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Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

computers, Materials & Continua 2022年第3期70卷 6127-6140页

作者： Mohamed Ali Ibrahim A.Abd El-Moghith Mohamed N.El-Derini Saad M.Darwish Higher Institute for Tourism Hotels and ComputerAl-SeyoufAlexandria21533Egypt Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University21526Egypt Department of Computer and Systems Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

Routing is a key function inWireless Sensor Networks(WSNs)since it facilitates data transfer to base *** attacks have the potential to destroy and degrade the functionality ofWSNs.A trustworthy routing system is essential for routing security andWSN *** methods have been implemented to build trust between routing nodes,including the use of cryptographic methods and centralized ***,the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node *** the moment,there is no effective way to avoid malicious node *** a consequence of these concerns,this paper proposes a trusted routing technique that combines blockchain infrastructure,deep neural networks,and Markov Decision Processes(MDPs)to improve the security and efficiency of WSN *** authenticate the transmission process,the suggested methodology makes use of a Proof of Authority(PoA)mechanism inside the blockchain *** validation group required for proofing is chosen using a deep learning approach that prioritizes each node’s *** are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting *** to testing data,our routing system outperforms current routing algorithms in a 50%malicious node routing scenario.

关键词： Wireless sensor networks trusted routing deep neural network blockchain markov decision

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Quantile-Based Maximum Likelihood Training for Outlier Detection 38

Quantile-Based Maximum Likelihood Training for Outlier Detec...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Taghikhah, Masoud Kumar, Nishant Šegvić, Siniša Eslami, Abouzar Gumhold, Stefan Faculty of Computer Science Technische Universität Dresden Dresden Germany Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Translational Research Lab Carl Zeiss Meditec AG Munich Germany

ISBN: (纸本)1577358872

Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning. Furthermore, unsupervised generative modeling of inliers in pixel space has shown limited success for outlier detection. In this work, we introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood. The experimental evaluation demonstrates the effectiveness of our method as it surpasses the performance of the state-of-the-art unsupervised methods for outlier detection. The results are also competitive compared with a recent self-supervised approach for outlier detection. Our work allows to reduce dependency on well-sampled negative training data, which is especially important for domains like medical diagnostics or remote sensing. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Maximum likelihood

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Cramer-Rao Bound Investigation of the Double-Nested Arc Array Virtual Extension and Butterfly Antenna Configuration 3rd

Cramer-Rao Bound Investigation of the Double-Nested Arc Arra...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： El-Rahman, Tarek Abd Salama, Amgad A. Saad, Walid M. El-Shafey, Mohamed H. Faculty of Engineering Computer and System Engineering Department Ain Shams University Cairo11517 Egypt The Egyptian Technical Research and Development Centre Cairo11618 Egypt

ISBN: (纸本)9789819947638

This paper exploits a sparse array representation, a novel double-nested arc array to generate a multiple virtual nested arc array that forms the virtual non-uniform circular array which has been presented and investigated. The nested arc array has been developed using higher-order statistics, namely using the fourth-order cumulants (FOC), to increase the array aperture. The extended array contains a hybrid of physical and virtual sensors which is distributed in a butterfly shape. The enhanced degrees of freedom (DOFs) in the resultant virtual array make it appropriate for over-and under-determined direction-of-arrival (DOA) estimation. The Cramer-Rao bound (CRB), the number of nested arc arrays, the structure of the double-nested arc related to each other, and the overlap between sensors for such an array are developed and investigated for various arrays. The proposed array performance is superior to that of the uniform one. Further, the proposed array is appropriate for two-dimensional (2D) DOA estimation problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Direction of arrival

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Enlightening the Student in Knowledge Distillation 48

Enlightening the Student in Knowledge Distillation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zheng, Yujie Wang, Chong Chen, Yi Qian, Jiangbo Wang, Jun Wu, Jiafei Ningbo University Faculty of Electrical Engineering and Computer Science China China University of Mining and Technology School of Information and Control Engineering China SenseTime Research China

ISBN: (纸本)9781728163277

Knowledge distillation is a common method of model compression, which uses large models (teacher networks) to guide the training of small models (student networks). However, the student may find a hard time absorbing the knowledge from a sophisticated teacher due to the capacity and confidence gaps between them. To address this issue, a new knowledge distillation and refinement (KDrefine) framework is proposed to enlighten the student by expending and refining its network structure. In addition, a confidence refinement strategy is utilized to generate adaptive soften logits for efficient distillation. The experiments show that the proposed framework outperforms state-of-the-art methods on both CIFAR-100 and Tiny-ImageNet datasets. The code is available at https://***/YujieZheng99/KDrefine. © 2023 IEEE.

关键词： knowledge distillation model compression structural re-parameterization

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Development of a Web Application for Evaluating Corporate Social Responsibility Practices

Development of a Web Application for Evaluating Corporate So...

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8th International Conference on Science, Technology and Innovation for Society, CITIS 2023

作者： Cuvi, Jonnathan Sigcha, Erik Siguenza-Guzman, Lorena Faculty of Engineering University of Cuenca Cuenca010107 Ecuador Department of Applied Chemistry and Systems of Production Faculty of Chemical Sciences University of Cuenca Cuenca010107 Ecuador Department of Computer Sciences Faculty of Engineering University of Cuenca Cuenca010107 Ecuador Faculty of Economic and Business Research Centre Accountancy KU Leuven Leuven Belgium

ISBN: (纸本)9783031519819

Corporate Social Responsibility (CSR) is an increasingly relevant concept for companies as it improves competitiveness and market perception. Incorporating CSR elements in commercial operations requires considering various aspects, such as employee and customer satisfaction, product quality, and analysis of the environmental impacts generated. In line with this, companies have shown a growing interest in CSR concepts and the application of tools to determine its level of integration in business activities. This paper aims to present a web application to assess the level of integration of CSR elements in Ecuadorian MSMEs (Micro, Small and Medium Enterprises). The development of the proposed software required the analysis of CSR concepts, norms, and standards to establish a basic framework adapted to the context of Ecuadorian companies. The web application and the evaluation method based on an adapted CSR framework are presented as results of this work. The developed application, tested with personnel from local textile MSMEs, automates the evaluation process and can be considered as a first component for a comprehensive evaluation platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Customer satisfaction

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A Survey of Acceptability and Use of IoT for Patient Monitoring

A Survey of Acceptability and Use of IoT for Patient Monitor...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Kumra, Shivam Savita Monika Manav Rachna International Institute of Research and Studies Computer Science and Engineering Faculty of Engineering and Technology India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

It's absurd for a clinical master to monitor a patient's pulse and body temperature all the time from a remote location using standard monitoring equipment. As a solution to this problem, we may develop an Internet of Things-based framework that makes use of a microcontroller and a user-friendly temperature sensor known as the DS18B20 (which is a kind of temperature sensor that provides 9-bit to 12-bit measurements of temperature). An emerging concept of a linked e-health system that includes Personal Health Devices has emerged due to recent advances in consumer electronics and rising healthcare expenditures. It was found that incorporating ICT into the healthcare sector may help maximize the availability of all medical resources while also providing dependable, efficient care for the elderly and those with physical impairments or long-term sickness. This article sheds some insight on the currently available methods for Internet of Things-based healthcare applications. An IoT-based healthcare system using Pattern Matching Algorithm is presented which gathers patient data from different sensors and sends messages alerting the caregiver and doctor in real time. It keeps track of the patient's physiological characteristics from a distance and makes early diagnoses of illness. Using a Wireless Body Area Network, this may be integrated into a wearable alert system. The suggested solution's network overhead and appropriateness for various settings are discussed, including how different wireless interfaces integrate with a cloud service. © 2022 IEEE.

关键词： Internet of things

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