Stress is a common mental health problem that affects individuals from all walks of life. It can manifest in various forms, such as emotional, physical, or cognitive distress. The detection of stress in individuals is...
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Depression is a global health crisis affecting millions. Workplace stress and unhealthy habits have risen, leading to more people with depressive symptoms. Early detection and prediction of depression are essential fo...
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This paper explores the importance of supply chain integrity in today's global business landscape, considering the substantial losses of over 500 billion annually attributed to counterfeit goods. It emphasizes the...
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The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities *** research demonstrated that the Protein-Protein Interaction(...
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The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities *** research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral ***,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical *** article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based *** classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this *** proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment ***,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.
Gathering all the necessary data is the major objective of this study so that a comprehensive picture can be painted of how new technologies might improve students' understanding of semantic content in digital tex...
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The traditional conduction of examinations needs to use physical copies of question paper and answer sheets, which are then evaluated by the evaluators. However, in a post-pandemic era, where the emphasis is more on t...
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Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recogniz...
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GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of mate...
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Multiple features of the complex network are exhibited among the nodes in time-varying social networks. Community detection has numerous approaches, but the majority of methods are restricted to disjoint community sit...
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We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ X is indexed by t ∈ T, and a neural autoregressive model is trained to map queries Q to T. GDR...
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We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ X is indexed by t ∈ T, and a neural autoregressive model is trained to map queries Q to T. GDR can be considered to involve information transmission from documents X to queries Q, with the requirement to transmit more bits via the indexes T. By applying Shannon's rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes T can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods. Copyright 2024 by the author(s)
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