In today's world, a typical job ad on the web attracts a massive number of applications in a short period of time. Manual screening of these resumes is not only time-consuming but also very expensive for the hirin...
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Allocating sources correctly within the ever-changing world of cloud computing is vital for maintaining uninterrupted guide of apps and offerings at the same time as preserving charges down. Machine mastering's fl...
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ISBN:
(纸本)9798350359756
Allocating sources correctly within the ever-changing world of cloud computing is vital for maintaining uninterrupted guide of apps and offerings at the same time as preserving charges down. Machine mastering's flexibility to accommodate unique duties and person conduct makes it an appealing option for assembly those desires. As a end result of factors including variable workloads, special application desires resource allocation in the cloud area provides a number of difficulties. Allocation strategies based on static parameters generally fail to fulfill these demanding situations. By integrating past facts, future predictions, and immediately feedback, MLT provide a promising opportunity for developing a flexible and powerful technique of allocating resources. This paper introduces a novel approach to cloud useful resource allocation referred to as Dynamic Resource Allocation with Reinforcement Predictive Learning (DRA-RPL). DRA-RPL combines reinforcement studying with predictive analytics to provide a flexible allocation mechanism that could respond to converting requirements in actual time. This technique seeks to find the candy spot between performance, efficiency, and cost to assure swift and powerful deployment of assets. DRA-RPL uses a cloud-based totally reinforcement mastering agent. The workloads, useful resource availability, and alertness performance are in reality some of the factors that this agent is continuously tracking. The technique uses predictive analytics to foresee useful resource demands primarily based on previous statistics and patterns. This predictive thing enables the reinforcement mastering agent count on future requirements. The simulation effects show the way the approach handles versions in surroundings and workload, imparting sturdy evidence of its effectiveness. With the ability to reinforce resource utilization, fee-effectiveness, and client delight across cloud-based totally offerings, DRA-RPL is a possible method that would help
Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementatio...
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The effectiveness of the Artillery shell is measured by its precision of hitting the target. During hitting the target, the intended firing path can be affected by the inherited source as well as external factors like...
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In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,mu...
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In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.
Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple ...
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Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme
Hate speech in social media has become a significant concern in today's age, making detecting and analyzing hate speech accurately an area of high importance. Sentiment analysis can aid in automatically detecting ...
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Wheat is essential everywhere. Wheat leaf diseases hinder growth. Wheat leaf disease identification is crucial to wheat quality and agriculture. This work presents an integrated machine learning strategy to improve wh...
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Wireless sensor networks (WSNs) have become integral to the Internet of Things (IoT) industry. In WSN, power consumption is essential as most sensor nodes are battery-operated and need to be replaced or recharged peri...
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Academic Emotion Detection is fundamentally a system for detecting emotions. The system's main goal was to identify feelings expressed while attending online lectures during the COVID-19 epidemic. The topic Academ...
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