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MobiTest – A Software for Mobile-Based Testing
No full textMobiTest 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 development of this application, a need arose for manual testing, which unfortunately resulted in the inefficient use of resources. MobiTest was designed to overcome these limitations by providing the ability to generate generic test scripts for any application as needed. It can efficiently and adaptively handle intricate tasks according to predefined parameters. This application thoroughly examines every possible detail, allowing the hacker to exploit the system
In Search of Suitable Methods for Cost-Benefit Analysis of Cyber Risk Mitigation in Offshore Wind: A Survey
No full textIn 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 narrowing the area scope, a systematic search across Scopus and Web of Science, yielded 18 articles, of which six met the selection criteria. It was found that the there was a lack of cost benefit analysis of cybersecurity solutions for, or set in, the area of offshore wind directly. From the analysis of the surveyed works, suggestions on future directions were given. The existing literature found lacks detailed cost modelling for offshore wind, beyond general breakdowns encompassing capital, maintenance, and labour/installation expenses, risk and scenario loss. Some of the literature used contextual factors such as compatibility and effectiveness of mitigation measures, effects on OT performance, geographical location, geopolitical context, and installed rated power which could be adapted to suit offshore wind. Since offshore operations contribute significantly to costs, cost modelling and consideration of other relevant factors pertaining to this area would be beneficial if explored. As an emerging area, in the future we expect this research to be a basis and a methodology that can be expanded with a larger data set from other publications in the field. Thus, it represents an opportunity to advance knowledge in offshore wind cyber-physical systems
Editorial Preview
No full textEffective 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 into everyday environments. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research
Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance
No full textWith the ever-increasing number of vehicles on the road, a faster reliable security system for university entry is needed. This paper presents an approach for Automatic Number Plate Recognition (ANPR) using deep learning and PP-OCRv3. The proposed approach utilizes a pre-trained object detection model to locate license plates, extracts a single frame of the license plate, performs license plate recognition, applies pre-processing techniques, and employs PP-OCRv3 for text extraction in real time. The system was tested with Malaysian vehicle plates, and its accuracy and speed of detection were evaluated. The results show the system's potential to be easily adapted to different camera systems, angles, and lighting conditions by retraining the deep learning model. The paper also explores various deep learning methods, such as CenterNet, EfficientDet, and Faster R-CNN, and their effectiveness in automated vehicle registration detection. The research methodology involves creating a dataset from Open Images Dataset V4, converting label text into XML files, and utilizing the TensorFlow model trained on the COCO dataset. The paper concludes with the synthetic evaluation of the trained models, comparing their performance based on precision, recall, and F1-score. Overall, the proposed approach highlights the potential of deep learning and PP-OCRv3 in achieving accurate and efficient ANPR systems
Performance Evaluation of YOLO Models in Plant Disease Detection
No full textPlant diseases significantly impact global agriculture, leading to substantial production losses and economic consequences. Timely disease detection can enhance crop yield, optimize resource utilization, reduce costs, and mitigate environmental effects, ultimately ensuring high-quality food production. Deep learning, specifically computer vision-based techniques, have proven invaluable in tasks like image classification, segmentation, and object detection. Deep Learning techniques such as You Only Look Once (YOLO) models are state of the art neural network algorithms used for accurate object detection. In this study, YOLOv5, YOLOv7 and YOLOv8 models were trained on CCL’20 dataset for citrus disease detection. Data augmentation techniques such as image translation, image scaling, flip, mosaic augmentations were implemented to improve the models’ performance during training phase. The model performance was evaluated using metric such as Mean Average Precision at 50% to 95% Intersection over Union score i.e. mAP@50-95. The results show that YOLOv8 model performs better than other variants and offers significant improvements over the benchmark performance from previous studies. The final hyper-parameter tuned model achieved 96.1% mAP@50-95 on testing data for citrus disease detection and mAP@50-95 of 95.3%, 96.0% and 97.0% for detection of Anthracnose, Melanose and Bacterial Brown Spot diseases, respectively. The trained model was able to detect single and multiple instances of same or different disease in an image showing the potential of recent YOLO models. The trained YOLOv8 model is deployed on Roboflow platform
Empirical Analysis of CI/CD Tools Usage in GitHub Actions Workflows
No full textAs 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 concurrent usage of the tools is calculated. Next, the tools are labeled based on their taxonomy. In our finding, the build automation has the biggest number of uses, while the test automation has the least number of uses. Our finding indicates the correlation between the tool category and the programming language used in the software project. Meanwhile, some tools cannot be classified into the existing taxonomy. This can lead to reevaluating the taxonomy structure of CI/CD tools
Editorial Preview
No full textThis editorial highlights all 18 papers in the June issue that deal with the practical aspects of Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), the Internet of Things (IoT), Computer Vision, e-learning, and other topics in Computer Science. This issue also includes suggestions for several worthwhile works that deserve further research. With effective from our third volume first issue, we will be publishing triannually in February, June and October
Characterization and Evaluation of Mechanical Properties of Carbon Nanotube Filler Epoxy Composite
No full textThe outstanding mechanical properties of carbon nanotubes (CNT) have made them the focus of extensive investigation. To characterize and study the effects of the different volume fraction of multi walled carbon nanotubes (MWCNTs) on the mechanical properties of the nanocomposites attempted. The purpose of this work is to use experimental techniques to ascertain the mechanical characteristics of the nanocomposite. Tests on mechanical tensile strength were conducted to see how the MWCNT filler content affected the reinforced epoxy nanocomposite. The result of altered percentage of MWCNTs on the mechanical properties of the composites had been inspected. Results showed that 0.2 wt% MWCNT addition has the best effect on the mechanical properties of the matrix.
Manuscript received: 10 Dec 2023 | Revised: 11 Feb 2024 | Accepted: 02 March 2024 | Published: 30 Apr 202
Performance Improvement Scheme of NIDS Through Optimized Intrusion Pattern Database
No full textNetwork-based intrusion detection systems (NIDS) are perceptively distributed devices within computer networks. They aim to examine traffic passing through the network on which they are installed passively. The database is the most vital part of network intrusion detection systems, as all the data converted information from the NIDS needs to be saved in a patterned structured manner. Understanding the usability of several available types of databases like central databases, Distributed databases, operational databases, etc., it is on the developer’s end to choose the most comprehensive one. Data transformation and performance speed are essential features that a stable database can handle. In this paper, we have analyzed the performance of multiple databases to find out the proficient way that favors NIDS optimization.
Manuscript received: 21 Dec 2023 | Revised: 26 Jan 2024 | Accepted: 10 Feb 2024 | Published: : 30 Apr 202
A Review on Mechanical Fuzzy Logic Control Cutters for Latex Glove
No full textLatex gloves are widely used in various industries, such as healthcare, laboratories, and manufacturing. Especially in the healthcare industry, it provides protection for doctors and nurses so that they will not get infected by viruses. The latex gloves contain some types of proteins that will trigger the allergic reactions of people with latex allergies. Therefore, before the latex gloves are sold on the market, protein concentration tests need to be done. In order to do the protein concentration tests, 2 cm by 2 cm samples of the latex gloves are needed. A cutter machine is needed in order to increase efficiency, save time, and also precisely cut. The samples can be obtained by cutting or stamping the latex gloves. In this paper, research in the literature that attempted to identify the mechanical cutters for latex gloves is reviewed. Furthermore, considering the ambiguity and variability in glove materials and cutting requirements, this paper explores the integration of fuzzy logic into cutter selection processes to accommodate uncertain criteria and optimize cutter performance in diverse operating conditions.
Manuscript received: 16 May 2024 | Revised: 3 July 2024 | Accepted: 10 Aug 2024 | Published: : 30 Sep 202