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 o...
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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
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 princi...
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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 meth...
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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.
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...
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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.
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 mo...
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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.
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 fi...
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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 ...
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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.
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...
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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
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 patie...
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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
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...
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