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Forecasting Directional Movement of Stock Prices using Deep Learning

Annals of Data science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Enhanced and Automatic Defect Detection in Thermography using Stacked Denoising Autoencoder 5

Enhanced and Automatic Defect Detection in Thermography usin...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Vesala, G.T. Ghali, V.S. Lakshmi, A. Vijaya Wang, Fei Yerneni, Naga Prasanthi Malla Reddy University School of Engineering Department of Computer Science Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Vijayawada India Amrita Sai Institute of Science and Technology Department of Electronics and Communication Engineering Paritala India Harbin Institute of Technology School of Mechatronics Engineering Harbin China

ISBN: (纸本)9798350376685

Active infrared thermography (AIRT) with frequency-modulated stimuli has emerged as a feasible and cost-effective non-destructive testing method for checking a variety of materials, with improved fault identification and depth resolution. However, assessing this non-stationary thermal response while reducing noise and inhomogeneous backgrounds is difficult in FMT. Conventional thermographic data analysis techniques use signal or image processing methods and supervised learning models, which are known for linear representation or feature extraction of thermographic data. This paper describes a stacked denoising autoencoder (SDAE) in frequency modulated thermography (FMT) that extracts non-linear information from temporal thermal profiles to improve flaw detection in a steel structure. The SDAE trains on temporal thermal profiles in order to generate denoised profiles at the other end. Furthermore, the latent space in the middle of the SDAE decreases dimensionality while highlighting flaws. Finally, the latent space of SDAE and local outlier factor (LOF) were coupled to form SDAE-LOF, which provided automatic fault detection in unsupervised learning passion. Experimentation results on a mild steel structure show an improvement in defect signature by reducing noise in temporal thermal profiles, and the proposed SDAE-LOF achieved an AUC of 0.81 in automatic defect detection. © 2024 IEEE.

关键词： Thermography (imaging)

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Energy optimization mechanism using metastable-smart grid (MSSG) 4

Energy optimization mechanism using metastable-smart grid (M...

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4th National Conference on communication Systems, NCOCS 2022

作者： Kannan, S. Pugazhendiran, P. Rishi Keshav, N. Arul Deva, A. Suresh Kumar, K. Ananth Kumar, T. Electrical and Electronics Engineering IFET College of Engineering Tamilnadu India Electronics and Communication Engineering IFET College of Engineering Tamilnadu India Computer Science and Engineering IFET College of Engineering Tamilnadu India

The Smart Grid model uses energy from renewable sources and utilities to manage energy in a way that ensures uninterrupted power supply for users. In order to operate independently from the main power grid, a DC micro grid is a small power system that generates and uses its own DC electricity. Solar panels, fuel cells, and wind turbines are the most typical power sources for DC micro grids, with batteries used to store any extra energy. The independence of the power grid is one feature of a DC micro grid that promotes increased lifetime. Building owners have more latitude to pursue their sustainability goals since they have control over the production, delivery, and consumption of power. Super-twisting provides a dependable method and an effective instrument for the control of uncertain nonlinear systems by addressing the basic flaws of conventional sliding mode control, notably large control effort and chattering. Fractional order controllers provide greater design freedom than conventional integer order controllers. The purpose of this work was to create a metastable-smart grid(MSSG) user end model that could dynamically manage and optimize energy produced by renewable energy sources and the utility to ensure that customers always have access to electricity. Controlling these many power sources using MSSG and delivering energy in accordance with each user's unique power plan is the responsibility of the central control unit of the distribution grid. The supply is only switched to solar by the central control unit if a user's power consumption falls below a predetermined threshold. Giving consumers with less purchasing power more clout as a result. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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Design and Analysis of a 4x4 Antenna Array 10

Design and Analysis of a 4x4 Antenna Array

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10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Bansal, Dipali Gupta, Sindhu Hak Kaur, Harleen Amity University Department of Electronics and Communication Engineering Uttar Pradesh Noida India Jamia Hamdard Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350384369

This research article explores the design and analysis of a 4x4 antenna array using MATLAB, a versatile and powerful computational tool widely employed for simulating and optimizing antenna systems. The antenna array is a fundamental component in modern wireless communication systems offering advantages in terms of enhanced gain, directivity and beam forming capabilities. The primary objective of this study is to optimize the performance of the 4x4 antenna array through MATLAB simulations considering crucial parameters such as radiation pattern, directivity and beam forming characteristics. The research leverages MATLAB's Antenna Toolbox to design the array and through a systematic simulation setup investigates the radiation pattern, optimizes directivity and assesses beam forming performance. The findings contribute valuable insights into the design and analysis of antenna arrays underscoring the significance of MATLAB as a robust tool for antenna system optimization. © 2024 IEEE.

关键词： MATLAB

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Improving Driver Safety: A Deep Transfer Learning Framework Optimized with Jaya Algorithm for Fatigue Detection 3

Improving Driver Safety: A Deep Transfer Learning Framework ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Veluchamy, S. Pasupuleti, Harini Saravanan, S. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Chennai India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

Detecting driver drowsiness to prevent car accidents is extremely important, leading to the demand for dependable monitoring systems. Recent studies are concentrating on utilizing cutting-edge technologies, particularly in driver-state monitoring, to address the continuous challenge of identifying effective characteristics for detecting drowsiness in visual data. This research provides a novel method for detecting driver drowsiness based on computer vision. The proposed system initiates by isolating the facial area in input images and extracting deep features using well-established models such as AlexNet, GoogLeNet, and MobileV2Net. To further enhance the system's accuracy, features from these models are optimally combined using the Jaya optimization technique. The experimental findings emphasize the effectiveness of this framework, achieving a noteworthy accuracy of 96.27%, and showcasing its potential to improve driver safety through better drowsiness detection. © 2024 IEEE.

关键词： Transfer learning

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Optimized Cascade and Elman Neural Network for CT Image-Based Brain Stroke Classification 1

Optimized Cascade and Elman Neural Network for CT Image-Base...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Papana, Krishna Reddy Nagakishore Bhavanam, S. Acharya Nagarjuna University Department of Electronics and Communication Engineering Andhra Pradesh Guntur India Department of Computer Science and Engineering Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350365337

The brain is an intricate organ that regulates a variety of bodily activities. Brain stroke happens when the artery that supplies blood to the brain is either blocked or breaks. The main objective is to propose image analysis techniques for identifying the brain stroke lesion. Thus, this provides an efficient solution for detecting and classifying stroke lesions present in CT brain images. This paper developed an Optimized Cascade and Elman Neural Network (OCENN) for the classification of brain stroke from the CT images. Initially, the CT images of brain stroke are gathered from online sources. After that, the noise removal of the image is obtained by using a wiener filter. The brain stroke lesions are segmented by using expectation-maximization (EM). After that, the important features are extracted. Finally, the OCENN is used to classify the brain stroke. To justify the performance of the proposed methodology, it will be implemented and performances will be evaluated. For comparison purposes, it will be compared with the conventional techniques with various parameters of accuracy, precision, recall, sensitivity, specificity, F _Measure, and kappa. The result will show that the proposed methodology will achieve efficient outcomes in system performance and enhance the accuracy of 98.60% in brain stroke detection. © 2024 IEEE.

关键词： computerized tomography

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Unsupervised Fuzzy Clustering-Based Vehicle Detection and Segmentation in Infrared Thermography 5th

Unsupervised Fuzzy Clustering-Based Vehicle Detection and Se...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ganesan, P. Leo Joseph, L.M.I. Sivakumar, V.G. Thulasi Prasad, S. Sathish, B.S. Sajiv, G. Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9789819718405

The identification of vehicles is indispensable task in this fast moving world where attentiveness is most important than ever before. The suggested methodology explains the effectiveness of fuzzy clustering algorithms for unsupervised vehicle recognition and segmentation in infrared thermography. The method takes use of the natural temperature variations that exist between moving objects and their environment, allowing for precise border delineation. Fuzzy clustering has shown to be a flexible method that offers a framework to organize pixels with comparable properties. By taking into account the uncertainty present in thermal imaging, fuzzy clustering presents a viable solution in the context of vehicle recognition and segmentation. Infrared thermography is more effective in challenging scenarios such as darkness, smoke, or dust. It is also well-suited for real-time scenarios due to fast thermal response. The proposed methodology finds applications in transportation management, security systems, industrial monitoring, and various thermal inspections. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy clustering

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Detection and Classification of Human Emotion Using Deep Learning Model

Detection and Classification of Human Emotion Using Deep Lea...

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Betageri, Deepa Yelamali, Vani School of Electronics and Communication Engineering KLE Technological University Hubli India Indian Institute of Information Technology Department of Computer Science & Engineering Dharwad India

ISBN: (纸本)9798331540685

Human emotions are the mental states resulting from neurophysiological changes that are diversely linked to human thoughts, feelings, and behavioral reactions. Facial expressions, eye movement and gestures are several nonverbal communication methods used in many human-machine interactions. Facial emotion recognition has been an essential field because it has many applications that convey the mental state and emotions of each individual. As there is no clear way to distinguish between emotions on a face and the complicated neural system that is connected to the face, recognizing human emotions is a difficult undertaking. In this paper, for students' facial expression recognition, we presented a model called Convolutional Neural Network (CNN). The proposed model consists of six convolutional layers, six max pooling with two fully connected layers. Using a Haar cascade detector, the system identifies the faces of students from input images. It categorizes them into seven different facial expressions, including surprised, fearful, disgusted, sadness, happiness, anger, and neutral. The FER2013 dataset yielded an accuracy rate of 77.22% for the proposed model. The comparative study is carried out with four other deep learning models. The models are Inception V3, VGG-16, VGG-19, Resnet50 and the accuracies achieved are 40%, 37%, 22.50%, 22.09%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Analysis of the Multidirectional Single Channel Speech Quality Improvement Using Dyadic Discrete Wavelet Transform in Comparison with Discrete Wavelet Transform for Improved Accuracy

Analysis of the Multidirectional Single Channel Speech Quali...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Kumar, J. Tharun Sungeetha, D. Padmakala, S. Saveetha School of Engineering SIMATS Department of Electronics and Communication Engineering Saveetha University Chennai India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Saveetha University Chennai India

ISBN: (纸本)9798350358926

Innovative dyadic discrete wavelet transform techniques are used to improve multi-directional single-channel speech accuracy. Materials and Methods: The experiment used a Kaggle dataset, which is open-access. The clinic calc tool was used to calculate with a 0.05 alpha, 0.80 G-power, and 0.2 last beta. Group 1 contains Stft and group 2 contains Dwt, each with 376 samples. Thus, 752 samples were used in this experiment. To measure accuracy, 80% of images are trained, 10% tested, and 10% validated, and performance analysis validates SPSS software's better methodology. Results: Dyadic discrete wavelet transform achieved 65% accuracy in speech quality detection and 55% in improvement, with a significance value of 0.001 (P © 2024 IEEE.

关键词： Discrete wavelet transforms

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Advancing Liver Cancer Diagnosis: Innovative Deep Learning Algorithms for Segmentation and Classification

Advancing Liver Cancer Diagnosis: Innovative Deep Learning A...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Suganthi, B. Vidhya, U. Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798331541170

Liver cancer is a significant worldwide health issue that requires precise diagnosis techniques and efficient treatment options. This paper presents sophisticated deep learning (DL) algorithms for the segmentation and classification of liver cancers, providing a thorough comparative comparison. The advancements in DL have led to significant progress in other domains. As a result, there is a rising excitement for using DL techniques in liver research to aid clinicians in diagnosing and treating medical conditions. The Improved Fully Convolutional Neural Network (IFCNN) has been introduced in this study as a solution to the problem of liver tumor segmentation in cases of liver cancer. The IFCNN has been mathematically designed to address this issue. Transfer learning improves the ability to apply knowledge gained from one patient group to other patient groups with different characteristics. Comparative studies performed against ResNet and VGG16 demonstrate higher sensitivity, specificity, and overall classification metrics. The algorithms' efficacy and real-time processing capabilities have been extensively validated on multiple datasets, including different kinds and stages of liver cancer. This paves the way for eventual incorporation into clinical processes. These advanced algorithms exhibit superior precision and efficiency compared to current models, offering significant potential for improving clinical practice. They provide more dependable tools for precise diagnosis and informed treatment planning in the fight against liver cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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