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A hybrid encryption for secure data deduplication the cloud

International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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StealthFace:Transfer Learning-Based Ensemble Model for Disguised Face Recognition using Skin-Segmented Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 1244-1260页

作者： Padmashree, G. Kotegar, Karunakar A Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Disguised face identification is challenging since people cover their identities by wearing masks, hats, sunglasses, or other disguises. These disguises dramatically modify face features, making identifying individuals a difficult task. In this study, we introduce StealthFace, an ensemble model that utilizes transfer learning and skin-segmented images to improve the recognition of disguised faces. To improve the accuracy and resilience of disguised face recognition, StealthFace leverages the power of deep learning algorithms, transfer learning, and ensemble methods. The framework employs pre-trained convolutional neural network models, such as ResNet50 and DenseNet121 for feature extraction and to learn discriminative features that are important in identifying individuals despite the disguises. The ensemble approach combines models’ predictions, utilizing their collective expertise and capturing multiple perspectives on disguised faces. Our ensemble model outperforms other state-of-the-art methods with an overall accuracy of 99.24% and contributes to enhancing security measures and advancing the development of face recognition systems. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Contrastive Learning

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Efficient straggler task management in cloud environment using stochastic gradient descent with momentum learning-driven neural networks

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Cluster Computing 2024年第4期27卷 4673-4685页

作者： Swain, Smruti Rekha Parashar, Anshu Singh, Ashutosh Kumar Lee, Chung Nan Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Kaohsiung804201 Taiwan

In the modern era, large-scale computing systems distribute tasks into smaller units, allowing them to be executed simultaneously, accelerating job completion, and reducing energy usage. However, cloud computing systems face a significant challenge: the Long Tail problem. This problem arises when a small subset of slow-performing tasks impedes the overall progress of parallel job execution, resulting in longer service response times and decreased system efficiency. To reduce task execution time and energy consumption, we propose an efficient straggler task management framework for cloud data centers in this paper. A neural network-based resource predictor is initially developed and tuned with the Stochastic Gradient Descent with Momentum mechanism to analyze and classify heterogeneous tasks into stragglers and non-stragglers. Then, after identifying the straggler tasks, they are further classified into two categories: Resource Hunters and Long-Tail stragglers, based on their specific resource requirements. A task management policy is implemented to achieve parallelism and enhance sustainability in the cloud infrastructure. Considering the task category, this policy effectively schedules and allocates resources among user job requests. To evaluate the effectiveness of the proposed work, extensive simulations are performed using the Google Cluster Dataset (GCD). The results obtained from these simulations are subsequently compared to state-of-the-art techniques for a comprehensive analysis. The experimental results reveal substantial improvements in various metrics, including power consumption and active servers showing reductions of up to 55.16% and 35%, respectively. Furthermore, there has been a reduction in execution time of up to 67.74%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Cloud computing

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in Engineering & sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © 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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Security Challenges and Privacy Issues in IoT Environment: A Systematic Review and Research Directions 23th

Security Challenges and Privacy Issues in IoT Environment: A...

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23rd International Conference on Intelligent Systems Design and applications, ISDA 2023

作者： Preeti Nath, Rajender Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9783031646492

IoT is a powerful piece of technology that can connect to anything and everyone at anytime, anywhere, and with any service available on a platform or network. The IoT needs to overcome several security and privacy concerns before it can be successfully used on a large and economically feasible scale. These security concerns include user and object privacy, IoT network access management, data security and user authentication etc. The communication stacks and protocols utilised by IoT technologies are different from those employed by conventional security countermeasures. This research paper examines the security concerns of the various platforms that enable IoT interconnection, their effects on the integrated IoT system, and potential future research areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Image Integrity Checking Using Watermarking in Cloud Computing: A Review 3rd

Image Integrity Checking Using Watermarking in Cloud Computi...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Rani, Jyoti Nath, Rajender Department of Computer Science and Applications Kurukshetra University Kurukshetra Thanesar India

ISBN: (纸本)9789819726707

In today’s era of cloud computing, modification and tampering of digital images on cloud storage have turn out to be easier due to proliferation of digital image processing tools. Consequently, tamper detection and integrity checking of images on remote server emerged as huge concern. Image watermarking techniques provide an efficient way to tackle such problems. This paper provides a systematic examination of existing watermarking methods currently in use in cloud computing environment. This paper also presents a comparative analysis of those techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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A Narrative Review of Developments in Knowledge Tracing in Last Decade 3

A Narrative Review of Developments in Knowledge Tracing in L...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jain, Ankush Mago, Neeru Panjab Univeristy Department of Computer Science and Applications Chandigarh India Panjab University Ssg Regional Centre Department of Computer Science and Applications Punjab Hoshiarpur India

ISBN: (纸本)9798331518592

Modern Teaching Methods suggest that the classroom interactions should be more student-centered rather than being teacher-centered. Intelligent Tutoring Systems (ITS) can play a vital role in making education more student-centered where student explores the content and tries to figure out answers to problems by themselves while teachers work as facilitators. However, modeling the students' knowledge effectively and accurately is crucial for the ITS to give intelligent recommendations and appropriate questions as per the level of understanding of student in a particular concept. This problem is known as Knowledge Tracing Problem. In this paper, we provide a narrative review and comparison, in an easily understandable manner, of the existing state-of-the-art knowledge tracing models developed in past 10 years. Additionally, we also classified 26 most popular knowledge tracing models among 7 groups based on their similarities. At the end, future directions of this field have been discussed. © 2024 IEEE.

关键词： Contrastive Learning

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Dynamic Analysis of Window’s Based Malware Using Reverse Engineering: A Case Study of Exmatter 23th

Dynamic Analysis of Window’s Based Malware Using Reverse En...

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23rd International Conference on Intelligent Systems Design and applications, ISDA 2023

作者： Surange, Geetanjali Khatri, Pallavi Hazra, Shubhankar Department of Computer Science and Applications ITM University Gwalior India

ISBN: (纸本)9783031646492

Malware analysis, is a crucial and cumbersome task, because of the continuously evolving characteristics of malware. Most of the malware damages the system resources or held them hostages for some unreasonable demands. Trojan is one of the most sought out malware by the attackers as it does destruction itself as well as it carries other types of malwares to the targets. This paper, proposes a framework for carrying out dynamic analysis of Trojan type malware, along with the mitigation methods and ways, by acquiring and executing the code at various stages of the malware in a controlled virtual environment. The experiment was carried out on, a virtual Window’s 11 machine, by following each step of the proposed framework. The analysis of the code is done using open source tools at various stages of the framework. The analysis carried out in the study results in identification of Indicators of Compromise (IOC) and strings that represent suspicious behavior and Tricks, Tactics and Procedures (TTP) of Exmatter malware, which further help to mitigate the effects of the attack. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Reverse engineering

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