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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Amir Al Sadi Marco Savi Davide Berardi Andrea Melis Marco Prandini Franco Callegati Department of Computer Science and Engineering - DISI University of Bologna (Bologna Italy) Department of Informatics Systems and Communication - DISCo University of Milano-Bicocca (Milano Italy)

In this work we demonstrate the integration of P4 enabled switches with high level AI techniques with the aim to improve efficiency and performance of DDoS detection and mitigation. Powerful ML-based strategies are adopted only when a suspicious behaviour is occurring in the network, and its activation is triggered by a coarser-grained and lightweight strategy fully executable in the data plane.

关键词： Technological innovation Cloud computing Conferences Pipelines Denial-of-service attack Real-time systems computer crime

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Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

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computers, Materials & Continua 2022年第5期71卷 3785-3801页

作者： Ashit Kumar Dutta S.Srinivasan Bobbili Prasada Rao B.Hemalatha Irina V.Pustokhina Denis A.Pustokhin Gyanendra Prasad Joshi Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Electronics and Communication Engineering Thirumalai Engineering CollegeKancheepuram631551India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking *** and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT *** this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted *** proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and *** proposed model involves two stage processes namely clustering and ***,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct ***,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted *** proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,*** proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 *** performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

关键词： Internet of things wireless sensor networks clustering routing metaheuristics cluster head selection QoS parameters

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Confidential Token-Based License Management 1

Confidential Token-Based License Management

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1st ACM International Workshop on Additive Manufacturing (3D Printing) Security, AMSec 2021

作者： Engelmann, Felix Speichert, Jan Philip God, Ralf Kargl, Frank Bösch, Christoph Department of Computer Science Aarhus University Aarhus Denmark Institute of Aircraft Cabin Systems TUHH Hamburg University of Technology Hamburg Germany Institute of Distributed Systems Ulm University Ulm Germany

ISBN: (纸本)9781450384803

In a global economy with many competitive participants, licensing and tracking of 3D printed parts is desirable if not mandatory for many use-cases. We investigate a blockchain-based approach, as blockchains provide many attractive features, like decentralized architecture and high security assurances. An often neglected aspect of the product life-cycle management is the confidentiality of transactions to hide valuable business information from competitors. To solve the combined problem of trust and confidentiality, we present a confidential licensing and tracking system which works on any publicly verifiable, token-based blockchain that supports tokens of different types representing licenses or attributes of parts. Together with the secure integration of a unique eID in each part, our system provides an efficient, immutable and authenticated transaction log scalable to thousands of transactions per second. With our confidential Token-Based License Management system (cTLM), large industries such as automotive or aviation can license and trace all parts confidentially. © 2021 ACM.

关键词： Blockchain

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Event-driven bi-fidelity control for efficient and reliable robotic manipulation

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Results in Engineering 2025年 26卷

作者： Alsaade, Fawaz W. Al-zahrani, Mohammed S. Alsaadi, Fuad E. Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper proposes a novel bi-fidelity control framework for robotic manipulators that integrates a high-fidelity model predictive control (MPC) scheme with a low-fidelity Long Short-Term Memory (LSTM) neural network surrogate. Unlike conventional fixed-schedule approaches, our method employs an event-triggered mechanism that dynamically selects the appropriate controller based on a real-time error metric. This mechanism ensures that the computationally intensive MPC is invoked only when the LSTM approximation deviates beyond acceptable bounds. We rigorously established the near-optimal performance and closed-loop stability of our approach via Lyapunov analysis under mild assumptions, instilling confidence in its reliability. The simulation results on a three-arm manipulator, subject to low-frequency sinusoidal and high-frequency chirp trajectories, demonstrate that the proposed approach achieves up to 80–90% reduction in MPC calls, significantly improving the accuracy of the follow-up and computational efficiency. These findings highlight the potential of integrating learning-based approximations with conventional optimization-based control to achieve reliable and time-efficient performance in complex robotic manipulation tasks. © 2025 The Authors

关键词： Predictive control systems

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Optimized efficient attention-based network for facial expressions analysis in neurological health care

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computers in Biology and Medicine 2024年 179卷 108822-108822页

作者： Munsif, Muhammad Sajjad, Muhammad Ullah, Mohib Tarekegn, Adane Nega Cheikh, Faouzi Alaya Tsakanikas, Panagiotis Muhammad, Khan Sejong University Seoul143-747 Korea Republic of Digital Image Processing Lab Department of Computer Science Islamia College Peshawar25000 Pakistan Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Institute of Communication and Computer Systems National Technical University of Athens Athens15773 Greece Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and *** code and data used in this work are available at: https://***/munsif200/Neurological-Health-Care. © 2024 Elsevier Ltd

关键词： Feature extraction

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Cloud-Edge-End Collaborative Computing-Enabled Intelligent Sharding Blockchain for Industrial IoT Based on PPO Approach

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IEEE Transactions on Mobile Computing 2025年

作者： Xiong, Xin Li, Meng Yu, F. Richard Zhang, Haijun Wang, Kan Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Carleton University Department of Systems and Computer Engineering ON OttawaK1S 5B6 Canada University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China

The security and reliability risks of industrial data have constrained the advancement of the Industrial Internet of Things (IIoT). Although blockchain can protect the security and reliability of industrial data through hash verification mechanisms, there are numerous challenges in the existing blockchain enabled IIoT systems, such as the trilemma of scalability, decentralization and security, high computational power consumption of consensus protocols and limited computational resources of industrial devices. To address these problems, an intelligent sharding blockchain enabled IIoT framework is proposed, in which the intelligent sharding based on the reputation mechanism and the adaptive switching for multi-consensus protocols are utilized to enhance the decentralization, security and scalability of blockchain. Considering higher requirement of computational power of the sharding blockchain, a cloud-edge-end collaborative computing framework is introduced, in which the parallel computational offloading and the Terahertz communication technology are utilized to enhance the cooperation of the cloud-edge-end networks. Furthermore, due to the highly dynamic nature of industrial devices and industrial data, we consider and design the optimization problem as a Markov decision process (MDP), which is solved via the Proximal Policy Optimization (PPO) algorithm. Simulation results show that our proposed scheme can minimize total delay and maximize transaction throughput while guaranteeing the safety as well as decentralization of blockchain enabled IIoT systems. © 2002-2012 IEEE.

关键词： Markov processes

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SSARS: Secure smart-home activity recognition system

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computers and Electrical Engineering 2025年 123卷

作者： Anna Palagan, C. Selvin Retna Raj, T. Muthuvairavan Pillai, N. Anish Pon Yamini, K. Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering DMI College of Engineering Tamil Nadu Chennai India Department of Computer Science and Business Systems R.M.D Engineering College Tamil Nadu Tiruvallur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

Smart homes provide assistance services that enhance the well-being, independence, and health of the residents, particularly the elderly. As techniques for human activity recognition in smart homes continue to advance, current methods face challenges such as insecure transmission of raw data and individual movement classification. To overcome these challenges, this study proposes Secure Smart-Home Activity Recognition System (SSARS). The proposed methodology utilizes an advanced preprocessing technique, AI-PSD, to reduce impulse noise in the data by combining adaptive interpolation (AI) and power spectral density (PSD). The Fractional Fast Fourier Transform (F-FFT) effectively captures statistical and dynamic aspects of human activities, offering a more detailed understanding of movement patterns. The extracted features are securely transmitted through encryption based on Factor private Key-based Elliptic Curve Cryptography (FK-ECC). Additionally, this study introduces the Pade activation function with a modified Physical Neural Network (P-PNN) to improve the system's classification ability. The proposed SSARS showed outstanding performance across various metrics, including an accuracy of 98.68 % and a precision of 98.93 % when compared with existing state-of-the-art approaches. © 2025 Elsevier Ltd

关键词： Adaptive interpolation-power spectral density Factor private key-based elliptic curve cryptography Fractional fast Fourier transform Human activity recognition Physical neural network Smart home Viola-Jones algorithm

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Automatic Classification of Heart Sounds Utilizing Hybrid Model of Convolutional Neural Networks 5th

Automatic Classification of Heart Sounds Utilizing Hybrid Mo...

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5th International Conference on New Trends in Information and communications Technology Applications, NTICT 2021

作者： Shyaa, Methaq A. Hasan, Ayat S. Ibrahim, Hassan M. Hamza, Weam Saadi Department of Information and Communication Systems Iraqi Ministry of Interior Baghdad Iraq Department of Computer Science College of Education for Pure Science University of Diyala Diyala Iraq University of Information Technology and Communication Baghdad Iraq

ISBN: (纸本)9783030934163

This paper introduces the employment of a deep learning model to develop an automated classification of heart sound into normal and abnormal. The Cardiology Challenge database 2016 (PhysioNet) has been employed in this work. This dataset is established with the assumption that the phonocardiograms (PCG) are extracted concurrently with the electrocardiograms (ECG). For each heartbeat, the PCG record on a time-domain basis is segmented and is converted to images. Then, these images feed the proposed convolutional neural network (CNN) model to perform the classification. Most previous methods used traditional machine learning techniques to classify the heartbeats. These methods depend on feature extraction and selection methods that are sensitive to different colors, shapes, and sizes. CNN has the ability to achieve feature extraction and classification automatically, unlike traditional machine learning methods which require manual feature extraction, and the performance relies on the chosen features. The proposed is designed by integrating multiple ideas including parallel convolutional layers and residual links. To prove that the proposed model is beneficial in terms of feature extraction and classification, the extracted features have been used to train the SVM classifier. The results show that the model can offer acceptable and comparable accuracy of classification. It has outperformed the previous methods by achieving an accuracy of 93.49% and 94.66% with the SVM classifier. High performance could help id saving many people’s lives. © 2021, Springer Nature Switzerland AG.

关键词： Cardiology

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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Smart Computing and Application (ICSCA), International Conference on

作者： Fahdah M. Al-Tbenawey Shoayee Alotaibi Marwa Salem Shamaila Mir Zahida Mir Department of Computer Science and Information Computer Science and Engineering College University of Ha'il Hail Saudi Arabia Department of Electrical Communication and Electronics Systems Engineering Faculty of Engineering Modern Science and Arts University (MSA) Cairo Egypt

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB.

关键词： Support vector machines Image segmentation Ultrasonic imaging Ultrasonic variables measurement Particle separators Neural networks X-rays

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