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Multi-objective cloud load-balancing with hybrid optimization

International Journal of computers and Applications 2023年第10期45卷 611-625页

作者： Geeta, Koppula Kamakshi Prasad, V. Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar India Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad India

In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas

关键词： Electric power utilization

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Hybrid feature selection method for predicting software defect

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Journal of engineering and Applied science 2024年第1期71卷 124页

作者： Anju, A.J. Judith, J.E. Department of Computer Science Engineering Eranad Knowledge City Technical Campus Kerala Manjeri India Department of Computer Science Engineering Noorul Islam Center for Higher Education Tamil Nadu Kanyakumari India

To address the challenges associated with the abundance of features in software datasets, this study proposes a novel hybrid feature selection method that combines quantum particle swarm optimization (QPSO) and principal component analysis (PCA). The objective is to identify a subset of relevant features that can effectively contribute to the accuracy of a predictive model based on an artificial neural network (ANN). The quantum particle swarm optimization algorithm is employed to optimize the selection of features by simulating the behavior of quantum particles in a search space. This approach enhances the exploration and exploitation capabilities, allowing for a more effective identification of relevant features. Furthermore, principal component analysis is integrated into the hybrid method to reduce dimensionality and remove multicollinearity among features, thereby improving the efficiency of the feature selection process. The proposed hybrid method is applied to software defect datasets, where the selected subset of features is fed into an artificial neural network for defect prediction. The performance of the hybrid model is compared with traditional feature selection methods, standalone QPSO, and PCA. Experimental results demonstrate the effectiveness of the hybrid approach in achieving superior predictive accuracy while reducing the dimensionality of the dataset. The proposed approach not only enhances prediction accuracy but also provides a more interpretable and efficient subset of features for building robust defect prediction models. © The Author(s) 2024.

关键词： Feature Selection

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Learning continuous network emerging dynamics from scarce observations via data-adaptive stochastic processes

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science China(Information sciences) 2024年第12期67卷 240-255页

作者： Jiaxu CUI Qipeng WANG Bingyi SUN Jiming LIU Bo YANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Public Computer Education and Research Center Jilin University Department of Computer Science Hong Kong Baptist University

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.

关键词： complex networks network dynamics emerging spatio-temporal dynamics neural processes

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Density Based Clustering Approach Combined with Fuzzy Logic for Elimination of Random Valued Impulse Noise

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Journal of The Institution of Engineers (India): Series B 2025年 1-14页

作者： Basu, Sulagna Bandyopadhyay, Aritra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions 1 Khan Road Mankundu West Bengal Chandannagar712139 India

The reduction of impulse noise is crucial in processing pictures since it directly impacts the patterns of noise present. This paper proposes a two-step technique, known as DCIFF (DBSCAN clustering identified fuzzy filter), to effectively eliminate impulsive noise from digital images. The suggested DCIFF methodology employs the DBSCAN clustering algorithm to identify and classify noise. Subsequently, a noise reduction technique is presented, which relies on the principles of fuzzy reasoning. The density-based clustering approach DBSCAN is utilized to identify commonalities throughout all 5 × 5 sized windows that are created by centering each of the pixels in the picture. Next, to properly reduce noise, a sized 7 × 7 window is formed with the centre of each identified disturbed pixel. The non-noisy pixel intensities are multiplied by their triangle fuzzy membership values, and then the products are added together. The total result is then divided by the sum of the fuzzy membership of all the non-noisy pixels’ intensities. The outcome is utilized to eliminate the noise from the central pixel that is affected by noise. The DCIFF is assessed using standard image evaluation criteria for various pictures. On the Lena image modified by random valued impulsive noise at 80% noise density, DCIFF provided peak-signal-to-noise ratios (PSNR) of 25.86 dB and structural similarity indexes (SSIM) of 0.8429. In contrast to prior research on the same topic, the current study findings consistently demonstrate striking outcomes. © The Institution of Engineers (India) 2025.

关键词： Fuzzy logic

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Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

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computer Systems science & engineering 2022年第10期43卷 77-86页

作者： V.Kumar N.Jayapandian P.Balasubramanie Department of Computer Science and Engineering Knowledge Institute of TechnologySalemIndia Department of Computer Science and Engineering Christ UniversityBangaloreIndia Department of Computer Science and Engineering Kongu Engineering CollegePerunduraiIndia

Through Wireless Sensor Networks(WSN)formation,industrial and academic communities have seen remarkable development in recent *** of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating *** most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering *** this method,new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each *** of effective energy use and the ability to decide the best method of attachments are *** Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high.A newer version of Gravity Analysis Algorithm(GAA)is used to solve this *** proposed new approach GAA is introduced to improve network lifetime,increase system energy efficiency and end delay *** results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA *** proposed method provides a set of data collection and increased throughput in wireless sensor networks.

关键词： WSNs Gravity analysis algorithm-GAA life time delay clustering algorithms-LTDCA end to end delay energy efficient

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Birch Clustering Approach for Detection of Random Valued Impulse Noise

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Journal of The Institution of Engineers (India): Series B 2024年 1-9页

作者： Basu, Sulagna Bandyopadhyay, Aritra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions 1 Khan Road West Bengal Mankundu712139 India

computer vision relies on image processing for autonomous driving, surveillance, and medical imaging. Clustering, an unsupervised learning approach, is essential for picture data organization and smooth pre-processing. Photo noise has been removed using K-Means, K-Medoid, and Fuzzy C-Means clustering methods. K-means, K-Medoid, and Fuzzy C-Means may not cluster huge datasets well with limited memory or CPU. Traditional clustering methods struggle to accommodate running durations and quality as dataset quantities rise. Birch clustering, or Balanced Iterative Reducing and Clustering utilizing Hierarchies, is frequently used in image processing because of its scalability and efficiency. Hierarchies help BIRCH summarize the dataset while maintaining as much information as feasible. The smaller summary follows the larger dataset. BIRCH is often used alongside other clustering methods to compress the dataset for the next step. Birch clustering is scalable, efficient in high-dimensional spaces, and can handle enormous datasets. Birch clustering regularly builds a tree structure to arrange images into a hierarchy of sub-clusters for effective segmentation and representation. Birch clustering divides images into sections by examining pixel intensities or characteristics for image segmentation. Birch clustering identifies typical centroids inside clusters to simplify feature extraction and allow meaningful picture data displays. Its noise reduction and data distribution adaptability make it suited for many academic and industrial image processing tasks. Birch clustering's hierarchical tree structure allows for scalability, unlike k-means' centroids-based clusters. Birch clustering creates a hierarchical tree using image data sub-clusters and centroids. It handles massive datasets thanks to its efficient memory storage. It offers scalable and efficient clustering with decreased computing complexity by dividing and combining data to create a hierarchy. The detection capabilitie

关键词： Image segmentation

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Enhancing reliability in coronary artery disease prediction using ensemble learning for key attribute identification

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Life Cycle Reliability and Safety engineering 2025年 1-14页

作者： Upadhyay, Sachin Sagar, Anil Kumar Roy, Nihar Ranjan Department of Computer Science and Engineering School of Engineering and Technology Sharda University Knowledge Park III Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology VIPS-TC Delhi Pitampura India

Cardiovascular disease (CAD) is a significant public health concern, affecting a large population worldwide. Early diagnosis and management of CAD can minimize the risk of acute myocardial infarction and improve patient outcomes. Assessment tools like SBP, cholesterol, pulse rate, and ST segment depression can help identify causes early and manage them effectively. Management includes medication therapy, healthy dietary habits, and exercise. Several machine learning (ML) methodologies have been researched to enhance CAD predictions, including AdaBoost, ANNs, J48, Decision Tree, K Nearest Neighbor (KNN), Naïve Bayes, and Random Forest. However, single models still lack sufficient capacity to address the complexity and flexibility of CAD. Ensemble learning, which uses multiple classifiers to boost predictability, has been employed to address these issues. The system was developed after benchmarking it with multiple classifiers on a Cleveland cardiac disease dataset. The ensemble method showed a 92.11% accuracy rate, far better than the highest performing classifier operating individually. This suggests the possibility of practical solutions for CAD prediction using ensemble methods, enabling precise early diagnosis and efficient targeted treatment. Comparing ensemble learning for CAD predictors reveals how these approaches can revolutionize medicine by enabling early diagnosis and personalized treatment plans. There is a need to further develop these methods for clinical application, such as creating practical tools for easier application by healthcare workers and integrating sophisticated techniques. In conclusion, ensemble learning methods represent significant advancements in CAD prediction, with superior performance in identifying critical attributes and enhancing predictive accuracy. As healthcare evolves with the integration of intelligent technologies, the adoption of ensemble learning methods holds great promise for enhancing patient outcomes and reducing the

关键词： ANN CAD Decision Trees Ensemble KNN Machine learning Random Forest

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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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