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

共 13588 条记录，以下是681-690 订阅

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2024 4th international conference on Artificial Intelligence and Signal Processing, AISP 2024

2024 4th International Conference on Artificial Intelligence...

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4th international conference on Artificial Intelligence and Signal Processing, AISP 2024

ISBN: (纸本)9798350320749

the proceedings contain 112 papers. the topics discussed include: explainable AI-based ECG heartbeat classification using deep learning models;miniaturized and multi band rectangular patch antenna loaded with ring shaped artificial magnetic conductor;real-time human pose estimation using media-pipe an artificial intelligence applications in health and fitness;classification of music genre using deep learning approaches;speech mastery detection using advanced natural language processing (NLP) and automatic speech recognition (ASR) techniques;a genetic algorithm for solving shortest path problem under neutrosophic environment;physics informed neural networks(PINNs) for Burgers' equation;and on-device input analysis to proactively enhance data security in large language models.

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Comparative Efficiency of Machine learning Models for Enhancing algorithms in Solving Multiextremal Multicriteria Problems 15th

Comparative Efficiency of Machine Learning Models for Enha...

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15th international conference on optimization and applications, OPTIMA 2024

作者： Konnov, Sergey Kozinov, Evgeny Barkalov, Konstantin Grishagin, Vladimir Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia

ISBN: (纸本)9783031791185

Solving a multicriteria optimization problem requires finding a whole set of independent variables corresponding to non-dominated criteria values (Pareto set). the complexity of these problems increases significantly in the case when the criteria are multiextremal. the paper presents several variations of the global optimization algorithm intended for approximate solving "black-box" multiobjective problems with multiextremal criteria. the proposed methods are based on a combination of the information-statistical approach and machine learning procedures aimed at increasing the efficiency of constructing the Pareto set approximation. As a novelty, the paper describes a new approach to using machine learning models within the algorithm of multicriteria optimization. Based on a representative computational experiment, the effectiveness of different versions of the proposed algorithm is estimated. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Detection of Alcohol Drug Consumption Among Youngsters using Eye Gazing Movement and Deep learning Techniques 4

Detection of Alcohol Drug Consumption Among Youngsters using...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Pyingkodi, M. Parvathavarthini, S. Palarimath, Suresh Deepa, D. Karthi, D. Maria John Paul, M. Kongu Engineering College Erode India University of Technology and Applied Sciences IT Department Ibri Oman

ISBN: (纸本)9798350385793

the intake of alcohol and drug consumption among young individuals poses significant societal challenges, necessitating effective preventive measures and early intervention strategies. this study suggests a unique method for predicting youth drug use that makes use of deep learning algorithms and eye gaze movement. this work objective is to use eye gaze movement and deep learning techniques to create a predictive model for young people's use of illicit drugs. the study design involves recruiting a diverse sample of young individuals and collecting their eye gaze data through eye-tracking devices. the collected eye gaze data is preprocessed, segmented, and fed into the deep learning models for feature extraction and prediction. Deep learning algorithms, including ResNet50, VGG19 and Alexnet are employed to extract relevant features and predict the likelihood of drug consumption based on the eye gaze data. the findings of this research highlight the significant relationship between eye gaze movement and illicit drug consumption among young individuals. the developed predictive model shows promising results in identifying high-risk individuals and providing a valuable tool for early intervention and prevention efforts. VGG19 outperforms ResNet 50 and AlexNet with highest accuracy of 0.96. It demonstrates the highest overall performance among the three models for the given task. © 2024 IEEE.

关键词： Contrastive learning

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Choosing Solutions for Current Problems in Operational Logistics Featuring Self-learning Systems 9

Choosing Solutions for Current Problems in Operational Logis...

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9th international conference on Automation, Robotics and applications (ICARA)

作者： Philipp, Wuddi Johannes, Fottner Tech Univ Munich Chair Mat Handling Mat Flow Logist Munich Germany

ISBN: (纸本)9781665489218

For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. this paper discusses in detail, how such a system can prioritize solutions to answer disturbances. Several ideas are part of the discussion on how to identify the most suitable or promising solution to a deviation. Furthermore, these different approaches converge to a single holistic approach via a step between. the rules, individual and holistic, also feature a mathematical expression. Furthermore, future possibilities for enhancement complete the paper.

关键词： self-learning systems control systems operational control logistics control decision-making

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A Bio-Inspired Optimal Short Pulse Generation in 1.2μm Optically Pumped Micro Disk Lasers for Medical applications 4

A Bio-Inspired Optimal Short Pulse Generation in 1.2μm Opti...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Ashok, P. Rimmya, C. Madhan, M. Ganesh Maharashtra Pune India College of Engineering Anna University Department of Electronics and Communication Engineering Tamilnadu Chennai India Madras Institute of Technology Anna University Department of Electronics Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350387490

Micro Disk Lasers (MDLs) pumped by 637nm external laser source and emitting light at 1.2112μ m, is investigated for short optical pulse generation. the need for a compact, portable, low-cost and effective pulsed laser source at 1.2112μ m for biomedical and clinical applications is the motivation behind this work. the device is pumped with optical pulses with square, haversine, trapezoidal and tangential hyperbolic shapes, under gain switching condition. the characteristics of the resultant short optical pulses at 1.2112μ m are analyzed. Chameleon Swarm optimization (CSO), a technique among the family of bio-inspired algorithms, is employed to deduce the optimal characteristics of the input optical pulse that can generate output pulses with maximum power, yet minimal pulse width. From the numerical analysis, it is predicted that trapezoidal input pulses produce optical pulses with peak power of O.032m W at a pulse width of 15.75ps, while tangential hyperbolic input pulse produce optical pulses with minimal pulse width of 8.3ps having a peak power of 0.0197mW. © 2024 IEEE.

关键词： Optical pumping

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Machine learning Approach for Detecting Money Laundering Transactions 4

Machine Learning Approach for Detecting Money Laundering Tra...

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Museba, Tinofirei College of Business and Economics University of Johannesburg Department of Applied Information System South Africa

ISBN: (纸本)9798350387988

Money laundering is a widespread criminal activity that presents difficult detection and prevention concerns. Machine learning algorithms have grown into effective tools in the past few decades to help in the detection of money laundering activities. the purpose of this paper is to apply ensemble methods, XGBoost and Linear Regression, to the detection of money-laundering-related suspicious financial activities. To increase overall forecast accuracy, ensemble methods integrate the predictions of various models. A complex gradient-boosting technique called XGBoost has demonstrated promising results in several applications, including the detection of money laundering. Further research is necessary to determine its precise advantages over other ensemble techniques in money laundering detection. With the use of feature engineering and model interpretability, traditional algorithms like linear regression have also been employed to find instances of money laundering. they may, however, be less efficient in detecting complicated patterns and nonlinear correlations associated with sophisticated money laundering operations. this paper intends to fill in these gaps by comparing the performance and effectiveness of XGBoost and Linear Regression in detecting suspicious financial transactions. To train the models, a Synthetic Financial dataset of classified suspicious and non-suspicious transactions will be compiled. Following that, the models will be tested using a variety of performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. the findings of this study will shed light on the advantages and disadvantages of ensemble approaches and traditional algorithms in the identification of money laundering. this study fosters an improved understanding of the best ways for detecting abnormal financial transactions, thereby supporting financial institutions and law enforcement authorities in their efforts to successfully combat money laundering. © 2024 IEEE.

关键词： Adaptive boosting

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Improving Energy Efficiency and Sustainability through Smart Energy Management 4

Improving Energy Efficiency and Sustainability through Smart...

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

作者： Shali, A. Vishal, N. Dhanush, S. Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798331508845

this research paper presents a Smart Energy Management System (SEMS) designed to tackle energy consumption challenges in rural areas. the project's main objective is to create an innovative solution that not only monitors energy use but also offers actionable insights for optimizing it [1]. the methodology combines hardware components like Arduino microcontrollers, ESP8266 Wi-Fi modules, and current sensors with software algorithms and machine learning techniques [3]. these are integrated into an energy monitoring system deployed in rural households [4]. the system collects energy consumption data, transmits it via Wi-Fi [2], and analyzes it in real-time, enabling users to make informed decisions and reduce energy consumption [5]. Key findings from the implementation include a noticeable reduction in energy usage [6], identification of energy-intensive appliances and peak demand periods [7], and the feasibility of integrating renewable energy sources like solar panels [8]. the implications are significant, as this system empowers residents to lower energy bills, reduce grid strain, and promote environmental sustainability [9]. It also has the potential for broader global applications in addressing energy challenges in remote regions [10]. this research contributes to the field of smart energy management, offering insights for policymakers, utility providers, and researchers working towards United Nations Sustainable Development Goal 7: Affordable and Clean Energy [11]. © 2024 IEEE.

关键词： Automation Cloud Computing Data Analysis Demand Response Energy Conservation Energy Consumption Patterns Energy Efficiency Energy optimization Energy Usage Visualization Environmental Sustainability HVAC Control IoT (Internet of things) Predictive Analytics Real-time Monitoring Remote Control Renewable Energy Sensor Networks Smart Building Technology Smart Grid Smart Meters

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A Specific Emitter Identification Method Based on Self-Supervised Representation learning 4

A Specific Emitter Identification Method Based on Self-Super...

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

作者： Shao, Mingyuan Deng, Pengfei Li, Dingzhao Lin, Rongbin Sun, Haixin Shenzhen Research Institute of Xiamen University Shenzhen China Xiamen University Xiamen China

ISBN: (纸本)9798350359589

Specific emitter identification techniques excel in discerning between various devices through their unique radio frequency fingerprints (RFF), thereby enhancing the efficiency of communication among devices. However, in non-cooperative communication environments, the labeled emitter signal is often scarce or even unavailable. We design an effective self-supervised learning (Self-SL) approach based on contrastive learning for SEI to address the extreme scenario with no labeled samples. Specifically, we employ data augmentation in conjunction with deep neural networks featuring contrast loss to extract generic RF fingerprint features from unlabeled data, enabling the discrimination of various devices. Experimental results demonstrate that the acquired generic features can attain 91% recognition accuracy using just a simple linear classifier. © 2024 IEEE.

关键词： contrastive learning self-supervised learning specific emitter identification Transformer

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Using optimization algorithms to Improve the Classification Of Diseases On Rice Leaf Of EfficientNet-B4 Model 23

Using Optimization Algorithms to Improve The Classification ...

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8th international conference on Intelligent Information Technology (ICIIT)

作者： Quynh, Anh Nguyen Quoc, Khang Nguyen Ngoc, Hoang Tran Quach, Luyl-Da FPT Univ Can Tho Vietnam

ISBN: (纸本)9781450399616

Rice is an important food source, so increasing rice yields is essential to disease management. Much related research has performed classification and disease detection on rice leaf using machine learning models. However, this study aims to synthesize data to evaluate rice leaf diseases through collected data and contribute new data sets. this data set uses optimization algorithms (RMSprop and Adam) combined with the EfficientNet-B4 model with learning rates of 0.01 and 0.001. the research showed that the optimal algorithm combined with the EfficientNet-B4 model gave high results of 93% (F1-Score) and an accuracy of 89%. the research results show the influence of optimal parameters on the models and find the most optimal parameter results.

关键词： EfficientNet-B4 transfer learning rice diseases rice leaf optimal algorithm

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Development of Enhance-Net Deep learning Approach for Performance Boosting on Medical Images 5th

Development of Enhance-Net Deep Learning Approach for Perfor...

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5th international conference on Data Science, Machine learning and applications

作者： Manoharan, Geetha Solanke, D. R. Acharjee, Purnendu Bikash Nayak, Chinmaya Kumar Sharma, Mukesh Kumar Sahu, Dillip Narayan SR Univ Sch Business Warangal Telangana India SGB Amravati Univ Dept Appl Elect Amravati 444602 Maharashtra India CHRIST Univ Pune Maharashtra India Sri Sri Univ Fac Emerging Technol Cuttack Odisha India Rungta Coll Pharmaceut Sci & Res Bhilai 490024 Chhattisgarh India Gangadhar Meher Univ Dept MCA Sambalpur Odisha India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. the A rapidly expanding area of study is the Deep learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. the musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.

关键词： Deep learning Medical image Enhance- net Dataset

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