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Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1708-1722页

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

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Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnosis 4

Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnos...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Pattnayak, Parthasarathi Mishra, Debaswapna Patnaik, Sanghamitra Mohanty, Arpeeta School of Computer Applications KIIT Deemed to Be University Bhubaneswar India OUAT Department of Computer Science and Application Bhubaneswar India School of Law KIIT Deemed to Be University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

High blood glucose levels are a defining characteristic of diabetes, with type 2 diabetes being the most prevalent form. Early detection and treatment can prevent or delay complications. To precisely anticipate type 2 diabetes risk, we applied machine learning techniques to identify unidentified correlations with a person's health condition. In order to find potential nonlinear correlations between the beginning of type 2 diabetes with an ensemble of health factors, this research used a binary classifier that was trained from scratch. Three datasets - MIMIC-III, MIMIC-IV, and NHANES - were used. The equal number of people who had or did not have type 2 diabetes have been included in one data set that was created by combining the two sets of data. Reliability was the key metric, given the balanced dataset. The model achieved ROC AUC score of 0.934 with accuracy ratings up to 86%, which were positive results of this investigation. To increase the model's dependability, more research is required to take into account multiple readings taken throughout period for an identical person. © 2024 IEEE.

关键词： Adversarial machine learning

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An Energy Efficient IPv6 Packet Delivery Outline for Industrial IoT Over G.9959 Protocol Based Wireless Sensor Network (WSN)

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SN computer science 2024年第7期5卷 1-11页

作者： Veena, R.S. Khambra, Geetanjli Sayal, Rishi Singh, Nagendra Dhandayuthapani, V. Bala Sar, Sumit Kumar Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Kanakapura Road Karnataka Bengaluru India Department of Computer Science and Applications The Bhopal School of Social Sciences MP Bhopal India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telangana Hyderabad 501506 India Department of Electrical and Electronics Engineering Trinity College of Engineering and Technology Telangana Karimnagar 505001 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science BIT Durg Chattisgarh Durg India

The world is witnessing a significant paradigm shift in the delivery and management of industrial services, driven by the need for automated manufacturing in industries such as mining. This transformation is pivotal as it leverages the Internet to interconnect people and devices, giving rise to a new field known as the Industrial Internet of Things (IIoT). IIoT represents the fusion of IoT’s computational capabilities with the principles of the industrial revolution, facilitating extensive scalability through the transition from IPv4 to IPv6. A crucial challenge within IIoT is the battery life of sensor nodes, which is directly influenced by the efficiency of their operating modules and protocols. Various protocols have been tested within IIoT-based Wireless Sensor Networks (WSNs) to address this issue. However, these protocols often fall short in terms of data rate performance, which is essential for supporting the diverse range of IIoT applications and services. This manuscript investigates the IPv6 packet delivery rate, focusing on energy efficiency and delay. The research identifies the G.9959 protocol as superior to IEEE 802.15.4 for IIoT adoption. Additionally, it examines the impact of bandwidth variation on energy efficiency. Through comprehensive simulations, the proposed scheme demonstrates superior performance compared to existing protocols, highlighting its potential for enhancing IIoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： G.9959 IIoT IPv6 OPD WSN

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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A Grading of PDN Based CC Networks 4

A Grading of PDN Based CC Networks

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Singh, Suresh Al-Farouni, Mohammed Singh, Devendra Varshney, Neeraj Arvind, Sudha Krishnaraj, N.S. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute of Technology Department of Computer Science & Engineering Dehradun India Gla University Department of Computer Engineering and Applications Mathura India Cmr Technical Campus Department of Ece Telangana Hyderabad India Department of Information Science and Engineering Nitte India

ISBN: (纸本)9798350360165

Network systems function based on rules that are intrinsically dynamic, subject to temporal circumstances set by outside events like host utilisation, bandwidth measurements, intrusion detection, or time-specific events. Software-defined networking (SDN) presents an opportunity to streamline network configuration through the provision of more sophisticated configuration tools. In an attempt to lower network monitoring costs and traffic overheads, we offer a software-defined cloud resource management solution that customises network resource distribution using a Flexible Analytical Hierarchy Procedure (Fuzzy-AHP). Through an Application Programme Interface, this framework may be easily integrated into cloud infrastructures that support SDN (API). We demonstrate how our system improves network resource management and efficiently responds to growing traffic requests using real-time data. Moreover, we confirm the performance of our framework using simulations. © 2024 IEEE.

关键词： and fuzzy analytical hierarchy process (fuzzy-AHP) Cloud computing network management resource management scheduling software-defined networking

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

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Smart Auto Image Captioning Using LSTM and Densenet Network 6

Smart Auto Image Captioning Using LSTM and Densenet Network

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arumugam, M. Vijayakumar, P. Siddharrth, G.M. Rohini, J. Karthikeyan, A. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350304480

The integration of semantic attention increase photo captioning ability. The premise underlying semantic attention-based techniques is that the model should prioritize words or qualities with high semantic value. Prior studies focused on the attribute detector and the captioning network as distinct entities. This hampers the best use of semantic data. Image captioning is the computational process of producing accurate written descriptions of an image's or video's visual content. Captioning images makes it easier to construct contextualized interpretations, allowing people to situate photographs within a larger context. Using photo captions can be useful in a variety of contexts, such as conducting research with an unlabeled image collection or attempting to identify. previously unknown patterns. Subtitles added to the photos using a Deep Learning Model. Recent advances in the fields of deep learning and natural language processing have made it much easier to generate captions for input photographs. This study will use neural networks to generate image captions on their own. The Long Short-Term Memory (LSTM) model, a type of recurrent neural network, used as an encoder, together with a pre-trained lexicon and visual attributes. This enables the extraction of image data and the generation of captions. © 2023 IEEE.

关键词： Deep Learning Model Densenet Network Long Short-Term Memory Remote Sensing

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Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images 2

Detection of Retinal Diseases in Retinal Optical Coherence T...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

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Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

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computers, Materials & Continua 2024年第9期80卷 3685-3711页

作者： Sagar Kavaiya Hiren Mewada Sagarkumar Patel Dharmendra Chauhan Faris A.Almalki Hana Mohammed Mujlid Smt.Chandaben Mohanbhai Patel Institute of Computer Applications Charotar University of Science and TechnologyChangaAnand388421India Electrical Engineering Department Prince Mohammad Bin Fahad UniversityP.O.Box 1664Al Khobar31952Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Chandubhai S.Patel Institute of TechnologyCharotar University of Science and TechnologyChangaAnand388421India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Kingdom of Saudi Arabia

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research ***,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor ***,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography ***,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m ***,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper ***,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV *** findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and *** results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

关键词： Physical layer security 3D positioning 6G communications UAV systems

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Advanced Fake Review Detection via Aspect Extraction and TPA-BiLSTM-CSSA 2

Advanced Fake Review Detection via Aspect Extraction and TPA...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, Deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. Dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BiLSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BiLSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

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