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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Quan, Teoh Chee Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Petaling Jaya Selangor Sunway City47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This survey provides detailed perspectives on Malaysian solar energy system adoption in relation to the economic outcome and environmental and policy context. This work has been done by using comprehensive keyword lists and inclusion criteria to confirm that the findings of this survey capture as many relevant publications as possible from the research period from 2010 to 2024. Unlike numerous papers that investigate such systems with mere commercial or industrial perspective, this survey aims at assessing residential installation, and providing accurate cost benefit analysis while showing the business belief of such schemes. It also examines various opinions, especially the Malaysian government policies and strategic incentives and staking its success on public awareness and acceptance. We equally allocate a considerable portion of this survey to discussing the environmental effects and the life cycle analyses comparing solar energy with other energy types. Notably, the survey is also under sustainable development goal 7 with the goal of achieving universal access to affordable reliable, sustainable and modern energy. Such a focus also makes the identified topics relevant for Malaysia, which is useful for references in subsequent projects and activities. These would include use of web-survey, the coverage on the residential sector, the ability to incorporate policy analysis and emphasis on sustainability to the study on solar energy usage in residential Malaysia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm Intelligence

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A comparative study of word embedding techniques for SMS spam detection 14

A comparative study of word embedding techniques for SMS spa...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Joseph, Prashob Yerima, Suleiman Y. School Computer Science and Informatics De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Cyber Technology Institute De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9781665487719

E-mail and SMS are the most popular communication tools used by businesses, organizations and educational institutions. Every day, people receive hundreds of messages which could be either spam or ham. Spam is any form of unsolicited, unwanted digital communication, usually sent out in bulk. Spam emails and SMS waste resources by unnecessarily flooding network lines and consuming storage space. Therefore, it is important to develop high accuracy spam detection models to effectively block spam messages, so as to optimize resources and protect users. Various word-embedding techniques such as Bag of Words (BOW), N-grams, TF-IDF, Word2Vec and Doc2Vec have been widely applied to NLP problems, however a comparative study of these techniques for SMS spam detection is currently lacking. Hence, in this paper, we provide a comparative analysis of these popular word embedding techniques for SMS spam detection by evaluating their performance on a publicly available ham and spam dataset. We investigate the performance of the word embedding techniques using 5 different machine learning classifiers i.e. Multinomial Naive Bayes (MNB), KNN, SVM, Random Forest and Extra Trees. Based on the dataset employed in the study, N-gram, BOW and TF-IDF with oversampling recorded the highest F1 scores of 0.99 for ham and 0.94 for spam. © 2022 IEEE.

关键词： Embeddings

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Federated Learning in STAR-RIS Aided SAGINs

IEEE Communications Standards Magazine

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IEEE Communications Standards Magazine 2025年第2期9卷 23-29页

作者： Wu, Min Guo, Kefeng Dev, Kapal Hamadi, Hussam Al Singh, Keshav Khowaja, Sunder Ali Li, Xingwang Space Engineering University School of Space Information Beijing101407 China Munster Technological University CONNECT Centre Department of Computer Science Ireland College of Engineering and IT University of Dubai United Arab Emirates National Sun Yat-sen University Institute of Communications Engineering Kaohsiung Taiwan Technological University Dublin School of Computing Faculty of Computing Digital and Data Ireland Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China

Satellite-aerial-ground integrated network (SAGIN) is considered to be an advanced architecture that offers seamless coverage, flexible access, and efficient connectivity. However, several challenges emerge during the construction of SAGINs, which include complex propagation environments and limited spectrum resources. The aforementioned challenges can be addressed in a cost effective manner by integrating simultaneous transmission and reflecting reconfigurable intelligent surfaces (STAR-RISs). The STAR-RISs are capable of reflecting and transmitting the incident signals passively in order to serve users on both sides of the panel. Given the self-organizing nature of STAR-RIS-aided SAGINs, meticulous resource allocation and transmission strategy adaptation are imperative to accommodate changes in multiple communication node environments. Recently, federated learning has been envisioned as a pioneering distributed machine learning (ML) approach for managing the multi-objective optimization problem in STAR-RIS-aided SAGINs. This approach strives to balance the trade-offs between wireless communication performance and model efficiency. In this work, we present the advantages and potential directions for employing federated learning in STAR-RIS-aided SAGINs through multi-objectives optimization. Furthermore, to illustrate the practical application of federated learning in STAR-RIS-aided SAGINs, we present two case studies utilizing federated deep reinforcement learning, based on the non-orthogonal multiple access (NOMA) scheme. © 2017 IEEE.

关键词： Deep reinforcement learning

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John Hollerton: a life in industrial analytical spectroscopy, part 2

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Spectroscopy Europe 2023年第1期35卷 21-26页

作者： Davies, Antony N. Cashyap, Mohan Hollerton, John SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom MASS Informatics Harpenden United Kingdom Hollerton Scientific Software Consultancy St Albans United Kingdom

An interview with John Hollerton, who retired from a long career at GSK, is presented. Among other things, Hollerton discusses the biggest innovation he has seen that advanced his science, the biggest hyped innovation which failed to deliver as promised, and the go-to instrument development techniques from the start of his career that are now seldom used.

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3D printing of PEEK–cHAp scaffold formedical bone implant

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Bio-Design and Manufacturing 2021年第1期4卷 44-59页

作者： Bankole I.Oladapo SAbolfazl Zahedi Sikiru O.Ismail Francis T.Omigbodun Oluwole K.Bowoto Mattew A.Olawumi Musa A.Muhammad School of Engineering and Sustainable Development De Montfort UniversityLeicesterUK Centre for Engineering Research School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldUK Mechanical Engineering Loughborough UniversityLoughboroughUK Faculty of Engineering Computing and mediaDe Montfort UniversityLeicesterUK Faculty of Engineering Environment and ComputingCoventry UniversityCoventryUK

The major drawback associated with PEEK implants is their biologically inert surface,which caused unsatisfactory cellular response and poor adhesion between the implants and surrounding soft tissues against proper bone *** this study,polyetheretherketone(PEEK)was incorporated with calcium hydroxyapatite(cHAp)to fabricate a PEEK-cHAp biocomposite,using the fused deposition modeling(FDM)method and a surface treatment strategy to create microporous architectures onto the filaments of PEEK lattice ***,nanostructure and morphological tests of the PEEK-cHAp biocomposite were modeled and analyzed on the FDM-printed PEEK-cHAp biocomposite sample to evaluate its mechanical and thermal strengths as well as in vitro cytotoxicity via a scanning electron microscope(SEM).A technique was used innovatively to create and investigate the porous nanostructure of the PEEK with controlled pore size and distribution to promote cell penetration and biological integration of the PEEK-cHAp into the *** vivo tests demonstrated that the surface-treated micropores facilitated the adhesion of newly regenerated soft tissues to form tight implant-tissue interfacial bonding between the cHAp and *** results of the cell culture depicted that PEEK-cHAp exhibited better cell proliferation attachment spreading and higher alkaline phosphatase activity than PEEK *** islands formed on the PEEK-cHAp composite after immersion in simulated body fluid of Dulbecco’s modified Eagle medium(DMEM)for 14 days and grew continuously with more or extended *** microstructure treatment of the crystallinity of PEEK was comparatively and significantly different from the PEEK-cHAp sample,indicating a better treatment of *** in vitro results obtained from the PEEK-cHAp biocomposite material showed its biodegradability and performance suitability for bone *** study has potential applications in the field of biomedical engineering to strengthen the conceptual kn

关键词： 3D printing PEEK-cHAp biocomposite Nanostructure Bone implant Composite morphing

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Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges

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Computers in Biology and Medicine 2025年 189卷 109834-109834页

作者： Ibrahim, Mahmoud Khalil, Yasmina Al Amirrajab, Sina Sun, Chang Breeuwer, Marcel Pluim, Josien Elen, Bart Ertaylan, Gökhan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands VITO Belgium Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our aim is to offer insights into their current and future applications in medical research, particularly in the context of synthesis applications, generation techniques, and evaluation methods, as well as providing a GitHub repository as a dynamic resource for ongoing collaboration and innovation. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered in previous reviews. The survey also emphasizes the aspect of conditional generation, which is not focused on in similar work. Key contributions include a broad, multi-modality scope that identifies cross-modality insights and opportunities unavailable in single-modality surveys. While core generative techniques are transferable, we find that synthesis methods often lack sufficient integration of patient-specific context, clinical knowledge, and modality-specific requirements tailored to the unique characteristics of medical data. Conditional models leveraging textual conditioning and multimodal synthesis remain underexplored but offer promising directions for innovation. Our findings are structured around three themes: (1) Synthesis applications, highlighting clinically valid synthesis applications and significant gaps in using synthetic data beyond augmentation, such as for validation and evaluation;(2) Generation techniques, identifying gaps in personalization and cross-modality innovation;and (3) Evalu

关键词： Generative adversarial networks

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (Soft computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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Soft computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

The author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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Investigation of screw assembly on cold-formed steel members: experimental and numerical studies

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Asian Journal of Civil engineering 2024年第4期25卷 3349-3362页

作者： Rouaz, Idriss Rafa, Sid Ali Bouzid, Haytham Saidani, Messaoud Ahmed-Chaouch, Ali National Center of Studies and Integrated Research on Building Engineering (CNERIB) Algiers Algeria Department of Sciences and Technology Faculty of Sciences and Technology Tissemsilt University Tissemsilt Algeria School of Energy Construction and Environment Faculty of Engineering and Computing Coventry University Priory Street Coventry CV1 5FB United Kingdom Faculty of Civil Engineering University of Science and Technology Houari Boumedienne (USTHB-LEEGO) Algiers Algeria

The strength of the Cold Formed Steel Members (CFS-M) assembly with screws connection has governed the global behavior of CFS Shear Wall Panel (CFS-SWP). Hence, the development of a Finite Element Modeling (FEM) with a reliable simulation taking into account the strength performances of these member assemblies strongly relies on experimental data. In this paper, the strength performances of Cold Formed Steel Member (CFS-M) assemblies in terms of failure modes, strength capacity and nonlinear force–displacement response is investigated through a rigorous experimental program. The results show that varying the thickness and the steel grade results in several failure modes, including CFS-M bearing, tilting and shear screw. Furthermore, increasing the number of screws assembly from one to two screws did not double the strength capacity of the assembly. In addition, a Finite Element Model (FEM) of the CFS-M assembly is developed to simulate and reproduce the assembly’s strength. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cold formed steel Experimental study Failure modes Finite element modeling Screw assembly Strength performances

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