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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4138 条记录，以下是301-310 订阅

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proceedings of the 4th African Human computer Interaction conference, AfriCHI 2023

Proceedings of the 4th African Human Computer Interaction Co...

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4th African Human computer Interaction conference, AfriCHI 2023

ISBN: (纸本)9798400708879

the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physical workers;an interface design methodology for serving machine learning models;lions out of bounds? reflections on digital technology and matristic design to address human-wildlife conflict;an integration model to enhance information systems administration and data sharing – a case of Namibian lower courts;unmasking trust: examining users’ perspectives of facial recognition systems in Mozambique;and investigating the efficacy of large language models in reflective assessment methods through chain of thoughts prompting.

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Enhancing Dermatology with machine learning: A Transfer learning Perspective 4

Enhancing Dermatology with Machine Learning: A Transfer Lear...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Al Jaziri, Maryam Bonny, Talal Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Detecting and diagnosing dermatological diseases is of paramount importance in ensuring early and accurate medical interventions, thus improving patient outcomes. In this context, our study presents a pioneering approach to derma disease detection utilizing machine learning algorithms. We introduce a novel methodology that leverages Deep learning - Keras, Deep learning - CNN, and Deep learning - Transfer learning algorithms to classify skin conditions. the results demonstrate the efficacy of our approach, achieving accuracy percentages of 56.79%, 55.16%, and 58.15% respectively, across different models. Among these, the Transfer learning algorithm emerges as the most accurate, underscoring its potential for revolutionizing dermatological diagnostics and offering a promising avenue for more precise and efficient disease detection. © the Institution of Engineering & Technology 2023.

关键词： Adversarial machine learning

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Phishing Attacks Detection Over machine learning Algorithms 4

Phishing Attacks Detection Over Machine Learning Algorithms

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4th international conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Alaoui, Safae Sossi Akssase, Brahim Farhaoui, Yousef Moulay Ismail University of Meknes Faculty of Sciences and Techniques Department of Computer Science Errachidia Morocco Moulay Ismail University of Meknes Faculty of Sciences Department of Computer Science Meknes Morocco

ISBN: (纸本)9798350311112

Cybercrimes have risen steadily since the Covid-19 epidemic. Although efforts are being made to detect cyberattacks, more scientific research is still needed as cybercriminals are constantly innovating and becoming more sophisticated. Phishing is still the most common attack and a major threat to both individuals and companies. this paper aims to select the appropriate machine learning algorithm to build an accurate model capable of detecting phishing attacks. After running several algorithms belonging to different ML categories, the obtained results show that Random Forest was the best algorithm with a high accuracy value of 98.36%. © 2023 IEEE.

关键词： COVID-19

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Rethinking on Misleading of machine learning in Bank Risk Warning System 4

Rethinking on Misleading of Machine Learning in Bank Risk Wa...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Xiao, Yan He, Zehao Liu, Yuyang Business School Guilin University of Technology Guilin China

ISBN: (纸本)9798400709449

machine learning is widely used in the bank risk warning system more and more popular. It has been able to provide early internal bank risk warning nowadays. However, its ability of external risk warning is limited and need a long way to go to meet the needs of bank risk warning system. this study shows that external bank risk warning information, especially the subtle information provided by the machine learning would mislead the bank risk warning system. Analyses indicated that machine learning relied heavily on the accuracy of data, typically provided predictive rather than deterministic results, overlooked or underestimated unexpected events during the learning process, and it might provide opposite predictive results when dealing with external subtle information. With the supports of external subtle information filtering criteria and bank simulation training center, machine learning could jointly improve its ability to process external subtle information and reduce the possibilities of misleading in bank risk warning system. © 2023 ACM.

关键词： machine learning

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machine learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis 4

Machine Learning Algorithms for Intrusion Detection Performa...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Irfan, B. Md. Poornima, V. Mohana Kumar, S. Aswal, Upendra Singh Krishnamoorthy, N. Maranan, Ramya Nalsar University of Law Department of Information Technology Telengana Hyderabad500101 India VISTAS Pallavaram Department of Computer Science Chennai India Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560054 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Vellore Institute of Technology Vellore Campus Department of Software Systems and Engineering Tamilnadu Vellore632014 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai600124 India

ISBN: (纸本)9798350300888

the security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. this study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. this research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. the results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems. © 2023 IEEE.

关键词： Decision trees

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Active learning with Data Augmentation Under Small vs Large Dataset Regimes for Semantic-KITTI Dataset 17th

Active Learning with Data Augmentation Under Small vs Large ...

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17th international Joint conference on computer Vision, Imaging and computer Graphics theory and applications (VISIGRAPP) / 17th international conference on computer Vision theory and applications (VISAPP)

作者： Duong, Ngoc Phuong Anh Almin, Alexandre Lemarie, Leo Kiran, B. Ravi Navya Machine Learning Paris France

ISBN: (纸本)9783031457241;9783031457258

Active learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. In this study we evaluate Bayesian active learning methods applied to the task of dataset distillation or core subset selection (subset with near equivalent performance as full dataset). We also study the effect of application of data augmentation (DA) within Bayesian AL based dataset distillation. We perform these experiments on the full Semantic-KITTI dataset. We extend our study over our existing work [14] only on 1/4th of the same dataset. Addition of DA and BALD have a negative impact over the labeling efficiency and thus the capacity to distill datasets. We demonstrate key issues in designing a functional AL framework and finally conclude with a review of challenges in real world active learning.

关键词： Active learning Point clouds Semantic segmentation Data augmentation Label efficiency Dataset distillation

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Agricultural Innovation: Unleashing Federated learning CNNs on Coffee Leaf Disease Severity Analysis 4

Agricultural Innovation: Unleashing Federated Learning CNNs ...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Jindal, Varun Kukreja, Vinay Bhattacherjee, Abhishek Rana, Samir Mehta, Shiva Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306118

this study adopts a novel strategy using federated learning with a Convolutional Neural Network (CNN) to identify and categorize Cucurbit leaf diseases. the research includes information from six customers, each with access to distinctive, local datasets of Cucurbit leaf illnesses divided into four severity categories, ranging from 1% to 25% to 50% to 75% to 100%. the use of federated averaging, which combines local model updates into a global one, is a crucial component of this work. this approach highlights a significant development in machine learning by using dispersed data without data centralization. the findings show that the model performed well, with accuracy, recall, and Fl-scores ranging from 85.93% to 94.85% across all customers, illuminating the model's strength and adaptability. the performance of the client model Cltt-3 was outstanding, with measures averaging a startling 95%. Additionally, findings from macro, micro, and weighted averages were similarly exceptional, demonstrating the model's adaptability to various class distributions and severity levels. In summary, this work shows the impressive effectiveness of federated learning with CNN in combating Cucurbit leaf diseases and sketches out possible application scenarios. the reliable findings confirm the efficacy of federated learning in a decentralized data environment while opening up fresh opportunities for the global model to be improved using lessons from local datasets. © 2023 IEEE.

关键词： Convolutional neural networks

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DRL-based Multi-Hop Relational Reasoning for Personnel Management Knowledge Graph 4

DRL-based Multi-Hop Relational Reasoning for Personnel Manag...

引用

4th international conference on computer Engineering and application, ICCEA 2023

作者： Liu, Shiang Li, Kaijiang Liu, Xiangqian Yu, Kaixuan College of Computer and Artificial Intelligence Zhengzhou University Henan China State Key Laboratory of Shield Machine and Boring Technology Henan China

ISBN: (纸本)9798350347548

In the personnel management knowledge graph, the absence of relations will lead to an incomplete knowledge graph, which affects downstream applications. While existing approaches can handle relational reasoning tasks, many neglect reasoning efficiency and implicit semantic information in multi-hop relational paths. therefore, we proposed a multi-hop relational reasoning model based on deep reinforcement learning to reason out paths with implicit semantic information. We optimize the proximal policy optimization algorithm to implement reasoning. the irrelevant actions masking scheme makes the reasoning more efficient. And the reward shaping make the reasoned multi-hop relational paths semantically interpretable. We construct a formal dataset containing a large number of triples to represent the personnel management knowledge graph. By comparing the experimental results on metrics about MAP, MRR, Hits@k, and frequency of paths with other baselines, our proposed model is more advantageous in the personnel management knowledge graph. In addition, experimental results on the dataset NELL-995 show that our proposed model's performance can compare to the mainstream methods. © 2023 IEEE.

关键词： Reinforcement learning

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machine learning Frameworks for Parking Guidance

Machine Learning Frameworks for Parking Guidance

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2023 IEEE international conference on Communications, Information, Electronic and Energy Systems, CIEES 2023

作者： Neykov, Neyko Stefanova, Svetlana University of Ruse 'Angel Kanchev' Dept. of Computer Science Ruse Bulgaria

ISBN: (纸本)9798350336917

A wide variety of open-source libraries and platforms support the creation of machine learning models that provide ready-to-use implementations of many popular algorithms available under various licenses. Such software frameworks reduce the time and effort of researchers and developers engaged in different projects where there is a need to use specific machine learning techniques. Case-based reasoning (CBR) involved in Parking Guidance Information System (PGIS) uses k-nearest neighbor (kNN) classification as well as several other machine learning methods in the various stages of building the action plan. In this analysis, we explore free toolkit options that can meet the needs of a specific input data system. We also consider additional requirements for efficient indexing and optimization of use cases, aiming to reduce the need for multiple programming languages and platforms and coding algorithms from scratch. © 2023 IEEE.

关键词： machine learning

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machine learning Based Object Detection and Voice Guidance Using Yolo Algorithm 4

Machine Learning Based Object Detection and Voice Guidance U...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sanjay, S. thakur, Mintu Kumar Kushal Gowda, Y. Jayapandian, N. Christ University Department of Cse Bangalore India

ISBN: (纸本)9798331518981

machine learning is used now a days in multidiscipline. Vision based or image-based application is very popular in healthcare and human interaction. the proposed model of this article is focus on visually challenged individuals to navigating the environment and ensure their safety by detecting nearby objects using the machine learning algorithm like YOLOV5 model. Secondary module is focus on alert intimation through speech notification. Clear vision is one of the God's gifts, unfortunately so human is having some issue in their vision, this kind of modern technology will help to solve this issue. this model is mainly help to identify the object at the same time spatial location. computer vision algorithms, particularly those that leverage convolutional neural networks, are subsequently employed to identify and categorize objects within the scene. the spatial location of these objects is determined, potentially using depth sensors or stereo cameras to calculate distance and relative position. this integration of technology not only aids in recognizing objects but also enhances spatial awareness, contributing to safer and more confident navigation for visually impaired individuals. the average precision level of proposed model is 91% and average Recall value is providing 0.88. © 2024 IEEE.

关键词： machine vision

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