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A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

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Annals of Data science 2023年第1期10卷 183-208页

作者： Kurani, Akshit Doshi, Pavan Vakharia, Aarya Shah, Manan Department of Computer Engineering Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering Indus University Gujarat Ahmedabad India Department of Chemical Engineering School of Technology Pandit Deendayal Petroleum University Gujarat Gandhinagar382007 India

From exchanging budgetary instruments to tracking individual spending plans to detail a business's profit, money-related organisations utilise computational innovation day by day. Here in this paper, we focus on the significance of innovation in accounts such as financial risk management and stock prediction. We discuss two significant algorithms that have a notable role in stock forecasting. Artificial Neural Networks (ANN), as absenteeism of some data points, does not hamper the network functioning. Secondly, Support Vector Machines (SVM) has several features, and due to simple decision boundaries, it avoids over-fitting. The paper first looks at the different technologies applied in stock market prediction. It examines how sentimental analysis, decision trees, moving average algorithm, and data mining is applied in various stock prediction scenarios. The paper covers the recent past studies to explore the concepts and methodologies through which ANN's and SVM's have been used. Additionally, the paper incorporates significant aspects of novel methods and technologies in which ANN as a hybrid model like ANN-MLP, GARCH-MLP, a combination of the Backpropagation algorithm and Multilayer Feed-forward network, yields better results. Simultaneously, SVM's have been successfully applied in stock prediction, giving an accuracy of about 60%–70% for simple SVM, which is further improved by combining methods like Random Forest, Genetic Algorithm more accurate outcomes. Further, we present our thoughts on where SVM's and ANN's stand as prediction algorithms and challenges like the time constraint, current scenarios, data limitation, and cold start problems were raised. Conclusively SVM and ANN played prominent roles in tackling these issue to an extent and can further be enhanced with their integration with other novel techniques resulting in hybrid methodologies. It will lead students, researchers and financial enthusiasts to more potent approaches for Stock forecasting. © 20

关键词： Machine learning ANN SVM Artificial neural network Support-vector machine Decision tree Backpropagation Stock market Autoregressive conditional heteroskedasticity Neural network Data mining

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GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Po...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Keyi Luo, Yeqi Yang, Weidong Xu, Jingyi Li, Zhijun Chen, Wen-Ming Fei, Ben School of Computer Science Fudan University China School of Mechanical Engineering Tongji University China Academy for Engineering and Technology Fudan University China

ISBN: (纸本)9798350368741

Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face challenges such as limited data diversity and inadequate augmentation for effective feature learning. To address these challenges, we propose GS-PT, which integrates 3D Gaussian Splatting (3DGS) into point cloud self-supervised learning for the first time. Our pipeline utilizes transformers as the backbone for self-supervised pre-training and introduces novel contrastive learning tasks through 3DGS. Specifically, the transformers aim to reconstruct the masked point cloud. 3DGS utilizes multi-view rendered images as input to generate enhanced point cloud distributions and novel view images, facilitating data augmentation and cross-modal contrastive learning. Additionally, we incorporate features from depth maps. By optimizing these tasks collectively, our method enriches the tri-modal self-supervised learning process, enabling the model to leverage the correlation across 3D point clouds and 2D images from various modalities. We freeze the encoder after pre-training and test the model's performance on multiple downstream tasks. Experimental results indicate that GS-PT outperforms the off-the-shelf self-supervised learning methods on various downstream tasks including 3D object classification, real-world classifications, and few-shot learning and segmentation. Project page: https://***/Luoyeqi1/***. © 2025 IEEE.

关键词： 3D Gaussian Splatting 3D understanding point clouds pre-training self-supervised learning

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Ensemble Learning for Soil Moisture Estimation: A Case Study of the Tibetan Plateau 24

Ensemble Learning for Soil Moisture Estimation: A Case Study...

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6th International Conference on Image, Video and Signal Processing, IVSP 2024

作者： Hao, Xiang Wang, Liejun He, Lijun Li, Fan School Of Computer Science And Technology Xinjiang University Urumqi China School Of Information And Communications Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798400716829

Soil moisture (SM) plays a critical role in agriculture, water management, climate modeling, ecosystem health, and hazard prediction. The majority of SM estimation methods rely on individual machine learning models, which are less robust and more prone to underfitting or overfitting. To overcome the issue, we design SM estimation models based on ensemble learning. The land-surface temperature (LST) has been identified as the most influential indicator of SM in prior research. However, the MOD11A1 LST product suffers from significant data gaps, rendering it unsuitable for direct application in SM estimation. To incorporate the crucial indicator of LST in SM estimation, we employ Random Forest (RF) to impute the missing data in the MOD11A1 LST product. Subsequently, we develop an RF-based LST reconstruction and estimation by utilizing the imputed MOD11A1 LST product and in situ soil temperature measurements. Next, we construct separate SM estimation models based on Extra Tree (ERT) and Light Gradient Boosting Machine (LightGBM), respectively, using the reconstructed LST and in situ measured SM. Finally, to enhance the accuracy of SM estimation, we construct an EL-Stacking-based SM estimation model by combining predictions from ERT or LightGBM with in situ SM measurements. The results substantiate the superior performance of our proposed EL-Stacking methods compared to other models. The root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of EL-Stacking were 0.0349-0.0657m3m-3, 0.0227-0.0525m3m-3 and 0.7305-0.9657, respectively. Furthermore, EL-Stacking effectively captures spatial dynamics and exhibits stronger consistency with in situ measurements. © 2024 ACM.

关键词： Soil moisture

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Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network

Enhancing Patient Well-Being in Healthcare Through the Integ...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Divakaran, Prakash Lingareddy, Nagulapalli Deepika, Sheri School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Ghaziabad India Himalayan University Itanagar India Vignan Institute of Technology and Science Department of CSE Deshmukhi India Sreyas Institute of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798350376470

This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. © 2024 IEEE.

关键词： Support vector machines

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YOLO Based Real Time Human Detection Using Deep Learning 4

YOLO Based Real Time Human Detection Using Deep Learning

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4th National Conference on Communication Systems, NCOCS 2022

作者： Jaswanth Kumar, Y.M. Valarmathi, P. School of Computer Science Engineering Vellore Institute of Technology Chennai600 127 India

Person identification which has been widely used in computer vision and one of the difficult applications employed in a variety of fields such autonomous vehicles, robots, security tracking, aiding visually impaired people, etc. As deep learning quickly developed, numerous algorithms strengthened the link between video analysis and visual comprehension. The goal of all these algorithms, regardless of how their network architectures operate, is to find many people inside a complicated image. The freedom of movement in an unknown environment is restricted by the absence of vision impairment, thus it is crucial to use modern technologies and teach them to assist blind people whenever necessary. We provide a system that will identify all potential daily multiple people and then prompt a voice to inform the user about both nearby and distant people. © Published under licence by IOP Publishing Ltd.

关键词： Network architecture

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Identification of High-Risk Areas for Geological Disasters using classification methods under complex environmental conditions 23

Identification of High-Risk Areas for Geological Disasters u...

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3rd International Conference on Artificial Intelligence, Automation and Algorithms, AI2A 2023

作者： Zhou, Jing-Wen Fu, Shao-Feng Li, Long-Hai Dong, Jun-Zhe School of Computer Science and Technology Xidian University China School of Cyber Engineering Xidian University China

ISBN: (纸本)9798400707605

Geological disasters result in significant human and property losses. It is imperative to identify areas prone to geological disasters for prevention and monitoring purposes. Identifying disaster-prone areas can be approached as a machine-learning classification problem. Various factors such as elevation, slope, aspect, terrain undulation, vegetation coverage, landform, and soil moisture content can be utilized as input variables for the classification algorithm to determine the probability of landslide occurrence within a specific range. Previous research in this area has been limited and fails to encompass diverse geological environments. This paper addresses this gap by utilizing a dataset collected within a province, which exhibits ample diversity and coverage. The dataset incorporates both continuous and discrete variables, allowing for a comprehensive evaluation of different classification algorithms. The primary objective of this study is to identify the most suitable classification algorithm for those conditions. By comparing the accuracy, ROC curves, and training duration of AdaBoost, cart, gbdt, xgboost, and random forest on multiple test datasets, it was determined that random forest yielded the best performance. The findings of this research can serve as a valuable reference for future related studies. © 2023 ACM.

关键词： Big data

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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A New Coati Optimization Algorithm for Binary Optimization 15th

A New Coati Optimization Algorithm for Binary Optimization

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Wang, Gui-Ling Chu, Shu-Chuan Pan, Jeng-Shyang Yang, Dongqiang Shieh, Chin-Shiuh College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Computer Science and Technology Shandong Jianzhu University Jinan China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

ISBN: (纸本)9789819700677

Coati Optimization Algorithm (COA) is a recently proposed heuristic optimization algorithm. The COA establishes phrases of exploitation and exploration by mimicking coati behavior. A comparison with twelve well-known optimization algorithms demonstrates the robustness of the COA. However, it can only be directly applied to continuous problems and its application in binary problems has not been well implemented. Therefore, a binary version (BCOA) is proposed in this paper. In BCOA, the positions of individuals in discrete space are mapped using sigmoid and V-shaped transfer functions. In addition, two experiments are underway to test the performance of BCOA. The comparison with the five optimization algorithms of the CEC2013 suite proved that BCOA has better optimization ability. Finally, this paper performed a Wilcoxon rank-sum test at a 5% significance level. The test shows that the proposed BCOA significantly outperforms the other algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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A review of research on super-resolution image reconstruction based on deep learning 8

A review of research on super-resolution image reconstructio...

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Yu, Wanjun Liang, Xiayan Shanghai University of Applied Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350369564

Currently, Super-resolution image reconstruction (SRIR or SR) is a research hotspot in the field of computer vision and image processing. With the development of deep learning, The technology of super-resolution image reconstruction based on deep learning has achieved some research results. In this paper, the concept of super resolution image reconstruction and typical deep network models of super resolution image reconstruction are described first, and then the advantages and disadvantages of different models are compared and analyzed. On this basis, the future of super-resolution image reconstruction methods based on deep learning is forecasted. © 2023 IEEE.

关键词： Generative adversarial networks

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Dual Attention Network for Retinal Layer and Fluid Segmentation in OCT 8

Dual Attention Network for Retinal Layer and Fluid Segmentat...

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Zhou, Zhilin Wu, Yan Peng, Zeyu Zhang, Yunqing Yan, Rugang Cao, Guogang Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai201418 China

ISBN: (纸本)9798350369564

Automatic segmentation of retinal optical coherence tomography (OCT) images has recently emerged as a significant research direction in medical machine learning. To address the task of retinal OCT image segmentation, we propose a method based on self-attention mechanism, which introduce a dual attention network to adaptively combine dependencies between local and global features for capturing rich contextual information. The spatial attention mechanism selectively aggregates weighted sums of features at each position, enabling selective integration of features. The channel attention mechanism selectively emphasizes interdependent channel features by considering the related factors among channels for weighted feature integration. We demonstrate promising segmentation performance on the publicly available Duke DME dataset, with an average Dice score of 0.824 and a Dice score of 0.53 for fluid segmentation, and the Dice scores of all seven retinal layers outperform other state-of-the-art techniques methods. © 2023 IEEE.

关键词： Optical tomography

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