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Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

computers, Materials & Continua 2022年第9期72卷 5041-5058页

作者： Maqsood Ahmad Noorhaniza Wahid Rahayu A Hamid Saima Sadiq Arif Mehmood Gyu Sang Choi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia86400 Parit RajaBatu PahatJohorMalaysia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38542Korea

Mental health signifies the emotional,social,and psychological well-being of a *** also affects the way of thinking,feeling,and situation handling of a *** mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in *** health problems are rising globally and constituting a burden on healthcare *** diagnosis can help the professionals in the treatment that may lead to complications if they remain *** machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric *** research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and *** challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the ***,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical *** framework consists of an ensemble model based on RF and GBM with a novel SPPM *** proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%*** framework seems suitable in the case of huge and more diverse multiclass ***,three

关键词： Mental health diagnosis machine learning depression shrewd probing diagnostic approach

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Comparative Analysis of Machine Learning Algorithms in Vehicle Image Classification 22nd

Comparative Analysis of Machine Learning Algorithms in Veh...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Asry, Nur Izzaty Muhammad Mustapha, Aida Mostafa, Salama A. Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia KM 1 Jalan Panchor Johor Pagoh84600 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Batu Pahat86400 Malaysia

ISBN: (纸本)9789819972395

Object identification in the area of computer Vision essentially focus on how robots are able to perceive in comparison of human eyes. The main task for vehicle detection is detecting vehicles from image sources. This study utilises Weka to categorise vehicle photos into 3 vehicle groups based on data mining approach. In feature extraction, three image filtering algorithms are used, which are Colour Layout, Edge Histogram, and Pyramid Histogram of Oriented Gradients (PHOG). The features extracted are then fed into five classification algorithms for a comparative analysis: Multilayer Perceptron (MLP), Simple Logistic, Sequential Minimal Optimization (SMO), Logistic Model Tree (LMT), and Random Forest. The experimental results showed that PHOG filtering algorithm produced the best set of features to accurately classify the vehicle images. During classification, the SMO classifier achieved the best accuracy of 46.3158%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Systematic Literature Review on Test Case Quality Characteristics and Metrics 3

Systematic Literature Review on Test Case Quality Characteri...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Obaid Barraood, Samera Mohd, Haslina Baharom, Fauziah Almogahed, Abdullah College of Computers and Information Technology Hadhramout University Department of Computer Science Hadhramout Yemen School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia

ISBN: (纸本)9798350305333

Companies are constantly optimizing their processes to increase the efficiency of software development, which heavily depends on software testing to fulfil stringent business requirements and with high-quality software released. The detection of software defects and the assurance of software quality are facilitated by high-quality test cases. However, the current metrics used to measure test case quality (TCQ) lack clarity. Therefore, this study aims to address this gap by identifying metrics and the corresponding characteristics that measure the TCQ. This paper presents a systematic literature review (SLR) focused on TCQ characteristics and metrics. By analyzing empirical papers published between 2010 and June 2018, a total of 23 papers were identified for inclusion in the review. Through this process, 16 characteristics and 50 metrics were identified. These characteristics and metrics were further analyzed, categorized, and finalized as the evaluation metrics for TCQ characteristics. The outcomes of this study contribute to the development of a comprehensive model for assessing TCQ. © 2023 IEEE.

关键词： Software testing

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An Analysis of Copy-Move Forgery Detection based on Discrete Cosine Transform against JPEG Compression and Brightness Changes Attack

An Analysis of Copy-Move Forgery Detection based on Discrete...

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Cyber Security and Computing (CyberComp), International Conference on

作者： Nurul Rihhadatul'aisy Hamidon Siti Fadzlun Md Salleh Nor Bakiah Abd Warif Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

Copy-move forgery (CMF) is an image manipulation in which a component of the image is copied and pasted onto another portion, usually to hide undesirable aspects of the image. The objective of copy-move forgery detection (CMFD) is to identify image regions that are identical or very similar. However, the tampered region in copy-move forgery is difficult to identify because the forge image appears to be identical from the original. In this research, Discrete Cosine Transform (DCT) which is a block based method, will be evaluated using F-measure metric to spot forged region on images with JPEG compression and brightness changes attack. To represent both attacks, this research used ten images in two available datasets which are CoMoFoD and GRIP datasets. These two attacks will be applied in image using GIMP editing software to three different parameters on compression quality and brightness manipulation. Experiments for this research were conducted using MATLAB. The results show that DCT-based copy-move forgery detection is able to detect at least 93% score for 90% compression quality but decreased to 88% for 70% quality. Meanwhile for brightness changes, DCT-based can detect at least 93% for (0.01,0.95) but decreased to 92% for (0.01,0.80) parameter.

关键词： Measurement Hands Image coding Brightness Transform coding Forgery Software Discrete cosine transforms Object recognition MATLAB

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Optimizing Variable-Strength Combinatorial Test Suite Generation via Fuzzy Adaptive Sine Cosine Algorithm (FASCA) 1

Optimizing Variable-Strength Combinatorial Test Suite Genera...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Zabidi, Nur Syabila Ibrahim, Noraini Rejab, Mazidah Mat Mamat, MasrulEhsan Nazir, Sumaira Mat Tuselim, Nurul Hawani Faculty of Computer Science and Information Technology Parit Raja Batu Pahat Malaysia National University of Modern Languages Department of Software Engineering Islamabad Pakistan Industrial Research Management Centre SIRIM Berhad Business Intelligence Section Shah Alam Malaysia

ISBN: (纸本)9798350387728

Combinatorial test suite generation is a critical aspect of software testing, particularly for systems with variable-strength interactions. Traditional optimization algorithms often struggle to efficiently generate minimal test suites while maintaining coverage. This research introduces the Fuzzy Adaptive Sine Cosine Algorithm (FASCA), a variant of the Sine Cosine Algorithm (SCA), tailored specifically for optimizing variable-strength combinatorial test suite generation. FASCA incorporates fuzzy adaptation mechanisms to dynamically balance exploration and exploitation, addressing the limitations of standard SCA in handling diverse interaction strengths. Experimental evaluations demonstrate that FASCA significantly reduces test suite size and execution time compared to the original SCA, offering a more efficient solution for combinatorial test optimization. These results highlight FASCA's potential as a robust tool for enhancing the efficiency of software testing processes. © 2024 IEEE.

关键词： Software testing

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Development of Student Face Recognition Attendance System

Development of Student Face Recognition Attendance System

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Student Conference on Research and Development (SCOReD)

作者： Muhamad Aiman Bin Mohd. Latif Ruhaya Ab. Aziz Hanayanti Hafit Faculty of Computer Science & Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Face recognition technology, a subset of biometric technology, has emerged as a crucial and revolutionary tool in various industries, including identity verification, security, and surveillance. In this study, face recognition technology has been employed to enhance the student attendance system at the faculty of computer science and information technology (FSKTM). The current attendance recording method, which requires students to scan a QR code during teaching and learning sessions, presents several challenges. It is time-consuming, prone to inaccuracies, and susceptible to misuse, such as remote attendance recording and QR code sharing, which can lead to errors and class disruptions. To address these issues, the Student Face Recognition Attendance System was developed to ensure attendance is recorded accurately, efficiently, and with integrity. The system incorporates three user roles, which are administrator, students, and lecturer, and focuses on managing student attendance data within educational institutions. Prototyping software process model was adopted for this project, emphasizing iterative development through phases including requirement gathering, quick decision-making, building and refining a prototype, user evaluation, and final product development. The system was developed using a combination of OpenCV and software such as PyCharm and Firebase, with Python as the programming language. This system is expected to significantly reduce the time required for attendance recording while minimizing errors, thereby improving the overall efficiency and reliability of the attendance process.

关键词： Training Face recognition Surveillance QR codes Switches Software User experience Recording Software reliability Security

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An Application of Multilayer Perceptron for the Prediction of River Water Quality 6th

An Application of Multilayer Perceptron for the Prediction o...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ghazali, Rozaida Fuzi, Norfar Ain Mohd Mostafa, Salama A. Khattak, Umar Farooq Ali, Rabei Raad Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Batu Pahat Parit Raja86400 Malaysia School of Information Technology UNITAR International University Petaling Jaya47301 Malaysia Department of Computer Engineering Technology Northern Technical University Mosul41000 Iraq

ISBN: (纸本)9789819930098

This study examines the use of an intelligent system to predict the quality of water flowing through the Johore Rivers. A neural network model of multilayer perceptron has been developed to predict the quality of water in three rivers, namely the Bekok River, Sayong River, and Johor River. The model has been trained and tested with the values of the pH parameters as input variables. The neural network model’s performance has been assessed using three performance metrics: mean squared error, CPU time, and prediction accuracy. By analyzing the relationship between the residuals and the model values, it will be possible to assess the model’s suitability. Based on the results from the implementation of the multilayer perceptron model, a method is developed for forecasting and monitoring the parameters associated with water quality obtaining an average accuracy of prediction score of 99.93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Analysis of the Influence of Service Quality and Audience Loyalty Interest in the Volleyball Tournament Events: A Case Study of Tulungagung Regency

Analysis of the Influence of Service Quality and Audience Lo...

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Human Engineering Symposium, HUMENS 2021

作者： Adji, Tito Pangesti Mansur Putro, Kukuh Hardopo Pratama, Kukuh Wahyudin Mustapha, Aida Sport Science Faculty Yogyakarta State University Yogyakarta Indonesia Shah Alam Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (纸本)9789811641145

Tulungagung Regency is one of the districts whose people are very enthusiastic about volleyball. The main research aim for this paper is to examine the service quality by the volleyball tournament organizer at Tulungagung and its relationships towards satisfaction and audience loyalty in the game of volleyball. This research adopts a descriptive methodology with mixed qualitative and quantitative approaches. The population in this study were a total of 100 volleyball viewers in Tulungagung Regency taken by random sampling. The data collection technique is using a questionnaire. From the analysis, the level of audience satisfaction towards event organizers involved in the arena is 46%, while for the influential category it reached 54%. Security is very influential, reaching 55%. The quality of the national level professional players who competed was very influential, reaching 72%. The ticket to the match has less effect alone, reaching to 25%. Means for entertainment are also very influential, reaching 33%. Regarding doorprize, it is less influential, whereby 63% has no effect. The quality of the field, the influential category reached 72, 24% is effective, and 4% had no effect. The findings are hoped to be beneficial to every event organizer that will hold volleyball tournament events in terms of ensuring quality of services and retaining audience loyalty. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interests Loyalty Professional athletes Service quality Volleyball tournament events

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A Survey of information Security Roles and Trends in the Quranic Perspective

A Survey of Information Security Roles and Trends in the Qur...

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Cyber Security and Computing (CyberComp), International Conference on

作者： Mohd Amin Mohd Yunus Nurul Hidayah Ab Rahman Isredza Rahmi A. Hamid Applied Information System Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Centre of Information Security Research Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

In this study, we surveyed the existing information security literature related to the Quran to contribute to the knowledge gap(s) in seeking the role of information security in the Quran research area. Materials published in English were located between January 2019 and February 2024 by searching various academic databases, including Google Scholar, IEEEXplore, ACM Digital Library, Springer and scienceDirect. Thirty-two publications were collected and classified into ten domains of information security Common Body of Knowledge to identify the research trends. The results show that Domain 3 (Cryptography and Digital Watermarking) is the most studied area, followed by Domain 2 (Ethical and Legal Issues) with eight publications and Domain 5 (Access Control) with three publications. Other domains consist of only 1 or 2 publications each. Our observation also showed that three recurring themes of studies that are the implementation of information security techniques to preserve digital Quran authenticity, the association of Quran verses in supporting information security approaches; and the development of information security techniques from the Quran epistemology. Overall, exploring the research trends and interconnected areas could lead to advancements in information security practices and Quranic studies, fostering how traditional wisdom influences modern technologies.

关键词： Surveys Ethics Law Databases information security Watermarking Market research Libraries Internet Cryptography

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Deep Learning Techniques in Mobile Edge Computing for Internet of Medical Things 3

Deep Learning Techniques in Mobile Edge Computing for Intern...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alawadhi, Abdulwadood Ahmad, R. Badlishah Almogahed, Abdullah Abrar, Ahmad Department of Computers and Information Technology Yemen Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia School of Computing Universiti Utara Malaysia InterNetWorks Research Laboratory Sintok-Kedah Malaysia

ISBN: (纸本)9798350305333

The fast expansion of the Internet of Medical Things (IoMT) has resulted in a ubiquitous home health diagnostic network. High patient demand results in high costs, short latency, and communication overload. As a result, 6G is the next generation of IoT, IoMT, and cellular networks, intending to considerably improve the quality of smart healthcare services through high throughput and decreased latency. So far, adopting cloud computing for time-critical applications and decreasing access delays to resources is difficult. Deep learning has been extensively employed to extract characteristics from complicated networks as artificial intelligence technology has advanced. On the other hand, deep learning models are often run in cloud computing data centres with tremendous processing resources. Conventional cloud computing systems primarily rely on the network, which has significant latency and security and privacy issues. One of the successful solutions is the Mobile Edge Computing (MEC) paradigm, which brings cloud computing services closer to the edge network and uses available resources. Mobile edge computing places computer and storage nodes near mobile devices at the Internet's edge, leading to considerable savings in system operating time, memory cost, and power usage. Deep learning is used in mobile edge computing to forecast changes in demand based on daily patient behaviours. It also prepares the network by scaling up network resources as required. This study aims to discuss IoMT and identfy the problems in deep learning for mobile edge computing technology and its applications. © 2023 IEEE.

关键词： Deep learning

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