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2018年第七届软件与计算机应用国际会议

作者： Kamal Z.Zamli Norasyikin Safieny Fakhrud Din Faculty of Computer Systems &Software Engineering Universiti Malaysia Pahang

software testing is a critical part of software development. Often, test suite sizes grow significantly with subsequent modifications to the software over time resulting into potential redundancies. Test redundancies are undesirable as they incur costs and are not helpful to detect new bugs. Owing to time and resource constraints, test suite minimization strategies are often sought to remove those redundant test cases in an effort to ensure that each test can cover as much requirements as possible. There are already many works in the literature exploiting the greedy computational algorithms as well as the meta-heuristic algorithms, but no single strategy can claim dominance in terms of test data reduction over their counterparts. Furthermore, despite much useful work, existing strategies have not sufficiently explored the hybrid based meta-heuristic strategies. In order to improve the performance of existing strategies, hybridization is seen as the key to exploit the strength of more than one meta-heuristic algorithm. Given such prospects, this research explores a hybrid test redundancy reduction strategy based on Global Neighborhood Algorithm and Simulated Annealing, called GNAA. Overall, GNAA offers better reduction as compared to the original GNA and many existing works.

关键词： Test Redundancy Reduction Simulated Annealing Global Neighborhood Algorithm

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Parameter Free Flower Algorithm Based Strategy for Pairwise Testing

Parameter Free Flower Algorithm Based Strategy for Pairwise ...

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2018年第七届软件与计算机应用国际会议

作者： Abdullah B.Nasser Kamal Z.Zamli Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang

Adopted to solve optimization problems, meta-heuristic algorithms aim to judiciously explore the search space in search of the good optimal solution. As such, the effectiveness of any particular meta-heuristic algorithm is heavily dependent on their control parameters, that is, to ensure balance exploration and exploitation. In the field of t-way testing, much work has been done to adopt meta-heuristic algorithms for generating interaction test suite(where t indicates the interaction strength). In this paper, we propose an Adaptive Flower Pollination Algorithm(AFPA) for pairwise testing. Unlike the original Flower Pollination Algorithm(FPA), our AFPA removes the static probability dependency inherent in FPA(i.e. for selection of local and global search operator). Specifically, we allow a dynamic and adaptive probability instead. The experimental results show that AFPA can produce the optimum results in many cases. AFPA also demonstrates its capacity to dynamically control global and local search based on the system configuration.

关键词： software Testing Pairwise Testing Meta–Heuristic Flower Pollination Algorithm Parameter Free

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Code-aware combinatorial interaction testing

arXiv

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arXiv 2019年

作者： Ahmed, Bestoun S. Gargantini, Angelo Zamli, Kamal Z. Yilmaz, Cemal Bures, Miroslav Szeles, Marek Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Management Information and Production Engineering University of Bergamo Italy Faculty of Computer Systems and Software Engineering University Malaysia Pahang Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic

Combinatorial interaction testing (CIT) is a useful testing technique to ad- dress the interaction of input parameters in software systems. In many ap- plications, the technique has been used as a systematic sampling technique to sample the enormous possibilities of test cases. In the last decade, most of the research activities focused on the generation of CIT test suites as it is a computationally complex problem. Although promising, less effort has been paid for the application of CIT. In general, to apply the CIT, practitioners must identify the input parameters for the software-under-test (SUT), feed these parameters to the CIT tool to generate the test suite, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper intro- duces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. We applied our approach to five reliable case studies. The results showed that this approach would help to detect new faults as compared to the equal impact parameter approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Black-box testing

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Fuzzy Adaptive Teaching Learning-based Optimization Strategy for the Problem of Generating Mixed Strength t-way Test Suites

arXiv

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arXiv 2019年

作者： Zamli, Kamal Z. Din, Fakhrud Baharom, Salmi Ahmed, Bestoun S. IBM Centre of Excellence Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak KuantanPahang Darul Makmur26300 Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Galleria 2 Manno-LuganoCH-6928 Switzerland

The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts. Copyright © 2019, The Authors. All rights reserved.

关键词： software testing

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Towards Optimal Search: An Efficient Search Algorithm for Arabic Text using Secant Method 2

Towards Optimal Search: An Efficient Search Algorithm for Ar...

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2nd Borneo International Conference on Applied Mathematics and engineering, BICAME 2018

作者： Kabir, Muhammad Nomani Alginahi, Yasser Ali, Jahan Benseghir, Hocine University Malaysia Pahang Faculty of Computer Systems Software Engineering Gambang Kuantan26300 Malaysia University of Windsor Dept. of Electrical and Computer Engineering WindsorONN9B 3P4 Canada University Malaysia Faculty of Engineering Technology Pahang Kuantan26300 Malaysia

ISBN: (纸本)9781538677254

Search techniques are an integral part for text authentication and plagiarism checks. In this work, our aim is to develop an efficient search algorithm for Arabic text (words and sentences) in a large database. The database contains the authentic source of information where information is ordered;thus the efficient algorithm can be used for searching the text in the database. The proposed search algorithm uses a quasi-Newton method - Secant method instead of the traditional binary search. The results show that the secant method has much faster convergence property than the binary search and hence the algorithm can find the search text in a shorter time period. © 2018 IEEE.

关键词： Database systems

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Model of Data Collection Control in the Internet of Things on the Basis of Social Network Technologies

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Journal of Physics: Conference Series 2021年第1期1864卷

作者： N A Zhukova A M Thaw E L Evnevich St. Petersburg Research Center of the Russian Academy of Sciences 14-th Linia VI No. 39 St. Petersburg 199178 Russia Faculty of Software Engineering and Computer Systems ITMO University Kronverkskiy prospekt 49 St. Petersburg 197101 Russia

The paper deals with the issues of data collection in the dynamic Internet of Things network. An approach proposed is based on the models and methods of social networks analysis along with clustering methods commonly used in this case. The developed hybrid model tracks contacts between nodes of the network thus creating 'social relationships' for each node and estimates the number and the frequency of 'social contacts' characterizing the degree of 'friendships'. After fixing the contacts between nodes the model takes into consideration only the nodes connected by 'friendship' relation. Data collection based on 'social structure' provides higher security level and shows the enhancement as regards energy saving and delays of data transfer.

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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Design of intelligent traffic controlling system using RF transponder 4

Design of intelligent traffic controlling system using RF tr...

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4th IEEE International Conference on Advances in Electrical and Electronics, Information, Communication and Bio-Informatics, AEEICB 2018

作者： Puviarasi, R. Ramalingam, Mritha Chinnavan, Elanchezhian Kalayan, Ajay ECE Department Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia School of Physiotherapy AIMST University Malaysia

ISBN: (纸本)9781538646069

Nowadays, the movement control is troublesome for the expanding traffic. The quick addition in vehicular excursions without relating growing in street space is achieving generous blockage in various parts. This makes trouble for the emergency vehicles such as ambulance, fire emergency and VIP vehicles to travel through the congested traffic. By analyzing different proposed systems, another arrangement can be actualized by making the traffic lights somewhat wise. To this issue, a framework utilizing computational intelligence is proposed to assign clear path for the emergency vehicles with the utilization of RF transponder and microcontroller. The significant part of this work is focused on reducing the inertness of the emergency vehicles with least interruption to customary traffic stream. © 2018 IEEE.

关键词： Microcontrollers

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Risk Factors Influencing the Construction Projects in Yemen from Expert's Perspective

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IOP Conference Series: Materials Science and engineering 2020年第1期712卷

作者： R A Bahamid S I Doh M A Al-Sharafi A R Rahimi Faculty of Civil Engineering Technology Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia Malaysia

This purpose of this study is to validate the important risk factors influencing the construction projects in a developing country of Yemen by using experts' evaluations and literature review. These factors influence the project's quality, time and cost. The semi-structured interviews were employed through online means using thirteen experts to achieve the set objective. Depending on the expert's overviews, out of 56 considered factors, 42 factors were assigned as the most related risk factors influencing the construction projects in Yemen. The obtained outcomes from this study can serve as the risk factors for building blocks in establishing a conceptual model to manage risks of the construction projects in Yemen.

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Review on the Effect of Student Learning Outcome and Teaching Technology in Omani’s Higher Education Institution’s Academic Accreditation Process

Review on the Effect of Student Learning Outcome and Teachin...

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2018年第七届软件与计算机应用国际会议

作者： Ragad M.Tawafak Muamer N.Mohammed Ruzaini bin Abdullah Arshah Awanis Romli Faculty of Computer Systems and Software Engineering University Malaysia Pahang Malaysia IBM Center of Excellence University Malaysia Pahang Faculty of Computer Systems and Software Engineering University Malaysia Pahang

Academic accreditation standards are defined clearly in Higher Educational Institutions（HEI） plans, management, and delivering results of assessment student with learning outcomes. In recent years, all universities and institutions work hard to increase their approval privileges, skills, achievements through getting the accreditation from HEI. The aim of this integrative review was to examine the current evidence on the impact of technology learning on student learning and academic performance and analysis the main factors of an institution accreditation process. The authors searched several electronic databases for relevant articles, with different learning techniques. The main contribution of this study lies in showing the general criteria’s of accreditation standards that focus on essential technology enhanced learning, student learning outcomes, and faculty technology experience on academic performance of universities. The result from this paper is showing the relations between what have been done and presented before with methods, factors, suggestions to identify these activities.

关键词： Higher Educational Institute （HEI） Student Learning Outcome （SLO） Technology Learning Quality Assurance Accreditation

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