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Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data

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Biomedical Materials and Devices 2025年第1期3卷 558-575页

作者： Ripa, Rokiya Uddin, Khandaker Mohammad Mohi Alam, Mir Jafikul Rahman, Md. Mahbubur Department of Computer Science and Engineering Dhaka International University Dhaka 1205 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka 1216 Bangladesh

Chronic liver damage is believed to be mostly caused by the Hepatitis C virus (HCV). About 90% of hepatitis C infections progress to chronic hepatitis. Acute HCV infection is a condition that frequently progresses to liver cirrhosis and eventually liver cancer;therefore, understanding this stage of the virus is essential. Molecular and serological testing approaches are often expensive and difficult to perform for diagnosing HCV infection. Machine learning technology can be effectively employed to identify patterns or associations for diagnosing HCV infection. The study utilized machine learning techniques to develop classification models for hepatitis C illness, aiming to anticipate the virus responsible for the infection. Our research integrates various machine learning algorithms, including Random Forest, Cat Boost, Bagging Classifier, SGD Classifier, Gaussian NB, Bernoulli NB, Multinomial NB, Linear Discriminant Analysis, ANN, and MLP. Prior to training the models, preprocessing methods, including normalization, filtering, and SMOTE, were used to improve the dataset’s attributes. The classification accuracy scores show encouraging results, with ANN scoring 79.63%, MLP scoring 46.29%, Bernoulli NB scoring 79.62%, Cat Boost scoring 98.14%, and Random Forest scoring 99.53%. Among classification methods, random forest demonstrates the highest accuracy in diagnosing HCV infection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification Deep learning Hepatitis C virus Machine learning Predictions

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Energy Storage System Using Digital Twins with AI and IoT for Efficient Energy Management and Prolonged Battery Life in Electric Vehicles

Energy Storage System Using Digital Twins with AI and IoT fo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Lakshmi, M. Sri Sarma, Vanam Ajay G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798331509828

This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehicles. Under typical operating conditions, the system exhibits predictive accuracy with mean absolute error relative to actual data equal to 0.042 and root mean square error equal to 0.055. These values reflect improvement in responsiveness of the system since latency has been reduced to 0.12 seconds with feedback response times established at 0.45 seconds. Moreover, it has increased by 15% in energy efficiency while promoting its longevity with a capacity fade rate measured at 0.0025% per cycle. Besides, the model shows strong anomaly detection capability - maintenance alert precision is found to be 95% while detection rate reaches76%. All these results point out the possibility that combining Digital Twin technology with AI and IoT may end up significantly boosting energy management systems for electric vehicles compared to more traditional approaches. © 2025 IEEE.

关键词： Artificial Intelligence (AI) Digital Twin technology Electric Vehicle (EV) Battery Management Energy Efficiency Optimization Internet of Things (IoT) Predictive Maintenance

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai China Ant Group Shanghai China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Fudan University School of Computer Science Shanghai China Tongji University Department of Control Science and Engineering Shanghai China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the- art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Enhancing EmoBot: An In-Depth Analysis of User Expectation and Satisfaction in an Emotion-Aware Chatbot 24

Enhancing EmoBot: An In-Depth Analysis of User Expectation a...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Mubassira, Taseen Hasan, Mehedi Noor, Jannatun Alim Al Islam, A.B.M. Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science and Engineering Brac University Dhaka Bangladesh

ISBN: (纸本)9798400711589

The research community has traditionally concentrated on emotion detection in emotion modeling, while emotion generation has garnered less focus. With the rise of artificial intelligence, numerous chatbots have been developed, but there remains a lack of standardized methods to evaluate them. In this study, we evaluate EmoBot, a state-of-the-art emotionally aware chatbot, through the lens of user expectations. By collecting and analyzing user feedback, we identify EmoBot’s strengths and weaknesses, providing a basis for future enhancements. We propose targeted improvements to enhance its user experience, especially in areas of underperformance, and offer a framework for assessing emotionally aware chatbots based on user expectations and experiences. © 2024 Copyright held by the owner/author(s).

关键词： Emotionally Aware ChatBot

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Autonomous embodied navigation task generation from natural language dialogues

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science China(Information sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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A Novel Lightweight Joint Source-Channel Coding Design in Semantic Communications

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IEEE Internet of Things Journal 2025年第11期12卷 18447-18450页

作者： Yu, Xianhua Li, Dong Zhang, Ning Shen, Xuemin Macau University of Science and Technology School of Computer Science and Engineering 999078 China University of Windsor Department of Electrical and Computer Engineering Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and non-local mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multi-scale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments. © 2014 IEEE.

关键词： Channel coding

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A transfer learning approach for continuous speech recognition system in Indian language sadri

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International Journal of Information technology (Singapore) 2025年 1-10页

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur 713209 India

Sadri is the most widely used language of the Chotanagpur Plateau region of India. This is primarily a spoken language and developing an automatic speech recognition (ASR) system in Sadri is extremely important. When we searched the literature, we found no open ASR system or relevant resources in Sadri. So, we worked on developing an ASR system in Sadri. We created a Sadri speech corpus of around 20 h, employing 53 native speakers. Then, we implemented the baseline model using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). As the size of the training data was not sufficient, we investigated a transfer learning technique to improve performance. We found that ASR data are openly available in other related languages such as Hindi and Bengali. We used Bengali and Hindi data of 100 h each in a transfer learning framework along with the Sadri data. In our experiments, we found that the transfer learning-based model outperforms the baseline model. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： ASR Deep learning Indian language Sadri Speech recognition

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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