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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11649 条记录，以下是401-410 订阅

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Smart Energy optimization Harnessing IoT and Advanced learning Techniques 4

Smart Energy Optimization Harnessing IoT and Advanced Learni...

引用

4th International conference on Sustainable Expert Systems, ICSES 2024

作者： Liyon Raj, S. Malleeshwaran, T. Prasanna, T. Alfred Daniel, J. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

the paper gives an extensive investigation of the research findings on the optimization of energy management in smart microgrids. the study specifically focuses on integrating renewable energy sources with the Internet of things. Smart microgrids have become an essential option due to the increasing global energy demand and environmental problems arising from the use of fossil fuels. these grids assure better efficiency in renewable energy usage energy storage, and improved user interaction using IoT-based technologies. this research investigates the application of machine learning algorithms to predict energy consumption patterns, electricity prices, and optimal energy storage management strategies. the main idea of the proposed mathematical model in this paper is to minimize the cost of energy procurement under the condition of a balanced energy supply/demand based on the optimization problem. this model is simulated with several scenarios using the linear programming tool of MATLAB software and confirms high cost savings and system efficiency. this study also investigates how EVs and BESS in the grid exert better optimization of smart energy systems. © 2024 IEEE.

关键词： Procurement Renewable energy sources Costs Machine learning algorithms Mathematical models Smart grids MATLAB Energy management optimization Energy storage

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Patterns Recognition of Sentimental Patterns from the X-Dataset using Deep learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

引用

4th International conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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optimization of Parameters in Additive Manufacturing Processes Using Rao Algorithms 4th

Optimization of Parameters in Additive Manufacturing Process...

引用

4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Pabari, Miit Chetan Patel, Pooja Rao, Ravipudi Venkata Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Additive manufacturing (AM) has revolutionized the manufacturing industry by enabling the creation of complex geometries and customized parts with unprecedented speed and precision. However, to fully unlock its potential, optimizing AM processes is imperative. this paper extensively explores the utilization of Rao optimization algorithms within the domain of additive manufacturing, with the primary objective of elevating operational efficiency, mitigating production expenditures, and elevating the overall standard of manufactured products. three case studies of additive manufacturing are considered in this paper to illustrate the practical implementation of Rao algorithms, showcasing remarkable improvements in terms of material utilization, build time reduction, and structural integrity enhancement.

关键词： Rao algorithm Additive manufacturing Process parameters optimization

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Leveraging LLM-Powered Chatbot for learning and optimization in Databases and Information Systems Education 4

Leveraging LLM-Powered Chatbot for Learning and Optimization...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Nageshwaran, Vinoth Nageshwaran, Sathish Business Insider New York United States Atos

ISBN: (纸本)9798331523923

this research work proposes a LLM-powered chatbot aimed at supporting students and educators in databases and information systems in higher education. the chatbot leverages the capabilities of LLM to facilitate interactive learning, offering real-time assistance on various database-related topics, including query formulation, data modeling, and system design. the chatbot incorporates advanced pre-processing techniques such as text cleaning, tokenization, stop words removal, and stemming/lemmatization to prepare user inputs for effective analysis. Feature extraction methods such as Bag-of-Words (BOW), n-grams, and Improved Term Frequency-Inverse Document Frequency (ITF-IDF) convert textual data into numerical forms, enhancing model understanding and accuracy. Additionally, deep learning algorithms, Hybrid Convolutional Long Short-Term Memory (HCLSTM) networks, are integrated to enhance the chatbot's ability to generate contextually relevant responses. optimization is achieved through the Improved Artificial Bee Colony (IABC) algorithm, which fine-tunes the chatbot's parameters for superior performance. through an interactive user interface, the chatbot provides personalized learning experiences, helping students grasp complex concepts in databases and information systems. Initial testing indicates the system's effectiveness in delivering insightful, tailored responses, making it a valuable tool for enhancing the learning process in higher education. © 2025 IEEE.

关键词： Students

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Detection of Control Valve Diagnosis and Pipeline Using Machine learning 4

Detection of Control Valve Diagnosis and Pipeline Using Mach...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Selvakarthi, D. Hakeem, A. Abdul Akilesh, D. Dharshini, S. Amjath, M. Mohamed Kongu Engineering College Department of Electronics and Instrumentation Engineering Perundurai Tamilnadu Erode638060 India

ISBN: (纸本)9798331529635

Machine learning has the potential for transforming the workplace across various industries. It can streamline repetitive tasks, improve decisionmaking, enhance safety, and increase operational efficiency. Rather than relying on static images from multiple angles to train object detection models, using video footage can simplify the process and reduce the need for large image datasets. Amazon Rekognition, a service from Amazon Web Services (AWS), leverages deep learning algorithms to analyze both images and videos. Its features include facial recognition, object detection and tracking, text recognition, and content moderation. In this project, AWS Rekognition is utilized to assess control valves and detect pipeline leaks. It also distinguishes between different types of control valves, such as normally open, normally closed, and those with positioners. this approach illustrates the effectiveness of video-based learning for object detection and the utility of cloud-based AI services in industrial diagnostics and monitoring. © 2024 IEEE.

关键词： Safety valves

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Predicting the Onset of Lifestyle Diseases Using Machine learning Techniques 4

Predicting the Onset of Lifestyle Diseases Using Machine Lea...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Prabagar, S. Babu, Tina Nair, Rekha R. Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Lifestyle diseases, including cardiovascular conditions, obesity, type 2 diabetes, and hypertension, are increasing global health concerns, particularly in developing countries. these non-communicable diseases are often attributed to daily habits such as poor diet, lack of exercise, smoking, and alcohol consumption. Early detection of these diseases is crucial due to their slow onset and the complexity of treatment once they manifest. this study explores the application of machine learning algorithms - such as decision trees, random forests, support vector machines (SVM), K-nearest neighbors (KNN), and Naive Bayes - in predicting the onset of lifestyle diseases. By utilizing key lifestyle factors as input data, the study evaluates the accuracy, precision, and recall of these models in diagnosing lifestyle diseases. the Random Forest algorithm emerged as the top performer with 88% accuracy. the results highlight the potential of machine learning to improve early disease detection, which could enable timely medical intervention and help reduce the long-term impact of lifestyle diseases on healthcare systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust optimization Strategy for Reserve Capacity via Source-Load-Storage Integration 4

Robust Optimization Strategy for Reserve Capacity via Source...

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4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Fang, Yuxuan Zhai, Haoyu Ma, Xurui Hu, Junjie North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing China

ISBN: (纸本)9798331532765

Due to the influence of external power supply environments and the frequent occurrence of extreme weather events, the power supply security of receiving-end power systems faces significant challenges, necessitating source-load-storage resources to provide more stable and reliable reserve capabilities. To address this issue, we propose a robust optimization dispatching strategy for source-load-storage resources that accommodates diverse system regulation demands. First, adjustable thermal power units are classified into cold, hot, and spinning reserve states based on response delay and regulation speed, and the adjustable characteristics of multiple resources, including various types of thermal units, electric vehicles, and distributed battery energy storage, are analyzed. Next, a robust optimization model for reserve capacity distribution of adjustable resources is developed, minimizing load shedding based on representative scenarios obtained through clustering. An iterative solution method is constructed to determine the optimal number of representative scenarios and the robust optimization strategy for reserve capacity. Finally, case studies validate the adaptability and effectiveness of the proposed robust optimization dispatching strategy in diverse regulation scenarios, supporting the adequate operation of power systems. © 2024 IEEE.

关键词： adjustable resource pool component receiving-end power system reserve capacity robust optimization

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Sarcasm Detection in Newspaper Headlines 4

Sarcasm Detection in Newspaper Headlines

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Harithikeswar Reddy, T. Vishnu Vardhan Reddy, A. Vekkot, Susmitha Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

In the current age of information saturation, where headlines serve as primary points of engagement, the pervasive use of sarcasm in news headlines poses a challenge for readers' comprehension, failing to discern sarcasm may lead to misunderstandings, misinformation dissemination, and harm journalistic integrity. this study addresses this critical issue and enhances the credibility of news sources by employing various ensemble learning algorithms and deep learning models with different types of word embedding, the evaluation includes various possible performance metrics and a complete correlation of the outcomes. XGBoost consistently outperforms other models with higher accuracy, precision, recall, and F 1-score in sarcasm detection. Among deep learning models, BiLSTM emerges as the top performer, achieving 0.86 accuracy, capturing nuanced language cues effectively and appears as the optimal model for sarcasm detection. BERT stands out with superior F1 scores, showcasing its exceptional ability in deciphering sarcastic language nuances. © 2024 IEEE.

关键词： Contrastive learning

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Levy Swallow Swarm optimization with Machine learning-based Intrusion Detection in IIoT 4

Levy Swallow Swarm Optimization with Machine Learning-based ...

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4th International conference on Distributed Sensing and intelligent Systems, ICDSIS 2023

作者： Alzahrani, Abdulrahman Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia

ISBN: (数字)9781837240258

ISBN: (纸本)9781839539954

With the recent evolution ofIndustry 4.0, integrated devices and sensors started to generate dynamic, extensive, and heterogeneous in Industrial Internet of things (IIoT) environments. the immense heterogeneous data probably raises the difficulties of data analysis procedures and security risks. As IIoT develops, cyber-attacks are more complex and diverse, making current anomaly detection methods’ operations less effective. Intrusion Detection System (IDS) had a major contribution to protecting IoT systems from cyberattacks by constructing complementing classical preventive security measures (e.g., encryption, authentication, and authorization). therefore, in this paper, we present a Levy Swallow Swarm optimization with Machine learning-based Intrusion Detection (LSSOML-ID) technique in IIoTenviornments. the presented LSSOML-ID technique focuses on the identification and classification of intrusions in IIoT environments. To achieve this, the LSSOML-ID technique uses a Z-score normalization algorithm to scale the input data into a uniform format. For feature selection purposes, the LSSO algorithm was used which reduces the number of features involved in the classification process. Finally, the Aquila optimizer (AO) algorithm with an extreme learning machine-based autoencoder (ELM-AE) model is proficiently employed for intrusion classification. To exemplify the significant outcomes of the LSSOML-ID technique, a thoroughset of experiments areconducted. the comprehensive outcomes highlighted the betterment of the LSSOML-ID approach over recent methods. © the Institution of Engineering & Technology 2023.

关键词： Network intrusion

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Comparitive Study of Intrusion Detection Using Hyper Heuristic Improved Particle Swarm optimization and Hyper Heuristic Firefly Algorithm Based Convolutional Neural Network 4

Comparitive Study of Intrusion Detection Using Hyper Heurist...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Aswanandini, R. Kg College of Arts and Science Department of Computer Science Coimbatore India

ISBN: (纸本)9798331529635

SVM has high impact on big data classification problems but it requires expert knowledge for determining the configuration. It includes the selection of parameters and kernel functions which have the ability of increasing the classification accuracy. therefore, configuration process of SVM is modelled here as multi-objective optimization problem with the parameters responsible for model complexity, false positive rate and false negative rate. the Hyper-Heuristic Improved Particle Swarm optimization (HHIPSO) methodology is developed for optimizing the multi-objective problem of SVM. It is the fusion of hyper-heuristic and improved PSO techniques. the second model proposed is HHFA-CNN model used for detecting the intrusions which learns both the host and network level features. the intrusion detection performance is enhanced by selecting optimal configurations for CNN using HHFA. Finally appropriate configuration of SVM is detected with the assistance of HHIPSO. the benchmark dataset namely NSL-KDD is utilized to assess the effectiveness of HHIPSO model and HHFA-CNN model. In this paper we are going to compare the results between Hyper heuristic Improved Particle Swarm optimization (HHIPSO) and Hyper heuristic Firefly Algorithm based Convolutional Neural Networks (HHFA-CNN) on the same data set. © 2024 IEEE.

关键词： Network intrusion

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