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Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models 1

Forecasting Stock Price by LSTM-CNN Hybrid Model and Compare...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Arora, Aditi Upadhyay, Yashaswi Shukla, Satvik Saket Abes Engineering College Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350377002

The The accurate forecasting of stock prices holds significant importance in devising effective trading strategies and making informed decisions about stock transactions. The inherent volatility in stock prices, driven by factors like geopolitical tensions, corporate earnings, and commodity costs, underscores the need for precise predictions. Domestic factors such as central bank policies, governmental decisions, inflation, and global market uncertainties also trigger stock price fluctuations. Estimating this volatility is a complex challenge for traders. A novel approach is presented in this research, proposing a hybrid model that integrates two distinct algorithms - Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). This model harnesses diverse data representations from stock time series to enhance price prediction accuracy. Comparative analyses are performed with alternative models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks) and Naïve Bayes to ascertain the most effective prediction paradigm with minimal error. The study demonstrates that combining temporal and image features extracted from the same dataset diminishes prediction errors compared to using these features independently. The hybrid model outperforms singular prediction models, highlighting a high enhancement. Among the individual models, the LSTM model exhibits superior performance in terms of prediction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Comparison of Machine Learning Algorithms for Parkinson's Disease Detection 14

Performance Comparison of Machine Learning Algorithms for Pa...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Gupta, Ipsha Vashisth, Dhwani Garg, Rakesh Amity University Dept. of Computer Science and Engineering UP India

ISBN: (纸本)9798350344837

Parkinson's Disease affects the neural system and can be detected using biomarkers, physiological and motor disruptions exhibited by patients. Several advancements and research on Machine Learning approaches have been carried out and tested to diagnose PD in the early stages of its inception by continuous monitoring of physical actions of patients. In this study we: (a) conduct a comprehensive analysis of existing machine learning algorithms to detect PD patients, (b) summarize and categorize the findings of efficiency of these algorithms, (c) test these algorithms on speech recordings of patients, (d) provide the model with best accuracy with an overview of all the models previously researched or currently in use. The benefits and shortcomings of the referenced models are thoroughly contrasted and explored for better accessibility for future research. © 2024 IEEE.

关键词： Support vector machines

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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Deepak, N.R. Paital, Santosh Kumar Verma, Saritha Choeyang, Tenzin Singh, Udit Kumar Bhavya Hkbk College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed. © 2024 IEEE.

关键词： Generative adversarial networks

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Load Flow Analysis and Optimization for Solar PV Integration in Power Systems 21

Load Flow Analysis and Optimization for Solar PV Integration...

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21st International Learning and Technology Conference, L and T 2024

作者： Shigdar, Basmah Binsalim, Haya Almatrafi, Lina Badaam, Salma Barkat, Enfel Effat University Dept. of Electrical & Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Saudi Arabia's Vision 2030 emphasizes renewable energy to drive sustainable development, particularly leveraging solar power due to abundant sunshine. However, integrating solar photovoltaic (PV) systems into existing grids presents multifaceted challenges. Voltage regulation, power quality issues, and reverse power flow disruption due to solar intermittency demand effective management strategies. Challenges, including voltage fluctuations, power quality issues, reverse power flow disruptions, and solutions like energy storage, advanced inverter control, demand response, and grid modernization, are explored to mitigate these challenges. A literature review and methodologies are presented, including PV generator modeling and power flow calculations. The proposed methodology is validated through case studies and a simulation based on the Institute of electrical and Electronics Engineers (IEEE) 30-Bus System, showcasing differences in voltage and angle outputs across nodes with integrated PV generators. © 2024 IEEE.

关键词： Power quality

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Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Enhanced Fetal Ultrasound Image Segmentation using Spatial A...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Verma, Harshita Patro, Bdk Rajkiya Engineering College Dept. of Computer Science and Engineering Kannauj India

ISBN: (纸本)9798350376876

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model's focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet's competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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A Study on the Evaluation of Cyber Security Vulnerabilities in Medical Software

A Study on the Evaluation of Cyber Security Vulnerabilities ...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo, Jeong-Hyun Koo, Jee-Hyun Seo, Byung-Suk Son, Sun-Young Ko, Kwang-Man Sang-Ji University Dept. of Computer Engineering Korea Republic of Sang-Ji University Dept. of Bigdata Science Korea Republic of Sang-Ji University Dept. of Electrical and Electronic Engineering Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we examine the cybersecurity vulnerability assessment method of medical software. Medical software processes patient sensitive data and is linked to various medical devices and systems in real time. Due to these characteristics, medical software is highly likely to be exposed to various cybersecurity threats such as ransomware, data leakage, and medical device hacking. Based on the international standard IEC TS 60601-4-5, we propose threat modeling, vulnerability scanning, and penetration testing as a methodology for assessing the security vulnerabilities of medical software. Through this, we can identify security vulnerabilities in advance and prepare measures to respond quickly. We can prevent security threats and improve the safety of medical software through response strategies such as security patches and updates, network separation, data encryption, and security education. In conclusion, strengthening the security of medical software is essential to maintain patient safety and the reliability of the medical system, and systematic security assessment and continuous response are required. © 2025 IEEE.

关键词： Cyber attacks

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Fairness-Aware Beam Placement Scheduling for LEO Satellites

Fairness-Aware Beam Placement Scheduling for LEO Satellites

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Eftetahi, Pooria Seyed Cai, Lin Sepahi, Amir University of Victoria Dept. Electrical and Computer Engineering VictoriaBC Canada

ISBN: (纸本)9798350362312

One of the primary objectives of the next-generation communication systems is to establish ubiquitous coverage. In this regard, Low Earth Orbit (LEO) satellite networks have attracted significant attention as a promising technology to achieve global coverage. However, fairly and efficiently managing resource allocation, especially beam placement scheduling, poses a significant challenge due to the large number of ground terminals and the vast coverage of LEO satellites. To address this challenge, we model the beam placement scheduling as a problem of minimizing terminal payload at the LEO satellite while considering fairness among terminals. In our proposed solution, we divide the coverage area into segments and use the advantage actor-critic (A2C) to schedule beams within each segment. Our simulations demonstrate that our approach outperforms the greedy method in terms of both throughput and fairness while improving first-time access to the LEO satellite for terminals. Moreover, it outperforms round-robin (RR) scheduling in terms of throughput. © 2024 IEEE.

关键词： Satellite communication systems

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using Image Processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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Clustering Data Log CITA Blockchain Performance Monitoring with Mean Shift Method

Clustering Data Log CITA Blockchain Performance Monitoring w...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ubaya, Huda Stiawan, Deris Suprapto, Bhakti Yudho Perdana, Muhammad Iqbal Universitas Sriwijaya Faculty of Computer Science Dept. of Computer Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Dept. of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

CITA is a high-performance enterprise blockchain system with six microservices (RPC, Auth, Consensus, Chain, Executor, and Network) that exchange information through Message Bus. There is a dataset in the form of performance logs from CITA in the format of CPU, Memory, Network, PReads, PWrite, and VMem taken in real-time using the Performance Monitoring Framework for Blockchain. This framework has the advantage of lower overhead, more details, and better scalability than other performance monitoring approaches, but there is no clustering feature in the monitoring results. Thus, it is necessary to include a clustering feature so that users know the performance directly without the need to analyze it more deeply. This research uses the Mean Shift algorithm, which does not require the number of clusters for the initial process but only determines the maximum distance of centroid displacement through calculation. The number of clusters estimated is 2 clusters, where CITA produces 7 cluster samples with cluster evaluation results with the highest average silhouette score from CITA, which is 0.555 in the Network-Received Network-Sent feature pair. So, it is known that the Mean Shift clustering algorithm produces goodness of fit results from clustering on the CITA Blockchain performance log, which means that the clusters are well separated and can be clearly distinguished. © 2023 IEEE.

关键词： Blockchain

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