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检索条件"任意字段=6th Asia Conference on Machine Learning and Computing, ACMLC 2024"

共 606 条记录，以下是31-40 订阅

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Conventional machine learning Approach for Waste Classification 6

Conventional Machine Learning Approach for Waste Classificat...

引用

6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Jangsamsi, Kharittha King Mongkuts Univ Technol Thonburi Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9798400716225

Waste management is a complex and challenging process, especially waste classification to sort waste by categories. the paper aims to overcome these challenges by proposing a waste classification approach that uses various feature extraction algorithms along with a support vector machine (SVM). the purpose is to identify the most effective feature for building a classification model, even with a low number of samples and high intra-class variance. SVM was used for classification while Fourier descriptors (FDs), histogram of oriented gradients (HOG), and local binary pattern (LBP) were used for feature extraction. the dataset used in this paper was obtained from *** and *** with different types of vision problems. the experimental results showed that classification with LBP feature extraction achieves the highest accuracy. this accuracy is higher than the experiments with other feature extractions.

关键词： waste classification feature extraction Fourier descriptors histogram of oriented gradients local binary pattern

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Weapon Detection System on Android Devices: A Portable machine learning Technology 24

Weapon Detection System on Android Devices: A Portable Machi...

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6th asia Pacific Information Technology conference (APIT)

作者： Castillo, Claire Mariel L. Trapero, Hazel A. Univ Philippines Cebu Cebu Philippines

ISBN: (纸本)9798400716218

the machine learning techniques paired with augmented reality (AR) systems extend the capabilities of surveillance systems for the social good through analyzing and extracting complex patterns in a simple and cost-effective AR-based app running on any mobile device. the potential of this app in crime prevention provides opportunities for this study to investigate the development of a portable, inexpensive surveillance system using an Android device that mainly focuses on detecting deadly weapons to promote public safety, especially in urbanized areas. the detection of deadly weapons beforehand is a proactive act before the crime happens. the study utilized the detection models offered by TensorFlow, an open-source machine-learning platform with a broad and adaptable ecosystem of tools and libraries. After the functional and performance tests, the study found that the AR-based weapon detection system could detect the weapons and recognize and classify them based on the computation of the detection confidence. thus, the cost-efficient and portable system showcased its potential as a proactive surveillance tool that detects and alerts for the weapon's existence. However, there is still a need to improve its detection capability for it to be able to increase its detection rate, especially in crowded areas, at far distances, in dark places, and those obstructed and concealed weapons.

关键词： Artificial and Computational Intelligence Mobile and Ubiquitous Solutions Weapon Detection Social Good machine learning TensorFlow Utilization Augmented Reality Android App

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A Cost-Effective Baugh-Wooley Approximate Multiplier for FPGA-based machine learning computing 6

A Cost-Effective Baugh-Wooley Approximate Multiplier for FPG...

引用

6th International conference on AI Circuits and Systems (AICAS)

作者： Vakili, Shervin Inst Natl Rech Sci Energie Mat Telecommun Res Ctr Montreal PQ Canada

ISBN: (纸本)9798350383638;9798350383645

Deep learning hardware accelerators commonly incorporate a substantial quantity of multiplier units. Yet, the considerable complexity of multiplier circuits renders them a bottleneck, contributing to increased costs and latency. Approximate computing proves to be an effective strategy for mitigating the overhead associated with multipliers. this paper introduces an original approximation technique for signed multiplication on FPGAs. the approach involves a novel segmentation method applied to the Baugh-Wooley multiplication algorithm. Each segment is optimally accommodated within look-up table resources of modern AMD-Xilinx FPGA families. the paper details the design of an INT8 multiplier using the proposed approach, presenting implementation results and accuracy assessments for the inference of benchmark deep learning models. the implementation results reveal significant savings of 53.6% in LUT utilization compared to the standard INT8 Xilinx multiplier. Accuracy measurements conducted on four popular deep learning benchmarks show an average accuracy degradation of 4.8% in post-training deployment and 0.7% after retraining. the source code for this work is available on Github(1).

关键词： approximate multiplier field-programmable gate array machine learning computing

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Telltale Twin - Predicting Latency of Unseen Subflow of Multipath Transmission Control Protocol Using machine learning 6

Telltale Twin - Predicting Latency of Unseen Subflow of Mult...

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6th International conference on Advancements in computing, ICAC 2024

作者： Wijethilake, tharindu Pitigala, Sachintha Weerakoon, Chinthanie Keppetiyagama, Chamath De Zoysa, Kasun University of Colombo School of Computing Colombo Sri Lanka University of Kelaniya Dalugama Sri Lanka

ISBN: (纸本)9798331517878

In Multipath Transmission Control Protocol (MPTCP), the subflows are not totally independent. Due to the correlation between subflows, information related to the unseen subflow can be extracted from the seen (observed) subflow. this opened a new avenue for network monitoring which was not available with traditional Transmission Control Protocol (TCP) connections. In this study, the network condition (latency) of the unseen subflow was predicted using information observed from the seen subflow. Data flow information from the observed subflow was encoded using six different ways and used across five different machine learning techniques to predict the latency class of the unseen sub-flow which expand the perimeter of the network monitoring. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantum machine learning Acceleration with Quantum Control Techniques 6

Quantum Machine Learning Acceleration with Quantum Control T...

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6th Iranian International conference on Microelectronics, IICM 2024

作者： Rashid, Sara Mahmoudi University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331511371

the integration of quantum computing with machine learning represents a significant frontier in computational research. this paper introduces an innovative approach that combines quantum control techniques with quantum machine learning (QML) to achieve notable advancements in performance and efficiency. the proposed methodology introduces a new quantum control framework designed to optimize quantum machine learning algorithms. this approach not only enhances computational speed but also improves the accuracy of quantum-enhanced support vector machines (SVMs) for classification tasks. the innovation lies in the application of advanced quantum control techniques, which are demonstrated to offer superior performance compared to existing methods. Additionally, the paper presents novel strategies for reducing power consumption in quantum computing systems. By incorporating dynamic quantum control, the proposed system achieves significant improvements in power efficiency, addressing one of the critical challenges in practical quantum computing applications. Benchmark tests across various datasets validate the effectiveness of the new method, showing advancements in processing efficiency and accuracy. the integration of machine learning creates new opportunities for advancements in research, potentially setting new standards in the field of quantum computing. these innovations underscore the potential for combining quantum control with machine learning to achieve groundbreaking improvements in computational capabilities and system efficiency, offering promising directions for future research and practical implementations. © 2024 IEEE.

关键词： Quantum computers

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A machine learning Approach for Context-Aware Input/Output Validation in Mobile Applications 6

A Machine Learning Approach for Context-Aware Input/Output V...

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6th International conference on Advancements in computing, ICAC 2024

作者： De Silva, Kushantha thilakarthna, thusithanjana Sri Lanka Institute of Information Technology Faculty of Graduate Studies Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

the vulnerability of mobile applications to security risks due to inadequate input/output validation has been high-lighted. Conventional rule-based methods have found it difficult to adjust to changing attack surfaces and various environments. A unique method for context-aware input/output validation in mobile applications has been suggested, utilizing machine learning. the creation of a machine learning model that can instantly detect abnormal patterns by examining past data, app context, and user input sequences has been investigated. An adaptive validation system has been implemented based on the model's output, dynamically modifying the validation criteria. the efficacy and user experience of the system has been assessed through penetration testing and user research. It is anticipated that the findings will aid in the creation of mobile applications that are both safer and easier to use. © 2024 IEEE.

关键词： Adversarial machine learning

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2024 6th International conference on Advancements in computing, ICAC 2024

2024 6th International Conference on Advancements in Computi...

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6th International conference on Advancements in computing, ICAC 2024

ISBN: (纸本)9798331517878

the proceedings contain 99 papers. the topics discussed include: galaxy morphology classification based on VGG19 deep convolutional neural network;machine learning and browser fingerprinting based approach for web bot detection;deep learning-based recognition system for hand-drawn sketches by children with down syndrome;mining haulage road maintenance prediction dashboard utilizing machine learning techniques;enhancing typing dynamics emotion recognition: a multi-class XGBoost approach for accurate sentiment detection;automated monitoring and analysis of avian behavior and hatching success using YOLOv8: a case study on red-wattled lapwings;and advanced convolutional neural networks for detecting and quantifying infections in coconut tree tissue cultures.

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Hybrid machine learning Techniques for Image Denoising Based on Wavelet Transform 6

Hybrid Machine Learning Techniques for Image Denoising Based...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Yuan, Qikun University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350374315

Wavelet theory has been widely applied in image processing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. this paper introduces a novel image denoising method. It leverages wavelet transform and machine learning algorithms, presenting a synergistic approach that combines discrete wavelet transform coefficients with Random Forests and CNNs. this method significantly enhanced image clarity without increasing computational demand. this article utilized wavelet transform to extract image features, and then trained the model with wavelet coefficients as training data through machine learning methods, integrating Random Forests and Convolutional Neural Networks to analyze the effectiveness of different wavelet coefficients, thereby optimizing the model's denoising performance. It has been proved effective in denoising performance after rigorous validation through quantitative comparisons of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the high-quality stereo image dataset Flickr1024. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring Enterprise Digital Transformation: the Perspective of Explainable machine learning 6

Exploring Enterprise Digital Transformation: The Perspective...

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6th International conference on machine learning, Big Data and Business Intelligence, MLBDBI 2024

作者： Tan, Zhonghao Peking University School of Software and Microelectronics Beijing China

ISBN: (纸本)9798331541798

Digital transformation (DT) significantly enhances enterprise efficiency, innovation, and competitiveness through cutting-edge technologies such as artificial intelligence (AI), cloud computing, and big data. this study employs interpretable machine learning (ML) models to investigate the determinants of DT outcomes. the primary aim is to assess the impact of these technologies on operational efficiency, innovation, and market competitiveness. Using the 2011-2022 CSMAR Enterprise Digital Transformation Database, we performed regression analyses and SHAP value assessments to elucidate the contributions of various factors. Our findings highlight the criticality of early adoption and ongoing advancement of digital processes. AI and cloud computing substantially improve digital outcomes, whereas broader innovation initiatives, though crucial, exhibit more variable effects. theoretically, this study enriches DT frameworks by integrating interpretable ML insights. Practically, it provides actionable strategies for enterprises to achieve superior digital outcomes and maintain a competitive edge. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Review on machine learning Advancements for Plant Disease Detection and Classification 6

A Comprehensive Review on Machine Learning Advancements for ...

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6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Almazrouei, Khawla Bonny, Talal College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Technology Innovation Institute Autonomous Robotics Research Centre Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351200

Plant diseases still exist and spread despite technological advancements in disease prevention. It is still a major concern for humanity as it affects the economy of agriculture-based countries and the food supply chain for countries where food is mostly imported. Using pesticides and insecticides without proper knowledge of disease is also concerning as it will not prevent or cure plant disease and will have adverse environmental effects. It is of utmost importance to recognize plant diseases most effectively, and the recent development in machine learning and vision transformer techniques has made this possible. this review discusses the latest developments in machine learning and vision transformer techniques for detecting and categorizing plant diseases in detail. © 2024 IEEE.

关键词： Adversarial machine learning

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