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

作者： Ye, Xinchen Zhang, Aokai Xu, Rui Li, Haojie DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350368741

Guided Depth Super-Resolution (GDSR) enhances low-resolution (LR) depth maps by leveraging high-resolution (HR) color images. The primary challenges involve achieving effective cross-modal data alignment and fusion, as well as incorporating multi-scale information within the Transformer architecture. To address these challenges, we propose a novel network architecture named DRMPNet which integrates two key components: Offset-based Detail Refinement (ODR) and Structure-guided Multi-scale Perception (SMP). ODR leverages offset calibration and window cross-attention to align and fuse LR depth maps with color images, effectively recovering local depth details. Meanwhile, SMP employs a structure generator and multi-scale cross-attention to capture scene details and structures at multiple scales, thereby enhancing the network's contextual understanding. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： Depth Map Guided Super-Resolution Transformer

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Empowering Computational Scientists with Data through Cutting-Edge Techniques for Comprehensive High-Performance Computing Analysis

Empowering Computational Scientists with Data through Cuttin...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Adlin Jebakumari, S. Sahoo, Girija Shankar Srilakshmi, K.H. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

In the era of exponential data expansion and ever-escalating computational demands, providing computational scientists with cutting-edge approaches for comprehensive high-performance computing (HPC) analysis is crucial. This study proposes a novel strategy, aiming to empower researchers with enhanced tools and approaches to capture the full potential of HPC resources. By using state-of-the-art algorithms and adopting data-driven decision-making, our suggested technique attempts to revolutionize HPC analysis. Through a combination of extensive algorithmic explanations and mathematical formulations, this article elucidates the key ideas underpinning our methodology. Furthermore, we give a set of illustrated flowcharts and code snippets for actual implementation. The suggested technique not only speeds calculations but also increases the quality and depth of analysis, addressing the rising complexity of current computing jobs. By putting data empowerment at the forefront, this strategy guarantees that computational scientists have the required means to generate significant insights from broad data sources. With this strategy, we intend to advance the field of HPC analysis ahead, helping scientists to traverse the problems of the data-driven future. © 2023 IEEE.

关键词： Decision making

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Investigating the Potential for Using AI to Improve the Performance of Big Data Access 3

Investigating the Potential for Using AI to Improve the Perf...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yuvaraj, K. Yuvaraj, S. Dhabliya, Dharmesh Rengarajan, A. Jain, Narendra Kumar Agrawal, Trapty Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

With the growing quantity of massive statistics, the need to correctly get entry to, mine, and analyse it has become ever more urgent. One approach to this project is to apply artificial intelligence (AI) methods to enhance the overall performance of massive information get entry to. AI strategies, such as neural networks and choice bushes, may be used to clear out huge quantities of information quick and as it should be. additionally, AI strategies may be tailor-made to precise applications, unlocking extra ability for progressed performance. This research will investigate the capacity of the usage of AI to enhance the overall performance of huge information get entry to. exclusive AI techniques might be tested and in comparison across various situations, to identify the handiest method. in the end, the blessings and downsides of using AI in large statistics get entry to might be mentioned, and guidelines on high-quality practices could be furnished. © 2023 IEEE.

关键词： approach blessings enhance precise unlocking

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Bridging Image Diagnostics with Advanced Computational Techniques for Hidden Feature Detection 3

Bridging Image Diagnostics with Advanced Computational Techn...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Manikandan, M. Dutta, Abhishek Pandeya, Megha Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

This research presents a comprehensive methodology for efficient medical image segmentation in radiology by synergistically combining the strengths of MobileNet and U-Net architectures. The process begins with the collection and preprocessing of diverse medical image datasets, including CT scans, MRI images, and X-rays. Preprocessing involves resizing, normalization, and data augmentation to enhance model robustness. MobileNet, known for its computational efficiency, serves as the feature extraction backbone. It efficiently captures hierarchical features from input medical images while minimizing computational requirements. Simultaneously, a U-Net-style architecture is integrated to leverage its exceptional segmentation accuracy. Skip connections are introduced to connect corresponding layers in both MobileNet and U-Net, facilitating the transfer of low-level and high-level features. Feature maps from both components are fused at multiple levels, combining MobileNet's efficiency with U-Net's accuracy. map, representing the final segmentation output. The hybrid model is trained using annotated medical image data with loss functions like Dice loss or cross-entropy. Fine-tuning and hyperparameter optimization are performed to enhance performance. The proposed methodology is applied to real-world radiology scenarios, such as tumor segmentation, organ delineation, and lesion detection. It significantly outperforms traditional methods, demonstrating superior accuracy and computational efficiency. © 2023 IEEE.

关键词： Feature extraction

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Multimodal Fake News Detection Based on Multi-source Heterogeneous Data Fusion 14

Multimodal Fake News Detection Based on Multi-source Heterog...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Yu, Yongxin Ji, Ke Gao, Yuan Chen, Zhenxiang Ma, Kun Wu, Jun University of Jinan School of Information Science and Engineering Jinan China University of Beijing Jiaotong School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350371024

In the era of information explosion, people are constantly exposed to a wealth of unreliable information. Nevertheless, societal stability and public trust may be seriously threatened by such unverified or even fabricated false information. In order to discriminate the fake news, several fake news detection approaches have been proposed. However, most of the current multimodal fake news detection methods only rely on news text and images from a single data source. Owing to the scarcity of multimodal information and the limited volume of data, the effectiveness of the majority of these methods is low. As a result, we embrace the approach of amalgamating multi-source heterogeneous data and propose a new network structure FusionNet. In this network structure, we creatively use web crawler technology to expand the data. We achieve double amplification of text and image as opposed to the prior single-mode amplification of text, and by data screening and cleaning, we create multi-source and multimodal datasets for various viewpoints on the same event. We also propose a novel modal fusion approach that leverages the inherent information in news content, integrates an attention mechanism, and explores the shared relevant features between original news and multi-source news. Through empirical results and analysis, our approach is superior to other methods. © 2023 IEEE.

关键词： Web crawler

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China Communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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Detection and Classification of Agricultural Pest and Vermin's using Full-Convolutional Neural Network 7

Detection and Classification of Agricultural Pest and Vermin...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Abdulrazzaq, Ali Raad Türkben, Ayça Kurnaz Kurnaz, Sefer Altinbas University Information Technology Istanbul Turkey Altinbas University Electrical and Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350383065

This paper comprehensively explores the development and implementation of Full-Convolutional Neural Networks (FCNs) for precise agricultural pest and vermin detection and classification. Precision farming, a cornerstone of modern agriculture, relies heavily on effective crop health management, with pest and rodent identification being a critical aspect. FCN, a specialized deep learning technique, exhibits unique capabilities by facilitating precise pest localization within crop images, regardless of input *** ensure the research's credibility and efficacy, a diverse array of data sources, encompassing drones, stationary cameras, and handheld devices, is tapped. High-quality images with superior resolution and ideal lighting conditions are essential for maximizing model performance. Additionally, dataset diversity and rigorous professional-grade annotation processes significantly bolster model robustness. Annotation tools such as Labelbox are harnessed for accurate pest and vermin delineation, employing advanced techniques like bounding boxes and segmentation masks, with multiple reviewers engaged to minimize *** paper presents a comprehensive roadmap for developing and deploying FCNs tailored to agricultural pest and vermin detection and classification. It offers invaluable insights into the seamless integration of cutting-edge technology into precision farming practices. The research thereby contributes to improving crop health management and promoting sustainable agricultural approaches, underlining its significance in the contemporary agricultural landscape. © 2023 IEEE.

关键词： Convolutional neural networks

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Analysis of Applied Cryptography for Secure VoIP Communications 5th

Analysis of Applied Cryptography for Secure VoIP Communicati...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： P, Anitha Agarwal, Trapty D, Ganesh Anand, Piyush School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

This paper presents an evaluation of the utility of cryptography for at ease Voice over net Protocol (VoIP) verbal exchange. It covers the fundamentals of secure conversation as well as elements of public key cryptography for at ease authentication and encryption. It also covers the hooked up consultation Protocol (ESP), a protocol utilized in VoIP for encryption and authentication in cozy communications. Subsequently, this paper examines the advantages of the usage of cryptographic techniques in VoIP as well as related safety challenges. The evaluation of the utility of cryptography for comfortable VoIP communications offers a foundation for the assessment of cozy communique protocols in VoIP implementations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Comparing Time Series Assist Vector Machines and Recurrent Neural Networks for Hyper Spectral Image Popularity 5th

Comparing Time Series Assist Vector Machines and Recurrent N...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Goswami, Shubhashish Pasha, Afroz Mishra, Awakash Krishnamoorthy, Ramkumar Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper examines the overall performance of two system mastering models, Time series help Vector Machines (SVMs) and Recurrent Neural Networks (RNNs), on classification of Hyper Spectral photographs (HSIs). The fashions are in comparison based totally on their recognition accuracy which includes the time requirements to build fashions. The paper additionally evaluates two varieties of preprocessing techniques: minimum Redundancy maximum Relevancy (MRMR) and precept aspect evaluation (PCA). The effects of the experiments highlight that SVMs and RNNs perform considerably distinct for HSI class and that counting on one version simplest may lead to exclusive type performance. Moreover, the experiments show that with increasing complexity, the quantity of time required to assemble the RNNs will increase significantly. The comparisons in the paper allow further exploration in the discipline and help the choice of the high-quality model with the aid of capacity practitioners and developers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Implementing Encryption Algorithms for the Protection of Wireless Networks 5th

Implementing Encryption Algorithms for the Protection of Wir...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahmad, Naseem Madhura, K. Agarwal, Trapty Zaidi, Taskeen Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

The use of encryption algorithms is more and more turning into a crucial issue of defensive wireless networks. Encryption algorithms may be used to cozy facts that is transferred over a Wi-Fi network while also presenting authentication and integrity protocols for the systems connected to the network. This paper discusses the significance of enforcing encryption algorithms for the safety of a wireless community. It describes the distinct encryption algorithms and protocols which are available, and the criteria for selecting the proper algorithm for exclusive varieties of Wi-Fi networks. It additionally provides unique implementation eventualities based totally on numerous sorts of Wi-Fi community structure in addition to the vast security threats in Wi-Fi networks that may be averted by imposing encryption algorithms. Finally, this paper offers guidelines on selecting and imposing the perfect encryption set of rules for safety of wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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