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Preemptive Min Max Optimal Cost Based Scheduling for Improving the Load Balancing in Virtualized Cloud Environment

SN computer science 2024年第6期5卷 1-12页

作者： Ravikumar, K. Saravanakumar, K. Viswanathan, Anand Department of Computer Science and Engineering RRASE College of Engineering Department of Computer Science and Engineering Sri Eshwar College of Engineering Department of Information Technology Ponjesly College of Engineering

Load balancing and scheduling are essential components of cloud computing that aim to optimize resource allocation and utilization. In a cloud environment, multiple virtual machines and applications compete for shared resources, and efficient load balancing and scheduling mechanisms are crucial for ensuring optimal performance and resource utilization. The prior methodologies attains higher utilization to allocate task due to makspan problem leads more deadlock occurrence and request failures with higher energy consideration. Furthermore, the paper examines the challenges and considerations in load balancing and scheduling, such as dynamic workload variations, heterogeneity of resources, and QoS requirements. To handle this issues, to propose an Efficient Load Balancing Based Predictive Priority-based Preemptive Min max priority Load balancer (PMin-Max LB) to improve the service optimality in Decentralized cloud server. Initially the user request accessibility and task progress is evaluated through Service level Workload Impact score (SLWIS). Then introducing Workload Scaling Job vector estimation is carried by Particle Swarm Optimization (PSO). By evaluating the Makespan Instance Scaling Changeover Virtualization (MISCV) task weights are assigned to virtual machine by applying Preemptive Min max priority Load balancer (PMin-Max LB) to reduce the workload to improve the performance. The proposed system improves the impact of load balancing and scheduling on cloud performance, scalability, and cost-effectiveness. Finally proposed system attains the potential advancements in load balancing and scheduling techniques improves the computing approach in virtualization process to reduce the workload burned to optimize the heterogeneity and Quality of service. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing Load balancer Particle swarm optimization Priority scheduling Task allocation Virtual machine

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Encryption of medical data based on blockchain and multi-chaotic maps

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Multimedia Tools and Applications 2025年第17期84卷 17871-17892页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In this work, the SHA-256 mapper of the blockchain has been utilized to secure medical data from brute-force attacks. The uniform distribution and lower correlation of the encrypted data are achieved using the multi-chaotic in the encryption approach. Also, the circular shift method is employed in the approach to avoid differential attacks. The encryption approach is applied to secure the patient information, medical signals (ECG and EEG), and medical images (Grayscale, RGB, and Hyperspectral). The performance of the proposed approach has been analyzed for twenty signals and fifty medical images. Performance metrics such as MSE, SNR, PSNR, entropy, histogram, variance, chi-square, NPCR, UACI, and SSIM have been considered to check the resilience of the proposed approach against various types of attacks. The proposed approach satisfies the requirements of performance metrics by achieving higher MSE values with lower SNR and PSNR values for the encrypted data. Furthermore, the proposed methodology attains an entropy approaching 8, signifying robust randomness and resistance against entropy attacks. The SSIM values for encrypted images are close to 0, indicating significant dissimilarity from the original images. The NPCR values surpassing 99.60% and UACI exceeding 33.40% demonstrate resilience against differential attacks in the proposed approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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Boosting cervical cancer detection with a multi-stage architecture and complementary information fusion

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Soft Computing 2025年第2期29卷 1191-1206页

作者： Sahoo, Pranab Saha, Sriparna Sharma, Saksham Kumar Mondal, Samrat Computer Science and Engineering Indian Institute of Technology Patna India Computer Science University of Maryland Baltimore CountyMD United States

Cervical cancer is one of the most fatal and prevalent illnesses affecting women globally. Early detection of cervical cancer is crucial for effective treatment. Pap smear tests are commonly used, but population-based screening is time-consuming, expensive, and requires expert physicians. computer-Aided Diagnosis (CAD) has shown promise in addressing this challenge. However, accurately predicting the disease using a single model can be difficult due to the complex data patterns involved. This research proposes a multi-stage architecture to improve cervical cancer screening. Initially, three pre-trained models are employed for image classification, after which the proposed advanced fusion technique is applied to combine the predictions. Additionally, we introduce a filtering approach in the third stage to refine the predictions. Unlike traditional fusion methods, the proposed architecture considers the confidence score of the base classifiers in making the final predictions on test samples. To enhance the performance of the models, we incorporate advanced augmentation techniques, including CutMix, CutOut, and MixUp. We assessed the performance of the proposed framework using a 5-fold cross-validation technique on two benchmark datasets. We evaluated the performance of the proposed framework through 5-fold cross-validation on two benchmark datasets. Remarkably, our framework achieved a classification accuracy of 97.62% and an F1-score of 97.64% on the SIPaKMeD dataset, demonstrating its effectiveness in accurately categorizing various cell types in the dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cervical cancer computer-aided detection Deep learning Ensemble learning

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A novel fault feature extraction method for planet-bearing based on generalized total variation model

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IEEE Sensors Journal 2025年第11期25卷 20870-20879页

作者： Wang, Zhile Guo, Yu Kang, Wei Chen, Xing Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China

Planet-bearing plays a critical role in supporting planet gear and transmitting power, yet their fault-induced vibrations are inherently modulated by time-varying transmission paths, and thus it brings significant challenges to fault feature extraction. Therefore, this means that it is imperative to develop a robust fault analysis methodology. For one thing, this paper adopts minimum noise amplitude deconvolution (MNAD) to deal with the fault signal of planet-bearing inner ring. It is different from the traditional blind deconvolution method, mainly by suppressing the marked area noise components, which indirectly makes the fault feature prominent. For another, the first-order total variation model is prone to fall into the gradient effect problem. It takes into account the regular term of higher order total variational to achieve the purpose of minimizing approximation. In addition, two gradient weight parameters are introduced to balance the difference operator with different orders. For this reason, the generalized total variational denoising model (GTVDM) is constructed, and then the angular domain signal filtered by MNAD is input into this model. Meanwhile, it utilizes the Split Bregman algorithm to acquire the optimal solution of the constructed model. The core analysis process transforms an objective function optimization problem into several sub-problems, significantly improving computational efficiency. Finally, the experimental validation using fault test data of planet-bearing inner ring demonstrates that the combined MNAD algorithm and GTVDM framework effectively, and the fault feature order and modulation sideband caused by time-varying transmission path are observed in the squared envelope order spectrum. © 2001-2012 IEEE.

关键词： Computational efficiency

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A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

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Modeling and assessment of the techno-economic analysis of biogas and its potential for the generation of electricity from water hyacinth biomass

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Journal of engineering and Applied science 2024年第1期71卷 1-19页

作者： Asante, Enoch Asiedu, Nana Yaw Sarpong, Samuel Agyemang, Emmanuel Okoh Ajani, Ibrahim Ntiamoah, Augustine Adjaottor, Albert Amatey Addo, Ahmad Renewable Energy Systems Engineering Department Koforidua Technical University Eastern Region Koforidua Ghana Chemical Engineering Department Faculty of Mechanical and Chemical Engineering College of Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Mechanical Engineering Department Faculty of Engineering Koforidua Technical University Eastern Region Koforidua Ghana Materials Engineering Department Faculty of Mechanical and Chemical Engineering College of Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Agricultural and Biosystems Engineering Faculty of Mechanical and Chemical Engineering College of Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana

The study presents the economic feasibility assessment of converting the produced biogas from water hyacinth biomass into electricity. Approximately, 0.3793 m3CH4/kgVS was generated from the water hyacinth biomass. The research indicated that the available water hyacinth population on the Lower Volta River in the year 2020 could potentially generate a methane yield of 53.676 × 106m3. The volume of methane gas generated had the potential to produce an annual electricity output of 110.792 × 106 kWh, which could be integrated into the national grid. The economic analysis indicated positive results with an initial total investment cost of $67,273,700. The project showed a positive net present value (NPV) of $8,923,769 and a levelized cost of 0.172 $/kWh. The simple payback and equity payback periods were determined to be 5.5 and 11.3 years, respectively. Furthermore, a sensitivity analysis conducted showed that the project’s NPV remained positive when variations in input parameters such as initial cost, operations, and maintenance cost were less than 15% sensitive range. However, a 30% reduction in the feed-in tariff cost resulted in a negative NPV. In conclusion, biogas production from water hyacinth biomass in Ghana can make a significant contribution to the country’s energy mix and help alleviate the energy shortfall in rural areas. © The Author(s) 2024.

关键词： Sensitivity analysis

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Regulation of crystal and microstructures of RETaO4（RE=Nd,Sm,Gd,Ho,Er） powders synthesized via co-precipitation

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Journal of Rare Earths 2025年第6期43卷 1246-1255页

作者： Jiang Tian Lin Chen Xunlei Chen Keren Luo Baihui Li Di Zhang Meng Wang Bing Xu Zhiyi Ren Shixiao Yan Xiaoliang Sun Chi Liu Jing Feng Faculty of Materials Science and Engineering Kunming University of Science and Technology Southwest United Graduate School Shanghai Electro-Mechanical Engineering Institute Shanghai Spaceflight Precision Machinery Institute

Ferroelastic rare earth tantalates（RETaO4） are widely researched as the next-generation thermal barrier coatings（TBCs）,and RETaO4powders are hugely significant for synthesizing their *** current research used chemical co-precipitation within an automated experimental device to synthesize RETaO4（RE=Nd,Sm,Gd,Ho,Er） *** device automatically monitored and controlled the solutions' pH,improving the chemical co-precipitation *** crystal structure and microstructure of the RETaO4powders can be controlled by changing the annealing temperature,and the materials undergo an m'-m phase *** m'-RETaO4powders exhibit nano-size grains,while m-RETaO4powders evince micron-size grains,altered by the annealing temperatures.A simultaneous thermal analysis estimates the reversive ferroelastic tetragonal-monoclinic phase transition ***,this research focuses on the synthesis,crystal structures,micro structures,and phase transition of the fabricated RETaO4powders.

关键词： Annealing temperatures Chemical co-precipitation method Crystal structures Microstructures Rare earth tantalates Rare earths

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Fast Parallel Magnetic Resonance Imaging Reconstruction Based on Sparsifying Transform Learning and Structured Low-Rank Model

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Journal of Shanghai Jiaotong University (science) 2025年第3期30卷 499-509页

作者： Duan, Jizhong Xu, Yuhan Huang, Huan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

The structured low-rank model for parallel magnetic resonance (MR) imaging can efficiently reconstruct MR images with limited auto-calibration signals. To improve the reconstruction quality of MR images, we integrate the joint sparsity and sparsifying transform learning (JTL) into the simultaneous auto-calibrating and k-space estimation (SAKE) structured low-rank model named JTLSAKE. The alternate direction method of multipliers is exploited to solve the resulting optimization problem, and the optimized gradient method is used to improve the convergence speed. In addition, a graphics processing unit is used to accelerate the proposed algorithm. The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging (JTL-PLORAKS), and the proposed algorithm is 46 times faster than the JTL-PLORAKS, requiring only 4 s to reconstruct a 200 × 200 pixels MR image with 8 channels. © Shanghai Jiao Tong University 2023.

关键词： Magnetic resonance imaging

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A Systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

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IEEE Access 2024年 12卷 53408-53423页

作者： Por, Lip Yee Ng, Ian Ouii Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes. © 2013 IEEE.

关键词： Authentication

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