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Efficient multiverse electro search optimization for multi-cloud task scheduling and resource allocation

Multimedia Tools and Applications 2024年 1-26页

作者： Ravi, Rupesh Pillai, Manu J. Department of Computer Science and Engineering TKM Institute of Technology Karuvelil Kerala Kollam691505 India Department of Computer Science and Engineering TKM College of Engineering Karicode Kollam India

Effective task scheduling and resource allocation have become major problems as a result of the fast development of cloud computing as well as the rise of multi-cloud systems. To successfully handle these issues, we provide a novel strategy in this study termed Efficient Multiverse Electro Search Optimization (EMESO). This study presents an Efficient Multiverse Electro Search optimization to improve work scheduling behaviour, with significance on electron transport near the nucleus and cosmological principles. The method uses exploration, exploitation, and local search to discover the optimal solution from a huge amount of potential alternatives. EMESO examines a broad search space to uncover optimal solutions for work scheduling and resource allocation by exploiting the concept of a multiverse, which represents a collection of parallel universes. While related to modern work, the proposed approach performs much better in big and extra-big workflow jobs than in smaller and midrange workflow jobs. It may decrease costs by 6%, shorten project timelines by 8%, and increase resource efficiency by 55% while still enabling all activities to be completed by the deadlines. The proposed approach significantly improves efficiency, resource utilization, and overall performance, making it one of the best options for managing complex cloud systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cloud computing

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Efficient Heuristic Replication Techniques for High Data Availability in Cloud

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computer Systems science & engineering 2023年第6期45卷 3151-3164页

作者： H.L.Chandrakala R.Loganathan Department of Computer Science and Engineering School of EngineeringPresidency UniversityIndia Department of Computer Science and Engineering HKBK College of EngineeringIndia

Most social networks allow connections amongst many people based on shared *** networks have to offer shared data like videos,photos with minimum latency to the group,which could be challenging as the storage cost has to be minimized and hence entire data replication is not a *** replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response *** simple and adaptive replication strategies exist,the solution is non-deter-ministic;the replicas of the data need to be optimized to the data usability,perfor-mance,and stability of the application *** resolve the non-deterministic issue of replication,metaheuristics are *** this work,Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Har-mony-Tabu search is proposed for effective placement and replication of *** on large datasets show the effectiveness of the proposed *** is seen that the bandwidth saving for proposed harmony-Tabu replication per-forms better in the range of 3.57%to 18.18%for varying number of cloud data-centers when compared to simple replication,Tabu replication and Harmony replication algorithm.

关键词： Cloud computing data replication bandwidth saving Tabu search Harmony search hybrid Harmony-Tabu search

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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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Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review

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Multimedia Tools and Applications 2025年第5期84卷 2293-2325页

作者： Jangid, Manisha Kumar, Rakesh Department of Computer Science & Engineering Central University of Haryana Haryana India

Recommendation systems (RS) have become prevalent across different domains including music, e-commerce, e-learning, entertainment, and social media to address the issue of information overload. While traditional RS approaches have achieved significant success in delivering recommendations, they still face issues including sparse data, diversity, cold start, and long tail problem. The emergence of deep learning as a prominent and extensively studied topic has shown significant potential in addressing these challenges in RS. Deep learning captures intricate patterns of interaction and precisely reflects user preferences, allowing for encoding complex abstractions in data representation and enhancing information processing capabilities. This paper provides an extensive survey of the existing literature on recommendation systems. We will begin by providing a foundational understanding of the core concepts and terminology of recommendation systems and significance of deep learning. Secondly, we talk about the original studies being conducted on deep learning methods and solutions to address "Cold start and long tail" challenges in recommendation. Thirdly, we examine the potential future directions of research pertaining to deep learning-based recommender systems (DLRS). Our review provides valuable insights for both researchers and practitioners in using deep learning to address challenges in recommendation and in developing effective and efficient recommendation systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Blockchain-Powered Authenticated Key Agreement Scheme With Reputation-Incentive Mechanism for Vehicle-to-Vehicle Communication in IoV

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IEEE Internet of Things Journal 2025年第13期12卷 25500-25515页

作者： Mukathe, Daniel Di, Wu Ahmed, Waheeb Worku, Tarik Dalian University of Technology School of Computer Science and Technology Dalian116024 China Tharaka University Department of Computer Science Marimanti193-60215 Kenya University of Aden Department of Computer Science and Engineering Aden009672 Yemen Injibara University Department of Computer Science Injibara6040 Ethiopia

The Internet of Vehicles (IoV) enhances road safety through real-time vehicle-to-vehicle (V2V) communication of traffic messages. However, V2V wireless connectivity poses security and privacy threats, as malicious adversaries can eavesdrop and modify V2V messages or compromise vehicle identity privacy. Existing authenticated key agreement (AKA) schemes attempt to address these threats but suffer from security flaws, computational inefficiency, high communication overhead, single points of failure, and trust deficits, making them unsuitable for resource-constrained and delay-sensitive IoV applications. To address the above challenges, we propose a blockchain-powered AKA scheme with a reputation-incentive mechanism (BAKARI) for V2V communication. BAKARI employs Schnorr signatures and lightweight elliptic curve cryptographic operations to improve computational efficiency, and minimizes communication overhead by completing the AKA phase with only two messages. BAKARI leverages blockchain ledger and smart contracts to maintain vehicle authentication information and V2V messages. Additionally, it incorporates a reputation-incentive model, where trustworthy vehicles are rewarded while malicious ones are penalized. A rigorous security analysis, including formal proof under the random or real model, informal analysis, and ProVerif verification, demonstrates BAKARI’s resilience against security and privacy threats. Performance evaluation shows that BAKARI balances computational efficiency, communication overhead, and security better than the benchmark schemes. Finally, simulations on Hyperledger Fabric and Veins frameworks validate BAKARI’s practicality in real-world IoV environments. © 2014 IEEE.

关键词： Blockchain

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An ensemble based approach for violence detection in videos using deep transfer learning

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Multimedia Tools and Applications 2025年第12期84卷 11001-11025页

作者： Kaur, Gurmeet Singh, Sarbjeet Department of Computer Science and Engineering UIET Panjab University Chandigarh India

The detection of violence in videos has become an extremely valuable application in real-life situations, which aim to maintain and protect people’s safety. Despite the complexities inherent in videos and the abrupt nature of violent actions, the field has seen several approaches, yet achieving consistent performance remains elusive, especially with advanced real-life datasets. Presenting a solution, the paper proposes a Bagging ensemble based approach comprising three pretrained models integrated with stacked Long Short-Term Memory (LSTM) to enhance individual model performance. This ensemble approach is rigorously analyzed on two publicly accessible datasets, RLVS and RWF-2000, providing remarkable accuracy (96.6%, 92.7%) and F1-scores (96.6%, 93.0%). Additionally, a cross-dataset analysis demonstrates the model’s ability to generalize across diverse datasets. Furthermore, a study of ablation highlighting the efficacy and optimal selection of components in augmenting the proposed ensemble’s efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2025年第16期84卷 16339-16356页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu 212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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Low-light image enhancement using the illumination boost algorithm along with the SKWGIF method

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Multimedia Tools and Applications 2025年第17期84卷 18651-18685页

作者： Radmand, Elnaz Saberi, Erfan Sorkhi, Ali Ghanbari Pirgazi, Jamshid Department of Computer Engineering University of Science and Technology of Mazandaran Behshahr Iran

Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main problems, the first of which is low visibility (i.e., small pixel intensities). Secondly, due to the low signal-to-noise ratio, noise also becomes prominent and obscures the image content. For this reason, images with low noise are usually employed in this application, a practice not possible in the real world. In this regard, a hybrid method is proposed which is based on the illumination boost algorithm (IBA) and weighted guided image filtering with steering kernel (SKWGIF). IBA is used to raise the values of low and medium intensity pixels while preventing excessive increases in high-value pixels. SKWGIF is employed to denoise and augment image details by adjusting its parameters based on the output of the previous step. The proposed method utilizes the LOL v1, LOL v2 and ExDark datasets Our hybrid method’s comprehensive approach to low-light image enhancement produced better outcomes than previous methods. Quantitative results have shown that the proposed model outperforms state-of-the-art (SOTA) models on the LOL v1 dataset, achieving a PSNR value of 18.80. Through the successful resolution of visibility and noise reduction issues, our approach led to notable gains in image quality measures, including SSIM, PSNR, BRISQUE, and NIQE. IBA’s ability to selectively boost intensity improved image visibility without producing overexposure artifacts, and SKWGIF effectively reduced noise while enhancing image details. The combined effect produced improved image quality that was superior to that of single techniques or already-used hybrid approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

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Recent Patents on engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham science Publishers.

关键词： Electroencephalography

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Multiview data fusion technique for missing value imputation in multisensory air pollution dataset

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3173-3191页

作者： Middya, Asif Iqbal Roy, Sarbani Department of Computer Science and Engineering Jadavpur University Kolkata India

The missing readings in various sensors of air pollution monitoring stations is a common issue. Those missing sensor readings may greatly influence the performance of monitoring and analysis of air pollution data. To address this problem, in this paper, a multi-view based missing value (MV) imputation method called MVDI (Multi-View Data Imputation) is proposed for air pollution related time series data. MVDI combines four models namely LSTM (Long-Short Term Memory), IDS (Inverse Distance Squared), SVR (Support Vector Regressor), and KNN (K-Nearest Neighbors) to estimate MVs. These four models are mainly employed to capture the variations in data from different views of the dataset. Here, different views represent different portions (subsets) of the actual dataset. The estimates of MVs from all the views are combined using a kernel function to get an overall result. The proposed model MVDI is evaluated on real-world air pollution dataset in terms of RMSE, MAE, MAPE, and R2. The experimental results show that MVDI dominates over the baseline methods namely AR (AutoRegressive), ARIMA (AutoRegressive Integrated Moving Average), RFR (Random Forest Regressor), ANN (Artificial Neural Network), LI (Linear Interpolation), NN (Nearest Neighbors), MI (Mean Imputation), CNN (Convolutional Neural Network), ConvLSTM (Convolutional LSTM). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Time series

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