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Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

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computer Modeling in engineering & sciences 2024年第9期140卷 2397-2424页

作者： Hongchi Liu Xing Deng Haijian Shao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89119USA

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi

关键词： Remote sensing image image dehazing deep learning feature fusion

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Integration of Blockchain and IPFS: healthcare data management & sharing for IoT Environment

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Multimedia Tools and Applications 2024年 1-22页

作者： Mishra, Rajiv Kumar Yadav, Rajesh Kumar Nath, Prem Department of Computer Science & amp Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & amp Engineering Delhi Technological University Delhi India Department of Computer Science HNB Garhwal University Srinagar India

The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial. Ensuring the security of the Internet of Things (IoT) ecosystem from unauthorized access is imperative. Blockchain technology emerges as a promising solution to address these security concerns. Nevertheless, the effectiveness of Blockchain in handling the extensive data generated by smart devices is challenged by the rapid pace of IoT data generation and the slower transaction validation speed within Blockchain networks. This research aims to resolve these issues by integrating Blockchain with the Inter-Planetary File System (IPFS), creating a robust framework for secure data recording on a distributed storage network while enabling authorized access to the stored data. The proposed mechanism involves defining and recording access policies and cryptographic hash content on the Blockchain network, while storing the actual IoT-generated data on IPFS to enhance the confidentiality, integrity, and availability (CIA) triad. Performance assessments of the proposed scheme demonstrate its security and practicality, validating its potential for real-world application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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Unique hash value based identical video sharing traffic flow optimization across social networks

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Multimedia Tools and Applications 2025年第13期84卷 11983-12011页

作者： Konyak, Chenlep Yakha Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima Dimapur797 103 India

Identical video sharing across social networks results in redundant video data traffic, consuming additional bandwidth, storage resources, and data usage. A novel method is proposed to optimise redundant video-sharing services across social media applications exclusively on the push model, which is based on creating a unique hash value identity (UHVID) to identify the video distinctly. The UHVID is generated based on the centroid of gradient orientation of the key frames. The key frames are extracted from the video based on colour histogram and structural differences. Using UHVID and video metadata, the server application validates and optimizes identical video content flow before actual video sharing. The end users’ privacy is not affected in the proposed framework, as the actual video is not used for comparison, only the hash values of the video. The statistical analysis has shown that in 14 test cases of 2700 traffic initiations and 39,180 transactions, 83.2% of redundant data of identical videos were optimised, resulting in 63.61% data savings, amounting to 19.75 GB. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital storage

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Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

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Multimedia Tools and Applications 2025年第18期84卷 19315-19342页

作者： Chaudhary, Kapil Department of Computer Science & Engineering BipinTripathi Kumaon Institute of Technology Gaucher Dwarahat Road Uttarakhand263653 India

This manuscript addresses the challenges in developing Automotive High-Performance Computing (HPC) systems for Future Smart Cars and Unmanned Mobile Vehicles (UMVs). We introduce a novel Unified Machine Vision (UMV) narrative methodology for operators overseeing UMVs. The integration of diverse sensors, Direction API software, and LoRa communication technology presents significant technical hurdles. To tackle these, we focus on integrating Direction API with LoRa in UMVs, incorporating open-source natural interaction OpenNI, and haptic driver inputs into the Master Control System (MCS). The MCS, using the Comp-GAP algorithm, serves as the central interface between the driver and machinery sensors, effectively managing commands to achieve multifaceted objectives. Additionally, we present the CCHA, CROMM-MSV parallel algorithm to mitigate power consumption issues in autonomous vehicles via centralized HPC. This new centralized Advanced Driver Assistance System (ADAS) methodology optimizes UMV energy consumption in air and enhances operational efficiency through parallel HPC techniques. Simulation results demonstrate stable behavior in parallel processing, validating the proposed methodologies. In summary, this manuscript offers a robust framework for HPC systems in Future Smart Cars and UMVs, paving the way for improved navigation and vehicular control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Cloud 2nd

Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Clou...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lalitha Devi, K. Deepa Thilak, K. Shanmuganathan, C. Kalaiselvi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9789819995172

In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving techniques reduce the amount of energy consumed based on proper task scheduling approaches. The existing fuzzy-based hybrid genetic algorithm considers job length to optimize the makespan. However this fuzzy genetic encoded chromosome (fuzzy-GEC) algorithm considers both makespan and energy consumption to optimize the calculation of fitness value for assigning the task to the virtual machine (VM) by considering the characteristics of the task and VM. According to these characteristics, a fuzzy genetic rule-based encoding scheme is developed to schedule the tasks onto the virtual machines to minimize makespan and energy consumption. The effectiveness of the new technique is evaluated against FCFS and conventional genetic scheduling algorithm using Google Cloud trace workload. The results show the efficiency of the developed approach for makespan and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual machine

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Per-user network access control kernel module with secure multifactor authentication

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Journal of Supercomputing 2024年第1期80卷 970-1008页

作者： Cheng, Sheng-Tzong Horng, Gwo-Jiun Hsu, Chih-Wei Su, Z.-Yu Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan

Network attacks, such as botnets stealing sensitive data, constitute a critical concern for administrators in the Internet area. Such attacks can be prevented using a network access control (NAC) scheme. However, existing NAC mechanisms all rely on the user account mechanism provided by the operating system. Herein, we propose a protocol involving a Linux security module for user-based NAC. The module requires neither user accounts nor secure user space;it loads signed rules and keys for a user from a USB security key, securely authenticates the user, and controls network permissions directly from the Linux kernel. Moreover, we present extensions for the protocol, developed using additional authentication methods and devices (such as a smartphone), to prevent man-in-the-middle, replay, and phishing attacks. The protocol can securely authenticate users from kernel space by using USB security keys and a user smartphone despite an insecure user space, is resistant to various attacks, and can guarantee that authorized users obtain their corresponding network privileges. At the same time, the analysis of Linux operating system security enhancement technology is proposed to achieve the goal of trusting machines. Through the use of empirical algorithm analysis, enhanced virtualization technology, and other methods, the risk of hacker intrusion on information platforms is reduced in multiple aspects, and management mechanisms are used to set and use resource permissions. On the other hand, after mastering the key technologies for network blocking development in the core layer of the system, there is also an opportunity to independently build modules for the security core of operating systems in special fields and future 5G/6G SDNs. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Network security

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Analyzing functional, technical and bibliometric trends of blockchain applications in education: A systematic review

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Multimedia Tools and Applications 2025年第8期84卷 4003-4048页

作者： Choudhary, Ankita Chawla, Meenu Tiwari, Namita Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

In an educational ecosystem, accurate and complete management of student’s records is a complex and error-prone process. Blockchain is a promising solution to add substantial value around enhanced efficiency, privacy protection, technological advancement, access control, and secure exchange of student course records. This work presents a systematic literature review to analyze the applications of blockchain technology in the educational domain addressing the gaps identified in the previous reviews. The technical papers published in English were searched by key terms related to blockchain and education on IEEE Xplore, science Direct, and six other databases. The risk of bias was assessed by considering all the crucial search terms preventing the accidental exclusion of any article. Many blockchain frameworks in the educational domain have been proposed;including some prototypes and implementations. The PRISMA framework was used for the evaluation of 150 distinct models, 124 of which were prototypes, pilots, or full implementation and the remaining 26 were only proposals. This work contributes a structured bibliometric, functional, and technical examination of these articles, assessing privacy, cost, performance, security, architecture, and standards to identify and bridge gaps. The study concludes an avalanche of proposals, with approximately half of them being prototypes, indicating that implementation and proof-of-concept pilots are expected. Blockchain is found to solve various record management issues at significant trade-offs between performance, storage, and cost. However, scalability issues, poor overall performance, and increased price are major limitations for implementing blockchain in education solutions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Records management

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The use of multi-task learning in cybersecurity applications: a systematic literature review

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Neural Computing and Applications 2024年第35期36卷 22053-22079页

作者： Ibrahim, Shimaa Catal, Cagatay Kacem, Thabet Department of Computer Science and Engineering Qatar University 2713 Doha Qatar Department of Computer Science and Information Technology University of the District of Columbia WashingtonDC20008 United States

Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field. © The Author(s) 2024.

关键词： Cybersecurity

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Multi-channel mantis search spatial attention convolutional neural network based hyperspectral image change detection

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Multimedia Tools and Applications 2025年 1-22页

作者： Divya, V.M. Department of Computer Science and Engineering East West Institute of Technology Visvesvaraya Technological University Belagavi560018 India

Hyperspectral imaging has transformed remote sensing by offering detailed spectral data for monitoring environmental changes, like urban development and terrestrial cover variations. However, existing change detection techniques often face challenges like underfitting and overfitting. In this research, a novel approach is introduced for hyperspectral image change detection using a Multi-Channel Mantis Search (MCMS). This innovative methodology leverages the power of a Multi-Channel Convolutional Neural Network (MCNN) to process each spectral band independently, facilitating the capture of unique features within each band. The input hyperspectral images are gathered from the Pavia University Dataset and the Indian Pines Dataset. This approach leads to significantly improved feature extraction, enhancing the discrimination of different materials in hyperspectral images. To further enhance the performance of the change detection model, the Mantis Search Algorithm (MSA) is integrated. The MSA is employed to optimize the model's hyperparameters, fine-tuning its architecture and training parameters to achieve superior results. The combination of MCNN and MSA improves hyperspectral image change detection accuracy, achieving 98.21% and 99.31% with the Indian Pines and Pavia University datasets correspondingly. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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