Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- MMU Press (252)
- University of Dayton (22)
- California Polytechnic State University, San Luis Obispo (16)
- San Jose State University (9)
- Western University (9)
-
- Department of Primary Industries and Regional Development, Western Australia (7)
- California State University, San Bernardino (6)
- Chapman University (6)
- Dakota State University (6)
- Louisiana State University (4)
- University of Denver (4)
- University of Nebraska - Lincoln (4)
- Kennesaw State University (3)
- Montclair State University (3)
- Portland State University (3)
- Southern Methodist University (3)
- Technological University Dublin (3)
- The University of Akron (3)
- University of Montana (3)
- City University of New York (CUNY) (2)
- Claremont Colleges (2)
- East Tennessee State University (2)
- Georgia Southern University (2)
- LSU New Orleans (2)
- Southern Adventist University (2)
- University of Nebraska at Omaha (2)
- University of San Diego (2)
- West Virginia University (2)
- Air Force Institute of Technology (1)
- Beirut Arab University (1)
- Keyword
-
- Machine Learning (62)
- Deep Learning (27)
- Artificial Intelligence (25)
- Natural Language Processing (15)
- Computer Science (11)
-
- Sentiment Analysis (10)
- Android (9)
- Random Forest (9)
- Convolutional Neural Network (8)
- Biosecurity (7)
- Classification (7)
- Computer Vision (7)
- Cybersecurity (7)
- Editorial Preview (7)
- Harvest (7)
- Support Vector Machine (7)
- Weed Seed Wizard (7)
- Weed science (7)
- Data Mining (6)
- E-Commerce (6)
- Long Short-Term Memory (6)
- Software Engineering (6)
- Algorithms (5)
- Blockchain (5)
- Computer science (5)
- Convolutional Neural Networks (5)
- Cyber Security (5)
- Deep learning (5)
- Internet of Things (5)
- Prediction (5)
- Publication Year
- Publication
-
- Journal of Informatics and Web Engineering (252)
- Computer Science Faculty Publications (22)
- Electrical and Computer Engineering Publications (9)
- Master's Theses (8)
- Biosecurity research reports (7)
-
- Master's Projects (6)
- Electronic Theses, Projects, and Dissertations (5)
- Masters Theses & Doctoral Dissertations (5)
- Computer Science and Software Engineering (4)
- Electronic Theses and Dissertations (4)
- Undergraduate Honors Theses (4)
- Department of Computer Science Faculty Scholarship and Creative Works (3)
- Engineering Faculty Articles and Research (3)
- Graduate Student Theses, Dissertations, & Professional Papers (3)
- LSU Doctoral Dissertations (3)
- Williams Honors College, Honors Research Projects (3)
- CMC Senior Theses (2)
- Computer Engineering (2)
- Computer Science and Engineering Theses and Dissertations (2)
- Dissertations, Theses, and Capstone Projects (2)
- Graduate Theses, Dissertations, and Problem Reports (ETD) (2)
- Honors College Theses (2)
- Honors Thesis (2)
- LSU New Orleans Theses and Dissertations (2)
- MS in Computer Science Project Reports (2)
- Other (2)
- Student Scholar Symposium Abstracts and Posters (2)
- University Honors Theses (2)
- All Dissertations (1)
- All Master's Theses (1)
- Publication Type
- File Type
Articles 211 - 240 of 417
Full-Text Articles in Other Computer Sciences
Ensemble Learning-Powered Url Phishing Detection: A Performance Driven Approach, Shougfta Mushtaq, Tabassum Javed, Mazliham Mohd Su’Ud
Ensemble Learning-Powered Url Phishing Detection: A Performance Driven Approach, Shougfta Mushtaq, Tabassum Javed, Mazliham Mohd Su’Ud
Journal of Informatics and Web Engineering
With the rapid growth in the usage of the Internet, criminals have found new ways to engage in cyber-attacks. The most common and widespread attack is URL phishing. The proposed system focuses on improving phishing website detection using feature selection and ensemble learning. This model uses two datasets, DS-30 and DS-50, each with 30 and 50 features. Ensemble learning using a voting classifier was then applied to train the model, achieving more accuracy. The combination of HEFS with random forest distribution achieved 94.6% accuracy while minimizing the number of features used (20.8% of the base feature set). The classifier works …
Wix For Web Development And The Application Of The Waterfall Model And Project Based Learning For Project Completion: A Case Study, Mawar Madiah, Ng Kai Xuen, Tan Yew Wen, Tan Zhi Heng, Chong Zhi Tian, Chan Jia Xuan
Wix For Web Development And The Application Of The Waterfall Model And Project Based Learning For Project Completion: A Case Study, Mawar Madiah, Ng Kai Xuen, Tan Yew Wen, Tan Zhi Heng, Chong Zhi Tian, Chan Jia Xuan
Journal of Informatics and Web Engineering
Website development without prior knowledge of HTML or programming experience would be a significant challenge. This study aims to share the students' experiences developing a website using Wix, a user-friendly website builder. The website was assigned as a project-based assessment of one of the courses required to complete a Foundation programme. The course was delivered using a project-based learning (PBL) approach in this context. The students worked as a group to write a project proposal, plan activities, develop the website, write reports, and present the outcome. As for the website development process, the study demonstrates the completion of the project …
Empirical Analysis Of Ci/Cd Tools Usage In Github Actions Workflows, Adam Rafif Faqih, Alif Taufiqurrahman, Jati H. Husen, Mira Kania Sabariah
Empirical Analysis Of Ci/Cd Tools Usage In Github Actions Workflows, Adam Rafif Faqih, Alif Taufiqurrahman, Jati H. Husen, Mira Kania Sabariah
Journal of Informatics and Web Engineering
As software systems grow larger and more complex, with rapidly changing requirements, manually managing code integration, testing, and deployment becomes extremely challenging. Continuous Integration and Continuous Deployment (CI/CD) practices and tools have emerged to help automate these processes. This research explores the usage of different categories of CI/CD tools within GitHub Actions workflow configurations across GitHub repositories. The five-tool categories analyzed are Version Control Management, Static Code Analysis, Build Automation, Test Automation, and CI/CD Servers. The data used in this research is from a dataset of GitHub Actions workflow configuration files. From the data, the usage is extracted and the …
Streamlining Dental Clinic Management For Effective Digitisation Productivity And Usability, Sin-Ban Ho, En-Yu Chew, Chuie-Hong Tan
Streamlining Dental Clinic Management For Effective Digitisation Productivity And Usability, Sin-Ban Ho, En-Yu Chew, Chuie-Hong Tan
Journal of Informatics and Web Engineering
Oral health is an integral part of overall health, and poor oral hygiene can lead to a variety of health problems. Modern oral care has greatly improved our quality of life, but the increasing demand for routine dental checkups and treatments calls for improved systems for managing patient records and appointments. While technology has significantly enhanced the efficacy and experience of dental care, many dental clinics still rely on paper records to record the patient’s oral condition, but these are not easily accessible to the patients for viewing. This study aims to address the issue by developing a Dental Clinic …
Knowledge-Based Word Tokenization System For Urdu, Asif Khan, Khairullah Khan, Wahab Khan, Sadiq Nawaz Khan, Rafiul Haq
Knowledge-Based Word Tokenization System For Urdu, Asif Khan, Khairullah Khan, Wahab Khan, Sadiq Nawaz Khan, Rafiul Haq
Journal of Informatics and Web Engineering
Word tokenization, a foundational step in natural language processing (NLP), is critical for tasks like part-of-speech tagging, named entity recognition, and parsing, as well as various independent NLP applications. In our tech-driven era, the exponential growth of textual data on the World Wide Web demands sophisticated tools for effective processing. Urdu, spoken widely across the globe, is experiencing a surge in, presents unique challenges due to its distinct writing style, the absence of capitalization features, and the prevalence of compound words. This study introduces a novel knowledge-based word tokenization system tailored for Urdu. Central to this system is a maximum …
Social Messaging Application With Translation And Speech-To-Text Transformation, Kang Qin Yip, Pey Yun Goh, Lee Ying Chong
Social Messaging Application With Translation And Speech-To-Text Transformation, Kang Qin Yip, Pey Yun Goh, Lee Ying Chong
Journal of Informatics and Web Engineering
Unlike traditional SMS or MMS, messaging apps offer a broader range of data transmission capabilities. The application utilizes a WIFI or internet connection and enables users to exchange information through various means such as text, voice, and multimedia files. However, popular messaging applications such as WeChat, Telegram, and WhatsApp have limitations in language translation and file uploading size. Thus, this project aims to address these limitations by developing a social messaging application that serves as a comprehensive communication tool. The application will facilitate both written and verbal communication by providing translation services for various languages, including voice messages. The proposed …
Crime Prediction Using Agent-Based Modeling, Yifei Gong
Crime Prediction Using Agent-Based Modeling, Yifei Gong
Dissertations, Theses, and Capstone Projects
Crime risk evaluation and crime prediction using agent-based modeling (ABM) have gained popularity in the field of computational criminology in recent years. Traditionally, researchers rely on statistical methods and machine learning models to predict crimes using historical data. ABM generates macro-level crime patterns in a bottom-up fashion by simulating the daily behaviors of autonomous entities, such as citizens and offenders. ABM takes into consideration the non-linear interactions between agents under complex social contexts. Currently, the comprehensive usage of ABM for criminological theory testing and urban policy evaluations calls for a unified software framework. In this research, we introduce CARESim, an …
Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi
Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi
LSU New Orleans Theses and Dissertations
This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …
Dancetag: Using Sensors To Improve Feedback Given To Dance Students, Yanelly Mego
Dancetag: Using Sensors To Improve Feedback Given To Dance Students, Yanelly Mego
Student Scholar Symposium Abstracts and Posters
The structure of dance classrooms has remained unchanged for several years. Very little, if any, technology has been incorporated to improve the quality of teaching. This has motivated our research project, whose goal is to capture dance movements with wearable sensors, to develop DANCETAG (Data Analytics and Notation with Captured Event Tagging). This is a platform that allows the gathering of data captured by Sony’s Mocopi sensors and annotating them with the dancer's movements. The Mocopi sensors make up a motion capture system. It is comprised of six small, round sensors that can be attached to velcro straps and clips. …
A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes
A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes
Honors Thesis
Frequency shift keying (FSK) is an old but powerful form of modulation that powered much of the early modems of the 1960’s, and the author felt inspired to make his own version of audio binary FSK modulation. He researched the general history and legacy of the Bell 103, a modem using FSK that defined telecommunication for the next few decades. Using research of the most common English characters of recent emails to determine which English characters should have the shortest bit length, a novel character encoding standard was created using variable bit rate. In addition, he has created a modulation …
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 …
Emojis And Miscommunication In Text-Based Interactions Among Nigerian Youths, Uduak Udoudom, Godwin William, Anthony Igiri, Ememobong Okon, Kalita Aruku
Emojis And Miscommunication In Text-Based Interactions Among Nigerian Youths, Uduak Udoudom, Godwin William, Anthony Igiri, Ememobong Okon, Kalita Aruku
Journal of Informatics and Web Engineering
This paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and …
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 …
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 …
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 …
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 …
Electric Vehicle Health Monitoring With Electric Vehicle Range Prediction And Route Planning, Jayapradha Jayaram, J Chetan, Barun Nayak
Electric Vehicle Health Monitoring With Electric Vehicle Range Prediction And Route Planning, Jayapradha Jayaram, J Chetan, Barun Nayak
Journal of Informatics and Web Engineering
The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among …
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 …
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 …
Prediction Of Student’S Academic Performance Through Data Mining Approach, Muhammad Mubashar Hussain, Shahzad Akbar, Syed Ale Hassan, Muhammad Waqas Aziz, Farwa Urooj
Prediction Of Student’S Academic Performance Through Data Mining Approach, Muhammad Mubashar Hussain, Shahzad Akbar, Syed Ale Hassan, Muhammad Waqas Aziz, Farwa Urooj
Journal of Informatics and Web Engineering
The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of …
Hybrid Crow Search And Rbfnn: A Novel Approach To Medical Data Classification, Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan
Hybrid Crow Search And Rbfnn: A Novel Approach To Medical Data Classification, Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan
Journal of Informatics and Web Engineering
The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network …
An In-Depth Analysis On Efficiency And Vulnerabilities On A Cloud-Based Searchable Symmetric Encryption Solution, Prithvi Chaudhari, Ji-Jian Chin, Soeheila Moesfa Bt Mohamad
An In-Depth Analysis On Efficiency And Vulnerabilities On A Cloud-Based Searchable Symmetric Encryption Solution, Prithvi Chaudhari, Ji-Jian Chin, Soeheila Moesfa Bt Mohamad
Journal of Informatics and Web Engineering
Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption …