The pursuit of trend prediction in diverse datasets has captivated the interest of researchers over the years. The significance of predictive modeling spans various domains, including business models, scientific resea...
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The burgeoning discipline of affective computing, which sits at the nexus of AI and psychology, aims to improve our capacity to comprehend and analyze human emotions as they manifest themselves in visual data. This ab...
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This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random...
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Space manipulators play an important role in the on-orbit services and planetary surface *** the extreme environment of space,space manipulators are susceptible to a variety of unknown *** to have a resilient guarante...
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Space manipulators play an important role in the on-orbit services and planetary surface *** the extreme environment of space,space manipulators are susceptible to a variety of unknown *** to have a resilient guarantee in failure or disturbance is the core capability of its future *** with traditional motion planning,learning-based motion planning has gradually become a hot spot in current ***,no matter what kind of research ideas,the single robotic manipulator is studied as an independent agent,making it unable to provide sufficient flexibility under conditions such as external force disturbance,observation noise,and mechanical ***,this paper puts forward the idea of“discretization of the traditional single manipulator”.Different discretization forms are given through the analysis of the multi-degree-of-freedom single-manipulator joint relationship,and a single-manipulator representation composed of multiple new subagents is ***,to verify the ability of the new multiagent representation to deal with interference,we adopted a centralized multiagent reinforcement learning *** influence of the number of agents and communication distances on learning-based planning results is analyzed in *** addition,by imposing joint locking failures on the manipulator and introducing observation and action interference,it is verified that the“multiagent robotic manipulator”obtained after discretization has stronger antidisturbance resilient capability than the traditional single manipulator.
Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive *** group communication plays a vital...
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Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive *** group communication plays a vital role in case of providing the integrity,authenticity,confidentiality,and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the *** secure group communications,the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service,particularly in the large groups with highly active ***,the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group *** this process,each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory *** of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group *** rekeying process is performed when a member leaves or adds into the *** performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost,rekeying process and formation of the *** is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods.
The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. H...
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The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things...
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A decentralized machine learning method known as federated learning (FL) for model-training has been discussed in this paper. FL is a subset of machine learning in which multiple clients(edge devices) train parts of a...
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Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much ...
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In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and p...
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In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and predicting the likelihood of students dropping out. To uncover these influential factors, feature selection methods are employed, while various machine learning models are used to predict students at risk of underperforming. Filter-based feature selection methods are commonly used in educational data mining due to their efficiency and ability to rank important features affecting academic success. However, because of their independence from classifiers and relying on a fixed threshold or predefined feature count, filter-based methods can sometimes negatively affect model performance. To address this, the present study introduces an optimized chi-square-based feature selection technique that dynamically selects the optimal features for each learning algorithm, ensuring that model performance is not compromised. The effectiveness of five classifiers—k-Nearest Neighbour (k-NN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR)—has been evaluated using three configurations: no feature selection, traditional chi-square feature selection, and proposed optimized chi-square based feature selection. These evaluations were conducted on two distinct student datasets, one from secondary schools (DS1) and another from engineering institutions (DS2). The results demonstrated that the optimized chi-square method consistently improved prediction accuracy across all classifiers. Additionally, a bagging-based ensemble classifier, constructed using the best-performing individual classifier, further enhanced predictive performance. The highest accuracies achieved were 94.62% for DS1 and 96.36% for DS2, outperforming traditional feature selection and ensemble methods. This study presents a scalable, reliable, and stable approach to s
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