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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Computer vision and Generative AI for yield prediction in Digital Agriculture 2

Computer vision and Generative AI for yield prediction in Di...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Majumder, Sayan Khandelwal, Yash Sornalakshmi, K. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer numerous economic, environmental and social benefits. Controlled Environment Agriculture (CEA) is providing more benefits since the weather and other environmental conditions is controlled and predictable. In CEA, the benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Generative AI to visually forecast the potential yield of a crop using stable diffusion and conditional prompts additionally implementing several Generative Adversarial Networks (GAN) architectures for data generation, augmentation and super-resolution. Our proposed system also adopts generative AI for real time monitoring of plants. studying their respective conditions and their autonomous cultivation and harvesting patterns. The dataset used contained over 8479 examples for different fruits alongside annotations. This data was used to train the detection models as well categorically feed the GANs for data generation. data generated by DCGAN has a structural similarity of 0.5-0.7 whereas data generated by pix2pix network returns us a structural similarity of 0.7-0.9. © 2024 IEEE.

关键词： Cultivation

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Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models

Advanced Arabic Alphabet Sign Language Recognition Using Tra...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Balat, Mazen Awaad, Rewaa Adel, Hend Zaky, Ahmed B. Aly, Salah A. Egypt-Japanese University of Science & Technology Cs & It Dept. Alexandria Egypt Fayoum University Faculty of Science Fayoum Egypt Badya University Faculty of Computing & Data Science Giza Egypt

ISBN: (纸本)9798350367560

This paper presents an Arabic Alphabet Sign Language recognition approach, using deep learning methods in conjunction with transfer learning and transformer-based models. We study the performance of the different variants on two publicly available datasets, namely ArSL2018 and AASL. This task will make full use of state-of-the-art CNN architectures like ResNet50, MobileNetV2, and EfficientNetB7, and the latest transformer models such as Google ViT and Microsoft Swin Transformer. These pre-trained models have been fine-tuned on the above datasets in an attempt to capture some unique features of Arabic sign language motions. Experimental results present evidence that the suggested methodology can receive a high recognition accuracy, by up to 99.6% and 99.43% on ArSL2018 and AASL, respectively. That is far beyond the previously reported state-of-the-art approaches. This performance opens up even more avenues for communication that may be more accessible to Arabic-speaking deaf and hard-of-hearing, and thus encourages an inclusive society. © 2024 IEEE.

关键词： Transfer learning

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Integrating local energetics into Maxwell-Calladine constraint counting to design mechanical metamaterials

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Physical Review E 2024年第2期110卷 025002-025002页

作者： Jason W. Rocks Pankaj Mehta Department of Physics Biological Design Center Faculty of Computing and Data Science

The Maxwell-Calladine index theorem plays a central role in our current understanding of the mechanical rigidity of discrete materials. By considering the geometric constraints each material component imposes on a set of underlying degrees of freedom, the theorem relates the emergence of rigidity to constraint counting arguments. However, the Maxwell-Calladine paradigm is significantly limited—its exclusive reliance on the geometric relationships between constraints and degrees of freedom completely neglects the actual energetic costs of deforming individual components. To address this limitation, we derive a generalization of the Maxwell-Calladine index theorem based on susceptibilities that naturally incorporate local energetic properties such as stiffness and prestress. Using this extended framework, we investigate how local energetics modify the classical constraint counting picture to capture the relationship between deformations and external forces. We then combine this formalism with group representation theory to design mechanical metamaterials where differences in symmetry between local energy costs and structural geometry are exploited to control responses to external forces.

关键词： Mechanical deformation Mechanical metamaterials Discrete symmetries Group theory

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Analysis of the Influence of Green Artificial Intelligence on Agricultural and Biological Fields Using Bibliometrics 5

Analysis of the Influence of Green Artificial Intelligence o...

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5th International Conference on Artificial Intelligence and data sciences, AiDAS 2024

作者： Anand, Jonitha Ab Ghani, Hadhrami Yusoff, Nooraini Kumar Thoti, Kiran Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia M.S. Ramaiah Institute of Management Bangalore India

ISBN: (纸本)9798331528553

The research examines how Green Artificial Intelligence (AI) impacts agricultural and biological domains by analysing relevant literature using bibliometrics. Green AI, which prioritizes sustainability and environmental awareness, is proving to be an asset in tackling the issues in agriculture and biology. The investigation examines a wide range of academic publications, patents, and research papers to identify significant trends, research topics, and the influence of Green AI on agricultural and biological fields. The research shows an increasing amount of literature investigating the combination of AI technology with green principles to improve resource management, boost agricultural output, and reduce environmental effects. This study uses analysis of citation networks, co-authorship patterns, and theme grouping to get insights into the present status and future trends of research in the fields of Green AI, agriculture, and biology. The data was extracted from the database maintained by Scopus using the PRISM Model. 11,142 article data points were used for the analysis of Green AI. This bibliometric study enhances comprehension of the changing environment of Green AI applications in agricultural and biological areas, providing vital recommendations for academics, policymakers, and practitioners working towards sustainable development objectives. © 2024 IEEE.

关键词： Biotic

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Implementation of CBDC System for the Customised Development of Finance Service System 5

Implementation of CBDC System for the Customised Development...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mehra, Devansh Vamsi, A. Sri Kavin, Prabhu Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350383522

CBDC-as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have - security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there;information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving. © 2024 IEEE.

关键词： CBDC DLT

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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Building Flood Resilience: A 3D Simulation Game for Public Awareness

Building Flood Resilience: A 3D Simulation Game for Public A...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Kadir, Tuty Asmawaty Abdul Xuan, Desmond Yap Wen Nazri, Siti NurAishah Faculty of Computing Center of Artificial Intelligence and Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Malaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan Malaysia

ISBN: (纸本)9798331530303

This article presents the development and evaluation of "Flood," a 3D simulation game designed to enhance public awareness of flood safety measures. The game features two distinct phases: the Pre-flood phase, where players prepare for impending flood conditions, and the Evacuation phase, where they must navigate various hazards to reach safety. By utilizing the Unity platform, "Flood" immerses players in realistic scenarios that promote learning through engagement. Results from a survey of 20 participants indicate that the game successfully simulates flood situations, with 80% of respondents affirming its effectiveness in conveying essential flood awareness. The majority of players reported learning crucial safety measures, such as avoiding power lines and preparing emergency bags. Furthermore, feedback highlighted high levels of engagement, with 70% rating the game at the highest level of interest. ©2024 IEEE.

关键词： Disasters

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