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Informatica (Slovenia) 2025年第13期49卷 143-162页

作者： Mohiuddin, Mohd Hasan Tamilselvan, Latha Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science &Technology Vandalur Chennai600048 India Department of Information & Technology B.S. Abdur Rahman Crescent Institute of Science &Technology Vandalur Chennai600048 India

Image deduplication is becoming increasingly important for cloud storage infrastructures to handle the increasing amount of multimedia material. Through increased storage efficiency, effective picture deduplication may optimize resources and save expenses. It also improves performance by facilitating quicker access, utilizing less bandwidth, and enhancing data integrity. Although heuristic-based classical deduplication techniques work well in various storage infrastructures, they cannot keep up with the dynamic nature of cloud storage resources. This study presents IDedupNet, a revolutionary DL-based framework that improves infrastructure performance in cloud computing by efficiently detecting duplicate and near-duplicate photos. Our approach leverages MobileNetV3 for feature extraction and CNN-based encodings for image deduplication, enabling it to manage duplicate photos in highly dynamic contexts efficiently. Additionally, we provide a Learning-Based Image Deduplication (LBID) approach that improves deduplication performance by extending the use of the IDedupNet model. Experimental evaluation demonstrates a high accuracy of 98.68% on benchmark datasets, consistently outperforming existing models. The underlying technique and deep learning framework may be easily integrated into real-time cloud storage systems to increase customer satisfaction and infrastructure efficiency. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Cloud storage

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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

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Alexandria Engineering Journal 2025年 127卷 357-373页

作者： Alzughaibi, Arwa Alanazi, Adwan A. Alshahrani, Mohammed Hilali Jaghdam, Ines Hassaballa, Abaker A. Applied college Taibah University Madinah Saudi Arabia Department of Computer Science and Information College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Sciences Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Mathematics Faculty of Science Northern Border University Arar73213 Saudi Arabia

The disease of Kidney stones is a risky disease for individuals all over the world. Many people with kidney stones in the early phase do not detect it as an illness, and it harms the organ gradually. Precise analysis of kidney illness is vital, as it is a major health concern that needs accurate detection for appropriate and effective treatment. CT scans are one of the most extensively accessible imaging models, and they are employed for effective diagnosis. Deep learning (DL) techniques are gradually identified as beneficial tools for analyzing illness in the medical field. However, present techniques employing deep networks often meet low accuracy and overfitting challenges, demanding further alteration for optimum performance. This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. The presented LFFOEDL-AKSD technique mainly focuses on detecting kidney stones using CI imaging. At first, the presented LFFOEDL-AKSD technique applies the pre-processing phase, which involves image resizing for uniform CT scan dimensions and data augmentation through transformations like rotation and flipping to reduce overfitting, sobel filtering (SF) sharpens edges, and the data is separated into training, validation, and testing sets for model development. The presented LFFOEDL-AKSD technique employs the swin transformer (ST) model for the feature extraction method. Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. Finally, the presented LFFOEDL-AKSD technique implements the flying foxes optimization (FFO) approach for the hyperparameter tuning involved in the ensemble learning models. An extensive experiment is conduct

关键词： Flying foxes optimization Ensemble deep learning Kidney stone detection CI imaging Swin transformer

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Machine Learning Vigilance: Safeguarding Networks Through Cyber Attack Detection 12th

Machine Learning Vigilance: Safeguarding Networks Through Cy...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sangeam, Ruchitha Ala, Rajitha Punyamurthy, Bheemeshwar Kanagala, Sai Sriharsha Kesari Swain, Nirmal Swetha, Rame Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderbad India

ISBN: (纸本)9789819601424

Embracing the latest technological strides holds immense promise for individuals, organizations, and governments. However, divergent perspectives exist on these advancements, particularly regarding safeguarding critical information, securing stored data platforms, and ensuring data accessibility. A pivotal challenge in the contemporary landscape is the escalating threat of digital terror ism, causing disruptions for individuals and institutions. This form of cybercrime, orchestrated by diverse entities like criminal organizations, skilled individuals, and digital activists, now poses a significant risk to public and national security. Intrusion Detection Systems (IDS) have been developed to address this pressing issue. The study leverages the recently introduced UNSWNB dataset to evaluate the efficacy of the chosen approach. The SVM algorithm demonstrated a commendable accuracy of 83.29. The company demonstrated its ability to effectively counter cyber threats by showcasing its potential. Moreover, they utilized a convolutional neural network (CNN) to achieve this feature outperformed, and achieved an accuracy level of 99.52 with your cybersecurity project. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and Networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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Improved Harmony Search Algorithm for SDN Controller Placement

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 167-179页

作者： Aldeen, Yousra Abdul Alsahib S. Sadiq, Ahmed T. Abed, Abeer E. Hussain, Omar F. Madni, Syed Hamid Hussain Department of Computer Science College of Science for Women University of Baghdad Iraq Department of Computer Science University of Technology Iraq Office of the Vice President for Scientific University of Baghdad Iraq Quality Assurance and Academic Performance Department University of Baghdad Iraq School of Electronics and Computer Science University of Southampton Malaysia Johor Bahru Malaysia

The Software-Defined Networking (SDN) paradigm decouples the control and the data plane. One of the most significant challenges in this paradigm is SDN controller placement optimization, since improper placement may dramatically influence latency, load balancing, and network resilience. The paper proposes the Improved Harmony Search Algorithm (IHSA) as a new approach for the placement of SDN controllers. To overcome the disadvantages of conventional optimization methods like HSA, GA, and PSO, adaptive parameter tuning, dynamic harmony memory management, and updating rules for enhanced memory are incorporated into the IHSA. The complete simulations of IHSA over small, medium, and large-scale SDN topologies have shown its robust supremacy in performance compared to the benchmark algorithms, which provide up to 23.5% less latency along with significant improvements in load-balancing and convergence-time. The optimization results of IHSA are well supported by the statistical validity based on the p-values and confidence intervals. The algorithm is scalable, adaptive, and very efficient. Hence, it makes a more realistic solution for real-time and dynamic SDN environments. The results show that IHSA has the potential to improve network performance in latency-sensitive applications such as IoT, cloud computing, and telecommunications, thus opening up resilient and high-performance network infrastructures. This is a practical application of IHSA, and subsequently, the recommendation given through this study is for its integration with datasets and machine learning techniques from real world to further improve optimization. © 2025 The Author(s).

关键词： Resource allocation

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Social Networks on WEB 3.0 12th

Social Networks on WEB 3.0

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Cidri, Anirudh AshaRedddy, M. Bhaskar, T. Vijaya Krishna, A. Suryanarayana, G. Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science & Engineering-AIML CMR College of Engineering Hyderabad India Department of Information Technology G. Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819601462

The "Social Network on WEB3.0" project is poised to revolutionize online social dynamics by leveraging blockchain technology to create a decentralized platform. This initiative prioritizes user control and security, aiming to address the shortcomings of existing Web 2.0 networks. Users will have unprecedented control over their data and privacy, with blockchain ensuring transaction irreversibility. Key goals include user empowerment, enhanced data security, decentralization, incentivized participation, and adherence to legal and privacy standards. The project incorporates a token-based incentive system to stimulate user engagement and collaboration, fostering a vibrant online community. Leveraging Ethereum and MetaMask for transactions and smart contracts highlights the project’s adaptability and scalability. With a commitment to ongoing enhancement and user feedback, the initiative heralds a significant shift toward a digital landscape characterized by user-centricity, heightened security, and privacy awareness, offering individuals unparalleled authority over their online interactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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AI, machine learning and deep learning in cyber risk management

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Discover Sustainability 2025年第1期6卷 1-21页

作者： Hamid, Ishrag Rahman, M. M. Hafizur College of Computer Science and Information Technology King Faisal University (KFU) Al-Ahsa 31982 Saudi Arabia

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in both climate change and cyber risk management has significantly bolstered their effectiveness. These advanced technologies enable organizations to handle extensive datasets, whether related to climate patterns or cyber activities, with greater efficiency, leading to more informed decision-making. In cybersecurity, AI and ML are utilized to predict potential threats, automate tasks, and manage cyber risks. However, challenges persist in ensuring the accuracy of information, understanding the functionality of models, and protecting individuals’ data. Ethical use of personal information and safeguarding privacy remain critical, with researchers needing to prioritize accuracy, transparency in AI models, and fairness in decision-making. Emerging technologies like edge computing, federated learning, explainable AI, and quantum computing are revolutionizing risk management and digital security. These advancements offer opportunities for better understanding risks, identifying threats more rapidly, and improving decision-making. Yet, issues such as data accuracy, model interpretability, and privacy concerns must be resolved to fully address cyber risks. This paper significantly contributes to understanding the role of AI, DL, and ML in cyber risk management by providing a thorough review of the literature and a discussion on both the challenges and future trends in the field. This paper explores AI, DL, and ML in the cyberrisk management through state-of-the-art analysis and future recommendations for further research. It discusses AI’s role in enhancing risk management, advancements in AI technologies, and their impact on traditional frameworks. The paper also highlights opportunities, challenges, and strategic and regulatory implications of AI in cybersecurity, concluding with future research directions. © The Author(s) 2025.

关键词： Artificial intelligence Challenges in risk management Climate change consequences Ethical use Implications Machine learning Privacy concerns Risk management Threat detection

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Customized IoT based Smart Irrigation for Terrace Plants 6

Customized IoT based Smart Irrigation for Terrace Plants

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priya, S. Padma Pavalarajan, S. Abarna, P.S. Manjula devi, C. Abinaya, B. Sukaina, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

An Internet of Things based smart irrigation system for terrace plants is a cutting-edge solution designed to automate and optimize the watering process, ensuring efficient use of water and promoting healthier plant growthSmart irrigation system may vary from field with crops of same variety to terrace plantation with plants of different varieties. As the water requirement may vary based on plant type, environmental condition, growth stage, it is intelligent to provide varying water supply. This Smart irrigation system employs soil moisture to monitor the water content of the terrace garden in real time. Some variety of plants may require minimum quantity of water. The sensors continuously collect data and send it to a central IoT platform or a cloud-based server, where intelligent algorithms analyse the data and determine the precise amount of water needed by the plants. Based on this analysis, the system automatically activates the irrigation mechanism whether it's a drip system, sprinklers, or any other appropriate watering method only when required, thus preventing water wastage and avoiding overwatering or underwatering. © 2025 IEEE.

关键词： Sprinkler systems (irrigation)

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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