewardhana, Danny Sebastian
Journal of Informatics and Web Engineering
College students, particularly those heavily involved in coursework, frequently do not prioritize physical fitness. Regular exercise is essential for preserving physical fitness and facilitating demanding academic tasks. Although there are other fitness programs available, such as GYM, jogging, CrossFit, and Yoga, users sometimes fail to make full use of them because the content is confined to exercise videos and descriptions. This matter highlights the progress of the Android application Kali Tech, which specifically concentrates on organizing and documenting calisthenics workouts. This program employs the concept of gamification by utilizing student achievement levels to encourage students to be diligent in doing …
Machine Learning Model For Predicting Net Environmental Effects, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Zhang Yujiao
Machine Learning Model For Predicting Net Environmental Effects, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Zhang Yujiao
Journal of Informatics and Web Engineering
Environmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and …
Fiber Break Prevention Using Machine Learning Approaches, Zhan Heng Ng, Tee Connie, Kan Yeep Choo, Michael Kah Ong Goh, Nurul Ain Abdul Aziz, Hong Yeap Ngo
Fiber Break Prevention Using Machine Learning Approaches, Zhan Heng Ng, Tee Connie, Kan Yeep Choo, Michael Kah Ong Goh, Nurul Ain Abdul Aziz, Hong Yeap Ngo
Journal of Informatics and Web Engineering
Modern fiber-optic communication systems are built around optical fiber, which allows data to be sent by emitting infrared light pulses. It is widely used by telecommunications firms and is essential to the smooth transmission of information in internet communication as well as the transmission of telephone signals. Nonetheless, optical fibers intrinsic fragility raises a problem, especially in areas where building projects are taking place. Especially nowadays construction-related impact and crushing pressures can cause physical damage that jeopardizes the fiber optic's integrity. Therefore, this research emphasizes the necessity of taking preventative and mitigating actions to reduce the possibilities of fiber optic …
Migraine Generative Artificial Intelligence Based On Mobile Personalized Healthcare, Michelle Ting-Ting Yong, Sin-Ban Ho, Chuie-Hong Tan
Migraine Generative Artificial Intelligence Based On Mobile Personalized Healthcare, Michelle Ting-Ting Yong, Sin-Ban Ho, Chuie-Hong Tan
Journal of Informatics and Web Engineering
Migraine is a complicated genetic disorder characterized by episodes of moderate-to-severe headaches that are usually unilateral and are frequently accompanied by nausea and increased sensitivity to sound and light. A migraine attack induces intense pain, hindering an individual from engaging in daily activities and potentially persisting for hours or even days. By the growth of the Internet of Things, we have new opportunities to try to apply it to the medical field. To identify the origin of a migraine, specialists need access to a patient's medical history and a comprehensive understanding of migraine symptoms for effective treatment. Determining the true …
Exploration Of The Impact Of Cyber Situational Awareness On Small And Medium Enterprises (Smes) In Malaysia, Tan Chee Keong, Sofiah Kadar Khan, Umar Farooq Khattak
Exploration Of The Impact Of Cyber Situational Awareness On Small And Medium Enterprises (Smes) In Malaysia, Tan Chee Keong, Sofiah Kadar Khan, Umar Farooq Khattak
Journal of Informatics and Web Engineering
The objective of this study is to explore the cyber situational awareness (CSA) level among the employees of small and medium-sized enterprises (SME) in Malaysia, by extending Endsley's situation awareness (SA) theory. It is crucial to understand the level of cyber situational awareness among employees as it sheds light on how well the employees understand the cyber threats and if they can handle them effectively. Literature has reviewed that SMEs are subject to a greater danger of cyber-attacks. Therefore, employees' awareness of cyber situations is of the utmost significance in studying cyber security. A convenient non-probability sampling method was chosen …
Climate Change Analysis In Malaysia Using Machine Learning, Anishalache Subramanian, Naveen Palanichamy, Kok-Why Ng, Sandhya Aneja
Climate Change Analysis In Malaysia Using Machine Learning, Anishalache Subramanian, Naveen Palanichamy, Kok-Why Ng, Sandhya Aneja
Journal of Informatics and Web Engineering
Climate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. Performance …
Path To A Healthy Work-Life Balance: Mobile Application For Work And Personal Life Mastery, Erfan Rahmani, Zarina Che Embi
Path To A Healthy Work-Life Balance: Mobile Application For Work And Personal Life Mastery, Erfan Rahmani, Zarina Che Embi
Journal of Informatics and Web Engineering
Drawing insights from diverse organizational methods, this study endeavors to facilitate effective self-development and organization in the face of contemporary demands as solutions for integrating goal tracking, event coordination, and task management within a unified calendar framework in a mobile application. There are three primary objectives of this study: firstly, to explore essential functionalities crucial for addressing the multifaceted challenges of modern life; secondly, to design and develop a mobile application that seamlessly integrates these functionalities; and finally, to evaluate the usability of the application through rigorous testing and feedback mechanisms. Envisaged deliverables include a fully functional mobile application designed …
Personalized Drug Recommendation System Using Wasserstein Auto-Encoders And Adverse Drug Reaction Detection With Weighted Feed Forward Neural Network (Waes-Adr) In Healthcare, J Omana, P. N Jeipratha, K Devi, S Benila, K Revathi
Personalized Drug Recommendation System Using Wasserstein Auto-Encoders And Adverse Drug Reaction Detection With Weighted Feed Forward Neural Network (Waes-Adr) In Healthcare, J Omana, P. N Jeipratha, K Devi, S Benila, K Revathi
Journal of Informatics and Web Engineering
In recent years, the use of deep learning approaches in healthcare has yielded promising results in a variety of fields, most notably in the detection of adverse drug reactions (ADRs) and drug recommendations. This paper promises a breakthrough in this field by using Wasserstein autoencoders (WAEs) for personalized medicine recommendation and ADR detection. WAEs' capacity to manage complex data distributions and develop meaningful latent representations makes them ideal for modeling heterogeneous healthcare data. This study intends to improvise the precision and efficiency of drug recommendation systems while also improving patient safety by combining WAEs and early ADR detection strategies. Previous …
Comparative Analysis Of Linear And Nonlinear Semg Methods For Detecting Muscle Fatigue During Dynamic Biceps Curls, Tang Ming, Ling Weay Ang, Sellappan Palaniappan
Comparative Analysis Of Linear And Nonlinear Semg Methods For Detecting Muscle Fatigue During Dynamic Biceps Curls, Tang Ming, Ling Weay Ang, Sellappan Palaniappan
Journal of Informatics and Web Engineering
Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established …
Enhancing Cybersecurity Awareness Through Gamification: Design An Interactive Cybersecurity Learning Platform For Multimedia University Students, Adlil Khaliq Abdul Razack, Mohamad Firdaus Mat Saad
Enhancing Cybersecurity Awareness Through Gamification: Design An Interactive Cybersecurity Learning Platform For Multimedia University Students, Adlil Khaliq Abdul Razack, Mohamad Firdaus Mat Saad
Journal of Informatics and Web Engineering
Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting …
Towards Analysable Chaos-Based Cryptosystems: Constructing Difference Distribution Tables For Chaotic Maps, Je Sen Teh, Abubakar Abba
Towards Analysable Chaos-Based Cryptosystems: Constructing Difference Distribution Tables For Chaotic Maps, Je Sen Teh, Abubakar Abba
Journal of Informatics and Web Engineering
Chaos-based cryptography has yet to achieve practical, real-world applications despite extensive research. A major challenge is the difficulty in analysing the security of these cryptosystems, which often appear ad hoc in design. Unlike conventional cryptography, evaluating the security margins of chaos-based encryption against attacks such as differential cryptanalysis is complex. This paper introduces a straightforward approach of using chaotic maps in cryptographic algorithms in a way that facilitates cryptanalysis. We demonstrate how a chaos-based substitution function can be constructed using fixed-point representation, enabling the application of conventional cryptanalysis tools such as the difference distribution table. As a proof-of-concept, we apply …
Development Of Virtual Reality-Based Left Brain System, Kok An Teo, Kok Swee Sim, Chean Khim Toa, Kai Liang Lew
Development Of Virtual Reality-Based Left Brain System, Kok An Teo, Kok Swee Sim, Chean Khim Toa, Kai Liang Lew
Journal of Informatics and Web Engineering
This paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and …
Pi Class – A Revolutionary Step Forward In Hybrid Class Management System, Qasim Hassan, Nabiha Faisal, Ayesha Anees Zaveri
Pi Class – A Revolutionary Step Forward In Hybrid Class Management System, Qasim Hassan, Nabiha Faisal, Ayesha Anees Zaveri
Journal of Informatics and Web Engineering
The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the …
Development Of Robot Feature For Stunting Analysis Using Long-Short Term Memory (Lstm) Algorithm, Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso
Development Of Robot Feature For Stunting Analysis Using Long-Short Term Memory (Lstm) Algorithm, Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso
Journal of Informatics and Web Engineering
Stunting prevalence in Indonesia persists as a significant challenge necessitating concerted efforts from all stakeholders. We developed robot for stunting analysis using deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs …
Cyber-Securing Medical Devices Using Machine Learning: A Case Study Of Pacemaker, Suliat Toyosi Jimoh, Shaymaa S Al-Juboori
Cyber-Securing Medical Devices Using Machine Learning: A Case Study Of Pacemaker, Suliat Toyosi Jimoh, Shaymaa S Al-Juboori
Journal of Informatics and Web Engineering
This study aims to enhance the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. The objectives are to pinpoint cybersecurity weaknesses, utilize machine learning to predict security breaches, and propose countermeasures based on analytical trends. The literature review highlights the transformation of pacemaker technology from basic, fixed-rate devices to sophisticated systems with wireless capabilities, which, while improving patient care, also introduce significant cybersecurity risks. These risks include unauthorized entry, data breaches, and life-threatening device malfunctions. The methodology in this study utilizes a quantitative research approach using the WUSTL-EHMS-2020 dataset, which includes network traffic features, patients' biometric …
Review On Automated Storage And Retrieval System For Warehouse, Alex Low Kai Jie, Sim Kok Swee, Lew Kai Liang
Review On Automated Storage And Retrieval System For Warehouse, Alex Low Kai Jie, Sim Kok Swee, Lew Kai Liang
Journal of Informatics and Web Engineering
The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context …
Sibling Discrimination Using Linear Fusion On Deep Learning Face Recognition Models, Rita Goel, Maida Alamgir, Haroon Wahab, Maria Alamgir, Irfan Mehmood, Hassan Ugail, Amit Sinha
Sibling Discrimination Using Linear Fusion On Deep Learning Face Recognition Models, Rita Goel, Maida Alamgir, Haroon Wahab, Maria Alamgir, Irfan Mehmood, Hassan Ugail, Amit Sinha
Journal of Informatics and Web Engineering
Facial recognition technology has revolutionised human identification, providing a non-invasive alternative to traditional biometric methods like signatures and voice recognition. The integration of deep learning has significantly enhanced the accuracy and adaptability of these systems, now widely used in criminal identification, access control, and security. Initial research focused on recognising full-frontal facial features, but recent advancements have tackled the challenge of identifying partially visible faces, a scenario that often reduces recognition accuracy. This study aims to identify siblings based on facial features, particularly in cases where only partial features like eyes, nose, or mouth are visible. Utilising advanced deep learning …
In Search Of Suitable Methods For Cost-Benefit Analysis Of Cyber Risk Mitigation In Offshore Wind: A Survey, Yvonne Hwei-Syn Kam, Kevin Jones, Robert Rawlinson-Smith, Kimberly Tam
In Search Of Suitable Methods For Cost-Benefit Analysis Of Cyber Risk Mitigation In Offshore Wind: A Survey, Yvonne Hwei-Syn Kam, Kevin Jones, Robert Rawlinson-Smith, Kimberly Tam
Journal of Informatics and Web Engineering
In recent years, notable incidents have highlighted the vulnerability of wind energy infrastructure, making cybersecurity crucial for the offshore wind industry. However, justifying the costs of cybersecurity measures is essential. A cost benefit analysis (CBA) is commonly utilised to support decision-making for risk mitigation. With a cost benefit analysis, risk mitigation strategies that strike an optimal balance between the costs of mitigation measures and the resulting risk reduction can be identified. This survey of literature was carried out to identify the existing proposed solutions for cost benefit analysis on cyber risk mitigation measures for offshore wind cyber physical systems. After …
Editorial Preview, Su-Cheng Haw
Editorial Preview, 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 particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing …
Mobitest – A Software For Mobile-Based Testing, Ayesha Anees Zaveri, Ramsha Mashood, Nabiha Faisal, Misbah Parveen, Naveera Sami, Mobeen Nazar, Saba Imtiaz
Mobitest – A Software For Mobile-Based Testing, Ayesha Anees Zaveri, Ramsha Mashood, Nabiha Faisal, Misbah Parveen, Naveera Sami, Mobeen Nazar, Saba Imtiaz
Journal of Informatics and Web Engineering
MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the …
Hybrideval: An Improved Novel Hybrid Metric For Evaluation Of Text Summarization, Raheem Sarwar, Bilal Ahmad, Pin Shen Teh, Suppawong Tuarob, Tipajin Thaipisutikul, Farooq Zaman, Naif R. Aljohani, Jia Zhu, Saeed-Ul Hassan, Raheel Nawaz, Ali R Ansari, Muhammad A B Fayyaz
Hybrideval: An Improved Novel Hybrid Metric For Evaluation Of Text Summarization, Raheem Sarwar, Bilal Ahmad, Pin Shen Teh, Suppawong Tuarob, Tipajin Thaipisutikul, Farooq Zaman, Naif R. Aljohani, Jia Zhu, Saeed-Ul Hassan, Raheel Nawaz, Ali R Ansari, Muhammad A B Fayyaz
Journal of Informatics and Web Engineering
The present work re-evaluates the evaluation method for text summarization tasks. Two state-of-the-art assessment measures e.g., Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Bilingual Evaluation Understudy (BLEU) are discussed along with their limitations before presenting a novel evaluation metric. The evaluation scores are significantly different because of the length and vocabulary of the sentences, this suggests that the primary restriction is its inability to preserve the semantics and meaning of the sentences and consistent weight distribution over the whole sentence. To address this, the present work organizes the phrases into six different groups and to evaluate “text summarization” problems, a …
Treatment Recommendation Using Bert Personalization, J Jayapradha, Yukta Kulkarni, Lakshmi Vadhanie G, Palanichamy Naveen, Elham Abdulwahab Anaam
Treatment Recommendation Using Bert Personalization, J Jayapradha, Yukta Kulkarni, Lakshmi Vadhanie G, Palanichamy Naveen, Elham Abdulwahab Anaam
Journal of Informatics and Web Engineering
This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all …
Performance Evaluation On E-Commerce Recommender System Based On Knn, Svd, Coclustering And Ensemble Approaches, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Heru Agus Santoso
Performance Evaluation On E-Commerce Recommender System Based On Knn, Svd, Coclustering And Ensemble Approaches, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Heru Agus Santoso
Journal of Informatics and Web Engineering
E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a …
Comparative Evaluation Of Machine Learning Models For Mobile Phone Price Prediction: Assessing Accuracy, Robustness, And Generalization Performance, Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata
Comparative Evaluation Of Machine Learning Models For Mobile Phone Price Prediction: Assessing Accuracy, Robustness, And Generalization Performance, Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata
Journal of Informatics and Web Engineering
These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting …
Investigation On Understanding The Numeracy Capacity Of Intellectual Disabled Students Using Enabling Technology Tools: Web Application, Ar And Ui/Ux, Vishvjit Thakar, Romany Thakar, Pratik Vyas
Investigation On Understanding The Numeracy Capacity Of Intellectual Disabled Students Using Enabling Technology Tools: Web Application, Ar And Ui/Ux, Vishvjit Thakar, Romany Thakar, Pratik Vyas
Journal of Informatics and Web Engineering
The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To …