检索结果-内蒙古大学图书馆

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Innovative Video Classification Method based on Deep Learning Approach

引用

Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

引用

Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applications

引用

International Journal of computers and Applications 2025年第1期47卷 1-16页

作者： Sepahvand, Majid Meqdad, Maytham N. Abdali-Mohammadi, Fardin Department of Computer Engineering Arak University Markazi Iran Intelligent Medical Systems Department Al-Mustaqbal University Babil Hillah Iraq Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

Human activity recognition systems using wearable sensors is an important issue in pervasive computing, which applies to various domains related to healthcare, context aware and pervasive computing, sports, surveillance and monitoring, and the military. Three approaches can be considered for activity recognition: video sensor-based, physical sensor-based, and environmental sensor-based. This paper investigates the related work regarding the physical sensor-based approaches to motion processing. In this paper, a wide range of artificial intelligence models, from single classifications to methods based on deep learning, have been reviewed. The human activity detection accuracy of different methods, under natural and experimental conditions poses several challenges. These challenges cause problems regarding the accuracy and applicability of the proposed methods. This paper analyzes the methods, challenges, approaches, and future work. The goal of this paper is to establish a clear distinction in the field of motion detection using inertial sensors. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

引用

China Communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

来源：评论

学校读者我要写书评

暂无评论

BGRU-MTRA: bilinear GRU networks with multi-path temporal residual attention for suspicious activity recognition

引用

Neural Computing and Applications 2025年第1期37卷 185-212页

作者： Pandey, Ajeet Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Suspicious activity recognition (SAR) is an active research field in computer vision and image processing due to the rapid demand for intelligent video surveillance systems. However, current automated systems focus too much on the temporal dynamics of events in videos and ignore the importance of spatial dynamics. The existing methods for SAR are complex and demand substantial resources. Hence, this paper proposes a novel trade-off architecture for SAR integrating the strengths of the multi-path temporal residual attention (MTRA) module with the bilinear-gated recurrent unit (BGRU) module. The MTRA module combines spatial feature extraction (SFE) with temporal residual attention network (TRAN) blocks for improving SAR by enhancing resilience to variations in object sizes, viewpoints, and motion patterns. It selects relevant action features, addresses vanishing gradient issues, and reduces spatial dimension. The BGRU module preserves spatial features, improving the model’s ability to recognize fine-grained features and complex temporal patterns for effectively recognizing suspicious activities. The BGRU-MTRA system yields recognition accuracy of 93.28%, 98.97%, 99.86%, and 99.66%, and 48.42% on the benchmark hybrid crime action (HCA), UT interaction (UTI), CAVIER, hockey fight (HF), and the most challenging UCF-crime datasets, respectively. Through comprehensive experiments, it is clear that the proposed method outperforms state-of-the-art methods over benchmark datasets with reduced parameters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

BDSLL: Biomedical Document Recommendation Using Knowledge-Empowered Semantic Intercepted Large Language Models 4th

BDSLL: Biomedical Document Recommendation Using Knowledge-Em...

引用

4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9789819757022

There is a requirement for document recommendation frameworks focusing on certain domains linked to medical sciences and biosciences like biomedical document recommendation in the era of the Web 3.0. This paper proposes a biomedical document recommendation strategy that realizes informative term generation using the encompassment of extraction of terms and categories and the TF-IDF. The framework also generates an ontology from the informative terms harvested from which features are selected to classify the dataset using a random forest classifier. The model also uses very strong knowledge bases that are highly specific to the domain like QIAGEN. It also uses WikiData which is generic but has a full cover of world knowledge to bridge the knowledge present in the datasets and the existing structure web. Metadata generation also increases the quantity of additional knowledge that is encompassed in the model. BioBERT is a strategic large language model that focuses on biosciences and biomedicine as its domain is used to classify the metadata to make it more permeable into the model. Semantic similarity is computed using NPMI, web overlap, SimRank, and Ricklefs and Lau index at different stages in the pipeline to facilitate quantitative reasoning through semantic relatedness computation. The salient semantic analysis also accelerates the overall semantic association into the proposed framework. This framework can demonstrate a precision of 96.56%, recall of 96.93%, and an FDR of 0.04. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Best Linear Controllers for Decentralized Linear Quadratic Systems

引用

Journal of Systems Science & Complexity 2025年第1期38卷 210-237页

作者： AFSHARI Mohammad MAHAJAN Aditya Institute for Robotics and Inteligent Machines Georgia Institute of TechnologyAtlanta 30332-0530USA Department of Electrical and Computer Engineering McGill UniversityMontreal H3A 0G4Canada

In this paper,the authors revisit decentralized control of linear quadratic(LQ)*** of imposing an assumption that the process and observation noises are Gaussian,the authors assume that the controllers are restricted to be *** authors show that the multiple decentralized control models,the form of the best linear controllers is identical to the optimal controllers obtained under the Gaussian noise *** main contribution of the paper is the solution ***,optimal controllers for decentralized LQ systems are identified using dynamic programming,maximum principle,or spectral *** authors present an alternative approach which is based by combining elementary building blocks from linear systems,namely,completion of squares,state splitting,static reduction,orthogonal projection,(conditional)independence of state processes,and decentralized estimation.

关键词： Decentralized estimation decentralized stochastic control linear quadratic systems team theory

来源：评论

学校读者我要写书评

暂无评论

LARSI-TPE-XGB: Short-Term Load Forecasting by Load-Adaptive Relative Strength Index and Fusion of Tree-Structured Parzen Estimator and XGBoost

引用

IEEE Transactions on Power Delivery 2025年第3期40卷 1318-1330页

作者： Liu, Jin-Xian Leu, Jenq-Shiou National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106216 Taiwan

Power load forecasting is essential for optimizing power generation and distribution efficiency. This paper proposes a novel method for daily average load forecasting, referred to as LARSI-TPE-XGB, which integrates the Load-Adaptive Relative Strength Index (LARSI) with the Tree-structured Parzen Estimator (TPE) and eXtreme Gradient Boosting (XGBoost). Our method significantly improves the accuracy and generalization ability of short-term load forecasting (STLF) by addressing limitations in feature extraction and hyperparameter optimization. The proposed LARSI enhances the forecasting model by adapting an improved Relative Strength Index (RSI) for power load prediction, while TPE optimizes the model's hyperparameters to dynamically adjust to time-series updates, thus mitigating the issue of XGBoost's sensitivity to hyperparameters in high-dimensional scenarios. Experimental results on real-world power load datasets demonstrate that LARSI-TPE-XGB reduces errors by 18.58% and 30.73% in root mean squared error (RMSE) across two different datasets compared to models without LARSI-TPE-XGB and outperforms state-of-the-art models, as confirmed by the Diebold-Mariano (DM) test. Our method consistently improves performance across various datasets, while we further investigate the influence of LARSI and other factors, such as weather conditions, on forecasting accuracy. © 1986-2012 IEEE.

关键词： Electric load forecasting

来源：评论

学校读者我要写书评

暂无评论

DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：