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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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Role of ERP as an Integrative Intelligent System for Business Enterprises 1st

Role of ERP as an Integrative Intelligent System for Bus...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Yasser, Ibrahim Khalifa, Fahmi Electronics and Communications Engineering Department Mansoura University Mansoura35516 Egypt Department of Electrical and Computer Engineering Morgan State University BaltimoreMD21251 United States

ISBN: (纸本)9783031823763

Enterprise Resource Planning (ERP) systems are now vital for organizations to integrate and manage their core business processes. This paper provides an overview of ERP systems, their functionalities, benefits, and challenges. ERP systems are comprehensive software solutions that enable organizations to streamline and automate various functions, such as finance, human resources, supply chain management, manufacturing, and customer relationship management. By integrating these functions into a centralized system, ERP promotes data consistency, enhances operational efficiency, and facilitates informed decision-making. Successful ERP implementation requires a thorough understanding of organizational requirements, effective change management strategies, strong leadership, and ongoing support and training. Furthermore, we touch base on emerging trends in ERP systems, such as cloud-based ERP solutions, mobile access, and integration with emerging technologies like artificial intelligence and analytics. These developments offer organizations greater flexibility, scalability, and real-time insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Public relations

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Investigation of Frequency Response of a Power Network Under Varying Penetration Levels of Renewable Energy Sources

Investigation of Frequency Response of a Power Network Under...

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2024 International Conference on electrical and computer engineering Researches, ICECER 2024

作者： Ayeleso, Ayokunle Okafor, Chukwuemeka Emmanuel Raji, A.K. Olasoji, Azeez O. Cape Pennisula University of Technology Department of Electrical Electronics and Computer Engineering Cape Town South Africa University of Cape Town Department of Electrical Engineering Cape Town South Africa

ISBN: (纸本)9798331539733

Numerous countries worldwide have prioritized the expansion of renewable energy sources (namely wind and solar photovoltaic) in their energy generation portfolio in order to maintain a healthier environment. Therefore, when the use of renewable energy sources grows, the traditional synchronous-based power generation decreases, leading to a reduction in power system inertia. This is because renewable energy sources do not naturally supply inertia, unless certain reserves are maintained. This study examines the characteristics of the system's frequency behaviors considering varying degrees of renewable energy integration. The study utilized power system software (DIgSILENT Power Factory) to model the IEEE 39-bus test system. The simulation results indicated that when the wind penetration reached 50%, the sudden failure of the largest machine caused a significant increase in rate of change of frequency (ROCOF) of -0.5579 Hz/s. This was in contrast to the ROCOF of -0.3707 Hz/s observed at 0% wind penetration. As a result, the minimum frequency dropped to 59.15Hz, which is below the acceptable threshold of 59.5Hz. © 2024 IEEE.

关键词： frequency instability Frequency response power system inertia rate of change of frequency

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A novel facial emotion recognition model using segmentation VGG-19 architecture

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International Journal of Information Technology (Singapore) 2023年第4期15卷 1777-1787页

作者： Vignesh, S. Savithadevi, M. Sridevi, M. Sridhar, Rajeswari Department of Electrical and Electronics Engineering National Insistute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Insistute of Technology Tiruchirappalli India

Facial Emotion Recognition (FER) has gained popularity in recent years due to its many applications, including biometrics, detection of mental illness, understanding of human behavior, and psychological profiling. However, developing an accurate and robust FER pipeline is still challenging because multiple factors make it difficult to generalize across different emotions. The factors that challenge a promising FER pipeline include pose variation, heterogeneity of the facial structure, illumination, occlusion, low resolution, and aging factors. Many approaches were developed to overcome the above problems, such as the Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) histogram. However, these methods require manual feature selection. Convolutional Neural Networks (CNN) overcame this manual feature selection problem. CNN has shown great potential in FER tasks due to its unique feature extraction strategy compared to regular FER models. In this paper, we propose a novel CNN architecture by interfacing U-Net segmentation layers in-between Visual Geometry Group (VGG) layers to allow the network to emphasize more critical features from the feature map, which also controls the flow of redundant information through the VGG layers. Our model achieves state-of-the-art (SOTA) single network accuracy compared with other well-known FER models on the FER-2013 dataset. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Classification Emotion recognition FER dataset VGG

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Low Power, High Speed Current Starved Ring Oscillator based ADC using 18nm FinFET Technology 15

Low Power, High Speed Current Starved Ring Oscillator based ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandrasekaran, Gokul Pavithara, P. Dinesh, P.M. Arun Kumar, V. Sharanyanivasini, J.S. Kavin Kumar, K. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Sona College of Technology Department of Electronics and Communication Engineering Salem636005 India Nandha Engineering College Department of Electrical and Electronics Engineering Erode India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi Perundurai638057 India

ISBN: (纸本)9798350370249

The goal of achieving high performance, low power consumption, and small area footprint is still central to the field of analog-to-digital converters (ADCs). This work describes a new methodology that enables the design of Current Starved Ring Oscillator (CSRO) architecture using 18nm FinFET technology. The CSRO based ADC meets the growing needs of contemporary electronic systems with promised improvements in speed, power efficiency, and portability. The intrinsic qualities of FinFET transistors are utilized by the CSRO architecture to produce high performance metrics such as improved sub threshold behavior, lower leakage currents, and superior gate control, all of which are essential for low power consumption and high speed operation. In addition, compared to conventional ADC designs, the 18 nm production node enables tighter component integration, resulting in a compact ADC design. The precise analog-to- digital conversion is made possible by controlling the oscillation frequency through the use of current starving techniques. In order to reduce power consumption, mitigate electromagnetic interference and extend the battery life, starving ring oscillators are employed. The ability of the GDI (Gate Diffusion Input) technique to lower space overhead and power consumption provides an effective solution for low-power and high-density applications in contemporary integrated circuits. The proposed CSRO based ADC with GDI technique experiences a delay of 1.208 ns and power consumption of 356.08 mW while the ADC without GDI technique involves a delay of 1.44 ns and 391 mW of power consumption. This shows that the proposed ADC exhibits 14% improvement inspeed and 9% improvement in power compared to the ADCwithout GDI technique. © 2024 IEEE.

关键词： ADC Current starved ring oscillator FinFET technology GDI technique

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Improved Key Agreement Based Kerberos Protocol for M-Health Security

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computer Systems Science & engineering 2022年第8期42卷 577-587页

作者： P.Thirumoorthy K.S.Bhuvaneshwari C.Kamalanathan P.Sunita E.Prabhu S.Maheswaran Department of Computer Science and Engineeirng Nandha Engineering CollegeErode638052India Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Electrical Electronics and Communication EngineeringGITAM UniversityBengaluru Campus561203India Department of Electronics and Communication Engineering Amrita School of EngineeringCoimbatoreAmrita Vishwa Vidyapeetham641112India Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India

The development of wireless sensor network with Internet of Things(IoT)predicts various applications in the field of healthcare and cloud *** can give promising results on mobile health care(M-health)and Telecare medicine information systems.M-health system on cloud Internet of Things(IoT)through wireless sensor network(WSN)becomes the rising research for the need of modern *** devices attached to the patients’body which is connected to the mobile device can ease the medical *** is the key connect for optimal performance of the m-health system that share the data of patients in wireless networks in order to maintain the anonymity of the *** paper proposed a secure transmission of M-health data in wireless networks using proposed key agreement based Kerberos *** patients processed data are stored in cloud server and accessed by doctors and *** data transfer between the patients,server and the doctors are accessed with proposed protocol in order to maintain the confidentiality and integrity of *** efficiency of the proposed algorithm is compared with the existing *** computing 100 devices it consumes only 91milllisecond for computation.

关键词： Health monitoring authentication preparation protocol kerberos key agreement

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

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仪器仪表学报 2024年第6期45卷 91-100页

作者： Yang Kang Wang Linyuan Gao Chao Chen Mozhi Tian Zhihui Zhou Dunzhi Liu Yang Department of Electrical and Computer Engineering University of FloridaGainesville 32611USA Department of Electrical and Computer Engineering Temple UniversityPhiladelphia 19122USA School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin 300072China

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring ***,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is *** paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based *** results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 *** the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 ***,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

关键词： structural health monitoring guided waves principal component analysis deep learning denoising dynamic environmental condition

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Smart Home Automation with Smart Metering using ZigBee Technology and Deep Belief Network 2

Smart Home Automation with Smart Metering using ZigBee Techn...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Murugesan, Sankar Cm, Avinash Bhuvanesh, A. Manoj, Sumitha Darney, P. Ebby Rajan S, Kani Siva Vel Tech Rangarajan Dr.Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India Rajarajeswari College of Engineering Department Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350327533

The smart metering in the smart home application is vital one to ensure the reduction of energy usage and become a hot topic in the research field with the increasing usage of energy in both industrial and residential. With the advance technologies the energy saving techniques are used for the smart homes. The Internet of Things (IoT) devices are used for monitoring, controlling, and detecting the devices for the smart home applications. In this study, a novel Deep Belief Neural Network (DBN) approach for analyzing the data that are gathered from smart home applications with IoT devices, and big data technologies which promises the smart metering in smart home applications is proposed. The energy consumption patterns and classifications are effectuated with the proposed DBN approach and ensures the safety of the home. Experimental study was made and analyzed the performance of the proposed wo rk in terms energy consumption of different devices of smart home applications. Our proposed approach surpasses all the other approaches for the smart metering based smart applications. © 2024 IEEE.

关键词： Deep Belief Neural Network Energy Smart Home Smart Metering Zigbee Protocol

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Smart Home Automation with Smart Metering Using ZigBee Technology and Deep Belief Networks - Optimization Using MB-ABCOA Algorithm 2

Smart Home Automation with Smart Metering Using ZigBee Techn...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Navaz, K. Mano Raja Paul, M. Yamuna Bee, J. Kavitha, S. Bhuvanesh, A. Mariappan, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India PSN College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Sri Eshwar College of Engineering Department of English Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350383652

In the domain of smart homes and smart metering, ZigBee technology plays a pivotal role within the energy efficiency of homes, albeit facing challenges such as packet transmission delays and uneven energy consumption among nodes. To address these issues while emphasizing energy efficiency, we propose the Energy-efficient ZigBee Directionally Opportunistic Short-cut Trees Routings (EP-ZDOSTR), which optimizes network lifetimes and throughput by prioritizing energy-efficient packet transmission. Complemented by a hybrid Monarchy Butterfly and Artificial Bees Colonies Optimizations Algorithms (MB-ABCOA) approach, EP-ZDOSTR demonstrates significant improvements in network performances metric, including higher throughputs, increased packet delivery ratios, reduced transmission delays, and minimized energy consumption. Comparisons with existing methods like ZigBee shortcut tree routing (ZSTR), ZigBee opportunistic shortcut tree routing (ZOSTR), and Energy Efficient shortcut tree routing (EPSTR) validate its superiority in enhancing overall network efficiency and reliability. Furthermore, integrating ZigBee with Deep Belief Networks (DBNs) holds promise for further enhancing network intelligence, enabling predictive analysis of energy usage patterns and optimization of network operations, thereby advancing the capabilities of smart home in the context of smart homes and metering systems within the IoT landscape. © 2024 IEEE.

关键词： Zigbee

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