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Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

Computers, Materials & Continua 2023年第11期77卷 2189-2204页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at Faculty of Electrical Engineering Faculty of EngineeringUniversiti TeknologiMalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti TeknologiSkudaiJohorMalaysia Department of Communication Engineering Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu PahatJohor86400Malaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to ***,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further *** such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication *** proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data *** modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,*** simulation outcomes are indeed viable for the practical warehouse model.

关键词： Visible light communication(VLC) industrial applications warehouse model light fixtures bit error rate(BER)

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A Two-Tier Anomaly-based Intrusion Detection Approach for IoT-Enabled Smart Cities

A Two-Tier Anomaly-based Intrusion Detection Approach for Io...

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2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Hamdan, Mosab Eldhai, Arwa M. Abdelsalam, Samah Ullah, Kifayat Bashir, Ali Kashif Marsono, M.N. Kon, Fabio Batista, Daniel Macêdo Department of Computer Science Brazil University of Science and Technology Faculty of Engineering Sudan Khartoum University Faculty of Mathematical Sciences and Informatics Sudan Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Universiti Teknologi Malaysia Faculty of Electrical Engineering Malaysia

ISBN: (纸本)9781665494274

The Internet of Things (IoT), like other network infrastructures, requires Intrusion Detection Systems (IDSs) to be protected against attacks. When deploying an IDS in IoT-based smart city environments, the balance between latency and node capacity must be considered, which justifies a distributed IDS with specific classifiers based on the location of the system processing nodes. This paper proposes a two-level classification technique for collaborative anomaly-based IDSs deployed on fog and edge nodes. A Gradient Boosting Classifier (GBC) is used in the lower layer classifier at the edge, while a Convolutional Neural Network (CNN) is used in the upper layer classifier at the fog. Experimentation has demonstrated that the suggested IDS architecture outperforms previous solutions. For instance, in some scenarios, when comparing our proposal with Random Forest, the former obtained an accuracy equal to 99.1%, while the latter obtained 95.3%. Furthermore, our proposal can better select the most important network traffic features, reducing 76% of the data to be analyzed and improving privacy. © 2023 IEEE.

关键词： Internet of things

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PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training

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Computers in Biology and Medicine 2024年 172卷 108287-108287页

作者： Zhang, Zitong Zhao, Lingling Gao, Mengyao Chen, Yuanlong Wang, Junjie Wang, Chunyu Faculty of Computing Harbin Institute of Technology Harbin150001 China Department of Medical Informatics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing211166 China

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods. © 2024 Elsevier Ltd

关键词： Forecasting

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Transforming Secondary School Chemistry Learning with Virtual Reality

Transforming Secondary School Chemistry Learning with Virtua...

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Student Conference on Research and Development (SCOReD)

作者： Chiou Sheng Chew Muhammad Nabil Shah Zafarin Nurul Hidayah Mat Zain Raihah Aminuddin Tse Guan Tan Kim On Chin College of Computing Informatics and Mathematics Universiti Teknologi MARA Melaka Branch Jasin Campus Melaka Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

In secondary schools, traditional chemistry instruction often depends on lecture-based approaches and occasional hands-on experiments, which may limit students’ grasp of complex concepts like chemical reactions and molecular behaviour. This study addresses the need for enhanced educational tools by introducing a Virtual Reality (VR) application designed to enrich chemistry learning through immersive, interactive experiences. The VR tool allows students to simulate chemical reactions with adjustable parameters, such as temperature and concentration, enabling safe, hands-on exploration of complex processes. Usability was assessed among 30 secondary school students in Malaysia using the System Usability Scale (SUS), a standardized measure that gauges user experience across factors like functionality and ease of use. Findings showed a strong average SUS score of 91.25, indicating high usability and positive engagement from users. These results suggest that incorporating VR into the chemistry curriculum can help make challenging topics more accessible and engaging, supporting the modernization of science education. Future studies will look to expand the sample size and investigate additional chemistry topics to validate the tool’s educational benefits further.

关键词： Temperature measurement Solid modeling Chemistry Temperature Education Virtual reality Chemical reactions User experience Safety Usability

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A Framework for Tele-rehabilitation Gaming System 6

A Framework for Tele-rehabilitation Gaming System

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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Privacy-Preserving Quantum Key Distribution Ensemble Paillier Cryptosystem for Securing IoT Based Smart Metering System 6

Privacy-Preserving Quantum Key Distribution Ensemble Paillie...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Hasan, Md Mehedi Hasan, Mohammad Kamrul Muniyandi, Ravie Chandren Islam, Shayla Goh, Say Leng Universiti Kebangsaan Malaysia Ukm Faculty of Information Science and Technology Selangor 43600 Malaysia Institue of Computer Science and Digital Innovation Ucsi University Malaysia Kuala Lumpur56000 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88400 Malaysia

ISBN: (纸本)9798350389692

The advancements of smart grid embedded Internet of things for monitor, control and collection of energy data from smart meter to data aggregator points. In this aspect two-way smart meter current challenges of data privacy and cyber-security concerns. To overcome these constraints numerous authenticated key agreements schemes considered to enhances performances and security. However, high latency and heavy cryptographic operation entails time and bits overheads. Moreover, quantum computing can break the classical encryption techniques and mathematical barriers of cyber defensive properties. Therefore, proposed quantum key distribution ensemble Paillier cryptosystem for maintains privacy-preserving and securing of smart metering infrastructures. The details security analysis showed proposed protocol can resistance various cyber-attacks with quantum computing threats. Furthermore, performances assessments based on extensive simulation of computation time and bits proposed protocols are leading position from existing schemes. Thus, the novel proposed key distribution enables efficiency, security, and data privacy which is emerging notions for the smart metering infrastructures. © 2024 IEEE.

关键词： Quantum computers

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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Computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data

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Computers, Materials & Continua 2022年第8期72卷 3665-3686页

作者： Mohammed BinJubier Mohd Arfian Ismail Abdulghani Ali Ahmed Ali Safaa Sadiq Faculty of Computing Universiti Malaysia PahangKuantanPahangMalaysia School of Computer Science and Informatics De Montfort UniversityLeicesterLE19BHUnited Kingdom School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonWulfruna Street WolverhamptonWV11LYUnited Kingdom

Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific ***,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published *** anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big ***,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership ***,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge *** study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data *** particular,the proposed method distributes the data into horizontal and vertical *** lower and upper protection levels are then used to identify the unique and identical attributes’*** unique and identical attributes are swapped to ensure the published big data is protected from disclosure *** outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.

关键词： Big data big data privacy preservation anonymization data publishing

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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The Lack of E-Commerce Customer Service and After Selling Follow-Up 1

The Lack of E-Commerce Customer Service and After Selling Fo...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Kanaan, Anas Ghassan Majdalawi, Yousef I. Farea, Mazen Mohammed Al-Dubai, Maged Mustafa Zakaria, Muhammad D. Bisht, Neeraj Petra University Faculty of Management and Financial Sciences Business & E-commerce Department Amman Jordan Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Faculty of Informatics and Computing UniSZA Malaysia Birla Institute of Applied Sciences Bhimtal India

ISBN: (纸本)9798350341737

With the rapid growth of e-commerce in recent years, customer service has emerged as a critical factor influencing customer satisfaction, loyalty, and overall success of online businesses. However, a concerning trend has been observed wherein several e-commerce platforms seem to overlook the significance of robust customer service and post-purchase follow-up initiatives. This research paper delves into the pressing issue of 'The Lack of E-commerce Customer Service and After-Selling Follow-Up' and its impact on customer experiences and business performance. This study employs a mixed-methods approach, combining quantitative data analysis and qualitative surveys to comprehensively assess the state of e-commerce customer service across a diverse range of online businesses. A large-scale survey is conducted to gauge the perceptions of customers regarding the effectiveness and responsiveness of customer support in resolving issues and queries pre- and post-purchase. Additionally, qualitative interviews are conducted with e-commerce companies' representatives to gain insights into their strategies, challenges, and perspectives concerning customer service and after-selling follow-up. The findings of this research indicate a significant disparity between customer expectations and the actual level of customer service offered by many e-commerce platforms. Analysis of the quantitative data reveals that customers frequently encounter delayed responses, ineffective communication channels, and unaddressed concerns, leading to reduced satisfaction and decreased repeat purchases. Furthermore, the qualitative data highlights the barriers faced bye-commerce companies in implementing robust customer service systems, such as resource limitations, technological constraints, and lack of organizational focus. In light of these findings, this research paper proposes practical recommendations and strategies for e-commerce businesses to enhance their customer service and after-selling follow

关键词： Sales

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