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

共 13501 条记录，以下是341-350 订阅

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Deep Reinforcement learning Indoor Autonomous Drone: Navigation in Unknown Environment Based on Relative Position Decision 4

Deep Reinforcement Learning Indoor Autonomous Drone: Navigat...

引用

4th international conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Phueakthong, Phuwanat Varagul, Jittima Pinrath, Nattawat School of Manufacturing Engineering Suranaree University of Technology Nakhon Ratchasima Thailand School of Industrial Engineering Suranaree University of technology Nakhon Ratchasima Thailand

ISBN: (纸本)9798331520038

this paper presents a new approach for indoor autonomous drone navigation using a deep reinforcement learning algorithm, specifically the Deep Deterministic Policy Gradient algorithm with position-based action (relative position) decisionmaking. Real-world tests with a quadrotor drone demonstrate the effectiveness of this method in practical applications. the unmanned aerial vehicle can safely navigate from a starting point to a destination without prior map knowledge, achieving smooth movements through a position control loop. the results confirm the suitability of this approach for real-world deployment. Compared to traditional map-based path planning algorithms like the A-star algorithm, the paths generated by our method show similar trends. Using relative position as an action in our approach allows the model to adapt to different robot platforms without retraining it when the kinematic model changes. © 2024 IEEE.

关键词： Deep reinforcement learning

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Kidney Disease Prediction using ML techniques 4

Kidney Disease Prediction using ML techniques

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Saha, Bishnu Pada Dash, Satya Ranjan Rout, Nikunj Kishore Patnaik, Aswini Prasad Mishra, Manoj Ranjan Kiit University School of Computer Engineering Bhubaneswar India Kiit University School of Computer Applications Bhubaneswar India Kalinga Institute of Medical Sciences Department of Nephrology Bhubaneswar India

ISBN: (纸本)9798350349856

Over the last two years there has been a wide progress in detecting causes of diabetic nephropathy, understanding its complications and preventing it. So far, techniques such as SVM, Decision Tree, KNN and ANN trained predictive models were used by researchers to monitor the progression of DN using T1- and T2-Weighted Imaging. We have discussed the importance of various Machine learning, Deep learning and artificial intelligence techniques used in curing diabetic nephropathy. Further a prospective of precise diagnosis of diabetic kidney disease is discussed to throw light on integrated bio-informatics analysis and machine learning algorithms. We also compare and analyze the research findings and solutions along with their challenges for a summarized view of the topic. In this research work, we have discussed about the Kidney diseases and the ML techniques wherever applicable. © 2024 IEEE.

关键词： Decision trees

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Optimal Gradient Descent Algorithm for LSTM Neural Network learning 4

Optimal Gradient Descent Algorithm for LSTM Neural Network L...

引用

4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Hodlevskyi, Yurii O. Vakaliuk, Tetiana A. Department of Software Engineering Zhytomyr Polytechnic State University Zhytomyr Ukraine Web and Mobile Solutions department Infopulse Ukraine Zhytomyr Ukraine Institute for Digitalization of Education of the NAES of Ukraine Kyiv Ukraine Kryvyi Rih State Pedagogical University Kryvyi Rih Ukraine

ISBN: (纸本)9798350374865

this research paper delves into an in-depth comparison of different gradient descent methods tailored for optimising long short-term memory (LSTM) recurrent neural networks (RNNs) using data generated from sinusoids with varying noise levels. Gradient descent is an optimisation technique extensively employed in machine learning, deep learning, and diverse scientific domains and serves as the focal point of investigation. this study's primary objective is to thoroughly comprehend the distinctive attributes, merits, and limitations of various gradient descent methods, thereby elucidating their effects on convergence speed, stability, and computational efficiency within the context of LSTM RNNs. through meticulous analysis and experimentation, this research contributes insights essential for informed decision-making in selecting and deploying gradient descent algorithms for LSTM RNN optimisation, thereby advancing the efficacy and reliability of recurrent neural network applications in real-world scenarios. © 2024 IEEE.

关键词： Mean square error

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Designing, Optimizing, and Manufacturing of Horizontal Wind Turbine Blades Using the Available Resources 4

Designing, Optimizing, and Manufacturing of Horizontal Wind ...

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Al-Wazer, Yhya Abdullah Al-Sharif, Gamil Abdullah Gen Corp Elect Ind & Renewable Energy Dept Machen Engn Sanaa Yemen

ISBN: (纸本)9798350354140;9798350354133

the research aims to enhance wind turbine blade performance by utilizing available technologies and considering constraints such as limited resources. the focus is on developing blade designs that optimize material use, manufacturing techniques, and performance while reducing costs. Alternative and cost-effective materials are considered for blade manufacturing. Analytical models and computational simulations are deployed to validate the initial design and analyze multiple parameters. Simulations and mold production are conducted before manufacturing the blades. Assembly, installation, and testing processes evaluate blade performance.

关键词： Aerodynamics Blade design Blade manufacturing Blade optimization Performance improvement Simulation Wind turbine blades

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Application of SAM-based prompting algorithm in camouflage object detection 5

Application of SAM-based prompting algorithm in camouflage o...

引用

5th international conference on Computer Engineering and Application (ICCEA)

作者： Yuan, Jiatang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

We propose a prompting algorithm based on SAM and apply it to the field of camouflage object detection. We extract the camouflaged object detection (COD) network features after weakly supervised learning and generate bounding box information and point prompt information to guide the SAM to segment the camouflaged objects in the image. the proposed method can effectively improve the performance of camouflage object detection network and has achieved good results on the mainstream camouflage object detection dataset.

关键词： SAM Camouflage object detection optimization algorithms

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Machine learning in Renewable Energy Systems 4

Machine Learning in Renewable Energy Systems

引用

4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Munir, Muhammad Sohail Shahid, Usama Nasir, Usama Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Fatima, Hoor Yaqub, Muhammad Atif Punjab Institute of Management and Professional Development Lahore Pakistan School of Business Computing and Social Sciences University of Gloucestershire Cheltenham United Kingdom Bahria University Lahore Campus Lahore Pakistan University of Central Punjab Lahore Faculty of Information Technology Lahore Pakistan Bahria University Lahore Campus Department of Computer Science Lahore Pakistan National College of Business Administration and Economics Lahore Pakistan Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350391183

From solar and wind power to hydropower and biomass, smart grids, the catalytic industry, and power storage and distribution, this paper covers the gamut of ML's present-day uses. Among the many domains covered by machine learning are classification issues and the methods used to solve them. Classification schemes have recently attracted a lot of interest as a tool for improving the deployment, administration, and optimization of Renewable Energy Systems. the integration of new data sources (such as innovative sensors) and improved algorithms for creating trustworthy data will improve the data flow between ML and systems. the growth of other important areas of data science, such big data analytics, will determine the significance of unsupervised and reinforcement learning in the energy industry. the development of sustainable uses of ML in non-industrial applications for energy management will be aided by massive implementations, specialized algorithms, and new technologies like 5G. this work aims to survey the state-of-the-Art classification algorithms for Renewable Energy situations, including both traditional and cutting-edge methods. Wind speed/power prediction, fault diagnostics in Renewable Energy Systems, power quality disturbance categorization, and other applications in alternative Renewable Energy Systems are some of the particular Renewable Energy topics covered in the paper's extensive literature analysis and discussion. Researchers and practitioners in the area may benefit from the paper's description of categorization algorithms and metrics used to Renewable Energy situations. © 2024 IEEE.

关键词： Adversarial machine learning

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An Approach to Recognize Efficient Deep learning Model for Pattern Recognition 11

An Approach to Recognize Efficient Deep Learning Model for P...

引用

11th international conference on Reliability, Infocom Technologies and optimization, ICRITO 2024

作者： Kaur, Amandeep Kapoor, Monit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

Pattern recognition using deep learning is one of the earliest and prevailing computer vision tasks and is a frequently used research area in today's world. Deep learning methods are currently being used in numerous applications such as Video and speech recognition, Computer Vision tasks, cybernetics, text mining, financial fraud detection, etc. DL is derived from classical neural networks which improvises them extensively. Recently developed DL algorithms have achieved excellent performance in various applications mentioned above. Despite the successful use of deep learning models in numerous domains, constructing a suitable deep learning model remains a difficult job. this difficulty arises from the dynamic and diverse nature of real-world issues and inputs. this paper emphasizes recent developments in deep learning, types of DL techniques, its applications and further highlights the challenges and findings to ease researchers in identifying research needs in the area of pattern recognition. © 2024 IEEE.

关键词： Character recognition

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Enhancing Kyphosis Disease Prediction: Evaluating Machine learning algorithms Effectiveness 4

Enhancing Kyphosis Disease Prediction: Evaluating Machine Le...

引用

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

作者： Praveen Kumar, K. Dondapati, Ramya Kalavakollu, HemaLatha Vyshnavi Patagarla, Nanditha Sri Sheikh, Tanveer Basha Raju, Peddiboyina T.V.V.K.P.ch. Department of Computer Science and Engineering Tadepalligudem534101 India

ISBN: (纸本)9798350385793

Kyphosis, characterized by the inward arching of the upper back, is often colloquially referred to as 'roundback' or 'hunchback' when a noticeable curvature is present. this condition typically arises due to weakened spinal bones, leading to compression or fractures. In children or adolescents, other forms of kyphosis may be attributed to spinal abnormalities or a progressive twisting of the spinal bones. While kyphosis can manifest at any age, it is most prevalent in teenagers. Multiple factors, ranging from poor posture and developmental issues to structural abnormalities in the spine, can contribute to its occurrence. this research introduces a machine learning approach aimed at predicting kyphosis disease, with a focus on enhancing early detection and improving patient outcomes. this research intends to incorporate different machine-learning approaches to biological data, such as Decision Trees and Random Forests, evaluate and assess the granularity of these algorithms. the findings underscore the significance of machine learning (ML) as a valuable tool for addressing biological problems in a broader context. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Microgrid optimization using Deep Reinforcement learning by utilizing the Energy Storage Systems 4

Smart Microgrid Optimization using Deep Reinforcement Learni...

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4th international conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Ahmed, Ibrahim Pedersen, Andreas Mihet-Popa, Lucian Ostfold University College Engineering Department Fredrikstad Norway

ISBN: (纸本)9798350306262

the need for power is expanding quickly, and as fossil fuels are depleted, alternatives based on renewable energy are emerging. In recent years, microgrids, which are based on distributed generation and storage systems, have been on an increasing trend. this generates various new opportunities, one of which is the capacity to monitor, control, and regulate the energy flow inside and outside of the microgrid. By making microgrid's distributed energy resources (DERs) and energy storage components economically viable with artificial intelligence and machine learning incorporated into the cost-optimization process, the demand for and growth of these technologies will be accelerated significantly. this paper focuses on employing reinforcement learning (RL) algorithms to control energy flow in an AC microgrid. By incorporating artificial intelligence and machine learning into the energy management system (EMS), the paper aims to optimize costs and facilitate the integration of renewable energy sources. the RL agent is designed to trade energy with the main grid, taking advantage of the energy storage system and achieving cost savings. the RL agent is tested using real spot prices data in Norway from a simulation model that combines Python and MATLAB-Simulink software programs for efficient co-simulation technique. Results show significant cost savings of around 14% for a simple model and 7.5% for a complex dynamic model. © 2024 IEEE.

关键词： Reinforcement learning

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Innovations in Education: A Deep Dive into the Application of Artificial Intelligence in Higher learning 4

Innovations in Education: A Deep Dive into the Application o...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Nugraheni, Anjar Sri Ciptorukmi Widono, Salim Saddhono, Kundharu Yamtinah, Sri Nurhasanah, Farida Murwaningsih, Tri Universitas Sebelas Maret Indonesia

ISBN: (纸本)9798350360165

the landscape of education is fast-changing, fueled by technological developments that are promising to tear down traditional methodologies of learning. Among these promising to be transformational forces with huge potential in improving higher learning experiences is Artificial Intelligence (AI). this research paper discusses the innovative applications of artificial intelligence within the higher education field, synthesized for its multifaceted impact on the teaching-learning process and administration. this means that at the heart of the effects of AI in higher education is the power to facilitate tailor-made education conducted in line with individual needs and requirements. these AI-based platforms use cutting-edge algorithms and data analytics. they analyze performance data of students and clearly indicate areas where they could have strengths, weaknesses, or improvement. Educators can then be able to derive insights from this that would help in making the tailoring of content and strategies for instruction with maximal learning outcomes and have students engaged in mastering the material from a higher level. Such AI-driven adaptive learning systems offer students dynamic and interactive study experiences where content delivery is adapted in real-time based on student progression and levels of understanding. the adaptive approach caters to the variance in paces of learning while it enables students to be in control of their learning journey through self-directed attitudes towards learning and critical thinking. © 2024 IEEE.

关键词： Contrastive learning

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