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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是121-130 订阅

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Automated Hyperparameter Optimization in machine learning for Stock Prediction 2

Automated Hyperparameter Optimization in Machine Learning fo...

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2nd international conference on Next Generation Intelligent Systems, ICNGIS 2022

作者： Bishwakarma, Sudip Tiwari Sharma, Gajendra School of Computing and Digital Media London Metropolitan University London United Kingdom Kathmandu University Department of Computer Science and Engineering Kathmandu Nepal

ISBN: (纸本)9781665467926

Stock prediction is the key area of focus in financial analysis. The growing amount of data and readily available machine learning algorithms has surged the amount of research in this field. This research in particular, involved in stock prediction of NEPSE using machine learning algorithms such as Linear Regression and LSTM. The research also studied traditional financial models such as ARIMA and GARCH. The analysis involved in manual and automated hyperparameter optimization via Optuna framework for single and stacked LSTM models. Initially, traditional financial models performed better than manually optimized LSTM variants. But the automated hyperparameter tuning approach significantly lowered the error loss and the single LSTM model best predicted the stock price with 7.21 RMSE score. https://***/SudipBishwakarma/MSc-Dissertation-2021. © 2022 IEEE.

关键词： Forecasting

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Inner Speech Classification using EEG Signals: A Deep learning Approach

Inner Speech Classification using EEG Signals: A Deep Learni...

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2nd IEEE international conference on Human-machine Systems (ICHMS)

作者： Van den Berg, Bram Van Donkelaar, Sander Alimardani, Maryam Tilburg Univ Dept Cognit Sci & AI Tilburg Netherlands

ISBN: (纸本)9781665401708

Brain computer interfaces (BCIs) provide a direct communication pathway between humans and computers. There are three major BCI paradigms that are commonly employed: motor-imagery (MI), event-related potential (ERP), and steadystate visually evoked potential (SSVEP). In our study, we sought to expand this by focusing on "Inner Speech" paradigm using EEG signals. Inner Speech refers to the internalized process of imagining one's own " voice". Using a 2D Convolutional Neural Network (CNN) based on the EEGNet architecture, we classified the EEG signals from eight subjects when they internally thought about four different words. Our results showed an average accuracy of 29.7% for word recognition, which is slightly above chance. We discuss the limitations and provide suggestions for future research.

关键词： Brain computer Interface (BCI) EEG Inner Speech Classification Deep learning Convolutional Neural Network (CNN)

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A smart android application with machine learning extension to operate computer and IoT devices 2

A smart android application with machine learning extension ...

引用

2nd international conference for Emerging Technology, INCET 2021

作者： Marakana, Meet Parmar, Amit Shah, Parth Chandubhai S Patel Institute of Technology CHARUSAT Smt. Kundanben Dinsha Patel Department of Information Technology Gujarat Changa India

ISBN: (纸本)9781728170299

In the era of smartphones and the Internet of Things (IoT) we can possibly do everything on our handheld devices. There are so many applications of Android, IOS, and more that can interact and operate nearly to all types of electronic devices. There are unique types of applications, which can operate a computer's software/functionalities through mobile phones only. In this paper, we have highlighted other available applications and introduced and proposed a new application, which interacts and operates a computer through voice commands and has many other functionalities related to IoT. As compared to other existing applications, it uses AO∗ machine learning algorithms which can reduce the delay of communicating the actions to any computer hardware, that is why it works efficiently as compare to its peer applications, and also has the functionality of controlling the performance embedded devices and the hardware resources of a computer system. © 2021 IEEE.

关键词： Smartphones

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Enhancing Brain Tumor Detection Classification by Computational Intelligence 2

Enhancing Brain Tumor Detection Classification by Computatio...

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2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Islam, Shaziya Udhayakumar, R. Kar, Saikat Pavitha, N. Aswal, Upendra Singh Saini, Dilip Kumar Jang Bahadur Rungta College of Engineering and Technology Bhilai India VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of English India Currently Enrolled with the University of Edinburgh Neuroscience Department United Kingdom Vishwakarma University Department of Computer Engineering Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Sawmi Rama Himalayan University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350346640

The need for early detection and treatment makes brain tumour possibly the most critical illness for which radiologists and neuro subject-matter professionals must be skilled. In recent years, computational intelligence (CI) and machine learning (ML) methods have evolved into effective smart instruments for the early detection of brain tumors. computer-aided diagnosis (CAD) methods primarily rely on various CI approaches in all phases of brain image analysis to build a smart consulting platform that can support radiologists in the detection and identification of brain tumors by offering a second option. Detection and classification of a brain tumour require time and are mainly reliant on the radiologist's skills and expertise. As the patient population has expanded, so has the quantity of data that should be processed, making previous techniques both expensive and inefficient. Deep learning (DL) methods have lately gained popularity in the field of CI for creating automated methods that can quickly and effectively detect and classify brain tumors. This study suggested a CNN-based brain cancer classification model (BCM-CNN) since DL makes it possible to use a pre-trained CNN framework for medical images, particularly for classifying brain tumors. After hyperparameter adjustment, an Inception-ResnetV2 training framework is developed. To enhance brain tumor detection, the approach uses widely employed pre-trained methods (Inception-ResnetV2), and its outcome is a binary 0 or 1 (Normal-0, Tumor-1). The empirical outcomes demonstrate that the BCM-CNN classifier outperformed the existing methods since the performance of CNN is enhanced through the CNN optimization's hyperparameters. Using the Task 1 dataset of BRaTS 2021, the BCM-CNN obtained 99.98% accuracy. © 2023 IEEE.

关键词： Deep learning

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machine learning and Statistics: A Study for assessing innovative Demand Forecasting Models 2

Machine Learning and Statistics: A Study for assessing innov...

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2nd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Moroff, Nikolas Ulrich Kurt, Ersin Kamphues, Josef Fraunhofer Inst Mat Flow & Logist Joseph von Fraunhofer Str 2-4 D-44227 Dortmund Germany

Besides increasing dynamics in market demands, companies strive to avoid short-term changes in their supply chain planning. Therefore, an essential lever to improve supply chain performance is the optimization of the demand forecast. In this regard, artificial intelligence is a widely adopted technique in Industry 4.0 that is associated with high expectations. Against this background, six different forecasting models from statistics and machine learning were evaluated in respect to forecast quality and effort for implementation. The results underline the potential of innovative forecasting models as well as the necessity for an intensive and application-specific evaluation of the advantages and disadvantages of the available approaches. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Demand Forecast machine learning Statistical Methods Deep learning

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A multi-view stable feature extraction network for SAR target recognition 2

A multi-view stable feature extraction network for SAR targe...

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2nd international conference on Image Processing, computer Vision and machine learning, ICICML 2023

作者： Xiao, Ziyi Zhang, Gong Dai, Qijun Wang, Zijun Nanjing University of Aeronautics and Astronautics College of Electronic and InformatioN Engineering Nanjing China

ISBN: (纸本)9798350331417

Since the viewing angle sensitivity of Synthetic Aperture Radar (SAR) image, targets' shape changes dramatically in different viewing angle, the accuracy of SAR target recognition base on deep learning in single view is often affected by this difference between various viewing angle of SAR. So the multi-view approaches have been proposed to solve this limitation by learning the targets' relationships between images from different viewings. In this paper, a multi-view stable feature extraction network (MV-SFN) is proposed which constructs stable feature that doesn't change with viewing angle by using multi-view images. It uses Convolutional Neural Networks (CNN) to gain image feature, then take Gate Recurrent Unit (GRU) to analyze varying regularity of series viewing features. At last a novel features fusion module was designed for fusing different viewing features according to their similarity, and finally going to classify. MSTAR database is used to validate MV-SFN method. © 2023 IEEE.

关键词： deep learning feature fusion multi-view SAR image viewing angle sensitivity

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LogE-Net: Logic Evolution Network for Temporal Knowledge Graph Forecasting 32nd

LogE-Net: Logic Evolution Network for Temporal Knowledge Gra...

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32nd international conference on Artificial Neural Networks (ICANN)

作者： Liu, Yuxuan Mo, Yijun Chen, Zhengyu Liu, Huiyu Huazhong Univ Sci & Technol Wuhan Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9783031442155;9783031442162

In recent years, research on predicting future events using temporal knowledge graphs by leveraging their rich structural and historical information has just begun. Due to their interesting application potential, they have gained more attention from researchers. Existing methods mainly focus on using evolution representation learning to aggregate and propagate recent graph structural information or integrate historical memory to infer the future. However, simply considering the historical repeated facts during reasoning is insufficient. Therefore, we propose a novel model called LogE-Net (Logic Evolution network) that simultaneously considers short-term facts and logical rules. Additionally, we propose a mutual-information control mechanism based on contrastive learning to control the freedom of temporal evolution. Finally, experiments on two public datasets demonstrate that our model consistently outperforms the state-of-the-art baseline on entity prediction and relation prediction tasks.

关键词： Knowledge graph Logic rule Evolution representation learning

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Face Detection for Real World application 2

Face Detection for Real World Application

引用

2nd international conference on Intelligent Engineering and Management, ICIEM 2021

作者： Brar, Dilpreet Singh Kumar, Amit Pallavi Mittal, Usha Rana, Pooja Lovely Professional University Dept. of Computer Science Engineering Punjab India

ISBN: (纸本)9781665414500

Face Detection has become a very prevalent issue in machine learning, not only in machine learning but in any field, one can think of. Due to this, it has gained a wide fan base and many people are working every day to improve the accuracy of object detection models using deep learning. But this improved performance comes at the price of increased computational overhead, which limits the ability of a machine learning model to be utilized on devices having small Graphical Processing Units. The core intent of this paper is to compare computation time for models such as Histogram of Oriented gradients (0.4 seconds) and ResNet (48.5 seconds) with BlazeFace (0.09 seconds), a model developed by google in the year 2020 and is a mobile device friendly model and fits well with real time application which need instant feedback and on top of that cannot handle bulky computations required for deep learning models. © 2021 IEEE.

关键词： Object detection

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Deaf Helper Mobile application for Interaction of Hearing Disorders Communities 2

Deaf Helper Mobile Application for Interaction of Hearing Di...

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2nd international conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Setyawan, Adrian Naphan, Gilbert Xervaxius Dynata, Kevin Friry, Jovano Edmund Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

People with hearing loss in this world have not received much serious attention from the authorities. This makes these sufferers confused in choosing learning media to interact with and isolated from their social environment. This application was created to help people with hearing loss to be noticed and understood from the way they communicate using sign language through the mobile application called Assistant for the Deaf, which has many features such as registration, interactive videos, sign language translator, forums, customer service, library, information, history, events, donations, and shops. The application is designed using use case diagrams and class diagrams modeling the database, and the implementation used Android Studio and MySQL database. © 2022 IEEE.

关键词： Audition

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Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

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2nd international conference on Image Processing, computer Vision and machine learning, ICICML 2023

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 IEEE.

关键词： deep learning Image pattern recognition MobileNetV3-small Wheat imperfect grains

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