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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是21-30 订阅

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An Overview of Various Approaches for Automated Skin Diseases Prognosis 7

An Overview of Various Approaches for Automated Skin Disease...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Supe, Meghana Bendre, Varsha PCCOE Dept. Electronics & Telecommunication Pune India

ISBN: (纸本)9798350304268

the majority of diseases in the world are skin-related. the complexity of the skin's structure makes treating skin problems a difficult task for the global health community. Because of this, early detection of this disease is crucial. Using diverse techniques, a lot of work has been done in the past, to diagnose the diseases. Early skin disease identification is aided by the development of a system that uses image processing, deep learning, and machine learning. the survey offers a thorough analysis of various approaches for skin disease predictions. © 2023 IEEE.

关键词： classifiers deep learning image processing machine learning neural network skin lesion

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Taxonomy of machine learning Techniques in Test Case Generation 7

Taxonomy of Machine Learning Techniques in Test Case Generat...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Singh, Ajmer DCRUST Murthal CSE Department Sonipat India

ISBN: (纸本)9798350397253

machine learning empowers computer systems to automatically learn and improve their performance without being explicitly programmed for each specific task. It has revolutionized many industries and continues to drive advancements in artificial intelligence, enabling computers to perform complex tasks with greater efficiency and accuracy machine learning algorithms use training data to learn patterns and make predictions or decisions about new data. It involves a range of techniques such as supervised learning, unsupervised learning, reinforcement learning, deep learning, and others. machine learning (ML) can be applied to test case generation in several ways: Like Prioritization, Test case reduction, Test case minimization etc. machine learning techniques can be beneficial in test case generation in several ways. this article reviews the research work on application of machine learning algorithms in test case generation. © 2023 IEEE.

关键词： software testing

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Comparative Analysis of Statistical Optimizers for Logistic Regression 7

Comparative Analysis of Statistical Optimizers for Logistic ...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Gaikwad, Madhava Doke, Ashwini Microsoft Bengaluru India CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350397253

Logistic regression is one of the method of statistics and closely related to machine learning. Optimization technique plays very important role while building any machine learning model. this research compares different optimization techniques which can be used for logistic regression;further it talks about which optimization technique can be the best optimization technique among the selected techniques to perform logistic regression based on prediction accuracy and loss obtained during experimental analysis. the obtained results demonstrate that RMSProp works well in practice and compares favorably to other optimization methods. © 2023 IEEE.

关键词： machine learning

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Early Fire Detection and Segmentation Using Frame Differencing and Deep learning Algorithms with an Indoor Dataset 24

Early Fire Detection and Segmentation Using Frame Differenci...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Tomas, John Paul Q. Dava, Jean Isaiah Espejo, Tia Julienne Medina, Hanna Katherine M. Doma, Bonifacio T. Mapua University Philippines

ISBN: (纸本)9798400716546

Deep learning models, such as YOLOv5, well-known for object detection, and U-Net, used for segmentation, are known for their respective capabilities within computer vision tasks. In this study, the researchers introduced a novel framework that uses YOLOv5 and U-Net models, in combination with frame differencing techniques, to achieve early fire detection in an indoor setting. YOLOv5 was trained on a diverse dataset consisting of fire, smoke, and non-fire scenarios, while U-Net was exclusively trained on fire data. Motion detection was then implemented using frame differencing that allowed to identify fire movements effectively. the developed framework achieved an overall accuracy of 88%, outperforming the standalone YOLOv5 model with its 81% accuracy. this improvement of 7% in detection performance was influenced by the incorporation of fire motion analysis which effectively reduced false positive results. In summary, the study presents a robust framework that significantly improves fire detection in indoor environments with the help of motion analysis alongside the used deep learning models. © 2024 ACM.

关键词： Object detection

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Joint Action Representation and Prioritized Experience Replay for Reinforcement learning in Large Discrete Action Spaces 23

Joint Action Representation and Prioritized Experience Repla...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Wei, Xueyu Xue, Wei Zhao, Wei Shen, Yuanxia Yu, Gaohang School of Computer Science and Technology Anhui University of Technology Maanshan243032 China Department of Mathematics School of Science Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450398633

In dealing with the large discrete action spaces, a joint action representation and prioritized experience replay method is proposed in this paper, which consists of three modules. In the first module, we use the k-nearest neighbor method to reduce the dimensionality of the original action space, generating a compact action space, and then the critic network is introduced to further evaluate and filter this compact space to obtain the optimal action. Note that the optimal action may have inconsistency with the actual desired action. then in the second module, we introduce a multi-step update technique to reduce the training variance when storing data in the replay buffer. In the third module, considering the existence of correlation between samples when sampling data, we assign the corresponding weight to the sample experience by calculating the absolute value of temporal difference error and use such a non-uniform sampling method to prioritize the samples for sampling. Experimental results on four benchmark environments demonstrate the effectiveness and efficiency of the proposed method in dealing with the large discrete action spaces. © 2023 ACM.

关键词： Reinforcement learning

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Decision Model of Ship Intelligent Collision Avoidance Based on Automatic Information System Data and Generic Adversary Imitation learning-Deep Deterministic Policy Gradient 23

Decision Model of Ship Intelligent Collision Avoidance Based...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Liu, Jiao Shi, Guoyou Zhu, Kaige Shi, Jiahui Wang, Yuchuang Navigation College Dalian Maritime University Liaoning Dalian116026 China Key Laboratory of Navigation Safety Guarantee of Liaoning Province China

ISBN: (纸本)9781450398633

Aiming at the problems that the current decision-making model of ship collision avoidance does not consider international Regulations for Preventing Collisions at Sea (COLREGS), ship maneuverability, and the need for a lot of training time, combined with the advantages of reinforcement learning and imitation learning, a ship intelligent collision avoidance decision-making model based on Generic Adversary Imitation learning (GAIL) is proposed: Firstly, the collision avoidance data in Automatic Information System (AIS) data is extracted as expert data;Secondly, in the generator part, the environment model is established based on Mathematical Model Group (MMG) and S-57 chart rendering, and the state space, behaviour space and reward function of reinforcement learning are constructed. the deep deterministic policy gradient (DDPG) is used to interact with the environment model to generate ship trajectory data. At the same time, the generator can constantly learn expert data;Finally, a discriminator can distinguish the expert data from the data generated by the generator is constructed and trained. the model training is completed when the discriminator cannot distinguish the two. In order to verify the performance of the model, AIS data near the South China Sea is used to process and extract collision avoidance decision data, and a ship intelligent collision avoidance decision model based on GAIL is established. After the model converges, the final generated data is compared with the expert data. the experimental results verify that the model proposed in this paper can reproduce the expert collision avoidance trajectory and is a practical decision model of ship collision avoidance. © 2023 ACM.

关键词： Collision avoidance

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Hybrid ML Algorithms for learning Disability Forecast in School Going Children Using Python in machine learning Techniques 7

Hybrid ML Algorithms for Learning Disability Forecast in Sch...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Margaret Mary, T. Prakash, V.S. Divya, K.S. George, Amalorpavam Kristu Jayanti College Karnataka Bengaluru India Affiliation Bangalore North University India Sambhram Academy of Management Studies Karnataka Bengaluru India

ISBN: (纸本)9798350340600

A learning disability is a neurological illness that impairs a child's ability to read, speak, and do a variety of other skills. the World Health Organization (WHO) estimates that learning disabilities impact 15% of youngsters [14]. the most important challenge for researchers to perform in order to identify learning disabilities early on is efficient prediction and accurate categorization. Our primary goal in this effort is to use soft computing to create a model for the prediction and categorization of learning disabilities. this study proposes a hybrid approach for enabling classification in order to enhance the performance of prediction and classification. this method incorporated classification's primary five techniques. Random Forest, Logistic Regression, Stochastic Gradient Descent, and K-Fold cross validation. In order to implement the system used python. Results analysis reveals the predict of learning disability in effectively © 2023 IEEE.

关键词： Python

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Predicting Students' Academic Success using machine learning Algorithms 7

Predicting Students' Academic Success using Machine Learning...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Nema, Tanmay Palwe, Sushila Mit World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350304268

this research attempts to predict the academic success of a student using machine learning algorithms and a thorough strategy that takes into account a variety of variables that affect students' results. the study examines a large dataset that contains information on the socioeconomic situation, involvement, and academic success of the students. To create predictive models that is able to find out the student who is at an academic risk of quitting and offer timely interventions to enhance their academic performance and prevent dropout, machine learning methods like Classifier based on Random Forest, Classifier Based on Decision Trees, Classifier using Logistic Regression, Voting based Classifier, and others will be used. To enhance students' achievement and raise the standard of education, policy decisions and resource allocation can be made using the knowledge gathered from the analysis. © 2023 IEEE.

关键词： Academic Success machine learning Random Forest Voting Classifier

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DDoS Attack Detection on Cloud computing Services using Algorithms of machine learning: Survey 7

DDoS Attack Detection on Cloud Computing Services using Algo...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sathvika, Chilla Satwika, Vuyyuru Sruthi, Yarrapothu Geethika, Maddali Bulla, Suneetha Swathi, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9781665464086

Nowadays cloud computing services have become the most popular internet-based computing and many organizations use their services. Due to this, many cyberattacks are happening in the cloud. One of those attacks is the Distributed-Denial-Of-Service (DDoS) attack. It floods unreal traffic, hence troubles the availability of the resources. this article is about DDoS attacks and detection of DDoS attacks using machine learning. there are many famous machine learning algorithms such as naïve bayes, random forest, support vector machines etc. these machine learning algorithms can be used to detect the DDoS attacks on cloud. there are several datasets available for the researchers to test their proposed models which include NSL-KDD, ICDX, CIDDS-001, CICIDS 2017 etc. this paper presents a detailed study on different machine learning based techniques proposed by various authors to detect the DDoS attack in the cloud environment. A brief explanation has been provided on the available datasets and further discussed about the general methodology. © 2023 IEEE.

关键词： Denial-of-service attack

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machine learning on Spectral Data from Miniature Devices for Food Quality Analysis - A Case Study 23

Machine Learning on Spectral Data from Miniature Devices for...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Asharindavida, Fayas Nibouche, Omar Uhomoibhi, James Liu, Jun Wang, Hui School of Computing Ulster University Northern Ireland United Kingdom School of Engineering Ulster University United Kingdom School of Electronics Electrical Engineering and Computer Science Queen's University of Belfast Northern Ireland United Kingdom

ISBN: (纸本)9781450398633

Food quality analysis can be carried out by spectral data acquired from spectrometers with its advantage of non-destructive way of testing. Portable and miniature spectroscopy can be a suitable solution when it meets the specifications such as portability, cost, and short processing time requirements, to enable ordinary citizens to use such a device in the fight against food fraud. Compared to more expensive, bulky, and non-portable devices, the data collected using miniature and portable spectrometers is of a lower quality and thus adversely affect the quality of the analysis. Research have been carried out to use machine learning (ML) classifiers on spectral data analysis for food quality assessment. the present work focuses on two aspects: firstly, preliminary exploratory statistical analysis is conducted on the real spectral data on different food products including oils, fruits and spices acquired from such miniature devices, which aims to evaluate and illustrate the distinctive characteristics of such spectral data, data distribution and difference in the spectra across multiple data acquisitions etc. along with a summary of the key challenges to face and explore. Secondly, a case study for the differentiation of extra virgin olive from adulterated with vegetable oil is provided to analyze and evaluate how some commonly used ML classifiers can be used for classification, while the impact of different preprocessing methods to improve the accuracy and efficiency is also provided. the case study demonstrates the good potential of using data analytics for spectral data from miniature device, although the overall performance of those ML classifiers is not exceptional (the classification rates of up to 83.32%) which is partially due to the quality of data, and partially due to limiting to only some classifiers. More elaborate data pre-processing and cleaning methods can be used to address the key challenges of the spectral data from miniature device, and other types

关键词： Data Analytics

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