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An enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 243卷

作者： Wang, Jujie Liu, Jing Jiang, Weiyi Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China

For the purpose of managing financial risk and making investment decisions, interval stock price forecasting is essential. Currently, decomposition integration frameworks are widely used in point-valued stock price forecasting studies, mainly focusing on mining internal information. However, point forecasts are difficult to adequately capture price uncertainty and may suffer from loss of volatile information. Therefore, this paper proposes an enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning. Firstly, the interval variational modal decomposition with feedback mechanism (FIVMD) is proposed to extract internal features and can decompose interval values into interval trend and residual. FIVMD not only solves the interval decomposition challenge, but also helps to improve the internal feature extraction capability. Secondly, while considering the influencing factors more comprehensively, appropriate feature selection and compression techniques can effectively achieve external feature extraction, obtain the best influencing factors, and improve the modeling capability of highdimensional data. Finally, the final prediction results are obtained by modeling the interval trend and residuals separately through the optimization algorithm and deep learning model to improve the prediction accuracy. The results of the empirical analysis reveal that the proposed interval decomposition integrated model has the smallest of the three evaluation metrics, where the values of interval mean average percentage errors (IMAPE) are 1.8188%, 1.1244%, 1.9001%, and 2.1542% respectively. This shows that the model is significantly more accurate and stable than the other comparative models, and it is a successful model for predicting intervalvalued stock prices.

关键词： Interval multi-scale decomposition Comprehensive feature extraction optimized deep learning Interval-valued stock price forecast

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optimized, robust, real-time emotion prediction for human-robot interactions using deep learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第4期82卷 5495-5519页

作者： Jaiswal, Shruti Nandi, Gora Chand Indian Inst Informat Technol Jhalwa Allahabad 211015 UP India Indian Inst Informat Technol Dept Informat Technol Jhalwa Allahabad 211015 UP India

To enable humanoid robots to share the social space,development in technology is required for natural interaction with the robots using multiple modes of communication such as speech, gestures, and share emotions with them. This research is targeted towards addressing the core issue of emotion recognition problem, which would require fewer computation resources and a much lesser number of network parameters, which will be more adaptive to compute on social robots for real-time communication. Any robots will have limited computation capability for run time actions and decisions. In the present investigation, Inception based Convolution Neural Network(CNN) Architecture is proposed to improve the emotion prediction. The proposed model has achieved improved accuracy of up to 6% improvement over the existing network architecture for emotion classification. The model was tested over seven different datasets to verify its robustness. In addition, real-time implementation capability is verified on humanoid robot NAO, which depicts its social behavior in real-time. The proposed model is reducing the trainable parameters to the extent of 94% as compared to vanilla CNN model, which indicates that its implementation ability in a real-time based application such as human-robot interaction. Rigorous experiments have been performed to validate the methodology, which is sufficiently robust and could achieve a high level of accuracy. Seven datasets are used to build a robust model. Finally, the model is integrated in a humanoid robot, NAO, in real-time. When averaged over all the emotions, the reduction in response time by 60% and 61% and improvement in prediction rate by 42% and 21% when compared in real-time environment with Vanilla CNN and state of the art model respectively.

关键词： Emotion classification optimized deep learning Convolution neural network Inception module Human-robot interaction Social robotics

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OLM-CPS: optimized learning Models for the Security of Constrained Cyber Physical Systems

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Procedia Computer Science 2025年 259卷 1845-1854页

作者： Zakir Ahmad Sheikh Yashwant Singh

Machine learning is widely used for securing cyber physical systems, but their computational demands pose challenges in constrained environments. This paper reviews and analyzes optimization methods for learning models, including feature engineering, hyper-parameter optimization, and optimizer selection. It also proposes a methodology to utilize optimized learning models for cyber physical system security. By optimizing learning models, they become feasible for deployment in constrained systems. The paper explores various optimization techniques to enhance learning model efficiency and effectiveness. Feature engineering transforms raw data into meaningful representations, hyper-parameter optimization finds optimal configuration settings, optimizer selection chooses the most suitable algorithm, and federated learning reduces the node level computational requirements and also preserves privacy of individual nodes. The proposed methodology integrates optimized models into cyber physical systems to improve security, considering their resource limitations. This research contributes to the advancement of machine learning in securing cyber physical systems. The findings highlight the benefits of optimization methods and emphasize the importance of optimized learning models in constrained environments, enhancing security measures.

关键词： optimized Machine learning optimized deep learning Constrained Cyber-Physical Systems Cyber Security CPS Security ML for Security Intelligent Security CPS for Automation

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An Intelligent Smart Dynamic Feature Analysis based Approach by utilizing deep learning to improve the Breast Cancer Detection

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IEEE Access 2025年 13卷 81643-81665页

作者： Shim, Seong-O Hussain, Lal Alsolami, Eesa Alkinani, Monagi H. University of Jeddah College of Computer Science and Engineering Department of Computer and Network Engineering Jeddah Saudi Arabia University of Azad Jammu and Kashmir Department of Computer Science and Information Technology King Abdullah Campus Chatter Kalas Muzaffarabad 13100 Pakistan University of Azad Jammu and Kashmir Department of Computer Science Neelum Campus Neelum Campus Athmuqam 13230 Pakistan University of Jeddah College of Computer Science and Engineering Department of Cybersecurity Jeddah Saudi Arabia University of Jeddah College of Computer Science and Engineering Department of Computer Science and AI Jeddah Saudi Arabia

Breast cancer remains one of the important global health concerns with high rates of mortality, highlighting the significance of more sophisticated diagnostic methods. Conventional methods, generally comprised of costly imaging and invasive biopsies, are of high burdens. Motivated by the limitations, the present study comes up with an innovative automated solution for the identification of breast cancer using deep learning analysis of mammograms. Moving away from the traditional approaches with inherent pre-processing and feature extraction constraints, this research focuses on a two-pronged improvement strategy: improved mammogram quality and highly optimized deep learning architecture. Specifically, we present a new optimized InceptionResNetV2 model significantly optimized through the thoughtful addition of large data augmentation to increase robustness, LeakyReLU activation to facilitate gradient flow and accelerate learning, and MeanDropout regularization to mitigate overfitting and improve generalization. The model was also trained using Quantization Aware Training (QAT) to enable efficient deployment on low-resource devices without significant performance degradation. The performance on our proposed approach for the massive mammogram dataset reflects an evident improvement in detection performance over traditional techniques. Our InceptionResNetV2 optimized achieved state-of-the-art accuracy with outstanding measures of 98.06% sensitivity, 97.05%, positive predictive value (PPV) and specificity of 99.60%, negative predictive value (NPV) of 86.83%, 97.94% accuracy, F1-score of 96.90%, Matthews correlation coefficient (MCC) of 90.67%, and AUC of 0.9939. The benefits of proposed system are that it can deliver a more efficient, precise, and possibly cost-effective diagnostic tool for breast cancer. Through synergistic integration of architectural optimization, sophisticated regularization methods, and deployment-aware training, our proposed system enables earlier

关键词： Breast Cancer Dynamics Feature Engineering optimized deep learning Post-Training Quantization Smart Model

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An Energy Aware Intelligent Fault Detection Scheme for IoT-Enabled WSNs

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IEEE SENSORS JOURNAL 2022年第5期22卷 4722-4731页

作者： Kaur, Gagandeep Chanak, Prasenjit Bennett Univ Sch Comp Sci Engn & Technol Greater Noida 201310 India Atal Bihari Vajpayee Indian Inst Informat Technol Dept Comp Sci & Engn Gwalior 474015 India Indian Inst Technol BHU Varanasi Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

Recent years have seen an explosion in the demand for the Internet of Things (IoT). IoT makes any physical object smart by providing sensing capability. The physical objects are embedded with sensors that form a network of sensors. However, sensors are vulnerable to faults due to energy depletion, software failures, and hardware failures. The existing fault detection schemes put huge computational overhead on the battery-limited and low computational capacity sensor nodes that are subject to the premature death of the sensor nodes. Also, they suffer from poor fault detection accuracy and huge false alarm rate that significantly reduce the overall performance of the networks. In this paper, an energy-aware intelligent fault detection scheme is proposed for IoT-enabled wireless sensor networks that significantly improve fault detection accuracy and reduce false alarm rate. A novel 3-Tier hard fault detection mechanism is used for detecting hardware unit faults of the sensor nodes. Furthermore, an optimized deep learning mechanism is used for various soft fault detection that prevents premature death of sensor nodes. The paper mathematically analyses the proposed scheme in terms of energy consumption, time complexity, and message complexity. Extensive simulations show the enhanced performance of the proposed scheme compared with the state-of-the-art algorithms in terms of fault detection accuracy, false alarm rate, false-positive rate, energy consumption, and network lifetime.

关键词： Fault detection Intelligent sensors Wireless sensor networks Circuit faults Internet of Things deep learning Substations Wireless sensor networks (WSNs) Internet of Things (IoT) fault detection energy optimized deep learning

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Influence of conditional holoentropy-based feature selection on automatic recommendation system in E-commerce sector

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 5564-5577页

作者： Sharma, Shambhu Nath Sadagopan, Prasanna Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Applicat Chennai India

This paper presents a new contribution to the recommendation system following E-commerce reviews. Here, it implements a dictionary-based sentiment analysis based on four main steps (i) Pre-processing (ii) Semantic word extraction (iii) Feature Extraction (iv) Classification. Initially, the tweets or reviews extracted from the database are subjected to pre-processing, which involves three processes: stop word removal, stemming, and blank space removal. In the semantic word extraction process, the semantic words from the dictionary will be extracted by matching with the extracted keywords. The next process is the feature extraction, which extracts joint holoentropy and cross holoentropy of all keywords, and further proceeds with conditional holoentropy-based feature selection process. Finally, the selected features are subjected to a classification process using deep Belief Network (DBN), which is a well-performing deep learning algorithm. Moreover, the main contribution of the paper relies on the improvement of DBN architecture, where the number of hidden neurons and activation function is optimally tuned by a hybrid optimization algorithm termed as Grey linked Chick Update-based Chicken Swarm Optimization (GCU-CSO). At last, the comparative analysis of proposed and conventional algorithms in terms of various performance metrics proves the efficacy of the proposed E-commerce recommendation system. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： E-commerce recommendation system Conditional holoentropy deep Belief Network optimized deep learning Grey linked chick update-based chicken swarm optimization

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Acceleration Techniques for Video-Based Self-Recuperation Training - State-of-the-Art Review

Acceleration Techniques for Video-Based Self-Recuperation Tr...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Ekambaram, Dilliraj Ponnusamy, Vijayakumar Srm Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Tamilnadu Kattankulathur India

ISBN: (纸本)9798350394580

In recent years, advancements in optimized deep learning (DL) models have strived to speed up self-recuperation training effectively. Video-based acceleration techniques enhance the self-recuperation process concerning fast recovery, increase patient engagement, individual progress tracking, optimized exercise plans, remote monitoring, effective feedback generation for correction of exercise poses, and reduced healthcare costs. This article explored the state-of-the-art comparison of various acceleration techniques for hardware and software in non-invasive self-recuperation. Also, we have deliberated some optimized DL methods to be deployed in the edge computers of users and developed accelerated hardware to achieve high precision outcomes with low computation and memory consumption on a home-based self-recuperation training system. Finally, this article addresses challenges and suggestions for future research directions on home-based self-recuperation acceleration techniques. © 2023 IEEE.

关键词： Acceleration techniques Action recognition FPGA GPU optimized deep learning Self-recuperation Video processing

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