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8th international conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Priyadharsini, M. Sugantha Mallika, S.S. Karuppasamy, K. Snegha, R. Sri Ramakrishna Engineering College Department of Information Technology Tamilnadu Coimbatore India Rvs College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Sri Ramakrishna Engineering College Tamilnadu Coimbatore India

ISBN: (纸本)9781665408165

Due to a variety of factors, pipelines are long and complicated for drug discovery and development. A set of tools are offered by machine learning (ML) approaches for improving discovery and decision-making for well- defined issues with large amount of good data. In this paper, analysis of digital pathology data, target validation and identification of prognostic biomarkers in clinical trials are all possibilities for machine learning. The context and methodology of the applications varied, with some generating precise forecasts and insights. The lack of interpretability and reproducibility of machine learning generated findings, which may limit their applicability, is one of the main obstacles of using machine learning. Systematic and thorough high dimensional data are still required in all fields. machine learning has the ability to speed up the drug discovery and development process and minimize failure rates by promoting data-driven decision making. © 2022 IEEE.

关键词： Decision making

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Technical research on machine learning framework based on optimization algorithm 2

Technical research on machine learning framework based on op...

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2nd international conference on machine learning and Intelligent Systems engineering, MLISE 2022

作者： Li, Yansong Railway Police College Zhengzhou China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In order to overcome the drawbacks of traditional machine learning algorithms and their frameworks, K-means algorithm and random forest classification algorithm are deeply analyzed, and improved AKM and ARF algorithms are proposed, and an AMLF machine learning application framework based on Spark platform technology is established. It can be seen from the verification results that the classification accuracy of the AKM algorithm in each data set is close to 100%, and it has strong data clustering ability. Furthermore, the AKM algorithm has a high acceleration in each data set, so the upgradeability is also relatively high. powerful. The ARF verification results show that it not only has a high classification accuracy, but also has strong upgradeability. © 2022 IEEE.

关键词： K-means clustering

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A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 3437-3472页

作者： Huang, An Chi Meng, Sheng Hui Huang, Tian Jiun Tsinghua Univ Dept Comp Sci Beijing 100190 Peoples R China Yango Univ Artificial Intelligence Coll Fuzhou 350015 Peoples R China Natl Kaohsiung Univ Dept Comp Sci & Informat Engn Kaohsiung 81154 Taiwan

The technology of big data analysis and artificial intelligence deep learning has been actively cross-combined with various fields to increase the effect of its original low single field. Precision components commonly used in electronic products use changes in the conductivity of semiconductors to process information. This study aims to review key milestones and recent developments in the semiconductor industry using artificial intelligence methods. For this systematic review, we searched academic networks between 2015 and 2022, including Nature, Elsevier, Springer, Taylor & Francis Online, Multidisciplinary Digital Publishing Institute, and the Institute of Electrical and Electronics Engineers. The literature reviewed is based on conference proceedings and journal articles, specifically covering the key achievements of the discussion paper, the key technologies used, experimental results, opportunities, and future research pathways. After searching on an academic website, we selected six major studies. In five of these studies, visual object detection, surface defect detection, machine production scheduling application, fault diagnosis and prediction, and monitoring of the manufacturing process were made using artificial neural networks, machine learning methods, and hybrid models. In addition, the studies covered independent, single methods or used more than two types of technologies for performance comparison. Finally, we reviewed the strengths and weaknesses of the literature. We also analysed various datasets, acquisition systems, and experimental scenarios. The review shows that as the number of studies conducted in manufacturing continues to increase, more research is needed to unearth key information that is often overlooked, all of which are challenges in refining science and overcoming real-world scenarios.

关键词： Semiconductor manufacturing Deep learning machine learning Process monitoring and defects diagnosis Product surface defect detection Automated production scheduling

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Enhancing Dynamic Ankle Joint Torque Estimation through Combined data Augmentation Techniques 10

Enhancing Dynamic Ankle Joint Torque Estimation through Comb...

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10th IEEE RAS/EMBS international conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Zhang, Haocheng Kizyte, Asta Wang, Ruoli KTH Dept Engn Mech Royal Inst Technol KTH MoveAbil Stockholm Sweden

ISBN: (纸本)9798350386523;9798350386530

Robotic-powered exoskeletons represent a promising avenue for aiding individuals with movement disorders in their daily activities and rehabilitation efforts. However, achieving precise joint torque estimation, particularly during dynamic movements, remains a significant challenge. While machine learning and deep learning techniques have been explored for estimation, their efficacy has been limited, especially in dynamic scenarios. Our target is to improve ankle joint torque estimation during dynamic movements by employing multiple data augmentation techniques. Augmentation methods did not significantly improve cases involving the same subject or session. However, our experiments reveal substantial performance gains when combining spatial and signal augmentation methods, particularly in scenarios involving different subjects. This indicated that when facing an over-fitting problem caused by a lack of subjects, a combined data augmentation method will be a proper solution to improve the predicting performance.

关键词： Exoskeleton (Robotics)

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Research of the Extreme learning machine as Incremental learning

Research of the Extreme Learning Machine as Incremental Lear...

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2022 international conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2022

作者： Abramova, Elena S. Orlov, Alexey A. Makarov, Kirill V. Vladimir State University Information Technology Faculty Vladimir Russia

ISBN: (纸本)9781665483698

The external environment of the world is dynamically changing, which requires the ability of continuous learning and memorization from intelligent systems. Iterative learning methods neural networks have smooth convergence, so they do not allow achieving such an effect. An exception is the Extreme learning machine (ELM), which allows for incremental learning. In the ELM, the input weights are randomly generated. The weights between hidden and output layers are computed in one step, resulting in a faster learning rate compared to traditional SLFN learning algorithms. Incremental training allows the neural network to retain existing data while at the same time adapting to new data. This article presents the research and assessment of the ELM as incremental learning. In particular, research of the sample size effect for different numbers of neurons in the hidden layer on the ELM performance is presented. © 2022 IEEE.

关键词： Intelligent systems

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Efficient-VGG16: A Novel Ensemble Method for the Classification of COVID-19 X-ray Images in Contrast to machine and Transfer learning 2

Efficient-VGG16: A Novel Ensemble Method for the Classificat...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Kumar, Sunil Kumar, Harish Department of Computer Engineering J. C. Bose University of Science and Technology YMCA Haryana Faridabad121006 India CSJM University UP Kanpur208024 India

Corona Virus Disease 2019 (COVID-19) is a contagious respiratory disease characterized by its high transmissibility and exponential spread, presenting persistent difficulties. This investigation sought to incorporate machine learning (ML) and transfer learning (TL) and introduced Efficient-VGG16, a novel ensemble approach that integrated the EfficientNet-B0 and VGG16 architectures to classify chest X-ray images as either COVID-19 positive or non-COVID. The COVID-Xray-5k dataset used in the research was split into different training and testing sets (with ratios of 80:20, 75:25, and 70:30), preprocessed, and then used to evaluate the approach's effectiveness. Throughout the investigation, all procedures operated effectively at the same selected image size, 224*224. Preprocessing included Gaussian blur to remove noise and relics, while Contrast Limited Adaptive Histogram Equalization (CLAHE) adjusted contrast. Among the employed ML classifiers, XgBoost attained the highest accuracy, with F1 scores of 95.78% and 71.56%, respectively. In addition, the research investigated the effectiveness of TL using pre-trained convolutional neural networks (CNNs) such as VGG16, MobileNetV3, ResNet101, Densenet201, and EfficientNet-B0. EfficientNet-B0 achieved the highest levels of accuracy and F1 score, 99.31% and 93.72%, respectively. Furthermore, the proposed Efficient-VGG16 achieved accuracy and an F1 score of 99.46% and 98.41%, respectively, and outperformed TL with EfficientNet-B0 and XgBoost. The investigation demonstrated the effectiveness of the Efficient-VGG16 for accurately classifying COVID-19. © 2024 Elsevier B.V.. All rights reserved.

关键词： Statistical tests

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Crop Yield Improvement with Weeds, Pest and Disease Detection

Crop Yield Improvement with Weeds, Pest and Disease Detectio...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Meena, S. Divya Susank, Munagala Guttula, Tarini Chandana, Srikurmum Hari Sheela, J. School of Computer Science and Engineering VIT-AP University Amaravathi522237 India

Detecting weeds, diseases, and pests in the agricultural field is a crucial concern for farmers. Weeds compete for light, nutrients, water, space and crop damage is caused by pests such as insects, mice, and birds and crop diseases pose a significant risk to food security. Farmers have relied on traditional methods to boost yield production in the past. The advancement of deep learning approaches helps to classify various types of photos in practice. For plant leaf disease detection, pest detection, and weed detection, we acquired many images of plant disease pairs, pest and weed images from varied crops. The data augmentation process is carried out because Deep learning works effectively with larger data sets. The neural model was created using multiple types of DCNN architecture, and the models were interpreted based on their accuracy and performance. By fine-tuning the hyperparameters and layers of the DenseNet201, Mobilenet, VGG16, Hyperparameter Search and InceptionV3 on agricultural image data. The fine-tuned InceptionV3 model outperformed by 87.85% when comparing the final value. Mobilenet and VGG16, on the other hand, achieved accuracy of 91.85% and 78.71% respectively, DenseNet model performs well with 99.62% accuracy whereas the Hyperparameter Search with 71.07% accuracy. The model's effective performance in weed,plant disease and pest disease classification is described by the produced resultant value. © 2023 The Authors. Published by Elsevier B.V.

关键词： Deep learning

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Using learning Analytics to Improve Students' Enrollments in Higher Education 2

Using Learning Analytics to Improve Students' Enrollments in...

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2nd international conference on Innovative Research in Applied Science, engineering and Technology (IRASET)

作者： Sghir, Nabila Adadi, Amina El Mouden, Zakariyaa Ait Lahmer, Mohammed Moulay Ismail Univ Higher Sch Technol Informat & Commun Syst Engn Meknes Morocco

ISBN: (纸本)9781665422093

In the last years there has been a growing interest in adopting learning analytics (LA) in higher and further education systems. LA assists the institutional stakeholders to enhance the learning process, ameliorate the teaching activities, make adequate decisions and take appropriate actions based on the collection, analysis, and reporting of data generated from individual learners. The learning analytics approach aims to achieve many objectives, one of them is prediction which is the center of this research. In this paper, we conduct a comparative study between three machine learning algorithms;Decision Tree (DT), Random Forest (RF), and Support Vector machine (SVM);to predict the stream of new enrollments in the first year of higher education. As a case study, the predictive model is applied to new enrollments in the first year of the University Diploma of Technology (DUT) at the Higher School of Technology in Meknes, Morocco (ESTM). The performance of the classifiers is tested with and without the use of SMOTE data balancing on a dataset of 53554 students collected between 2016 and 2019. The obtained results show the best algorithm to predict the most accurate enrollments of students.

关键词： learning analytics Prediction machine learning Decision Tree Random Forest Support Vector machine

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Sentiment Analysis using Supervised machine learning 2

Sentiment Analysis using Supervised Machine Learning

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2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Chauhan, Hritik Rathore, Kanishka Singh Rajan, V Gokul School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665437899

The dataset is the IMDB dataset which comprise of the reviews of 50k movies and it is available on the Kaggle, which has 2 columns in it. The two columns in the dataset contains the reviews and the sentiment which will help us to identify the reviews is a positive review, or a negative review about the movie. machine learning is a modern-day concept in which the machine can be trained in the same manner as an infant baby learns the things in an environment, with the use of machine learning certain patterns are used from the data to give the machine a learning, reasoning and predicting ability. The aim is to train and build a machine learning model that will be able to predict whether a movie review provided is a positive or negative review, this is also known as binary text Classification which will be done by using Scikit-learn library, which is a very common machine learning library used in python programming language. © 2022 IEEE.

关键词： Classification (of information)

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A machine learning-Aware data Re-partitioning Framework for Spatial datasets 38

A Machine Learning-Aware Data Re-partitioning Framework for ...

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38th IEEE international conference on data engineering (ICDE)

作者： Chowdhury, Kanchan Meduri, Venkata Vamsikrishna Sarwat, Mohamed Arizona State Univ Tempe AZ 85281 USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Spatial datasets are used extensively to train machine learning (ML) models for applications such as spatial regression, classification, clustering, and deep learning. Most of the real-world spatial datasets are often too large, and many spatial ML algorithms represent the geographical region as a grid consisting of several spatial cells. If the granularity of the grid is too fine, that results in a large number of grid cells leading to long training time and high memory consumption issues during the model training. To alleviate this problem, we propose a machine learning-aware spatial data re-partitioning framework that substantially reduces the granularity of the spatial grid. Our spatial data re-partitioning approach combines fine-grained, adjacent spatial cells from a grid into coarser cells prior to training an ML model. During this re-partitioning phase, we keep the information loss within a user-defined threshold without significantly degrading the accuracy of the ML model. According to the empirical evaluation performed on several real-world datasets, the best results achieved by our spatial re-partitioning framework show that we can reduce the data volume and training time by up to 81%, while keeping the difference in prediction or classification error below 5% as compared to a model that is trained on the original input dataset, for most of the ML applications. Our re-partitioned framework also outperforms the state-of-the-art data reduction baselines by 2% to 20% w.r.t. prediction and classification errors.

关键词： Spatial machine learning Spatial data Training Time Reduction Training data Volume Reduction

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