检索结果-内蒙古大学图书馆

Adaptive Electric Vehicle Charge Controllers Using Deep Reinforcement learning for Real-Time Optimization 1

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kavitha, N. Bdair, Mohammad Chandrasekar, P. Veerakumar, S. Kumar, M. Hema Kumar, Vinay Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of EEE Tamil Nadu Chennai India School of Architecture Computing & Engineering University of East London London United Kingdom Bannari Amman Institute of Technology Department of EEE Tamil Nadu Erode India Sona College of Technology Department of ECE Tamil Nadu Salem India B N M Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331518578

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent Deep Reinforcement learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow Deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution. © 2025 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A machine learning based Smart Grid for Home Power Management using Cloud-Edge Computing System

A Machine Learning based Smart Grid for Home Power Managemen...

引用

2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Kavya, B.M. Sharmila, N. Naveen, K.B. Mallikarjunaswamy, S. Manu, K.S. Manjunatha, S. Ks Insititute of Technology Dept of Electronics and Communication Bengaluru India Jss Science and Technology University Dept of Electrical and Electronics Engineering Mysore India Engineering Bgs Institute of Technology Dept of Electronics and Communication Nagamangala India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India Don Bosco Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Global Academy of Technology Dept of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350306927

In recent years, a huge amount of data has been communicated between internet of things (IoT) devices. Apart from these dependencies, many fields such as smart grids, smart cities, and smart homes are incorporating IoT devices. This work focuses on refining issues faced with existing power grids, such as unidentified fault detection, prediction of power generation, and utilisation at the consumer side of existing power grids. To overcome this problem, a machine-learning-based smart grid using computing systems is designed to control renewable energy power generation, power prediction, fault detection, and utilisation at the consumer side. This is achieved using cloud-edge computing. As per the simulation analysis, the proposed work shows improved performance in comparison to convolution neural network (CNN) based cloud computing with respect to power utilisation, throughput, and fault detection of 0.25%, 0.12%, and 0.35%, respectively. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based on Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

引用

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

作者： Nainwal, Ankita Sharma, Vishal Varshney, Neeraj Shirish Boob, Nandini Reddy, N.V. Uma Reddy, K. Jyothsna Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use. © 2025 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Detecting Zero-Day Attacks using Advanced Anomaly Detection in Network Traffic

Detecting Zero-Day Attacks using Advanced Anomaly Detection ...

引用

Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Asma Anjum P. Rama Subramanian R. Stalinbabu Deepthi Kothapeta K. Santha Sheela B. Jegajothi Computer Science and Engineering HKBK College of Engineering Bangalore Computer Science and Engineering P.S.R Engineering College Sivakasi Artificial Intelligence and Machine Learning M. Kumarasamy College of Engineering Karur Computer Science and Artificial Intelligence SR University Department of CSE Velammal College of Engineering and Technology SRS Tech Solutions Chennai

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Zero-day attacks pose a significant challenge to cybersecurity due to their unpredictable nature and the lack of labeled attack data for training conventional detection models. This research proposes a Hybrid Deep Anomaly Detection Model that integrates Autoencoders, Transformer-Based Detection, and Isolation Forest to enhance the identification of zero-day attacks in network traffic. The Autoencoder component learns normal traffic patterns and identifies deviations based on reconstruction errors. A Transformer-based model captures temporal dependencies in network traffic using self-attention mechanisms, improving the representation of evolving attack behaviors. Finally, an Isolation Forest refines anomaly detection by isolating outliers and reducing false positives. The model is trained and evaluated on the CSE-CIC-IDS2018 dataset, which contains diverse cyberattack scenarios, ensuring a robust performance assessment. The results of the proposed model achieve 97.2% accuracy and a false positive rate of 2.8%, significantly outperforming existing approaches such as Autoencoder-only, LSTM-based detection, and Random Forest classifiers. The proposed framework enhances zero-day attack detection, minimizes false alerts, and ensures real-time adaptability in modern cybersecurity environments.

关键词： Deep learning Adaptation models Autoencoders Intrusion detection Forestry Telecommunication traffic Transformers Feature extraction Real-time systems Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Deep learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Improving Eeg Signal-Based Emotion Recognition Using a Hybrid Gwo-Xgboost Feature Selection Method

SSRN

引用

SSRN 2023年

作者： Asemi, Hanie Farajzadeh, Nacer Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Artificial Intelligence and Machine Learning Research Laboratory Azarbaijan Shahid Madani University Tabriz Iran

Emotion, as one of the most important functions of the brain, plays an important role in human daily life, and it is related to many cognitive functions such as language comprehension, decision-making, attention, and concentration. Due to the ever-increasing number of computer systems employed in our lives, it is of great importance to be more aware of the emotional state of the user. In addition, developing reliable systems that recognize emotion accurately and are compatible with real-life applications is a very useful and challenging task. Emotion detection through electroencephalogram signals allows direct assessment of the human’s internal state and is considered an important factor in the interaction between humans and external devices. Therefore, in this research, a method for predicting emotions using electroencephalogram signals is presented. In this research, various features are extracted in time, frequency, and time-frequency domains, and informative features are selected using Grey Wolf Optimizer algorithm. Next, to predict emotions in two dimensions of Arousal and Valence, the selected features are given to the XGBoost algorithm. The proposed method was evaluated on the DEAP dataset, and prediction accuracies of 89.63% and 89.03% were obtained for Arousal and Valence respectively;these were respectively 10.67% and 12.41% more than the best predominant method. © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

The Effective Way of using machine learning Classifier Technique to Predict the Heart Muscle Condition 4

The Effective Way of using Machine Learning Classifier Techn...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vimal, V.R. Joany, R.M. Rao, Kolli Himantha Krishnammal, P. Muthu Rashid, Zakhara Abdel Hassan Safi, Hassn Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai Thandalam India Sathyabama Institute of Science and Technology Department of Ece Chennai India Saveetha Engineering College Institute of Cse Department of Artificial Intelligence and Machine Learning India Vitap University School of Electronics Engineering Andhra Pradesh Amaravati India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Free-text is categorised into predetermined groups in text classification, a crucial field of machine learning. Due to its many uses, such as sentiment analysis, topic labelling, language identification, and spam filtering, it has attracted a great deal of study interest. Ontologies and machine learning give the knowledge of data relations or patterns, which greatly increases the text categorization efficiency. The goal of this study is to diagnose myocardial infraction among patients by machine learning classifiers. The code that goes with the created approach is meant to be flexible and work with any text categorization system. In this research, we present an improved architecture that makes use of ontology to improve the efficacy of text categorization and diagnose myocardial infraction. In particular, we provide a novel ontology-driven text categorization technique that leverages the extensive semantic data from the Disease Ontology (DOID). We examine previous research and argue that ontology-based text categorization strategies are better than traditional techniques in a number of criteria, including as F-measure, Accuracy, Precision, and Recall. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

GeoAgriGuard: AI-Driven Pest and Disease Management with Remote Sensing for Global Food Security

引用

Remote Sensing in Earth Systems Sciences 2025年第2期8卷 409-422页

作者： Sharada, K. Choudhary, Shailee Lohmor Harikrishna, T. Dixit, Rinku Sharma Suman, Sanjay Kumar Ayyappa Chakravarthi, M. Bhagyalakshmi, L. Department of Computer Science and Engineering GITAM (Deemed to Be University) AP Visakhapatnam India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of AIML Annamacharya University Andhra Pradesh Rajampet India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Departmentt of CSE-Data Science KSR Institute of Technology and Science KKR & AP Guntur India Department of ECE Rajalakshmi Engineering College TN Chennai India

Agricultural productivity is increasingly threatened by the growing frequency and severity of pest and disease outbreaks, exacerbated by climate change and evolving environmental conditions. The GeoAgriGuard AI-Driven Pest and Disease Management System integrates remote sensing technologies with advanced AI models to address pest and disease outbreaks in agriculture. Utilizing multi-spectral and hyper-spectral satellite and drone imagery, the system processes crop health data through comprehensive preprocessing steps, including radiometric, geometric, and atmospheric corrections. Vegetation indices such as NDVI and NDWI further enhance the data quality, ensuring accurate detection of crop stress. A hybrid deep learning ensemble, combining models like ResNet, Transformer, DenseNet, and AutoEncoders, processes this data to predict and classify pest and disease occurrences. The proposed model achieved a remarkable accuracy of 97.81%, significantly outperforming existing models such as SVM (82.45%) and CNN (90.12%). By integrating these AI predictions with GIS-based spatial analysis, the system generates real-time risk maps, enabling farmers to take timely and localized actions. Performance metrics, including precision (96.72%), recall (95.83%), and F1-score (96.27%), demonstrate the system’s reliability in real-world applications. GeoAgriGuard provides a scalable, data-driven solution for enhancing crop protection and improving global food security. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： AI-driven agriculture Crop disease Deep learning Food security GIS NDVI Pest detection Precision agriculture Remote sensing Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

machine learning for Blood Donors Classification Model Using Ensemble learning 3rd

Machine Learning for Blood Donors Classification Model Using...

引用

3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： El-rashidy, Nora El-Ghamry, Amir ElSayed, Nesma E. Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Faculty of Computers and Information Mansoura University Mansoura Egypt School of Engineering and Computer Science Hosted By Global Academic Foundation University of Hertfordshire Garden City Egypt Faculty of Computer Science and Engineering New Mansoura University Mansoura Egypt Delta Higher Institute for Management and Accounting Information Systems Mansoura Egypt

ISBN: (纸本)9789819947638

Blood transfusion is in constant demand, as it is required for several medical procedures and life-or-death operations. This study is motivated by the fact that the need for blood transfusions is steadily on the rise because of accidents, operations, diseases, etc. such that the ability to accurately predict the number of blood donors allows medical personnel to predict the future blood supply and plan accordingly to recruit enough volunteers to meet demand. We attempt to handle this supply–demand gap by using a predictive model that identifies potential donors. We model the probability of a person donating blood, depending on five specific features, using machine learning techniques. This paper implements a machine learning pipeline that includes data preprocessing and donor classification. Several ensemble models have been developed. The results show that the random forest model led to the best test set accuracy (96.3%), which beat other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： machine learning

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：