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Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

computer Systems Science & engineering 2023年第7期46卷 27-42页

作者： D.Viji S.Revathy Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia

Cloud storage is essential for managing user data to store and retrieve from the distributed data *** storage service is distributed as pay a service for accessing the size to collect the *** to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage *** potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data *** creates a complex nature to increase the storage consumption under *** resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud ***,preprocessing is done using the sparse augmentation ***,the preprocessed files are segmented into blocks to make *** block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the *** on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a ***,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match *** implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

关键词： Cloud computing deduplication hash indexing relational content analysis document clustering cloud storage record linkage

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A Novel Deep Reinforcement Learning Approach for Stability-Based Parallel Disassembly Sequence Planning Problem

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SN computer Science 2025年第3期6卷 1-19页

作者： Kumar, M. R. Mahesh Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Parallel Disassembly Sequence Planning (DSP) deals with obtaining the order of disassembling the product with multiple parts disassembling simultaneously. Existing studies derive the product’s disassembly order based on the requirement for liaison data. However, the absence of stability data does not ensure that all the product parts are disassembled without damage. Since the order of retrieval of the product can also be obtained from stability data, this reduces the requirement of an initial precedence graph based on liaison data. The complexity of the problem increases if we adopt different representations for precedence and stability conditions of the product for constraint checking throughout the disassembly process. This paper constructs a stability-based precedence graph based on only the product’s stability data. The developed graph represents the precedence of the stable parts for disassembly, and the same is utilized to formulate the problem as a model-free deep Reinforcement Learning (RL) problem. A novel Exploration–Exploitation (EE) strategy-based Deep Q-Learning (DQL) algorithm is proposed to parallel DSP minimizing direction changes. Our effective reward mechanism accelerates the learning process for all the conditions and objectives of the problem considered. Based on past performance, the proposed algorithm dynamically decides the exploration and exploitation strategy to avoid local optima. We applied the proposed algorithm on five different-scale products and compared the results with uniform-sampling-based model-free deep RL algorithms. It showed significant improvements, particularly in cumulative rewards and disassembling all the parts for large-scale products with early convergence. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Q-learning Deep Q-Networks Disassembly sequence planning Parallel disassembly Precedence graph Stability graph

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Image based Android Malware Detection System using ResNet50 and VGG16 15

Image based Android Malware Detection System using ResNet50 ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gundla, Rajeshwari Gengaje, Sachin R. Department of Computer Science & Engineering Walchand Institute of Technology Solapur India Department of Electronics and Computer Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9798331300579

Attacks on mobile devices, such as smartphones and tablets, have increased as a result of their rising popularity. One of the most significant risks is mobile malware, which may lead to both security breaches and financial losses. Mobile malware will probably continue to develop and spread since it is used to commit different types of cybercrimes on mobile devices. As the Android operating system has gained popularity, mobile malware targets it especially. Users' security is seriously jeopardised by the quick spread of Android malware apps, which also makes it challenging to manually and statically analyse harmful files. Therefore, it is essential to accurately identify and categorise Android harmful files. There has been some discussion of Convolutional Neural Network (CNN) based approaches in this area, however there is still opportunity for performance enhancement. In this work, using two well-known deep learning models, ResNet-50 and VGG16, we present a transfer learning strategy to effectively detect the Android malware files. By converting malicious and benign APK files into grayscale images, the proposed model is trained on the AndroHealthCheck dataset. On the AndroHealthCheck dataset, our model outperforms state-of-the-art works in terms of accuracy, recall, precision, and F1 measures. © Grenze Scientific Society, 2024.

关键词： Android malware

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Hobby Based Learning for Automata Theory and Computability 2

Hobby Based Learning for Automata Theory and Computability

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Pallavi, N. Darshna, A. Naik Manjula, L. Keerthi, P. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Impact Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306415

In the traditional form of instruction, the teacher and pupil converse face to face. The discussion is started by the instructor, who generally discusses the material from the required textbooks. Many students might not find the concepts intriguing, which would reduce their ability to concentrate and pay attention in class. This makes learning challenging. A novel teaching strategy that allows pupils to learn a subject depending on their interests has been implemented in the classroom. With this approach, students apply their own hobbies to the subject they wish to learn. It focuses on interest-driven learn. This method focuses on interest driven learning where students apply their Personal interest to the subject they want to learn. A prime example of interest-driven learning is hobby-based learning. Modelling enthusiasm and making the topics relevant to their hobby would make the classroom student centric learning and interest driven learning where student would learn the topic self-directed, enjoy learning process, gain deeper insight on the topic and also develop their hobbies. Hence hobby-based learning was introduced to the classroom for the subject Automata Theory and Computability. A comparative study was made on analysis and data obtained from classrooms with hobby based learning and conventional learning handled by the same instructor on the subject Automata Theory and Computability where hobby-based learning gave significantly best feedback than conventional learning. © 2024 IEEE.

关键词： Hobby based learning interest driven learning student centric learning

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Enhancing Malware Detection: A Comparative Analysis of Ensemble Learning Approaches 1

Enhancing Malware Detection: A Comparative Analysis of Ensem...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Ajvad Haneef, K. Shashank, N.S. Madhu Kumar, S.D. National Institute of Technology Department of Computer Science & Engineering Calicut India National Institute of Technology Department of Computer Science & Engineering Calicut India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392111

Malware represents the greatest threat to cybersecurity and serves as the weapon of choice for carrying out nefarious activities in the digital realm. The proliferation of malware poses significant risks to individuals and organizations. Therefore, malware detection is essential for companies to strengthen their cyberspace, protect sensitive data and minimize financial losses from cyberattacks. Ensemble learning methods have emerged as powerful tools in this domain, leveraging the strengths of multiple models to enhance detection accuracy and reliability. We present a comparative analysis of ensemble learning approaches for malware detection using the EMBER dataset. We evaluate six ensemble models, including Random Forest, AdaBoost, Extra Trees, XGBoost, LightGBM, and Bagging, based on key performance metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that all ensemble models demonstrate strong performance in malware detection, with Bagging ensemble achieving the highest accuracy and overall performance. The results highlight the effectiveness of ensemble learning techniques in enhancing malware detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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A Comprehensive Analysis of Feature Selection Techniques and Their Impact on Earthquake Damage Prediction

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Iranian Journal of Science and technology - Transactions of Civil engineering 2025年 1-35页

作者： Feroz, Shejuti Binte Sharmin, Nusrat Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Earthquake damage prediction is vital to ensure occupants of buildings are not injured and substantial financial losses can be avoided. Algorithms based on machine learning are prevalent in this field. This study conducts an in depth analysis of the impact of feature selection on machine learning models for earthquake damage prediction. A feature selection method is critical for reducing data dimensionality, improving model accuracy, and mitigating overfitting. The Bat Algorithm, Particle Swarm Optimization, and Simulated Annealing were evaluated alongside twelve traditional feature selection techniques, such as Information Gain, Chi-square Test, Fisher’s Score, and LASSO Regularization. A comprehensive dataset from the 2015 Gorkha earthquake in Nepal was used to assess the predictive performance of three machine learning models-Decision Tree, Random Forest, and LightGBM. The novelty of this work lies in applying a broad spectrum of feature selection methods—many of which are being used for the first time—on the real-world dataset from the 2015 Gorkha earthquake in Nepal. A significant role is played by feature selection in the improvement of model accuracy and efficiency. Out of all the models, the Forward Feature Selection and Correlation Coefficient showed the best trade-off between processing time and accuracy. This study contributes to future disaster preparedness and structural resilience initiatives by highlighting the significance of meticulous feature selection and providing a useful framework for improving earthquake damage prediction models. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Deep learning Earthquake prediction Machine learning

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Con-Fog: Consensus-Driven Fog Node Selection in FU-Serve Platform for IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 24686-24697页

作者： Imandi, Raju Roy, Arijit Sethi, Kamalakanta Pavan, Kumar Guizani, Mohsen Indian Institute of Information Technology Computer Science and Engineering Group Sri City India Indian Institute of Technology Department of Computer Science and Engineering Patna India Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform. © 2014 IEEE.

关键词： Fog computing

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Chaos Theory Based Gravitational Search Algorithm For Medical Image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

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SN computer Science 2024年第6期5卷 750页

作者： Moeed, Syed Abdul Karnati, Ramesh Ashmitha, G. Mohammad, Gouse Baig Mohanty, Sachi Nandan Department of Computer Science and Engineering Kakatiya Institute of Technology and Science Department of Computer Science and Engineering Vardhaman College of Engineering School of Computer Science & amp Engineering (SCOPE) VIT-AP University

The Internet of Things (IoT) has revolutionized our lives, but it has also introduced significant security and privacy challenges. The vast amount of data collected by these devices, often containing sensitive information, makes them prime targets for cyberattacks. Traditional security methods struggle to keep pace with the evolving threat landscape of the interconnected IoT world. In response, this paper explores the transformative potential of federated learning (FL) in safeguarding both the privacy and security of IoT devices. FL keeps data on individual devices, only sharing updated models, not raw data, thus protecting user privacy and eliminating the need for a central data storage server, which reduces the risk of data breaches and security vulnerabilities. The effectiveness of the proposed model is evaluated using established open-source datasets like NSL-KDD and UNSW NB15, ensuring real-world applicability. Analysis of the dataset's features enabled the development of a model utilizing federated learning. Notably, the proposed model achieved superior performance with FL in detecting attacks on IoT networks. Furthermore, this research investigates the transformative potential of FL to address the inherent security and privacy challenges plaguing traditional, centralized data collection methods in the ever-expanding realm of IoT devices. FL empowers collaborative learning on distributed datasets, allowing devices to collectively improve security without compromising user privacy. The findings of this study demonstrate the promise of FL as a secure and privacy-preserving solution for the future of IoT. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberattacks Federal learning Internet of Things Machine learning Security and privacy

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