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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

International Journal of Computers and applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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CNN-DBO: Dung Beetle Optimization Based Urban Area Change Detection Using Satellite Images

CNN-DBO: Dung Beetle Optimization Based Urban Area Change De...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

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Plasma-assisted fabrication of multiscale materials for electrochemical energy conversion and storage

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Carbon Energy 2025年第2期7卷 144-171页

作者： Chen Li Tengfei Zhang Zhong Qiu Beirong Ye Xinqi Liang Xin Liu Minghua Chen Xinhui Xia Chen Wang Wangjun Wan Yongqi Zhang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina Key Laboratory of Engineering Dielectric and Applications(Ministry of Education) School of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbinChina State Key Laboratory of New Textile Materials and Advanced Processing Technology Wuhan Textile UniversityWuhanChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhouChina School of Materials Science and Engineering Zhejiang UniversityHangzhouChina Zhejiang Academy of Science and Technology for Inspection and Quarantine HangzhouChina

Plasma,the fourth state of matter,is characterized by the presence of charged particles,including ions and *** has been shown to induce unique physical and chemical ***,there have been increased applications of plasma technology in the field of multiscale functional materials'preparation,with a number of interesting *** review will begin by introducing the basic knowledge of plasma,including the definition,typical parameters,and classification of plasma *** this,we will provide a comprehensive review and summary of the applications(phase conversion,doping,deposition,etching,exfoliation,and surface treatment)of plasma in common energy conversion and storage systems,such as electrocatalytic conversion of small molecules,batteries,fuel cells,and *** article summarizes the structure-performance relationships of electrochemical energy conversion and storage materials(ECSMs)that have been prepared or modified by *** also provides an overview of the challenges and perspectives of plasma technology,which could lead to a new approach for designing and modifying electrode materials in ECSMs.

关键词： electrocatalysts energy conversion and storage nanomaterials plasma

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Neighbor-relation aware low-rank multi-view subspace clustering

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Multimedia Systems 2025年第3期31卷 1-16页

作者： Jia, Hongjie Wang, Tengteng Song, Heping School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang212013 China

Low-rank multi-view subspace clustering seeks to uncover complex relationships in high-dimensional data by leveraging multiple views and low-rank representations. However, two key challenges persist: (1) Existing methods emphasize low-rank consistency through nuclear norm regularization but often overlook the unique low-rank features of individual views. (2) Constructing the affinity matrix directly from the coefficient matrix can introduce excessive connections, degrading clustering performance. To address these challenges, we propose neighbor-relation aware low-rank multi-view subspace clustering (NLMSC), a unified model that integrates view-specific low-rank structure learning with neighbor-aware affinity fusion. NLMSC decomposes the coefficient matrix into smaller factor matrices to capture the underlying low-rank structures of each view and constructs a sparser, more interpretable affinity matrix by reliable neighbor selection. These affinity matrices are then fused into a block-diagonal consensus graph through a self-weighting strategy for spectral clustering. Extensive experiments on diverse multi-view datasets demonstrate NLMSC’s superior performance and effectiveness in challenging clustering tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Low-rank structure Multi-view clustering Neighbor-relation awareness Subspace clustering

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A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment

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Multimedia Tools and applications 2025年第7期84卷 3767-3792页

作者： Verma, Ankit Gupta, Amit Kumar Kumar, Vipin Rajak, Akash Kumar, Sushil Panda, Rabi Narayan Department of Computer Applications KIET Group of Institutions Delhi-NCR Ghaziabad India Computer Science & Engineering KIET Group of Institutions Ghaziabad Delhi-NCR India

People who lead hectic lives daily suffer from a variety of illnesses, including diabetes, high blood pressure, hypertension, etc. For someone to survive, they must become aware of these illnesses promptly. The Internet of Things (IoT) and cloud computing are the two critical prerequisites for digital healthcare. In the present research, the attacked data are detected and removed using the security module to enhance the security of the healthcare system. However, an accurate prediction mechanism is needed for the early diagnosis of the diseases. To predict the sickness and its severity more accurately, a unique Dragon Fly-based Generalised Approximate Reasoning Intelligence Control (DF-GARIC) is devised in this article. This system was primarily responsible for preprocessing the cloud medical records entered into the system. Additionally, the regression algorithm extracts the relevant features. Based on the retrieved features, the dragonfly function is used to classify the disease and estimate its severity. Subsequently, a warning is given to the providers for the abnormal condition via SMS or e-mail. The system validated a higher accuracy level of 99.8% from the MATLAB execution. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Glasses-free 3D LED display based on a radial barrier

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Optics Express 2025年第9期33卷 19616-19626页

作者： Zheng, Changxiong Surman, Phil Chen, Zhidong Sun, Xiao Wei Department of Electrical and Electronic Engineering Institute of Nanoscience and Applications Southern University of Science and Technology Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

Conventional glasses-free 3D displays have a limitation of an optimum viewing distance (OVD). This limitation is unfriendly for multi-user scenarios, with users at varying viewing distances. This article introduces a glasses-free 3D display method designed to support multi-user viewing from different distances. The method uses a radial barrier as the optical component, replacing a conventional parallel barrier or lenticular lens. This enables the angle between the optimum viewing plane (OVP) and the horizontal plane to be adjustable. To mitigate moiré patterns, a diffusion film is employed. The most important aspect of our current work is the development of a sub-pixel mapping algorithm for the radial barrier that considers the LED sub-pixel sharing. This paper describes the algorithm and the prototype hardware that is an updated version of a historical projection system developed in the mid-20th century. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Volume rendering

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Recent advances in porous structures for oxygen reduction reaction

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Progress in Natural science:Materials International 2025年第1期35卷 83-97页

作者： Ying Chen Huawei Wang Yujing Li Beijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications.School of Materials Science and Engineering Beijing Institute of Technology Fuel Cell Products Business Weichai Power Company Limited

As environmental concerns grow, there is a global shift from traditional fossil energy. Hydrogen fuel cells are gaining attention, but high material costs limit their adoption in large scale. The oxygen reduction reaction at the cathode of fuel cell, which dominates the electrochemical kinetics, relies mainly on platinum-group-metal(PGM) catalysts, hence research is focused on minimizing PGM or developing non-PGM catalysts. Porous materials possess unique physicochemical properties, and have shown great potential in advancing catalyst technology in fuel cells. This review summarizes the recent fundamental and technological advances of porous catalysts for oxygen reduction reactions including PGM and non-PGM catalysts, discusses the mechanisms underlying the enhanced catalytic activity through structural design and surface modification. It also highlights the comprehensive impact of porous materials in oxygen reduction reactions and membrane electrode assemblies,the main challenges, and provides perspectives on research of porous catalyst.

关键词： Oxygen reduction reaction

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Comparative Analysis of RL-Based Resource Allocation Methods for Optimization in 5G MMWave Network 5th

Comparative Analysis of RL-Based Resource Allocation Methods...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Shilpa, V. Ranjan, Rajeev School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9783031770746

In this study, resource allocation techniques based on reinforcement learning (RL) for 5G millimeter wave (mmWave) networks are compared and analyzed. The high bandwidth and large available spectrum in mmWave networks offer a great opportunity for high data rate communication but also pose significant challenges in terms of resource allocation. RL, being a powerful tool for decision making in dynamic and uncertain environments, has been widely studied for resource allocation in mmWave networks. In this paper, we survey the state-of-the-art RL-based resource allocation methods for mmWave networks and we also discuss the challenges and open research directions in this field. This study presents an effective method for balancing stable, low-capacity mmWave links with reliable, high-capacity backhaul links designed using stochastic geometry and analytical channel modeling. We also propose a backhaul resource allocation strategy. To increase system utility with a mmWave network’s limited backhaul capacity, a neural network-based backhaul resource allocation has been proposed. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： 5G mobile communication systems

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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qLIF: Mitigating the memory and computation overhead to implement spiking convolutional neural networks

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Neural Computing and applications 2025年第15期37卷 9011-9024页

作者： Li, Silong Wang, Ningning Kang, Xinyu Yu, Chunlin Ye, Terry Tao Department of Electronic and Electrical Engineering Southern University of Science and Technology Xueyuan Avenue Guangdong Shenzhen518055 China Institute of Nanoscience and Applications Southern University of Science and Technology Xueyuan Avenue Guangdong Shenzhen518055 China

Spiking neural networks (SNNs) have many advantages in achieving lower power neuromorphic computation. In this paper, we propose new methods that can further mitigate the memory spaces and computation resources that are needed for spiking convolutional neural networks (SCNNs) implementation. Traditional leaky-integrate-and-fire (LIF) mechanism, which is widely adopted in SNN, demands large memory spaces to store the membrane potentiation values for integration, through experiments, we find out that for many layers, the potentiation values can be quickly leaked (qLIF) and no extra memory spaces are needed to store the values from past events. With the adoption of qLIF mechanism, convolution between the input matrix and the kernel matrix can be simplified into a look-up-table (LUT) operation (called ConvLUT in this paper), without the need of computation resources to perform the dot-product. Through experiments on MNIST, CIFAR-10, CIFAR-100 and DVS128-Gesture datasets with SCNNs, we have verified that both qLIF mechanism and ConvLUT method can greatly reduce the memory spaces as well as computation resources in SCNN, the computation latency can also be improved. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolution

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