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Integration of IOT and optical networks for enhanced connectivity and performance

Journal of Optical Communications 2025年

作者： Rani, Rajni Tyagi, Sanjive Sharma, Vikas ECE Department KCC Institute of Technology & Management 2B-2C Knowledge Park-III Uttar Pradesh Greater Noida201306 India Department of Computer Science Faculty of Engineering and Technology Swami Vivekanand Subharti University Uttar Pradesh Meerut250005 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Swami Vivekanand Subharti University Uttar Pradesh Meerut250005 India

The integration of Internet of things (IoT) and optical networks represents a significant evolution in modern communication systems. Optical networks, known for their high bandwidth and low latency, can provide the necessary backbone to support the vast data exchange required by IoT devices. This paper explores the potential of combining IoT with optical networks, analyzing how this convergence can enhance connectivity, increase performance, and tackle the growing demands of future smart environments. Challenges, opportunities, and future directions for this integration are discussed, alongside case studies and applications that showcase the benefits of such an integrated approach. © 2025 Walter de Gruyter GmbH. All rights reserved.

关键词： Internet of things

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Discrepancy-Guided Reconstruction Learning for Image Forgery Detection

arXiv

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arXiv 2023年

作者： Shi, Zenan Chen, Haipeng Chen, Long Zhang, Dong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of CSE The Hong Kong University of Science and Technology Hong Kong

In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus on the discrepant-specific patterns (e.g., noises, textures, and frequencies), our method has a greater generalization. Specifically, we first propose a Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual patterns. DisGE consists of two branches, where the mainstream backbone branch is used to extract general semantic features, and the accessorial discrepant external attention branch is used to extract explicit forgery cues. Besides, a Double-Head Reconstruction (DouHR) module is proposed to enhance genuine compact visual patterns in different granular spaces. Under DouHR, we further introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine compact visual patterns, such that the forgery detection capability on unknown patterns can be improved. Extensive experimental results on four challenging datasets validate the effectiveness of our proposed method against state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Next-Generation Artificial Intelligence Solutions for Comprehensive Analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： H. Manoj T. Gadiyar S Vinod Kumar Vijilius Helena Raj Dinesh Kumar Yadav Mortada Kareem Sabi Hasoni Sorabh Lakhanpal Department of Computer Science and Business System Canara Engineering College India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh AlTalb Collage of Nursing National University of Science and Technology Dhi Qar Iraq Lovely Professional University Phagwara India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method’s superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape.

关键词： Heating systems Adaptation models Accuracy Heuristic algorithms Neural networks Linear regression Predictive models Prediction algorithms Optimization Genetic algorithms

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Elevating Digital Experiences by Enhancing Service Delivery through Revolutionary Cloud Services and Applications

Elevating Digital Experiences by Enhancing Service Delivery ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V Asha S Sreekanth Ginni Nijhawan K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This research introduces an approach named “Cloud-Driven Personalized Experience Enhancement” to satisfy the escalating demand for enhanced digital connectivity. Via cutting-edge cloud services and applications, the CPExE framework provides a groundbreaking solution for digital platforms. The framework is built upon three core components: Together with the Quantum-Inspired Data Analytics and Evaluation (QIDAE) and Personalized Content Recommendations (PRA), Dynamic Resource Allocation and Adaptation (DRAA) completes the framework. By leveraging quantum-inspired approaches, QIDAE analyzes vast datasets with outstanding effectiveness and exactness improving processes for data handling and personalization. This strategy allows for swift detection of patterns while making certain that data-driven understandings are both on time and precise. By changing cloud resources according to user requests the DRAA module guarantees uninterrupted access to computing power and data storage. By managing resources effectively in real-time it decreases latency and improves service speeds. By using complex algorithms to assess user habits and choices the PRA system boosts the digital experience. PRA uses both content-focused filtering and collaborative screening strategies to provide users with engaging content that matches their unique preferences. By applying sophisticated analytics and resource management methods with content personalization CPExE boosts the user experience and develops cloud technology efficiency. This framework signifies an important progress toward developing dynamic and tailored online environments.

关键词： Heating systems Histograms Data analysis Heuristic algorithms Scalability Memory Dynamic scheduling Real-time systems User experience Resource management

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Research on Liver Fat Sequencing Measurement Method Combined with Super-pixel Segmentation 3

Research on Liver Fat Sequencing Measurement Method Combined...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Jiao, Xu Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

The number of patients with nonalcoholic fatty liver is increasing. Liver fat quantification is the most reliable indicator for the diagnosis of non-alcoholic fatty liver disease, and it is of great value for the early diagnosis and condition monitoring of non-alcoholic fatty liver disease. Therefore, it is of great significance to quantitatively analyze liver fat. In this work, we utilize the superpixel segmentation method based on depth neural network to preprocess the MRI image, we can simplify the representation of the image and obtain good organ boundary information. In addition, we also understand the popular involution operator and improve it through combining the medical image segmentation model u-net to obtain a better region of interest. The segmentation accuracy of the proposed method is 1.82 percentage points higher than that of u-net model. On this basis, the proton density and fat fraction of liver are calculated by medical formula, achieving the sequencing measurement of liver fat. The experimental results show that the proposed method is efficient and feasible. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Smart Contracts Resilient against Malicious Attacks in Permission-less Blockchain

Smart Contracts Resilient against Malicious Attacks in Permi...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Anjaneyulu Endurthi Akhil Khare Osmania University Hyderabad India Computer Science and Engineering Department Rajiv Gandhi University of Knowledge Technologies (RGUKT-IIIT) Basar India CSED Maturi Venkata Subba Rao Engineering College Hyderabad Telangana India

The applications of Blockchain are rapidly growing across all the sectors and blockchain has become a one- stop solution to achieve data integrity. For both small and large businesses, blockchain can be used to enhance security, privacy, and data openness. A block gets added in the blockchain after reaching an agreement among the peers using a consensus algorithm. In a permission-less blockchain, the peers can behave maliciously. A consensus algorithm should prohibit malicious nodes/peers from adding invalid blocks to the blockchain, controlling the network while also granting equal rights to all nodes and mandating their participation in the consensus process. Smart contracts are the programs stored in a blockchain that run when specific conditions are met. There are certain attacks on blockchain smart contracts — Reentrancy attacks, Arithmetic overflow/underflow attack, Short address attack, Denial of service etc. The root cause of such attacks on smart contracts are the vulnerabilities existing in the smart contracts. This paper aims at correcting the vulnerabilities in the smart contracts thus eliminating such malicious attacks in the permission-less blockchains.

关键词： Data privacy Data integrity Smart contracts Process control Consensus algorithm Blockchains Peer-to-peer computing

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Transformer Embeddings for the Implementation of a Semantic MOOC Recommender System

Transformer Embeddings for the Implementation of a Semantic ...

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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Sara Assami Najima Daoudi Rachida Ajhoun Department of computer science Ecole Nationale Supérieure d'Informatique et d'Analyse des Systèmes of Mohammed V University Rabat Morocco Department of data and knowledge engineering Ecole des Sciences de l'Information Rabat Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Semantic web technologies aim to interconnect objects using descriptors that identify their characteristics and improve information retrieval. In Massive Open Online Courses (MOOCs), the learning contents semantic links and their pertinence for learner needs are essential to motivate learners and reduce dropout rates. This work aims to develop a semantic recommender system prototype that uses external data sources on learners to identify their knowledge, skills, and interests to recommend MOOCs. First, this paper traces the knowledge-based recommendation of MOOCs and its data representation in the literature. Afterward, it explores a dataset of a MOOC platform to extract MOOCs content-related data, and a dataset of a professional social media to extract knowledge-based characteristics of a learner from his/her social profile. For the implementation of the semantic recommender, three different techniques: TF-IDF, GloVe, and USE models are used to compute the semantic similarity of learner needs and MOOCs content. The semantic MOOC recommender system implementation showed the advantages of using different data sources and the increased performance of a MOOC recommender system based on transformer models like the USE model.

关键词： computer aided instruction Electronic learning Social networking (online) Computational modeling Semantics knowledge based systems Data visualization Transformers Data models Recommender systems

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Research on Liver Segmentation of CT Image Based on Deep Active Contour Model 3

Research on Liver Segmentation of CT Image Based on Deep Act...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： He, Shanshan Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

Liver cancer is one of the liver diseases with a high morbidity and mortality. At present, surgery is still the main treatment method for liver cancer. Therefore, it is particularly important to accurately segment the liver on CT imaging. Deep learning-based algorithms struggle to derive shape priors due to the irregular shape of the liver and blurring organ borders in CT images. We present a two-stage 3D deep active contour model 3DAC based on the V-net and CV models, in which the neural network is guided to learn the global features by regressing the signed distance map. The signed distance function can also be used as an initialization level set function. Then, the active contour model is extended to three dimensions to enable evolution of the overall liver surface. The advantages of the proposed method for liver segmentation were validated on the AbdomenCT-lK dataset. Compared with the DALS method, the Dice coefficient and the Jaccard coefficient are improved by 4.29% and 4.58%, the ASD and 95HD indicators are improved by 5. 96mm and 10. 04mm, which proves the effectiveness and robustness of the proposed method. © 2022 IEEE.

关键词： Image segmentation

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Advanced Simulation and Optimization Strategies in Thermo-Fluid Dynamics: A Deep Learning Approach to Enhancing Heat Transfer in Evaporative Cooling Systems

Advanced Simulation and Optimization Strategies in Thermo-Fl...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： B Rajalakshmi V Alekhya Sorabh Lakhanpal Irfan Khan Maha Chasib Munshid Ramya Maranan Department of Computer Science New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Collage of Nursing National University of Science and Technology Dhi Qar Iraq Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article describes a novel technique to study and improve evaporative cooling systems to move heat better. The system uses the GA, CNN, LSTM, and FVM algorithms. Each thermofluid dynamics study approach provides something unique. The CNN approach independently acquires spatial information from raw modeling data to study fluid movement. Temporal linkages in sequential data reveal heat’s movement over space and time via LSTM. GAN adds continuing antagonistic processes to the dataset, strengthening the model. GA adjusts GAN settings to improve data, whereas FVM simulates realistic fluid dynamics to improve heat transport. Ablation studies demonstrate each algorithm’s distinct contributions and importance. Performance comparisons reveal that the recommended framework outperforms others in accuracy, processing time, memory utilization, convergence rate, resilience, and adaptability. Bar charts, line charts, pie charts, and histograms show how the recommended method compares to others. The framework is precise, efficient, and versatile, making it ideal for improving thermo-fluid dynamics models in evaporative cooling systems. Researchers and engineers seeking to improve heat transfer in evaporative cooling systems can utilize it. The proposed architecture is full and adaptable to thermal-fluid utilization issues.

关键词： Heuristic algorithms Evaporative cooling Generative adversarial networks Data models Convolutional neural networks Long short term memory Optimization

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TREC: transient redundancy elimination-based convolution 22

TREC: transient redundancy elimination-based convolution

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Guan Feng Zhang Jiesong Liu Hsin-Hsuan Sung Ruofan Wu Xiaoyong Du Xipeng Shen Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Computer Science Department North Carolina State University

ISBN: (纸本)9781713871088

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.

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