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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391-400 订阅

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A Data-Driven Machine learning Approach for Human Gender Classification Using Explainable AI 4

A Data-Driven Machine Learning Approach for Human Gender Cla...

引用

4th international conference on Computer, Communication, Control and Information Technology, C3IT 2024

作者： Dipto, Shakib Mahmud Habib, Adria Binte Mim, Nadia Tasnim Eunus, Salman Ibne Orchi, Nabiha Tasnim University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350389128

Since gender recognition contains a wealth of information about the differences between male and female characteristics, it is crucial and essential for many applications in commercial domains, such as human-computer interaction applications and computer-aided physiological or psychological analysis. there are lots of scientific studies that contain different approaches to classify gender. Apart from those, we have included Explainable Artificial Intelligence to interpret the usability of the model. In this paper, we study a gender classification dataset to predict gender from tabular data of specific human body features such as hair length, forehead length, height, nose width, and length. We have used several Machine learning (ML) algorithms such as - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Stochastic Gradient Descent (SGD) to predict gender. After that, we performed a comparison study and implemented Lime on the best performing ML model to interpret the prediction of the model. the Gaussian Naive Bayes architecture with the dataset shows a reasonable accuracy of 98%, proving the usability of the model. © 2024 IEEE.

关键词： Contrastive learning

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Application of Evolutionary Computational algorithms to Estimate Responses in Milling Al 7075 4th

Application of Evolutionary Computational Algorithms to Esti...

引用

4th international conference on Advanced Engineering optimization through Intelligent Techniques

作者： Kulkarni, Rushikesh D. Mali, Rahul A. Gupta, T. V. K. Visvesvaraya Natl Inst Technol Dept Mech Engn Nagpur 440010 Maharashtra India Pimpri Chinchwad Coll Engn Dept Mech Engn Pune 411044 Maharashtra India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

the study is on investigating the machinability aspects of Al7075, a high-strength and lightweight alloy commonly used in automobiles, aircraft structures, defense equipment, etc. the work presented includes cutting forces, specific power, surface roughness, and tool wear while milling Al7075 alloy. A central composite design in response surface methodology is used to determine the optimum parameters, i.e., cutting speed, feed rate, and depth of cut with above responses. the prediction models are developed followed by ANOVA analysis to understand the role of input parameters on responses. these models are in good agreement with experimental results, followed by parametric optimization using desirability approach. Simultaneously, the evolutionary algorithms like real-coded genetic algorithm, teaching-learning-based optimization, and JAYA are employed for selecting the machining parameters and corresponding responses. the optimized results show that JAYA and TLBO algorithms are better to effectively minimize the responses in milling Al-7075.

关键词： Al7075 RSM ANOVA CCD Multi-object optimization RCGA TLBO JAYA

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Robust Statistical Models for Identifying Inauthentic Online Reviews 4

Robust Statistical Models for Identifying Inauthentic Online...

引用

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

作者： Swapna, D. Shanmuga Sundari, M. Khadhyothi Sreeja, G. Sriya Varma, S. Rajeshwari, M. Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (纸本)9798331515720

Review authenticity is a key factor in determining trustworthiness in the internet marketplace. However, the threat of fake reviews remains constant, endangering both fair competition and customer confidence. the rise of fake reviews undermines the credibility of these platforms and poses a significant challenge to users seeking genuine feedback. In response, this study proposes a machine learning (ML) approach for detecting fake *** research begins by collecting a dataset comprising both genuine and fake reviews from various online platforms. Features are extracted from the textual content of these reviews, including linguistic characteristics, sentiment analysis, and meta-data such as review length and rating distribution. Several ML models are employed for classification, such as Naive Bayes, Random Forest, and Support Vector Machines. these algorithms are assessed using measures like accuracy, precision, recall, and F1-score after being trained on the dataset. In addition, methods like hyperparameter tuning and cross-validation are used to improve the performance of ***, the findings suggest that ML-based approaches hold promise for mitigating the problem of fake reviews, thereby enhancing the reliability of online review platforms and empowering consumers to make informed decisions. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Evaluation of Irregular Shape Strip-Packing algorithms 5th

Comparative Evaluation of Irregular Shape Strip-Packing Algo...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Giovenali, Niccolo Bruno, Giulia Chiabert, Paolo Politecn Torino Turin Italy

ISBN: (纸本)9783031807596;9783031807602

the nesting problem of 2D shapes, which has impactful application in the cutting and packing fields, has been studied for many years. Previous papers are mainly focused on proposing new algorithms and prove their efficiency in terms of packing density or computation time. However, the results are reported only on few datasets and the comparison is done only with respect to few competing algorithms. the aim of the paper is to analyse and compare the results obtained by strip-packing algorithms published in the last 20 years. the results show that the effectiveness of the algorithms varies widely across different datasets, and there is a lack of comprehensive benchmarking that considers both the quality of solution and the computational time required to achieve it. Furthermore, since no algorithm clearly outperforms all the others, further methods to address the nesting problem with reinforcement learning and neural networks could be investigated to improve the generalization ability on the nesting problem.

关键词： Irregular strip-packing 2D nesting Meta-heuristics Combinatorial optimization Packing Cutting reinforcement learning Artificial intelligence

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Comparing Collaborative Filtering algorithms in AI-Powered Recommendation Systems for E-Commerce 4

Comparing Collaborative Filtering Algorithms in AI-Powered R...

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

作者： Aamer, Najmuddin Mittal, Tanusha Muthusamy, Nantha Kumar Battula, R. S. S. Raju Ajith, P.S. Soudagar, ManzooreElahi M. Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Maharashtra Dehradun410003 India OntrailInc Dba Uprise San FranciscoCA94104 United States Aditya University Department of Computer Applications Andhra Pradesh Surampalem533432 India Brindavan College of Engineering Department of Mathematics Karnataka Bengaluru560063 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

the quick growth of e-commerce has brought attention to how important efficient recommendation systems are to enhancing user experience and accomplishing business objectives. this paper investigates the effectiveness of collaborative filtering algorithms in AI-powered recommendation systems, with a focus on user-based collaborative filtering (UBCF). A comprehensive method is employed to guarantee consistency across different rating scales, beginning with data normalisation to standardise user-item interaction matrices. To decrease dimensionality and maintain the most significant patterns of user-item interaction, Principal Component Analysis (PCA) is employed for feature selection. the classification system uses similarity measures and UBCF to predict user preferences based on historical data. the algorithms are evaluated on key metrics like precision, recall, and RMSE using publically available datasets. the outcomes demonstrate the impact of preprocessing and dimensionality reduction on UBCF's performance as well as its efficacy in providing customised recommendations. this research contributes to the improvement of collaborative filtering algorithms for e-commerce systems, which will raise customer satisfaction and enable scalability in dynamic online markets. © 2025 IEEE.

关键词： Collaborative filtering

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AUTOSGM: A UNIFIED LOWPASS REGULARIZATION FRAMEWORK FOR ACCELERATED learning 49

AUTOSGM: A UNIFIED LOWPASS REGULARIZATION FRAMEWORK FOR ACCE...

引用

49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Somefun, Oluwasegun A. Lee, Stefan Mathews, V. John Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

ISBN: (纸本)9798350344868;9798350344851

this paper unifies commonly used accelerated stochastic gradient methods (Polyak's Heavy Ball, Nesterov's Accelerated Gradient and Adaptive Moment Estimation (Adam)) as specific cases of a general lowpass regularized learning framework, the Automatic Stochastic Gradient Method (AutoSGM). For AutoSGM, we derive an optimal iteration-dependent learning rate function and realize an approximation. Adam is also an approximation of this optimal approach that replaces the iteration-dependent learning-rate with a constant. Empirical results on deep neural networks comparing the learning behavior of AutoSGM equipped with this iteration-dependent learning-rate algorithm demonstrate fast learning behavior, robustness to the initial choice of the learning rate, and can tune an initial constant learningrate in applications where a good constant learning rate approximation is unknown.

关键词： stochastic gradient method accelerated learning learning algorithms optimization deep learning.

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Design of Multi-Intelligent Body AGV Path Planning Algorithm under Cooperative Stochastic Game Based on Reinforcement learning Improvement 4

Design of Multi-Intelligent Body AGV Path Planning Algorithm...

引用

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

作者： Wang, Jinyu Southwest Jiaotong University SWJTU-Leeds Joint School Chengdu China

ISBN: (纸本)9798350361643

the advent of Automated Guided Vehicles (AGVs) in industrial automation and intelligent logistics has underscored the importance of efficient path planning to augment production efficacy and reduce operational costs. Traditional path planning techniques, while effective in static or less complex environments, often fail to address the dynamic and uncertain conditions present in facilities utilizing multiple AGVs. this paper investigates the application of Multi-Agent Reinforcement learning (MARL) as a novel approach to overcome these challenges, offering a promising avenue for optimizing multi-AGV path planning. By treating AGVs as agents that learn optimal strategies through environmental interactions, MARL facilitates a nuanced consideration of both individual actions and the interplay between multiple agents. this study synthesizes recent advancements in MARL applications for AGV path planning, highlighting significant contributions, such as the development of enhanced algorithms for various operational scenarios and the introduction of the RFM-AGV algorithm. the latter focuses on crucial aspects of MARL design - convergence, learning speed, and scalability - demonstrating its effectiveness in cooperative tasks through theoretical analysis and comparative experiments. this work not only showcases the potential of MARL in revolutionizing AGV path planning but also sets the stage for future research directions in the logistics and manufacturing sectors. © 2024 IEEE.

关键词： Reinforcement learning

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A Hypothesis-based Dictionary learning Model 4

A Hypothesis-based Dictionary Learning Model

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

作者： Meng, Linghang Gu, Yuantao Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350359589

Recent research in machine learning and signal processing has focused on modeling data with linear combinations of elements from learned dictionaries. We propose a hypothesis-based framework for classification tasks that classifies signals according to their structured dictionary representations. the framework considers that signal representations display different structures depending on their label information, and it makes hypotheses to analyze how likely a signal belongs to a particular class. the likelihood of belonging to a class is revealed by the patterns of representations on the dictionary, which allows for distinguishing signals from different categories. Additionally, our proposed framework is computationally more efficient than traditional sparse dictionaries. We validate the efficiency and performance of our framework on standard datasets used for evaluating dictionary learning models. © 2024 IEEE.

关键词： Deep Dictionary learning Dictionary learning

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Performance optimization in Agro-Vision by Assessing the Impact of Data Normalization and Standardization on Deep learning Models 4th

Performance Optimization in Agro-Vision by Assessing the Imp...

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4th international conference on Digital Technologies and applications (ICDTA)

作者： Zarrouk, Yassine Bourhaleb, Mohammed Rahmoune, Mohammed Hachami, Khalid Hamdaoui, Hajar Al Kaddouri, Hanae Mohammed First Univ ENSAO LaRSA Oujda Morocco Mohammed First Univ LAPABE FSO Oujda Morocco Mohammed First Univ ENSAO SmartICT Oujda Morocco

ISBN: (纸本)9783031686740;9783031686757

Potato late blight is one of the commonserious diseases, caused by Phytophthora infestans, with major risks for agriculture production and food supply. this study addresses this challenge by critically evaluating the effect of normalization, image-wise standardization, and dataset-wise standardization preprocessing techniques on a YOLOv8m model designed for blight detection. the reported results of the normalization show it remains robust for generalization, especially in the case of unseen data with an mAP50 of 99.4%. At the same time, imagewise standardization still is an acceptable alternative with an mAP50 of 73.3%. Dataset-wise standardization is reported to showlesser efficacy in newdata scenarios resulting in 21.7% of mAP50. the YOLOv8m has a compact and streamlined architecture that projects preprocessing to be a core factor in disease detection, paving the way for further advances in precision agriculture.

关键词： Deep learning Crop disease detection Image preprocessing techniques Performance optimization

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Empowering Computing: AI and CPU Synergy 4

Empowering Computing: AI and CPU Synergy

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kaushik, Priyanka Chamarthi, Subbarao Wasim, Javed Khatoon, Naghma Kumar, B Rajesh Tyagi, Nancy Chandigarh University Dept. of Cse Chandigarh India Lingayas Vidyapeeth Dept. of Mechanical Engineering Faridabad India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Computing & It Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

the objective of this research is to redefine the future of computing systems, focusing on enhancing performance, efficiency, and adaptability by identifying the cutting-edge innovations driven by AI in CPU design. this study conducts a comprehensive literature review and empirical evaluations to survey front-end optimizations, new architectural concepts, and emerging paradigms that could potentially revolutionize CPU technology. By leveraging advanced artificial intelligence techniques such as machine learning, deep learning, and reinforcement learning, novel CPU architectures and optimization strategies can be explored, unlocking unprecedented computational capabilities. the integration of AI methods into CPU architecture addresses key concerns including scalability, energy efficiency, security, and reliability. Moreover, real- world case studies across various domains such as autonomous systems, healthcare informatics, and edge computing demonstrate the transformative potential of AI- driven CPUs. this paper provides insights into the roadmap for the next wave of innovation in AI-infused computing, outlining potential avenues for further research and collaboration. © 2024 IEEE.

关键词： Deep reinforcement learning

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