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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Acikalin, Utku Umur Caskurlu, Bugra Abul, Osman Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400716928

Wireless Sensor Networks (WSNs) play a crucial role in various domains, from commercial to military, enabling precise monitoring of environmental and other conditions. However, the transmission of sensitive data in WSNs poses security challenges, particularly concerning eavesdropping attacks. While cryptographic methods offer protection, they often incur high energy costs, making them impractical for battery-powered WSNs. Our problem formulation, based on graph theory, reduces to the Secluded Paths problem for which no polynomial solution yet to known. Motivated by the challenge of reducing the eavesdropping risk through intelligent routing, we propose a scalable optimization approach using heuristics and local search techniques. We present a solution framework, followed by an experimental evaluation to demonstrate its efficacy. This paper contributes to the advancement of secure and efficient data transmission in WSNs. © 2024 ACM.

关键词： Sensitive data

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Deepfake Video Detection Using Inception_Resnet_V2: A Convolutional Neural Network Approach 16

Deepfake Video Detection Using Inception_Resnet_V2: A Convol...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Praveen Kumar, Mandala Kumari, V Valli Lakshmi Durga, K Swathi Andhra University College of Engineering Department of Computer Science and Systems Engineering Visakhapatnam530003 India

ISBN: (纸本)9798331505264

Deepfake videos, generated by sophisticated AI algorithms, pose significant challenges to the authenticity and trustworthiness of multimedia content on the internet. In this study, we propose a deepfake video detection system utilizing the Inception_ResNet_v2 architecture, a deep convolutional neural network renowned for its effectiveness in image classification tasks. The system is trained and evaluated on the Deepfake Challenge Dataset sourced from Kaggle, comprising a diverse set of real and manipulated video clips. We preprocess the dataset, extracting frames from each video and augmenting them to enhance model generalization. The Inception_ResNet_v2 model is then fine-tuned using transfer learning, leveraging pre-trained weights from ImageNet to expedite convergence and improve performance. Through extensive experimentation and evaluation, we demonstrate the efficacy of our approach in accurately distinguishing between genuine and deep-fake videos. The proposed system achieves promising results in detecting deep-fake videos, showcasing its potential utility in combating the proliferation of synthetic media and safeguarding the integrity of digital content. Our findings underscore the importance of leveraging advanced machine learning techniques for addressing the evolving challenges posed by deep-fake technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Skill Mastery Solutions: Your Path to Excellence 10

Skill Mastery Solutions: Your Path to Excellence

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Saranya, E. Ajay, K. Hardika, S. Jayasudha, C. Kishore, R.E. Sri Eshwar College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

'Skillstorm' is an innovative and competitive website that harnesses the power of cutting-edge technologies to create a safe and engaging online environment. Developed primarily using Python and Flask, Skillstorm integrates machine learning algorithms to combat cyberbullying, ensuring a positive user experience. This project leverages HashMaps for efficient data management and employs a dynamic front-end interface built with HTML, CSS, and JavaScript. The backend of Skillstorm is powered by Python and Flask, providing a robust and scalable foundation for handling user data, interactions, and website functionality. Skillstorm utilizes machine learning techniques to automatically detect and mitigate instances of cyberbullying and harassment. This proactive approach ensures a safe and welcoming environment for all users. HashMaps are employed for efficient data storage and retrieval, optimizing the performance of the website and enhancing user experience. 'Skillstorm' represents a forward-thinking and comprehensive project that combines Python, Flask, machine learning, HashMaps, HTML, CSS, and JavaScript to create a competitive website that prioritizes user safety and positive interactions in an increasingly digital world. Powered by cutting-edge Python frameworks, we have created a dynamic and immersive experience that will ignite your passion, inspire excellence, and propel you to new heights of achievement. At SkillStorm, we believe that true talent knows no borders. © 2024 IEEE.

关键词： Python

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

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Neural Computing and Applications 2024年第34期36卷 21545-21560页

作者： Alam, Tanweer Gupta, Ruchi Ahamed, N. Nasurudeen Ullah, Arif Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah P.O.Box. 170 Madinah42351 Saudi Arabia Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Abdul Kalam Technical University Lucknow India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation

关键词： Reinforcement learning

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A Robust Security Detection Strategy for Next Generation IoT Networks

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computers, Materials & Continua 2025年第1期82卷 443-466页

作者： Hafida Assmi Azidine Guezzaz Said Benkirane Mourade Azrour Said Jabbour Nisreen Innab Abdulatif Alabdulatif Technology Higher School Essaouira Cadi Ayyad UniversityEssaouira44000Morocco IMIA Laboratory MSIA TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia50050Morocco CRIL-CNRS Artois UniversityLens62300France Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia

Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating *** connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented *** ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and *** these approaches,Intrusion Detection systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of *** study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification *** combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of *** analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT *** results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack *** the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of *** work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.

关键词： IoT security intrusion detection RF KNN SVM EL NSL-KDD Edge-IIoT

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Latest Innovations in Intelligent Network-on-Chip Architectures: A Systematic Review 17

Latest Innovations in Intelligent Network-on-Chip Architectu...

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17th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2024

作者： Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331506421

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures. © 2024 IEEE.

关键词： Federated learning

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A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： Shaha Al-Otaibi Rahim Khan Hashim Ali Aftab Ahmed Khan Amir Saeed Jehad Ali Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200Pakistan Department of Computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server *** IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority ***,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are *** paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other ***,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline ***,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication ***,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT ***,proposed authentication and existing state-of-the-art approaches have been s

关键词： Internet of things authenticity security location communication

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An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

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computers, Materials & Continua 2023年第2期74卷 3315-3332页

作者： Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Arts at MuhayelKing Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing ***,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management *** study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human *** goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous *** HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and *** addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 ***,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

关键词： Hazardous waste image classification ensemble learning deep learning intelligent models human health weighted voting model

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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A novel hybrid deep learning framework for pedestrian detection based on thermal infrared and visible spectrum images

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Multimedia Tools and Applications 2025年 1-27页

作者： Defaoui, Mahassine Koutti, Lahcen El Ansari, Mohamed Lahmyed, Redouan Masmoudi, Lhoussaine LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir80000 Morocco Informatics and Applications Laboratory Faculty of Science My Ismail University Meknes Morocco Conception and Systems Laboratory Faculty of Sciences Mohamed V University Rabat Morocco

For many intelligent systems, designing accurate pedestrian detection approaches is a fundamental task. This paper describes a novel hybrid system to detect pedestrians using both visible and thermal infrared sensors. The proposed method is achieved in two primary stages. The first stage, hypotheses generation (HG), aims to extract the regions that represent suspected pedestrians from the image. It is performed using both thermal and visible images of the same scene. Here, an improved deep learning-based solution with a convolutional neural network named PedTherm-U-net is proposed. It segments the thermal images and maps the segmentation results into the corresponding visible image to extract the ROIs (candidate pedestrians). The second stage, hypotheses verification (HV), classifies the ROIs extracted from the first stage into pedestrians and non-pedestrians. In the HV step, a novel PedVis-ResNet framework to classify the extracted ROIs is introduced. It is a modified version of ResNet model dedicated to classify the visible pedestrian samples. The proposed method has been tested on the publicly accessible OSU Color-Thermal dataset, and the obtained results demonstrate the effectiveness of the proposed method when compared to the recent state of the art pedestrian detection approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Intelligent systems

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