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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image 7

Machine-Learning-Scheme to Detect Choroidal-Neovascularizati...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Taniar, David Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Faculty of Information Technology ClaytonVIC3800 Australia Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Eye is a fundamental sensory organ and any disease in eye will severely affect the sensory signal evaluation and conclusion making capability of the brain. The Choroidal-Neovascularization (CNV) is one of the harsh eye diseases in which a new blood-vessel grow from the choroid. Usually, the major cause of CNV is due to wet Age-Related-Macular-Degeneration (ARMD) and the formed new vessel will cause a leak in fluid which makes the retinal wet. The untreated CNV will lead to vision loss. In this research, detection of CNV using Optical-Coherence-Tomography (OCT) is presented using 484 images (242 Healthy and 242 CNV). In this work, a Machine-Learning-Scheme (MLS) is developed to examine the resized OCT of 256x256 pixels and the stages of this MLS includes;pre-processing, feature extraction, Mayfly-Optimization-Algorithm (MFA) based feature reduction, and two-class classification. The experimental outcome of this technique confirmed that the Fine-Gaussian-SVM (SVM-FG) classifier helped to accomplish an improved classification accuracy (>92%) compared to the alternative classifiers of this study. © 2021 IEEE.

关键词： Optical tomography

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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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Multi-Classification of Imbalance Worm Ransomware in the IoMT System 21

Multi-Classification of Imbalance Worm Ransomware in the IoM...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Hameed, Shilan S. Selamat, Ali Latiff, Liza Abdul Razak, Shukor A. Krejcar, Ondrej University Teknologi Malaysia Kuala Lumpur54100 Malaysia Information Technology Koya University Koya44023 Iraq School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor Skudai81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Skudai81310 Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

ISBN: (纸本)9781643683164

Worm-like ransomware strains spread quickly to critical systems such as IoMT without human interaction. Therefore, detecting different worm-like ransomware attacks during their spread is vital. Nevertheless, the low detection rate due to the imbalanced ransomware data and the detection systems' disability for multiclass simultaneous detection are two apparent problems. In this work, we proposed a new approach for multi-classifying ransomware using preprocessing, resampling, and different classifiers. The proposed system uses network traffic NetFlow data, which is privacy-friendly and not heavy. In the first phase, preprocessing techniques were used on the collected and aggregated ransomware traffic, and then an optimized Synthetic Minority Oversampling Technique (SMOTE) was used for resampling the low-class samples. After that, four classifiers were applied, namely, Bayes Net, Hoeffding Tree, K-Nearest Neighbor, and a lightweight Multi-Layered Perceptron (MLP). The experimental results showed that the efficient preprocessing ensured accurate and simultaneous ransomware detection while the resampling technique improved the detection rate, F1, and PRC curve. © 2022 The authors and IOS Press. All rights reserved.

关键词： Malware

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A Review Paper on Student-Graduate Advisory Expert system

A Review Paper on Student-Graduate Advisory Expert system

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2020 International Conference on computing and Information technology, ICCIT 2020

作者： Tawafak, Ragad M Alfarsi, Ghaliya Romli, Awanis Jabbar, Jasiya Malik, Sohail Iqbal Alsideiri, Abir AlBuraimi College Information Technology Department Buraimi Oman University Malaysia Pahang Faculty of Computing College of Computing and Applied Science Malaysia

ISBN: (纸本)9781728126807

Academic advising of the postgraduate students is considered as a fundamental rule and one of the significant positive services and responsibilities for the academic staff follow up in most of the e-leading educations. The expert system is considered as one of most achievement areas in artificial intelligence and highly appreciated for application with e-learning and teaching methods. The aim of this paper, is to survey the most expert systems used in advisory model and replaced with the manual registration and advising system of face to face communication and discussion in Instructor office. The method used of determined the key factors related to student advising and expert system models though open access databases. The finding of this section reviews out nine selected expert systems in order to explore the strengths, and limitations of existing expert system approaches to advising undergraduate students. © 2020 IEEE.

关键词： Expert systems

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A multi-criteria decision analysis framework for evaluating deep learning models in healthcare research

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Decision Analytics Journal 2024年 13卷

作者： Drissi, Nidal El-Kassabi, Hadeel Serhani, Mohamed Adel Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada The Faculty of Applied Sciences & Technology Humber College Institute of Technology & Advanced Learning Toronto ON Canada College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Selecting the appropriate deep learning (DL) model for healthcare research poses a significant challenge due to the diversity of evaluation criteria and the complex nature of health-related tasks, where a single metric like accuracy is often insufficient. Motivated by the need for a structured, multi-criteria approach, this study proposes a Multi-Criteria Decision Analysis (MCDA) framework using the Analytic Hierarchy Process (AHP). Our primary contribution is the development of a comprehensive decision-making framework that integrates multiple evaluation criteria, such as accuracy, sensitivity, specificity, and computational complexity, alongside empirical data from existing literature to systematically compare DL models. The framework was validated through a use case involving the selection of the best DL model for diagnosing COVID-19 using X-ray images, where we compared eight popular models, including ResNet34, SqueezeNet, and AlexNet, and it was also evaluated through comparative scenarios using traditional methods, including weighted sum, weighted average, and accuracy-based evaluation. Quantitative results show that SqueezeNet achieved the highest score in the AHP framework (88.64), while ResNet34 performed best in traditional methods such as weighted sum (588.49) and accuracy ranking (98.33%). A sensitivity analysis further demonstrated the impact of varying criteria weights, showing how changes in the importance of accuracy and precision, influenced model ranking. These findings highlight the flexibility and robustness of the AHP framework in addressing the complexities of model selection in healthcare research. The implications of this work suggest that a structured, data-driven evaluation approach can provide more nuanced and reliable insights compared to traditional methods like single-metric evaluations, ultimately supporting more informed decision-making in healthcare applications. © 2024 The Author(s)

关键词： Analytic hierarchy process Data analysis Deep learning Healthcare research Multi-criteria decision analysis

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Lithium Chloride-Mediated Enhancement of Dye Removal Capacity in Borneo Derived Nanocellulose-Based Nanocomposite Membranes (Ncms)

SSRN

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SSRN 2024年

作者： Rahman, Md Rezaur James, Anthonette Said, Khairul Anwar Mohamed Khandaker, Mayeen Uddin Namakka, Murtala Kuok, King Kuok Department of Chemical Engineering and Energy Sustainability Faculty of Engineering University Malaysia Sarawak Kota Samarahan94300 Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Jalan Simpang Tiga Kuching Sarawak93400 Malaysia Applied Physics and Radiation Technologies Group CCDCU School of Engineering and Technology Sunway University Selangor Darul Ehsan47500 Malaysia

Demand for sustainable materials for membrane fabrication is rapidly increasing, particularly in Sarawak coastal areas. nanocellulose emerges as a promising alternative for membrane development due to its availability, intrinsic biodegradability, and high strength. However, the hydrophobicity of nanocellulose limits its applicability in water treatment operations. hence necessitating the need for innovative solutions. Herein, we examine the potential of bamboo-derived nanocellulose to enhance nanocomposite membrane-based water treatment systems by incorporating lithium chloride as a pore-forming additive. Nanocomposite membranes (NCMs) were synthesised using a LiCl-doped solution of nanocellulose and polyvinylidene fluoride (PVDF) through phase inversion and controlled solidification methods. The results show that nanocellulose incorporation induces structural alterations in NCMs, with reduced size in cellulose microfibrils. The crystallinity of nanocellulose was enhanced through interactions between PVDF and nanocellulose. The integration of nanocellulose along with lithium chloride was observed to have a significant impact on water permeation flux, attaining 104 L/m2h due to improved hydrophobicity and reduced fouling. In addition, the developed NCMs recorded 93% methylene blue (MB) removal within 10 minutes. Revealing high potential of NCMs as an alternative and sustainable material for the development of nanocomposite membranes to mitigate contemporary challenges of water scarcity. © 2024, The Authors. All rights reserved.

关键词： Nanocomposites

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Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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Offline Writer Identification using Deep Convolution Neural Network 2

Offline Writer Identification using Deep Convolution Neural ...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2022

作者： Durou, Amal M. Aref, Ibrahim A. Erateb, Suleiman El-Mihoub, Tarek A. Ghalut, Tarik Emhemmed, Adel Saad University of Tripoli Faculty of Information Technology Tripoli Libya Lancaster University School of Computing and Comms Lancaster United Kingdom Coventry University Engineering and Digital Technologies Coventry United Kingdom Jade University of Applied Sciences Department of Engineering Science Wilhelmshaven Germany University of Tripoli Electrical Engineering Department Tripoli Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Deep convolutional neural networks (DCNN) are efficient in solving different pattern recognition problems and have been applied to extract image features (IFs). This paper investigates using deep learning (DL) techniques to improve the performance of the writer identification (WI) process. This work presents a novel approach for WI tasks by combining a DL technique with machine learning (ML). A convolutional neural network (CNN) is employed as a feature extractor along with a ML algorithm to classify those features. The standard Alex-Net model is utilized to extract IFs that located in the fully connected layers (FCLs). The support vector machine (SVM) model is selected as the classifier due to its efficient capabilities to improve identification performance (IP). The proposed model is tested using various types of the datasets, namely the Islamic Heritage Project (IHP) and Clusius. Furthermore, IAM and ICFHR-2012 datasets have been employed for benchmarking the proposed model. The results demonstrate the model achieves superior performance. © 2022 IEEE.

关键词： Convolutional neural networks

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Artificial Bee Colony assisted Spectrum Sharing Scheme for NOMA based Cognitive Radio Networks 4

Artificial Bee Colony assisted Spectrum Sharing Scheme for N...

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4th International Conference on Electronics, Communication and Aerospace technology, ICECA 2020

作者： Sultan, Kiran King Abdulaziz University Faculty of Applied Studies Department of Computing and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781728163871

In this paper, a cooperative spectrum sharing scheme is proposed for NOMA based cognitive radio networks comprising of primary and secondary networks. The primary network consists of a primary transmitter PT communicating with a primary receiver PU, whereas the secondary network consists of a NOMA based secondary transmitter ST communicating with L secondary users SUs. The primary terminals are separated far apart therefore ST provides assistance as a relay in order to enable their end-to-end communication. Each terminal is equipped with a single antenna therefore end-to-end communication is accomplished in two time-slots. In first timeslot, PT transmits its signal to ST and at the same time one best SU retransmits the primary signal of the last frame to PU. In second time-slot, ST transmits the superimposed signals of primary and secondary networks. In this scenario, sum rate of SUs is maximized while ensuring to guarantee the QoS of PU. Artificial Bee Colony (ABC) global optimization algorithm is employed to solve this transmit power allocation problem which quickly converged to the best solution. © 2020 IEEE.

关键词： Cognitive radio

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