Identifying druggable proteins,which are capable of binding therapeutic compounds,remains a critical and resource-intensive challenge in drug discovery.To address this,we propose CEL-IDP(Comparison of Ensemble Learnin...Identifying druggable proteins,which are capable of binding therapeutic compounds,remains a critical and resource-intensive challenge in drug discovery.To address this,we propose CEL-IDP(Comparison of Ensemble Learning Methods for Identification of Druggable Proteins),a computational framework combining three feature extraction methods Dipeptide Deviation from Expected Mean(DDE),Enhanced Amino Acid Composition(EAAC),and Enhanced Grouped Amino Acid Composition(EGAAC)with ensemble learning strategies(Bagging,Boosting,Stacking)to classify druggable proteins from sequence data.DDE captures dipeptide frequency deviations,EAAC encodes positional amino acid information,and EGAAC groups residues by physicochemical properties to generate discriminative feature vectors.These features were analyzed using ensemble models to overcome the limitations of single classifiers.EGAAC outperformed DDE and EAAC,with Random Forest(Bagging)and XGBoost(Boosting)achieving the highest accuracy of 71.66%,demonstrating superior performance in capturing critical biochemical patterns.Stacking showed intermediate results(68.33%),while EAAC and DDE-based models yielded lower accuracies(56.66%–66.87%).CEL-IDP streamlines large-scale druggability prediction,reduces reliance on costly experimental screening,and aligns with global initiatives like Target 2035 to expand action-able drug targets.This work advances machine learning-driven drug discovery by systematizing feature engineering and ensemble model optimization,providing a scalable workflow to accelerate target identification and validation.展开更多
Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide ...Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.展开更多
Dialogue State Tracking(DST)is a critical component of task-oriented spoken dialogue systems(SDS),tasked with maintaining an accurate representation of the conversational state by predicting slots and their correspond...Dialogue State Tracking(DST)is a critical component of task-oriented spoken dialogue systems(SDS),tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values.Recent advances leverage Large Language Models(LLMs)with prompt-based tuning to improve tracking accuracy and efficiency.However,these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts,without explicitly modeling the complex dependencies between slots and values.In this work,we propose PUGG,a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer to implement a memory encoder.PUGG explicitly extracts slot values via GPT-2 and employs Graph Attention Networks(GATs)to model and reason over the intricate relationships between slots and their associated values.We evaluate PUGG on four publicly available datasets,where it achieves stateof-the-art performance across multiple evaluation metrics,highlighting its robustness and generalizability in diverse conversational scenarios.Our results indicate that the integration of GPT-2 substantially reduces model complexity and memory consumption by streamlining key processes.Moreover,prompt tuning enhances the model’s flexibility and precision in extracting relevant slot-value pairs,while the incorporation of GATs facilitates effective relational reasoning,leading to improved dialogue state representations.展开更多
Experience is a sociological concept and builds over time. In a broader sense, the human-centered equivalents of experience and trust apply to D2D interaction. Ubiquitous computing (UbiComp) embeds intelligence and co...Experience is a sociological concept and builds over time. In a broader sense, the human-centered equivalents of experience and trust apply to D2D interaction. Ubiquitous computing (UbiComp) embeds intelligence and computing capabilities in everyday objects to make them effectively communicate, share resources, and perform useful tasks. The safety of resources is a serious problem. As a result, authorization and access control in UbiComp is a significant challenge. Our work presents experience as an outcome of history (HI), reliability (RL), transitivity (TR), and Ubiquity (UB). This experience model is easily adaptable to a variety of self-regulating context-aware access control systems. This paper proposes a framework for Experience-Based Access Control (EX-BAC) with all major services provided by the model. EX-BAC extends attribute-based access control. It uses logical device type and experience as context parameters for policy design. When compared with the state-of-the-art, EX-BAC is efficient with respect to response time.展开更多
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Centre)support program(IITP-2024-RS-2024-00437191)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Identifying druggable proteins,which are capable of binding therapeutic compounds,remains a critical and resource-intensive challenge in drug discovery.To address this,we propose CEL-IDP(Comparison of Ensemble Learning Methods for Identification of Druggable Proteins),a computational framework combining three feature extraction methods Dipeptide Deviation from Expected Mean(DDE),Enhanced Amino Acid Composition(EAAC),and Enhanced Grouped Amino Acid Composition(EGAAC)with ensemble learning strategies(Bagging,Boosting,Stacking)to classify druggable proteins from sequence data.DDE captures dipeptide frequency deviations,EAAC encodes positional amino acid information,and EGAAC groups residues by physicochemical properties to generate discriminative feature vectors.These features were analyzed using ensemble models to overcome the limitations of single classifiers.EGAAC outperformed DDE and EAAC,with Random Forest(Bagging)and XGBoost(Boosting)achieving the highest accuracy of 71.66%,demonstrating superior performance in capturing critical biochemical patterns.Stacking showed intermediate results(68.33%),while EAAC and DDE-based models yielded lower accuracies(56.66%–66.87%).CEL-IDP streamlines large-scale druggability prediction,reduces reliance on costly experimental screening,and aligns with global initiatives like Target 2035 to expand action-able drug targets.This work advances machine learning-driven drug discovery by systematizing feature engineering and ensemble model optimization,providing a scalable workflow to accelerate target identification and validation.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621,Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management).
文摘Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ITRC(Information Technology Research Centre)support program(IITP-2024-RS-2024-00437191)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Dialogue State Tracking(DST)is a critical component of task-oriented spoken dialogue systems(SDS),tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values.Recent advances leverage Large Language Models(LLMs)with prompt-based tuning to improve tracking accuracy and efficiency.However,these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts,without explicitly modeling the complex dependencies between slots and values.In this work,we propose PUGG,a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer to implement a memory encoder.PUGG explicitly extracts slot values via GPT-2 and employs Graph Attention Networks(GATs)to model and reason over the intricate relationships between slots and their associated values.We evaluate PUGG on four publicly available datasets,where it achieves stateof-the-art performance across multiple evaluation metrics,highlighting its robustness and generalizability in diverse conversational scenarios.Our results indicate that the integration of GPT-2 substantially reduces model complexity and memory consumption by streamlining key processes.Moreover,prompt tuning enhances the model’s flexibility and precision in extracting relevant slot-value pairs,while the incorporation of GATs facilitates effective relational reasoning,leading to improved dialogue state representations.
文摘Experience is a sociological concept and builds over time. In a broader sense, the human-centered equivalents of experience and trust apply to D2D interaction. Ubiquitous computing (UbiComp) embeds intelligence and computing capabilities in everyday objects to make them effectively communicate, share resources, and perform useful tasks. The safety of resources is a serious problem. As a result, authorization and access control in UbiComp is a significant challenge. Our work presents experience as an outcome of history (HI), reliability (RL), transitivity (TR), and Ubiquity (UB). This experience model is easily adaptable to a variety of self-regulating context-aware access control systems. This paper proposes a framework for Experience-Based Access Control (EX-BAC) with all major services provided by the model. EX-BAC extends attribute-based access control. It uses logical device type and experience as context parameters for policy design. When compared with the state-of-the-art, EX-BAC is efficient with respect to response time.
