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An efficient optimal load balancing algorithm for distributed file systems in cloud environment

International Journal of Networking and Virtual Organisations 2024年第2期30卷 134-151页

作者： Nebagiri, Manjula H. Latha, P.H. Department of Computer Science Atria Institute of Technology Bengaluru India Department of Information Science Sambhram Institute of Technology Bengaluru India

Efficient operations in distributed environments can be obtained by load balancing (LB). LB has turned out to be a vital and interesting research area with respect to the cloud owing to the swift augmentation of cloud computing, and the more services together with better outcomes demand of the clients. The work has developed a framework named an efficient optimal LB (EOLB) for distributed files system to beat the challenges faced in LB. LB was done by means of the framework centred on node distribution together with task distribution. Centred upon the data aspects as well as cloud servers, say CPU in addition to memory usage, together with disk IO occupancy rate, etc., it renders task distribution. Experimental analysis exhibits that the framework attains a better response rate of 74.68 ms, and a processing time (PT) of 0.43 ms, in addition, remains to be efficient when weighed with the prevailing methods. © 2024 Inderscience Enterprises Ltd.

关键词： Distributed cloud

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Implementation of ANN and response surface method for dual-fuel CI engine optimization and prediction using water infusion and biofuel

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Multimedia Tools and Applications 2025年 1-43页

作者： Pramanik, Sabyasachi Department of Computer Science & Engineering Haldia Institute of Technology Haldia India

In this study, tests were done to see what would happen if hydrogen (H2) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial with pure diesel, the engine was modified to run on two fuels by adding H2 through the inlet pipe and LO as direct injection (DI) gasoline. In this study, the energy fraction of H2 ranged from 5 to 10%. A 3% volume mixture of water (W) and diesel-LO mixtures was used to study how NOX emissions were affected. Using a common back- transmission method, an Artificial Neural Network model was created to forecast the correlation between engine output reactions and input variables (load, LO, and H2). The goal of response surface methodology is for improve the motor input settings for low pollution and high heat efficiency. The trials were designed using response surface methodology (RSM), and the optimization procedure was conducted using RSM. An artificial neural network (ANN) was used to predict the test engine’s emission patterns and performance. The results showed that ANN and RSM were both very good and accurate modeling strategies. When 10% H2 + LO25 was used, the BTE rose to 31%, up from 20% for clean LO and 28% for 12% H2 + LO25 + 2%W means. Compared to clean LO operation, the cylinder pressure raised in 15% H2 mode by up to 63 psi. The measured value for the HRR, under H2 premixed combustion framework was 81 k Joule/CA deg in 10% H2, that is significantly greater than the value for every LO processes. The highest NOX release for 10% H2 + LO 25% was discovered at 690 ppm peak load, while it dropped to 645 ppm in gasoline that had been emulsified in water. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gasoline

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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HandRT: Simultaneous Hand Shape and Appearance Reconstruction with Pose Tracking from Monocular RGB-D Video

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5625-5635页

作者： Kalshetti, Pratik Chaudhuri, Parag Indian Institute of Technology Bombay Department of Computer Science and Engineering India

We propose a method to reconstruct a personalized hand avatar, representing the user's hand shape and appearance, from a monocular RGB-D video of a hand performing unknown hand poses under unknown illumination. Our method, HandRT, jointly optimizes hand pose, shape, appearance, and lighting parameters using a physically-based shading model in a differentiable rendering framework incorporating Monte Carlo path tracing. HandRT extends our previous work, Intrinsic Hand Avatar, by relaxing the assumption of a known coarse hand pose and utilizing depth data in the optimization. Specifically, we introduce an articulated registration energy based on iterative closest point over the depth point cloud that enables reconstruction from unknown hand poses via tracking across frames. Thus, we can reconstruct the avatar from arbitrary poses with high accuracy without relying on an off-the-shelf 2D joint detector at each frame. Further, HandRT is capable of precisely tracking the reconstructed avatar from either RGB or RGB-D input. Our evaluation demonstrates that our method outperforms existing hand avatar reconstruction methods on all commonly used metrics while producing significantly accurate mesh compared to state-of-the-art hand mesh recovery methods by a large margin on public and our captured datasets. To facilitate future research, we release our code at https://***/pmkalshetti/handrt © 1979-2012 IEEE.

关键词： Mesh generation

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Emotion mining for early suicidal threat detection on both social media and suicide notes using context dynamic masking-based transformer with deep learning

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Multimedia Tools and Applications 2025年第13期84卷 11729-11752页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India

Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental health states leading to suicidal tendencies. The purpose is to recognize the emotions influencing individuals’ mental states, potentially leading to suicidal behavior. This study seeks to identify specific emotions like anger, depression, anxiety, guilt, fear, stress, and sadness within suicide-related texts sourced from both social media and suicide notes. We introduce a novel suicide severity assessment method emphasizing emotion detection across categories like suicidal ideation, suicide planning, attempted, committed, suicide notes, describing, and cannot dare. By identifying high-intensity negative emotions, our approach enables early detection of potential suicide threats. Existing studies often struggle to capture nuances and semantic relationships, particularly in suicide-related contexts. To address this, we propose a context dynamic masking based on bidirectional long short-term memory along with a multi-head self-attention and convolutional neural network (CoDyn-BMHSA-CNN) model, capturing bidirectional contexts and maintaining variable-length sequences. Our model achieves accuracies of 98.2% and 97.4%, with F1 scores of 95.5% and 93.8% for social media and suicide note datasets, respectively. We perform ablation tests to pinpoint the key components of our proposed models. Furthermore, a thorough comparative analysis evaluates the model’s performance across various contexts and platforms. Experimental results illustrate the superior effectiveness of our model in identifying emotions from text sequences compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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MTUNet + + : explainable few-shot medical image classification with generative adversarial network

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Multimedia Tools and Applications 2025年第11期84卷 9219-9239页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science and Engineering National Institute of Technology Patna India

Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the hurdles of subjective interpretation and reliance on specialized expertise. Deep learning algorithms show improvements in automating medical image analysis, reducing radiologists' burden, and potentially enhancing patient outcomes. However, these algorithms require substantial quantities of high-quality labelled data for effective training and refinement. This paper proposes an innovative approach that harnesses few-shot learning (FSL) and generative adversarial networks (GANs) to overcome conventional methods' limitations in medical image classification. FSL, capable of learning from limited labelled examples, holds promise for scenarios where labelled data is scarce. However, the lack of interpretability in existing FSL models impedes their clinical adoption. To tackle this, this paper proposes a explainable FSL network, "MTUNet + +," which integrates an attention mechanism to emphasize relevant regions in medical images. Furthermore, integrating a generative adversarial network, enhances the performance of MTUNet + + by generating synthetic medical images. Systematically eliminating misleading synthetic images improves the reliability and accuracy of medical image classification. Empirical evaluation on benchmark datasets underscores the effectiveness of the approach, achieving 85.19% and 69.28% accuracy for the HAM10000 and Kvasir datasets, respectively. This paper contributes to advancing AI-driven solutions in clinical practice, facilitating enhanced patient care and streamlined workflows within real-world healthcare settings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Examining the quality of learned representations in self-supervised medical image analysis: a comprehensive review and empirical study

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Multimedia Tools and Applications 2025年第9期84卷 6295-6325页

作者： Pani, Kaliprasad Chawla, Indu Department of Computer Science & IT Jaypee Institute of Information Technology Noida India

Medical Image Analysis (MIA) is integral to healthcare, demanding advanced computational techniques for precise diagnostics and treatment planning. The demand for accurate and interpretable models is imperative in the ever-evolving healthcare landscape. This paper explores the potential of Self-Supervised Learning (SSL), transfer learning and domain adaptation methods in MIA. The study comprehensively reviews SSL-based computational techniques in the context of medical imaging, highlighting their merits and limitations. In an empirical investigation, this study examines the lack of interpretable and explainable component selection in existing SSL approaches for MIA. Unlike prior studies that randomly select SSL components based on their performance on natural images, this paper focuses on identifying components based on the quality of learned representations through various clustering evaluation metrics. Various SSL techniques and backbone combinations were rigorously assessed on diverse medical image datasets. The results of this experiment provided insights into the performance and behavior of SSL methods, paving the way for an explainable and interpretable component selection mechanism for artificial intelligence models in medical imaging. The empirical study reveals the superior performance of BYOL (Bootstrap Your Own Latent) with resnet as the backbone, as indicated by various clustering evaluation metrics such as Silhouette Coefficient (0.6), Davies-Bouldin Index (0.67), and Calinski-Harabasz Index (36.9). The study also emphasizes the benefits of transferring weights from a model trained on a similar dataset instead of a dataset from a different domain. Results indicate that the proposed mechanism expedited convergence, achieving 98.66% training accuracy and 92.48% testing accuracy in 23 epochs, requiring almost half the number of epochs for similar results with ImageNet weights. This research contributes to advancing the understanding of SSL in MIA, providin

关键词： Supervised learning

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Exploring Nonuniform Structure of NSRTDs for Characterizing MACA Rules in the Null-Boundary Condition

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Complex Systems 2025年第1期34卷 91-127页

作者： Hazra, Suvadip Banerjee, Som Dalui, Mamata Chakraborty, Bidesh Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad58009 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern generation. This paper examines a two-state three-neighborhood nonuniform finite CA with a specified number of fixed points. We utilize a graph-based tool known as the next state rule minterm transition diagram (NSRTD) to analyze the spatiotemporal behavior of these cellular automata (CAs) across all lengths. We devise methods for synthesizing nonuniform single length cycle multi-attractor CAs (MACAs), a specialized class of irreversible CA with a predefined number of fixed points for any arbitrary length. Despite the method’s exponential worstcase time complexity, it offers the advantage of selecting rules for each CA cell from a set of candidate rules, ensuring the desired number of fixed points. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： cellular automata fixed-point attractor length cycle multi-attractor cellular automata MACA next state rule minterm transition diagram NSRTD

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Malicious iOS Apps Detection Through Multi-Criteria Decision-Making Approach

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 207-220页

作者： Bhatt, Arpita Jadhav Sardana, Neetu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Indonesia

In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for user’s privacy, have increased. Information on smartphones is perhaps, more personal than compared to data stored on desktops or computers, making it an easy target for intruders. After Android, the most prevalently used mobile operating system is Apple’s iOS. Both Android and iOS follow permission-based access control to protect user’s privacy. However, the users are unaware whether the app is breaching the user’s privacy. To combat this problem, in the paper we propose a hybrid approach to detect malicious iOS apps based on its permissions. In the first phase, weights have been assigned to app permissions using multi-criteria decision-making (MCDM) approach namely Analytic Hierarchy Process (AHP), and in the second phase machine learning& ensemble learning techniques have been employed to train the classifiers for detecting malicious apps. To test the efficacy of the proposed method dataset comprising 1150 apps from 12 app categories has been used. The results demonstrate the proposed approach improves the efficacy of detecting malicious iOS apps for majority of categories. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Differential privacy

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Hybrid scientific article recommendation system with COOT optimization

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Data science and Management 2024年第2期7卷 99-107页

作者： R.Sivasankari J.Dhilipan Department of Computer Science SRM Institute of Science and TechnologyChennai600089India

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.

关键词： Recommendation system COOT optimization Citation network Classification Long short-term memory(LSTM)

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