检索结果-内蒙古大学图书馆

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

Journal of Internet technology 2024年第3期25卷 455-463页

作者： Ho, Wen-Jen Hsieh, Hsin-Yuan Tsai, Chia-Wei Software Technology Institute Institute for Information Industry Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Energy conservation

来源：评论

学校读者我要写书评

暂无评论

M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

引用

Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

引用

International Journal of computers and Applications 2024年第12期46卷 1152-1165页

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Boosting cervical cancer detection with a multi-stage architecture and complementary information fusion

引用

Soft Computing 2025年第2期29卷 1191-1206页

作者： Sahoo, Pranab Saha, Sriparna Sharma, Saksham Kumar Mondal, Samrat Computer Science and Engineering Indian Institute of Technology Patna India Computer Science University of Maryland Baltimore CountyMD United States

Cervical cancer is one of the most fatal and prevalent illnesses affecting women globally. Early detection of cervical cancer is crucial for effective treatment. Pap smear tests are commonly used, but population-based screening is time-consuming, expensive, and requires expert physicians. computer-Aided Diagnosis (CAD) has shown promise in addressing this challenge. However, accurately predicting the disease using a single model can be difficult due to the complex data patterns involved. This research proposes a multi-stage architecture to improve cervical cancer screening. Initially, three pre-trained models are employed for image classification, after which the proposed advanced fusion technique is applied to combine the predictions. Additionally, we introduce a filtering approach in the third stage to refine the predictions. Unlike traditional fusion methods, the proposed architecture considers the confidence score of the base classifiers in making the final predictions on test samples. To enhance the performance of the models, we incorporate advanced augmentation techniques, including CutMix, CutOut, and MixUp. We assessed the performance of the proposed framework using a 5-fold cross-validation technique on two benchmark datasets. We evaluated the performance of the proposed framework through 5-fold cross-validation on two benchmark datasets. Remarkably, our framework achieved a classification accuracy of 97.62% and an F1-score of 97.64% on the SIPaKMeD dataset, demonstrating its effectiveness in accurately categorizing various cell types in the dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cervical cancer computer-aided detection Deep learning Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

A Survey on Logic-Locking Characteristics and Attacks

引用

Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 1073-1087页

作者： Subbiah, Karthik Chinnathevar, Sujatha Electronics and Communication Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India Computer Science and Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India

Integrated circuits (ICs) are ubiquitous and a crucial component of electronic systems, from satellites and military hardware to consumer devices and cell phones. The computing system’s foundation of trust is the IC. Most semiconductor businesses are shifting to fabless manufacturing and outsourcing to foundries worldwide as integrated circuit feature sizes continue to decrease. That puts the design business at risk for several things, such as unauthorized overproduction, resale on the black market, and illegal copying brought on by intellectual property theft. Logic locks offer one solution in which the chip’s actual functioning is "locked" with a key only known to the inventor. The design will only function as intended if specific keys are pressed. Unlocking overproduced chips should be impossible for supply chain attackers since designers open them after manufacturing them. Logical locks against the risk of overproduction are the main subject of this research. We examine current locking systems, define features based on crucial processing, and identify commonalities and discrepancies between the employed attacker models. This research paper is intended to assist scientists, IP distributors, and SoC developers in rapidly investigating and comprehending the most recent technologies that should be considered and analyzed for additional research on logic-locking techniques. © The Institution of Engineers (India) 2024.

关键词： Locks (fasteners)

来源：评论

学校读者我要写书评

暂无评论

Machine and deep learning techniques for the prediction of diabetics: a review

引用

Multimedia Tools and Applications 2025年第18期84卷 19425-19549页

作者： Modak, Sandip Kumar Singh Jha, Vijay Kumar Department of Computer Science & Engineering Sarala Birla University Jharkhand Ranchi India Department of Computer Science & Engineering Birla Institute of Technology Mesra Ranchi India

Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 700 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multi factorial metabolic disease, its diagnostic criteria are difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of the medical diagnosis process. With the development of Data mining, researchers find that machine learning and deep learning, playing an important role in diabetes prediction research. This paper is an in-depth study on the application of machine learning and deep learning techniques in the prediction of diabetics. In addition, this paper also discusses the different methodology used in machine and deep learning for prediction of diabetics since last two decades and examines the methods used, to explore their successes and failure. This review would help researchers and practitioners understand the current state-of-the-art methods and identify gaps in the literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

An optimization driven deep belief network model for crop yield prediction in IoT based smart agriculture

引用

Multimedia Tools and Applications 2024年 1-23页

作者： Patel, Jaideep Chandel, Sonal Telang Sonker, Abhilash Department of Computer Science Engineering Sagar Institute of Research and Technology Bhopal India Department of Computer Science Engineering Indian Institute of Information Technology Bhopal India Department of Information Technology Madhav Institute of Technology & Science Gwalior India

Crop yield Prediction based on environmental, soil, water, and crop parameters has been an active area of research in agriculture. Many studies have shown that these parameters can have a significant impact on crop yields, and that accurate predictions of crop yields can be made by considering them together. Despite the progress made in crop yield prediction, several challenges still remain, such as the lack of high-quality and diverse data, the high cost of technology, and the lack of domain knowledge for certain crops and regions. Thus, Crop yield forecasting models can reasonably estimate yields, but better yield forecasting performance is still desirable. Thus, in order to enhance the accuracy of crop yield prediction in IoT, a novel optimization driven deep belief neural network model is developed in this study. IoT sensors can collect data on various environmental factors such as temperature, humidity, soil moisture, as well as crop parameters such as growth stage and leaf area index. These data can then be used to train a proposed deep learning-based model, namely an optimization driven deep belief neural network (DBNN) along with an effective crocodile optimization algorithm, to predict crop yields. To improve the performance of DBNN, adaptive crocodile optimization (ACO) algorithm is used. Using this algorithm, weight parameters of DBNN are chosen optimally. Results of the article depict that the proposed model achieve accuracy of 97.26%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Face Recognition Using LBPH and CNN

引用

Recent Advances in computer science and Communications 2024年第5期17卷 48-58页

作者： Shukla, Ratnesh Kumar Tiwari, Arvind Kumar Mishra, Ashish Ranjan Department of Computer Science & Engineering Shambhunath Institute of Engineering & Technology Uttar Pradesh Prayagraj211012 India Department of Computer Science & Engineering Kamla Nehru Institute of Engineering & Technology Uttar Pradesh Sultanpur228118 India Department of Computer Science & Engineering Rajkiya Engineering College Uttar Pradesh Sonbhadra231206 India

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security;however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result. © 2024 Bentham science Publishe.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

引用

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

来源：评论

学校读者我要写书评

暂无评论

Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

引用

Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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：