检索结果-内蒙古大学图书馆

An explainable deep learning approach for stock market trend prediction

Heliyon 2024年第21期10卷 e40095页

作者： Muhammad, Dost Ahmed, Iftikhar Naveed, Khwaja Bendechache, Malika CRT-AI and ADAPT Research Centres School of Computer Science University of Galway Ireland Department of Software Engineering University of Europe for Applied Sciences Potsdam Germany Swedish Center for Digital Innovation University of Gothenburg Sweden ADAPT Research Centre School of Computer Science University of Galway Ireland

Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.9%, compared to 85.7% for random forest, 60.07% for support vector machine, and 52.45% for logistic regression. Furthermore the proposed model excels in F1-score, with a 94.85% performance, compared to 77.95% for random forest, 21.02% for support vector machine and 46.23% for logistic regression, across four real world diverse datasets. Additionally, we employ explainable AI (XAI) techniques, SHAP and LIME, to enhance interpretability, enabling stakeholders to understand the key factors driving predictions. The SHAP analysis reveals the top 10 most important/influential features, enabling feature reduction while maintaining performance. Interestingly, while accuracy slightly decreases with top 10 features, precision, recall, and F1-score improve, suggesting a trade-off between comprehensiveness and performance. These results demonstrate the potential for practical application in financial decision-making, providing a balance between interpretability and predictive power that can support investors in risk management and strategic planning. © 2024 The Author(s)

关键词： Deep neural network Explainable AI Machine learning Stock market trend prediction Time series prediction

来源：评论

学校读者我要写书评

暂无评论

Innovative Evaluation Framework for Consumer Electronics-Enabled Intelligent Transportation Systems: Leveraging q-Rung Picture Fuzzy Hypersoft Schweizer-Sklar Aggregation Operators

IEEE Open Journal of the Computer Society

引用

IEEE Open Journal of the computer Society 2025年第01期6卷 834-845页

作者： Mohammed, Badiea Abdulkarem Dhumras, Himanshu Shukla, Varun Bajaj, Rakesh Kumar Al-Mekhlafi, Zeyad Ghaleb University of Ha'il Department of Computer Engineering College of Computer Science and Engineering Ha'il 81481 Saudi Arabia Chandigarh Group of Colleges Department of Applied Sciences Advanced Centre of Research & Innovation (ACRI) Chandigarh Engineering College Jhanjeri Punjab Mohali 140307 India Allenhouse Institute of Technology Dean Research Kanpur 208008 India Jaypee University of Information Technology Department of Mathematics Waknaghat Himachal Pradesh Solan 173234 India University of Ha'il Department of Information and Computer Science College of Computer Science and Engineering Ha'il 81481 Saudi Arabia

The development of intelligent transportation systems (ITS), supported by consumer electronics, is important for modern urban mobility. However, to evaluate these systems, complex, uncertain and interrelated factors, such as technological feasibility, economic viability, and risk assessment, must be addressed. Traditional multi-criteria decision-making (MCDM) approaches do not manage these uncertainties well. This article presents a novel assessment framework based on new Schweizer-Sklar (SS) aggregation operations and the q-rung picture fuzzy hypersoft set (q-RPFHSS) to tackle this problem. These operators enhance decision-making accuracy by modeling sub-parameterized uncertainties. The proposed method is applied to rank five cities for the deployment of ITS, showing excellent flexibility and precision compared to existing methods. Comparative analyses illustrate the robustness of the proposed approach in strategic decision-making for ITS, ensuring a more reliable and adaptive evaluation process. © 2020 IEEE.

关键词： consumer electronics-enabled intelligent transportation system multi-criteria decision-making q-rung picture fuzzy hypersoft set Schweizer-Sklar aggregation operators

来源：评论

学校读者我要写书评

暂无评论

Classifying Psychiatric Patients Using Machine Learning

Classifying Psychiatric Patients Using Machine Learning

引用

Information Technology (ACIT)

作者： Hmmam M. Alrjoob Raed Alazaidah Radwan Batyha Hayel Khafajeh Esraa Abu Elsoud Ala'a Saeb Al-Sherideh Ali Elrashidi Muhyeeddin Alqaraleh Waleed Amer Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyper Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty on Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.

关键词： Machine learning algorithms Correlation Psychology Feature extraction Classification algorithms Bayes methods Information technology Random forests Bagging Logistics

来源：评论

学校读者我要写书评

暂无评论

Framework of software design patterns for energy-aware embedded systems 15

Framework of software design patterns for energy-aware embed...

引用

15th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2020

作者： Schaarschmidt, Marco Uelschen, Michael Pulvermuller, Elke Westerkamp, Clemens Faculty of Engineering and Computer Science University of Applied Sciences Osnabrück Germany Software Engineering Research Group University of Osnabrück Germany

ISBN: (纸本)9789897584213

With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable. © Copyright 2020 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Comparison Model for Forecasting the Total Population in Bangkok

Comparison Model for Forecasting the Total Population in Ban...

引用

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Nattanicha Tipwong Anuchit Jitpattanakul Wikanda Phaphan Chalermrat Nontapa Sakorn Mekruksavanich Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Statistics Faculty of Science Chiang Mai University Chiang Mai Thailand Research Group in Statistical Learning and Inference Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

The primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok’s overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, department of Provincial Administration. This dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. The second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. The evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification

arXiv

引用

arXiv 2023年

作者： Yang, Ziyuan Teoh, Andrew Beng Jin Zhang, Bob Leng, Lu Zhang, Yi College of Computer Science Sichuan University Chengdu China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Macau China School of Software Nanchang Hangkong University Nanchang China School of Cyber Science and Engineering Sichuan University Chengdu China

Palmprint as biometrics has gained increasing attention recently due to its discriminative ability and robustness. However, existing methods mainly improve palmprint verification within one spectrum, which is challenging to verify across different spectrums. Additionally, in distributed server-client-based deployment, palmprint verification systems predominantly necessitate clients to transmit private data for model training on the centralized server, thereby engendering privacy apprehensions. To alleviate the above issues, in this paper, we propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. PSFed-Palm draws upon the inherent physical properties of distinct wavelength spectrums, wherein images acquired under similar wavelengths display heightened resemblances. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images. Specifically, a spectrum-consistent loss that enforces the model parameters and feature representation to align with their corresponding anchor models is designed. Finally, we impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift. This measure guarantees spectrum consistency while protecting data privacy, as there is no need to share local data. Extensive experiments are conducted to validate the efficacy of our proposed PSFed-Palm approach. The proposed PSFed-Palm demonstrates compelling performance despite only a limited number of training data. The codes will be released at https://***/Zi-YuanYang/PSFed-Palm. Copyright © 2023, The Authors. All rights reserved.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

MIND-WANDERING DETECTION MODEL WITH ELECTROENCEPHALOGRAM 18

MIND-WANDERING DETECTION MODEL WITH ELECTROENCEPHALOGRAM

引用

18th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2021

作者： Rungsilp, Chutimon Piromsopa, Krerk Viriyopase, Atthaphon U-Yen, Kongpop Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Research Group on Applied Digital Technology in Medicine Chulalongkorn University Bangkok Thailand Future Innovation Science and Technology Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789898704337

The study of mind-wandering is popular since it is linked to the emotional problems and working/learning performance. In terms of education, it impacts comprehension during learning which affects academic success. Therefore, we sought to develop a machine learning model for an embedded portable device that can categorize mind-wandering state to assist people in keeping track of their minds. We utilize a low-channel EEG to record the brain state and to build the predictive model because of its practicality and user-friendly. Most machine learning experiments in mind-wandering using EEG exhibit good individual-level performance. For the group-level technique, only a few research has developed a model. As a result, the goal of this research is to achieve a high-accuracy group-level model. Thus, Leave One Participant Out Cross Validation (LOPOCV) was used to assess the model correctness. This study shows that using a baseline normalization technique assists feature extraction and improves performance. The model was built using a support vector machine (SVM), and the best model achieved an accuracy value of 75.6 percent. © 2021 Virtual Simulation Innovation Workshop, SIW 2021. All rights reserved.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

引用

IEEE Transactions on Network science and engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing

arXiv

引用

arXiv 2023年

作者： Lu, Rongwei Jiang, Yutong Mao, Yinan Tang, Chen Chen, Bin Cui, Laizhong Wang, Zhi Shenzhen International Graduate School Tsinghua University Shenzhen518000 China The AI Cloud Business Group Baidu Incorporated Beijing100085 China The Department of Computer Science and Technology Tsinghua University Beijing100084 China The Department of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Federated Learning (FL) in mobile environments faces significant communication bottlenecks. Gradient compression has proven as an effective solution to this issue, offering substantial benefits in environments with limited bandwidth and metered data. Yet, it encounters severe performance drops in non-IID environments due to a one-size-fits-all compression approach, which does not account for the varying data volumes across workers. Assigning varying compression ratios to workers with distinct data distributions and volumes is therefore a promising solution. This work derives the convergence rate of distributed SGD with non-uniform compression, which reveals the intricate relationship between model convergence and the compression ratios applied to individual workers. Accordingly, we frame the relative compression ratio assignment as an n-variable chi-squared nonlinear optimization problem, constrained by a limited communication budget. We propose DAGC-R, which assigns conservative compression to workers handling larger data volumes. Recognizing the computational limitations of mobile devices, we propose the DAGC-A, which is computationally less demanding and enhances the robustness of compression in non-IID scenarios. Our experiments confirm that the DAGC-R and DAGC-A can speed up the training speed by up to 25.43% and 16.65% compared to the uniform compression respectively, when dealing with highly imbalanced data volume distribution and restricted communication. © 2023, CC BY-NC-SA.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

arXiv

引用

arXiv 2024年

作者： Bhattacharjya, Abanti Islam, Md Manowarul Uddin, Md Ashraf Talukder, Md. Alamin Azad, A.K.M. Aryal, Sunil Paul, Bikash Kumar Tasnim, Wahia Abdulllah Almoyad, Muhammad Ali Moni, Mohammad Ali School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Riyadh13318 Saudi Arabia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Department of Basic Medical Sciences College of Applied Medical Sciences King Khalid University Saudi Arabia Artificial Intelligence & Data Science Faculty of Health and Behavioural Sciences The University of Queensland Australia

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously. © 2024, CC BY.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：