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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

Alexandria engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

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Exploring Algorithmic Efficiency of A-Star and Dijkstra for Optimal Route Planning in Green Transportation

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International Journal of Intelligent Transportation Systems Research 2025年 1-11页

作者： Elshaer, A.M. Elmanfaloty, Rania A. Abou-Bakr, Ehab Elrakaiby, Mahmoud Saada, Khaled College of Artificial Intelligence Arab Academy for Science Technology and Maritime Transport P.O. B 1029 Alamein Egypt Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University P.O.B 21589 Jeddah Saudi Arabia Department of Computer Science and Information Technology Jeddah International College P.O.B 23831 Jeddah Saudi Arabia Department of Computer Engineering Higher Institute of Engineering and Technology El- Beheira Egypt Faculty of Information Technology & Computer Science Sinai University– Arish Branch Arish Egypt

Efficient navigation across complex maps is a critical challenge in domains such as GPS systems, robotics, and sustainable logistics. Optimal route planning plays a key role in minimizing resource consumption, reducing environmental impact, and improving overall navigation efficiency. This study evaluates and compares the A-Star and Dijkstra algorithms to determine the most effective approach for shortest path computation in map-based routing. Our analysis examines both algorithms under identical map configurations, considering computational complexity, memory usage, and execution time. The results indicate that A-Star generally outperforms Dijkstra’s algorithm in identifying the shortest and most efficient route. While Dijkstra’s algorithm guarantees optimality, it requires more memory and a higher number of iterations, making it less efficient in large-scale scenarios. Empirical findings reveal that the time complexity of Dijkstra’s algorithm is O(V²), completing in 0.22 s, whereas A-Star achieves a faster execution time of 0.07 s with a time complexity of O(E). Regarding space complexity, Dijkstra consumes O(V + E) memory, using 179,241 to 190,961 bytes, while A-Star requires less, at O(V), using 159,287 to 163,919 bytes. These insights provide a comparative framework for selecting the appropriate algorithm based on map characteristics and computational efficiency. Our findings contribute to the development of sustainable pathfinding solutions, facilitating energy-efficient transportation and logistics. © The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2025.

关键词： Global positioning system

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A Modular-based Strategy for Mitigating Gradient Conflicts in Simultaneous Speech Translation

A Modular-based Strategy for Mitigating Gradient Conflicts i...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Xiaoqian Du, Yangfan Wang, Jianjin Ge, Yuan Xu, Chen Xiao, Tong Chen, Guocheng Zhu, Jingbo School of Computer Science and Engineering Northeastern University Shenyang China College of Computer Science and Technology Harbin Engineering University Harbin China NiuTrans Research Shenyang China

ISBN: (纸本)9798350368741

Simultaneous Speech Translation (SimulST) involves generating target language text while continuously processing streaming speech input, presenting significant real-time challenges. Multi-task learning is often employed to enhance SimulST performance but introduces optimization conflicts between primary and auxiliary tasks, potentially compromising overall efficiency. The existing model-level conflict resolution methods are not well-suited for this task which exacerbates inefficiencies and leads to high GPU memory consumption. To address these challenges, we propose a Modular Gradient Conflict Mitigation (MGCM) strategy that detects conflicts at a finer-grained modular level and resolves them utilizing gradient projection. Experimental results demonstrate that MGCM significantly improves SimulST performance, particularly under medium and high latency conditions, achieving a 0.68 BLEU score gain in offline tasks. Additionally, MGCM reduces GPU memory consumption by over 95% compared to other conflict mitigation methods, establishing it as a robust solution for SimulST tasks. © 2025 IEEE.

关键词： GPU Memory Efficiency Gradient Conflict Mitigation Multi-task Learning Simultaneous Speech Translation

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Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs) 6th

Identification of Real or Fake Currency Using Semi-supervise...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mukherjee, Anupam Ghosh, Anupam Department of Computer Science & Engineering Siliguri Institute of Technology West Bengal Siliguri India Department of Computer Science & Engineering Netaji Subhash Engineering College West Bengal Kolkata India

ISBN: (纸本)9789819780891

Traditional paper money and contemporary electronic money are two significant forms of exchange. However, due to the new and improved methods that counterfeiters are using, it is now becoming an increasingly important issue. This work suggests a machine-assisted method that has been created to distinguish between fake and real currency. Generative adversarial networks (GANs), a cutting-edge machine learning model, are used for this purpose. To develop a model that can be used for supervised prediction, GANs utilize semi-supervised learning. Semi-supervised learning is an exceptional approach in building a model that is fit for supervised prediction. It offers a multitude of benefits, allowing the training of unlabeled data while achieving highly accurate predictions. In this work, detecting counterfeit money using GANs involves a multi-step methodology that leverages the power of deep learning for accurate identification. Preprocess the data by resizing, normalizing, and augmenting the images to ensure uniformity and improve model generalization. The discriminator minimizes the loss when making correct classifications, while the generator minimizes the loss when the discriminator classifies its generated images as genuine. Indian banknotes were subjected to this procedure. Modern feature identification and image processing techniques were employed to develop the overall strategy of a valid input. The trials demonstrate that a high-precision system may be created to identify real paper money. During the training process, the generator network and the discriminator networks update their loss functions. Once training is complete, the model can accurately identify real money notes from fake ones with an 80% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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Joint Power Control and Multi-Path Routing for Internet of Underwater Things in Varying Environments

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IEEE Internet of Things Journal 2025年第11期12卷 15197-15210页

作者： Xu, Cangzhu Song, Shanshan Liu, Jun Pan, Miao Xu, Gaochao Cui, Jun-Hong Jilin University Department of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Smart Ocean Technology Co. Ltd Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Chengdu China Jilin University College of Computer Science and Technology Changchun130012 China

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput. © 2014 IEEE.

关键词： Routing Reliability Power control Routing protocols Energy efficiency Relays Internet of Things Sensors Reliability engineering computer network reliability

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Classification of System Failures in Cyber-Physical Systems Using Heterogeneous Edge Enhanced Graph Attention Networks 3

Classification of System Failures in Cyber-Physical Systems ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Obidhusin, Saef Chandra, N. Subhash Balamurugan, D. Premalatha, J. Kumararaja, K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Computer Science and Engineering Salem India Dhanalakshmi Srinivasan College Of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

From the past few years, Cyber-Physical Systems (CPS) have rapidly evolved in various sectors by integrating computing, networking, and physical processes. CPS facilitates the management of critical infrastructure such as transportation, healthcare, and energy. Even though there are advanced technologies, which are vulnerable to failures led to severe consequences including unpredictable interactions between cyber and physical components. Therefore, to address these issues a robust model is proposed by using Heterogeneous Edge-Enhanced Graph Attention Network (HEE-GAT). Initially, the data is collected from BoT-IoT database which consists of huge traffic data. Then, the collected data is preprocessed using Z-score normalization is applied to standardize feature distributions while preserving network traffic characteristics. Furthermore, for selection of significant features Skill Optimization Algorithm (SOA) is utilized. Finally, HEE-GAT is utilized for effective classification of system failures in CPS. Unlike existing failure detection models such as LSTM-GNN hybrids, which struggle with capturing heterogeneous dependencies and dynamic edge relationships in CPS data, HEE-GAT enhances graph-based learning by assigning dynamic edge weights and utilizing attention mechanisms to distinguish between different failure types with higher accuracy. The proposed HEE-GAT model acquired accuracy of 95% when compared to the existing Fractional Artificial Protozoa Optimization enabled Spiking VGG-16 (FAPO Spiking VGG-16). © 2025 IEEE.

关键词： Service oriented architecture (SOA)

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Black Hole Attack Detection Using Dynamic Threshold Values and Residual Energy in VANETs

Black Hole Attack Detection Using Dynamic Threshold Values a...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shalom Priscilla, I. Senthil Kumar, C. Buvaneswari, B. Shiva Kumar, M. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India SRM Madurai College for Engineering and Technology Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798331512965

In black hole attack, a hostile vehicle or node within the VANET pretends to have the optimal route to the destination, convincing other nodes to route their data packets through it as they head towards the destination, trailing behind the malicious node, receives the data, it either discards the packets completely or forwards them selectively, causing disruptions in communication. To overcome this dilemma the research discusses a novel approach for categorizing nodes in AODV (Adhoc On demand vector Routing Protocol) networks according to their residual energy (RE) and forward rate (FR). With the goal to identify nodes that are considered energy-constrained, the proposed approach defines a residual energy threshold (RET) and includes dynamic threshold values (MinDTV and MaxDTV) for the forward rate. Three categories are applied to nodes: 1. (Trusted), 2. (Intermediate), and 3. (Untrusted or Energy-Constrained). This methodology results in a more trustworthy and energy-efficient network by dynamically updating node selection based on present forwarding behavior and levels of energy. The proposed approach boosts the overall strength, longevity as well, and integrity of RPL networks through the elimination of energy-depleted and unstable nodes;hence, it is especially suitable for applications in scenarios with scarce resources and malicious activity. © 2025 IEEE.

关键词： Black holes

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Thiyagarajan, Gomathi Swathi, Y. Cmr Institute of Technology Department of Computer Applications Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates. © 2025 IEEE.

关键词： Marketplaces

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Frequency Attacks Based on Invertible Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 292-300页

作者： Shao, Ming-Wen Yang, Jian-Xin Meng, Ling-Zhuang Hu, Zhi-Yong China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China China University of Petroleum College of Control Science and Engineering East China Qingdao266580 China

Adversarial attacks reveal the vulnerability of classifiers based on deep neural networks to well-designed perturbations. Most existing attack methods focus on adding perturbations directly to the pixel space. However, the perturbations generated by these methods may be easily perceived by humans. To alleviate the aforementioned problem, we propose a novel high-frequency attack based on invertible neural networks (HA-INN) that relies on INNs to adds perturbations to the high-frequency space of the image instead of the pixel space. In this way, we can fool the classifier while the perturbations are not easily detected by humans. Specifically, we introduce INNs to separate the high-frequency and low-frequency components of the image. And the low-frequency components are guaranteed to be reconstructed as the original image, while the high-frequency components are replaced with resampled high-frequency latent variables with additional adversarial information. Then, the low-frequency components and the high-frequency components with adversarial information are inversely fed into the INN to generate effective adversarial examples. Extensive experiments on two datasets (CIFAR-10 and CIFAR-100) show that our method can generate misleading and transferable cross-architectural adversarial examples with greatly reduced computational resource requirements. Under the white-box setting, the attack success rate of CIFAR10 and CIFAR100 is 99.8% and 99.74%, respectively. Further, under the black-box setting, the adversarial examples generated by our method are more effective than the other methods. © 2020 IEEE.

关键词： Deep neural networks

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