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共 24666 条记录，以下是291-300 订阅

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Enhancing the Performance of Machine Learning Classification models

Enhancing the Performance of Machine Learning Classification...

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international Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Cihat Talat Akpinar Özcan Koşar Akif Durdu Computer Engineering Research and Development Elfatek Elektronik Ltd. Co. Konya Turkey Department of Electrical and Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Turkey

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study aims to compare and improve the performance of four different machine learning algorithms (Naive Bayes, Multi-Layer Perceptron (MLP), Decision Trees, and Support Vector Machines (SVM)) in classification problems. The analyses emphasize the impact of data preprocessing steps and hyperparameter optimization on model performance. As part of the data preprocessing, missing values were imputed, categorical data were transformed into numerical data, and normalization procedures were applied. It was observed that normalization significantly enhanced the performance of the MLP and SVM algorithms in particular. Furthermore, additional improvements in accuracy rates were achieved through hyperparameter optimization. Naive Bayes and Decision Trees were found to exhibit stable performance regardless of data scaling. This study demonstrates that proper data preprocessing and model selection can significantly enhance algorithm performance in classification problems.

关键词： Support vector machines Accuracy Machine learning algorithms Data preprocessing Hyperparameter optimization Data models Bayes methods Classification algorithms Numerical models Decision trees

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Reactive Navigation of the Ackermann Steering Robot in Unknown Environments

Reactive Navigation of the Ackermann Steering Robot in Unkno...

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international conference on Autonomous Robots and Agents, ICARA

作者： ANA ŠELEK IVAN HRABAR SAŠA ŠTEKOVIĆ Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Autegra D.O.O. Zagreb Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Mobile robots are now widely used in numerous real-world applications that have complex navigation requirements, especially in environments used by humans. This requires highly accurate navigation that can be performed in realtime. In this paper, a method for generating a smooth motion of nonholonomic mobile robots is proposed. It enables robots to move optimally toward the desired goal and allows fast path replanning when encountering unknown or dynamic obstacles. The method generates smooth, collision-free trajectories based on clothoids, ensuring high computational efficiency and suitability for realtime path planning. By applying a smoothing algorithm, the proposed method improves the robot's efficiency in terms of travel time and trajectory length from start to goal, as demonstrated by a comparison with model predictive control.

关键词： Smoothing methods Navigation Dynamics Real-time systems Trajectory Computational efficiency Mobile robots Collision avoidance Testing Predictive control

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All YIN No YANG: Geometric Abstraction of Oil Paintings with Trained models, Noise and Self-reference 14th

All YIN No YANG: Geometric Abstraction of Oil Paintings with...

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14th international conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2025, held as part of EvoStar 2025

作者： Arandas, Luís Ionescu, Iulia Khan, Murad Grierson, Mick Carvalhais, Miguel University of the Arts London Creative Computing Institute London United Kingdom i2ADS Faculty of Fine Arts University of Porto Porto Portugal

ISBN: (纸本)9783031901669

The rapid development of Diffusion models and the declarative nature of interfaces developed for the public require automation methods, where media production can harness natural language as a mode of representation but not necessarily of interaction with humans. This article describes an image-to-video Diffusion system which removes practitioners from the process of defining prompts when producing images with conditional reference, documenting a set of results with a custom dataset of oil paintings. Our research focuses on the appropriation of trained model ensembles that are coordinated to produce indefinite sets of frames with occasional human intervention utilising timeline-based architectures. The proposed system automates a CLIP-guided DDPM with a supplementary depth estimation model and through a set of compositing techniques we found that results with coincidental and diverging descriptions can be useful for moving-image element composition. Our experiments focus on the representation of human figure and its morphological transformation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Depth-mask compositing Language-guided automation Oil painting diffusion Predictive abstraction Timeline-based architectures

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Mechatronics Design and Simulation of a novel Hybrid Ground/Aerial robot for Infrastructure Inspection

Mechatronics Design and Simulation of a novel Hybrid Ground/...

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international conference on Autonomous Robots and Agents, ICARA

作者： Mutaz Ryalat Mohammad Ahasan Hisham Elmoaqet Natheer Almtireen Mechatronics Engineering Dep. German Jordanian University Amman Jordan

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Robots with hybrid or multimodal mobility in the land, air, and water environments have rapidly developed in recent years. In domains like rescue, exploration, inspection, and environmental monitoring, where multi-domain missions are frequent, there is growing interest in the search for more versatile and adaptable robots. The purpose of the paper is to design and simulate a hybrid land/air robot that has high maneuverability and efficient use of battery power. The robot is capable of both terrestrial and aerial locomotion, which increases its flexibility, utility, and robustness. The hybrid mobility mode improves the robot's versatility and mobility, drawing inspiration from species such as birds and insects. This conference paper significantly advances the field by providing an indepth examination of the design and simulation of a hybrid drone capable of seamless land and aerial operation. The innovative simulation framework established in this work serves as a strong foundation for analyzing the performance of both unmanned aerial and ground vehicles. Furthermore, it deepens our understanding of the ROS framework and demonstrates its integration with simulators such as Gazebo, including the incorporation of a detailed SolidWorks model into the Gazebo environment.

关键词： Solid modeling Mechatronics Service robots Inspection Mathematical models Land vehicles Robustness Software tools Robots Drones

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A Multitask Genetic Programming Approach with ANew Individual Representation to Symbolic Regression

A Multitask Genetic Programming Approach with ANew Individua...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Jing Liang Wenjing Li Yahui Jia Ying Bi School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China School of Future Technology South China University of Technology Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Symbolic regression is a task aimed at auto-matically discovering and constructing mathematical models from data that describe the complex relationships between inputs and outputs. Evolutionary multitask methods have demonstrated powerful capabilities across various domains, but research in the field of symbolic regression remains relatively limited. To further explore the potential of evo-lutionary multi task algorithms in symbolic regression, this paper proposes a multitask Genetic Programming (GP) approach with a new individual representation (IRMTGP), which encodes shared knowledge as a shared tree, explicitly facilitating knowledge transfer between tasks. Specifically, in IRMTGP, each task's solution is composed of a shared tree and an unshared tree, where the shared tree captures common knowledge across tasks, and the unshared tree focuses on learning the characteristics of individual tasks. Furthermore, to learn the optimal shared and unshared trees, a new fitness function and evolutionary process are designed. Comparing IRMTGP with GP and Multifactorial GP on six multi task problems composed of four symbolic regression benchmarks, IRMTGP achieves smaller regression errors and better generalization capabilities in most cases.

关键词： automation Heuristic algorithms Genetic programming Benchmark testing Multitasking Mathematical models Artificial intelligence Regression tree analysis Knowledge transfer

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth models Software Reliability Prediction Swarm Intelligence

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Predictive Modeling of Eye Cancer Using Deep Learning Techniques: An Analysis

Predictive Modeling of Eye Cancer Using Deep Learning Techni...

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automation and Computation (AUTOCOM), international conference on

作者： Simran Raj Nipun Setia Geet Sahu Hitesh Kalra Richa Tiwari Kalyan Acharjya Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

one such application is the Detection and Diagnosis of Eye cancer disease with the help of Deep learning techniques in Medical Imaging. Overview: Eye cancer ocular melanoma is a rare cancer that grows within the cells inside your eye. Detecting and diagnosing AD early on is key for successful treatment and patient outcomes. Consequently, there is a rise in the interest of utilizing deep learning methods to develop predictive models for early eye cancer detection and diagnosis. In this paper, we have conducted a comprehensive survey on eye cancer prediction using deep learning tools to understand the current state-of-the-art predictive modeling. We start by studying the related works on applying deep learning in medical imaging, specifically for eye cancer detection. We then delve into the varying deep learning architectures and methodologies incredibly successful in this quest, including convolutional neural networks CNNs or recurrent neural networks RNNs. We also discuss these models' performant data sources and preprocessing methods. We then evaluate the models based on accuracy, sensitivity, specificity, and other measures. We present here the strengths and weaknesses of each approach and recommendations for future work in this area.

关键词： Deep learning Accuracy Sensitivity Recurrent neural networks Predictive models Cancer detection Data models Convolutional neural networks Cancer Biomedical imaging

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Hybrid Quantum Model for Digital Media Processing for Metal Surface Defect

Hybrid Quantum Model for Digital Media Processing for Metal ...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Joy Saha Shreyansh Jain Roopa M Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Processing Digital media such images is a crucial step for any image classification task such as surface defect detection or any deep learning task which include Digital Media Processing. Ensuring the detail capturing of each and every feature of the input image from each and every pixel is must in order to build a robust system with minimal error or no error. While it may not be possible and causes the struggle for classical computing methods to efficiently capture and process complex patterns and subtle details. Utilising quantum computing for digital image processing gives an advantage over classical methods as it operates on high dimensional space and leverages quantum parallelism to explore numerous possibilities simultaneously. This enable the extraction of more complex features that might be otherwise missed improving the model’s capability to flag defects with greater accuracy and efficiency. Quantum computing’s potential to enhance feature extraction can significantly boost the performance of classification models, leading to dependable and precise surface defect detection.

关键词： Deep learning Analytical models Quantum computing Computational modeling Digital images Media Parallel processing Feature extraction Surface treatment Defect detection

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Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively

Deep Reinforcement Learning for Adaptive Job Shop Scheduling...

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Mechatronics and robotics Engineering (ICMRE), international conference on

作者： Eddi Miller Anna-Maria Schmitt Tobias Kaupp Andreas Schiffler Jan Schmitt Institute of Digital Engineering Technical University of Applied Sciences Würzburg-Schweinfurt Schweinfurt Germany Center for Robotics Technical University of Applied Sciences Würzburg-Schweinfurt Schweinfurt Germany

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

In contemporary manufacturing, efficient operation of robotic cells is critical and Job Shop Scheduling (JSS) faces challenges such as machine failures and shifting priorities. Deep Reinforcement Learning (DRL) addresses these challenges by enabling adaptive scheduling. DRL empowers robotic cells to dynamically optimize schedules which can increase resilience drastically. Through simulations and real-world use, DRL-based approaches outperform traditional methods, improving adaptability and efficiency. Integrating DRL into scheduling processes enables better navigation of uncertainties, enhancing productivity and competitiveness in modern manufacturing. This contribution focuses on these problems and provides a DRL approach for robotic cells to dynamically schedule production orders when facing random tool breakdowns. Using the e-greedy policy, it allows the agent to explore the environment and choose different actions to generate rewards. Apart from reacting to random machine failures, the provided model enables collaborative robotic processes to further shorten the makespan and increase the utilization of industrial robots paired with machine tools.

关键词： Productivity Schedules Adaptation models Job shop scheduling Uncertainty Service robots Deep reinforcement learning Manufacturing Robots Resilience

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Predicting Flood Incidences in Kerala Using Machine Learning models

Predicting Flood Incidences in Kerala Using Machine Learning...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Shivappriya S N Indrani C Sriram K Sneha Nagarajan Srima E S Yazhini S ECE Department Kumaraguru College of Technology Coimbatore Tamil Nadu India Maths Department Bannari Amman Institute of Technology Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

One of the most important and lethal natural disasters of this century is flooding. The deficiency of a comprehensive flood forecasting system has resulted in significant losses of life and property. This has highlighted the necessity of having a reliable flood prediction system. This paper examines the development of the most efficient flood determining model, which combines the use of Machine Learning technique with a robust, efficient and accurate flood expectation framework to provide essential aid and assistance to the local population and government. We have used K-Nearest Neighbors (KNN) classifier, Logistic Regression, Ensemble Learning methods, Decision Trees, and Random Forest in order to determine flood. SAR is also used to map flood affected areas. To this end, Logistics Regression consistently provided the most accurate (87.5 % accuracy) flood detection outcomes. The framework helps the public authority set aside funds for salvage works and start moving chores before the flood causes a disturbance in town.

关键词： Logistic regression Accuracy Machine learning algorithms Disasters Predictive models Nearest neighbor methods Prediction algorithms Floods Ensemble learning Random forests

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