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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13504 条记录，以下是371-380 订阅

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Evaluating Artificial Neural Networks and Emerging Machine learning Methods for Engine Performance and Emissions Prediction: A Review 4

Evaluating Artificial Neural Networks and Emerging Machine L...

引用

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

作者： Galat, Umesh Rewatkar, Rajendra Kothare, Chandrakant Datta Meghe Institute of Research and Higher Education Aids Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Shri Shankarprasad Agnihotri Collge of Engineering Department of Mechanical Engineering Maharashatra Wardha India

ISBN: (纸本)9798331523923

these due to the shift towards the cleaner energy generation and newer and sophisticated engine technologies the prediction of the performance and emission of engine is of vital importance for the designer for the purpose of ANR as well as the emission control standards. this review also assesses the performance of ANNs and the new machine learning techniques in this area. ANNs especially the single layer and multi-layer networks have shown good levels of predictions of engine performance and emission for ethanol-gasoline, bioethanol and other fuel blends. For example, the models based on Levenberg-Marquardt optimization and the Bayesian regularization have revealed essential correlation coefficients and minimized mean squared errors. Based on comparative analyses, the further improvement of the predictive ability and speed of ANNs is stressed, including applications of ANNs integrated with other approaches like Response Surface Methodology (RSM), and other developing approaches like Extreme learning Machines (ELM) and Support Vector Regression (SVR). However, challenges are concluded to remains including data quality, model complexities and computational resources. this review reveals that, for tackling these issues and enhancing the progress in developing efficient and sustainable engine solutions, detailed databases and refined mathematical modeling approaches are required. the directions for future research are to obtain better quality data, enhance computational time, and determine more fuel blends and engine settings for the model. © 2025 IEEE.

关键词： Adversarial machine learning

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AI Powered Crypt-Analysis for Identification of Encryption Algorithm 4

AI Powered Crypt-Analysis for Identification of Encryption A...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Radhika, K. Verma, Shanti Sathya, R. Kathirvel, T. Vishnu, K.Harish Rajan, N.Mukesh Kongu Engineering College Department of Mathematics Perundurai India Lokmanya Institute of management and computer applications Gujarat Technological University Ahmedabad India Kongunadu College of Engineering and Technology Tamil Nadu Trichy India

ISBN: (纸本)9798331540364

In modern world, cryptographic algorithms are critical for securing sensitive data, ensuring confidentiality, and protecting against cyber threats. With the increasing number of encryption algorithms and modes, identifying which algorithm was used in a ciphertext becomes a difficult job. To tackle this we present a machine learning-based solution that automates the identification of cryptographic algorithms using the Random Forest classifier. the system processes a large dataset of 'encrypted-data', consisting of 39 different algorithms and modes, by first extracting key features such as byte frequency and entropy from the ciphertexts. these features are used to create a 257 -length cipher-descriptor for classification. the dataset is split into 19 subsets, each containing data from two algorithms, and 19 different Random Forest models are trained. During testing, a sequential model loading approach is employed to manage computational resources. the best-performing model, in terms of confidence score, is used to predict the encryption algorithm for a given input ciphertext. Finally, the system is integrated with a Gradio web interface, allowing for real-time, user-friendly interaction. Our approach provides a scalable and accurate tool for cryptographic analysis, significantly improving upon traditional methods. © 2024 IEEE.

关键词： Encryption algorithms

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Exploring Apple Silicon's Potential from Simulation and optimization Perspective 24th

Exploring Apple Silicon's Potential from Simulation and Opti...

引用

24th international conference on Computational Science (ICCS)

作者： Struniawski, Karol Konopka, Aleksandra Kozera, Ryszard Warsaw Univ Life Sci SGGW Inst Informat Technol Ul Nowoursynowska 159 PL-02776 Warsaw Poland Univ Western Australia Sch Phys Math & Comp 35 Stirling Highway Perth WA 6009 Australia

ISBN: (纸本)9783031637742;9783031637759

this study explores the performance of Apple Silicon processors in real-world research tasks, with a specific focus on optimization and Machine learning applications. Diverging from conventional benchmarks, various algorithms across fundamental datasets have been assessed using diverse hardware configurations, including Apple's M1 and M2 processors, NVIDIA RTX 3090 GPU and a mid-range laptop. the M2 demonstrates competitiveness in tasks such as BreastCancer, liver and yeast classification, establishing it as a suitable platform for practical applications. Conversely, the dedicated GPU outperformed M1 and M2 on the eyestate1 dataset, underscoring its superiority in handling more complex tasks, albeit at the expense of substantial power consumption. With the technology advances, Apple Silicon emerges as a compelling choice for real-world applications, warranting further exploration and research in chip development. this study underscores the critical role of device specifications in evaluating Machine learning algorithms.

关键词： Machine learning optimization Simulation Apple Silicon Extreme learning Machine

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Transfer learning-Based Evolutionary Multi-task optimization 18th

Transfer Learning-Based Evolutionary Multi-task Optimization

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18th international conference on Bio-Inspired Computing: theories and applications (BIC-TA)

作者： Li, Shuai Zhu, Xiaobing Li, Xi Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China

ISBN: (纸本)9789819722716;9789819722723

Multi-task optimization (MTO) is an emerging research topic to optimize multiple related tasks simultaneously. It aims to enhance task interrelationships by leveraging shared information and features, thereby improving model performance. Evolutionary transfer optimization (ETO), applied to address multitask problems using evolutionary algorithms, incorporates the principles of transfer learning. It utilizes knowledge and experience from source tasks to expedite the optimization process of target tasks. We introduce a transfer learning-based strategy where valuable information from one task is transferred as comprehensively as possible to another task. this article proposes an idea that is based on joint distribution adaptation (JDA) and employs population individual replacement methods as knowledge transfer, differential evolution as the underlying optimizer, called transfer learning-based evolutionary multi-task optimization algorithm (TLEMTO). To validate the effectiveness of the proposed algorithm, the experiment is conducted on CEC17 multi-task optimization problem benchmarks, the results show that TLEMTO is superior to the compared state-of-the-art algorithms.

关键词： Evolutionary transfer optimization Multi-task optimization Differential evolution Transfer learning Joint distribution adaptation

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Scripting the Skies : Beyond the Horizon with Machine learning in Drone Shaped Bird Recognition 4

Scripting the Skies : Beyond the Horizon with Machine Learni...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： thamizhkani, B. Shalini, S. Jeyanthi, S. Aishwarya, R. Kumar, S. Senthil Anitha, R. New Prince Shri Bhavani College of Engineering and Technology Department of Eee Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Applications Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of It Chennai India

ISBN: (纸本)9798331508845

Support Vector Machine (SVM) is a Machine learning approaches has been used for bird species identification. Feature extraction from bird photos is used to train the SVM classifier. these features include morphological traits, color histograms, and texture descriptors. the experimental findings show that the SVM model works well for identifying bird species from photos. To begin, SVM's classification performance is very sensitive to the hyperparameters - the kind of kernel and regularization parameter - selected with great care. Also, support vector machines (SVMs) often have trouble handling noisy data or feature spaces with a lot of dimensions since they are so sensitive to the features that are *** contrasted with probabilistic classification model like logistic regression or Gaussian Naive Bayes, SVM's lack of intrinsic probabilistic outputs makes it less interpretable. Finally, SVM could have scalability problems with big datasets since training duration and storage needs go up with features and samples. Notwithstanding these caveats, support vector machines (SVMs) continue to be a hot commodity when it comes to machine learning classification algorithms, and bird species detection is no exception. To overcome SVM's limitations in bird recognition applications, future studies can investigate hybrid techniques or other algorithms. © 2024 IEEE.

关键词： Birds

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Research on Energy Efficiency optimization of IRS-NOMA Wireless Communication System Based on Deep Reinforcement learning 4

Research on Energy Efficiency Optimization of IRS-NOMA Wirel...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Liu, Jinyu Shen, Yangguang Wu, Tao Li, Liwei Shandong University of Science and Technology Department of Electrical Information Jinan China Jiangsu University School of Electrical and Information Engineering Zhenjiang China Xi'an University of Posts and Telecommunications School of Electronic Information Engineering Xi'an China University College London London United Kingdom

ISBN: (纸本)9798350361643

In this study, the design of an energy-efficient IRS-NOMA wireless communication system based on Non-Orthogonal Multiple Access (NOMA) and Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) technologies is investigated. the study uses the Deep Deterministic Policy Gradient (DDPG) algorithm, a deep reinforcement learning approach, to jointly optimize the transmit beamforming vectors at the base station and the coefficient matrix at the STAR-RIS for maximizing Energy Efficiency (EE). this approach addresses the optimization challenges inherent in traditional methods. © 2024 IEEE.

关键词： Reinforcement learning

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Distributed Deep learning for Real-World Implicit Mapping in Multi-Robot Systems 24

Distributed Deep Learning for Real-World Implicit Mapping in...

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24th international conference on Control, Automation and Systems

作者： Jeong, Eunju Gwak, Jihun Kim, Taeho Kang, Dong-oh Elect & Telecommun Res Inst Daejeon 34129 South Korea BnTSoft Co Daejeon 34129 South Korea

ISBN: (纸本)9798331517939;9788993215380

Using a distributed learning framework and 2D LiDAR data, this paper presents a novel approach for distributed navigation and environmental mapping. Each robot collects distance data in real time to create a local map, which is then shared and integrated among the robots through wireless communication. the framework uses DiNNO (Distributed Neural Network optimization) for distributed learning to collaboratively optimize navigation paths and improve mapping accuracy. DiNNO effectively balances computational load and communication overhead while providing superior accuracy and efficiency compared to other algorithms. Continuous data collection and real-time map updates ensure adaptability to dynamic environments. Experimental results demonstrate the effectiveness of the system and highlight its potential for a variety of autonomous navigation and mapping applications. this approach, as enhanced by DiNNO, offers significant advantages in terms of communication efficiency and mapping accuracy, thus providing a robust solution for dynamic and complex environments.

关键词： Distributed learning Neural Network optimization Implicit Mapping Multi-Robot Systems Real-World Robotics

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Research on the Application of Image Preprocessing Technology Based on Computer Deep learning in Wireless Communication Systems 4

Research on the Application of Image Preprocessing Technolog...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Xu, Xiaoqing School of Information Engineering Eurasia University Shaanxi Xi'an China

ISBN: (纸本)9798350359589

this paper proposes a radio frequency signal identification method based on deep neural network. First, this article abstracts the radio frequency signal into a plane diagram and converts the radio frequency signal identification problem into object detection in image recognition. then, it is combined with the latest international artificial intelligence technology to further enhance the intelligence of radio frequency signals. At the same time, the ability to identify complex electromagnetic interference is enhanced. then this paper proposes a method to identify interference signals through radio spectrum images based on deep learning technology, and designs experiments to verify the identification accuracy of this method. the effectiveness of this method was verified through field sampling of a total of 5,432 samples of 15 categories. the proposed method can correctly identify the wave pattern and frequency characteristics of radio frequency signals, with an accuracy rate of 87.14%. the simulation results show that this method is universal and has good identification accuracy for interference at other frequency points. © 2024 IEEE.

关键词： convolutional neural network deep network image object detection radio frequency learning Radio wave recognition

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Predictive Modeling of Liver Diseases using Artificial Intelligence 4

Predictive Modeling of Liver Diseases using Artificial Intel...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Srivastava, Sarthak Antal, Reena Chandigarh University Department of Mathematics Punjab Mohali India

ISBN: (纸本)9798331515720

Modern medical care generates large amounts of multimodal patient data, which many physicians find challenging to assess and synthesize into actionable knowledge. In the last few decades, AI has shown to be a helpful tool in this field. Hepatology is not an exception, as the application of AI techniques for the diagnosis and treatment of liver disease is the subject of an increasing amount of published research in the field. Artificial Intelligence (AI) has now enabled a faster access to fresh data on patients' chronic liver diseases, such as non-alcoholic fatty liver disease and liver fibrosis. this study determines the predictive capacity of machine learning, an application of artificial intelligence, for liver disease. the liver disease prediction (LDP) approach, which can be used by researchers, students, stakeholders, and health professionals to forecast liver illness, is introduced in this work. Random Forest, Linear Discriminant Analysis, Decision Tree, Classification and Regression Trees and BRDT are the five approaches that are selected. R and Python are used to assess the accuracy and determine which classification technique best predicts liver illness. the results showed that BRDT achieved the maximum accuracy of 93%, while the auto encoder network achieved an accuracy of 94.6%, which is more than acceptable and might be considered for the prediction of liver illness. © 2024 IEEE.

关键词： AI algorithms Data analysis liver disease machine learning prediction

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Machine learning-Based Wireless Network Resource Allocation Strategy 4

Machine Learning-Based Wireless Network Resource Allocation ...

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4th international conference on Digital Signal and Computer Communications, DSCC 2024

作者： Liu, Yuheng Communication University of China Beijing China

ISBN: (纸本)9781510681538

this paper introduces HetGNN, a novel machine learning framework for wireless network resource allocation, blending graph neural networks with long short-term memory networks to adeptly manage graph-structured and temporal data. An adaptive resource allocation algorithm, rooted in Actor-Critic reinforcement learning, dynamically refines strategies, ensuring efficient interaction between agents and their environment. Comparative analyses with benchmark algorithms on a simulation platform highlight HetGNN's superiority in enhancing spectrum and energy efficiency, alongside user experience. the study paves the way for advanced applications in 5G/6G networks, emphasizing the integration of federated and transfer learning for future intelligent wireless network developments. © 2024 SPIE.

关键词： Reinforcement learning

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