This paper presents the implementation of ARQ-PROP II, a limited-depth propositional reasoner, via the compilation of its specification into an exact formulation using the satyrus platform. satyrus' compiler takes...
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Software differs from most manufactured products because it is intangible. This characteristic makes it difficult to detect, control, and understand how it evolves. This paper presents an approach based on software vi...
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Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been util...
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery process. However, these existing methods are primarily uni-tasking, either designed to predict drug-target interaction (DTI) or generate new drugs. Through the lens of pharmacological research, these tasks are intrinsically interconnected and play a critical role in effective drug development. Therefore, the learning models must be utilized in such a manner to learn the structural properties of drug molecules, the conformational dynamics of proteins, and the bioactivity between drugs and targets. To this end, this paper develops a novel multitask learning framework that can predict drug-target binding affinities and simultaneously generate new target-aware drug variants, using common features for both tasks. In addition, we developed the FetterGrad algorithm to address the optimization challenges associated with multitask learning particularly those caused by gradient conflicts between distinct tasks. Comprehensive experiments on three real-world datasets demonstrate that the proposed model provides an effective mechanism for predicting drug-target binding affinities and generating novel drugs, thus greatly facilitating the drug discovery process.
This paper presents an analysis of the applicability of Sparse Kernel Principal Component Analysis (SKPCA) for feature extraction in speech recognition, as well as, a proposed approach to make the SKPCA technique real...
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Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer...
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Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.
This work introduces a new technique that enables SDSMs to categorize dynamically and accurately memory sharing patterns in both classes of regular and irregular applications. The categorization is carried out automat...
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Complexity and dynamism of day-to-day activities in organizations are inextricably linked, one impacting the other, increasing the challenges for constant adaptation of the way to organize work to address emerging dem...
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ISBN:
(纸本)9789898425065
Complexity and dynamism of day-to-day activities in organizations are inextricably linked, one impacting the other, increasing the challenges for constant adaptation of the way to organize work to address emerging demands. In this scenario, there are a variety of information, insight and reasoning being processed between people and systems, during process execution. We argue that process variations could be decided in real time, using context information collected. This paper presents a proposal for a business process line cycle, with a set of activities encapsulated in the form of components as central artefact. We explain how composition and adaptation of work may occur in real time and discuss a scenario for this proposal.
Document management system makes user to access information anytime and anywhere. The purpose of this research is to analyze what variables have impact on the intention to use of document management system. To achieve...
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We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to...
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We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research fo...
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Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.
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