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Journal of Network Intelligence 2024年第1期9卷 28-50页

作者： Ma, Shaowei Pirouz, Matin Wu, Jimmy Ming-Tai College of Computer Science and Engineering Shandong University of Science and Technology 579 Qianwangang Road Qingdao266590 China Department of Computer Science California State University Fresno California United States Department of Information Management National Kaohsiung University of Science and Technology No1 University Rd. Yanchao Dist. Kaohsiung City824 Taiwan

In the financial markets, artificial intelligence technology is widely used for stock forecasting. The precision of forecast results and the timing of transactions are crucial. In this paper, ten stocks from the U.S. and Taiwan stock markets serve as the research objects, and the long short-term memory neural network (LSTM) is employed to analyze the stock price fluctuations. To optimize trading signals, using particle swarm optimization (PSO) with a faster convergence speed. Kelly Criterion is used to calculate the optimal investment ratio, and the stop-loss double threshold is chosen as the stop-loss strategy for stock trading. The experiment demonstrates that not only does the trading method proposed in this paper improve investors’ returns, but it also reduces their risks. © 2024, J. Network Intell. All rights reserved.

关键词： Long short-term memory

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Highly Efficient Three-Level AC-AC Converter with Identical In-Phase and Antiphase Buck-Boost Operations

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IEEE Transactions on Power Electronics 2024年第8期39卷 9929-9942页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Al Zaabi, Omar Mousavi, Seyyed Mohammad Javad Babaei, Ebrahim National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates University of Tabriz Faculty of Electrical and Computer Engineering Tabriz51666 Iran Near East University Engineering Faculty Nicosia99138 Turkey

In this article, a new coupled-inductors based three-level bipolar buck-boost ac-ac converter is proposed. The proposed converter can produce highly efficient and symmetric in-phase and antiphase buck and boost modes of operation with much lower component voltage and current stresses. This reduces the operation and design complexity, and helps to achieve a high-efficiency operation. The high-frequency modulating switches of the proposed converter are implemented with coupled-inductor-based dual-buck phase leg, producing three-level input and output voltages, and providing inherent protection from voltage-source short-circuit issue during switch transitions. The other salient features of the proposed converter are no-commutation issue, no need for RC snubbers or dedicated safe-commutation algorithms, provision of high-quality and continuous input/output currents, and support for nonresistive loads. An in-depth circuit analysis of the proposed converter is provided based on the proposed switch modulation strategies. The guidelines for component design/selection are discussed, followed by the comparisons with state-of-the-art three-level ac-ac converters. Finally, practical circuit verifications are performed on a laboratory-assembled prototype. © 1986-2012 IEEE.

关键词： Buck-Boost converter

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Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

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computers, Materials & Continua 2024年第5期79卷 2673-2696页

作者： Hina Naz Zuping Zhang Mohammed Al-Habib Fuad A.Awwad Emad A.A.Ismail Zaid Ali Khan School of Computer Science and Engineering Central South UniversityChangsha410003China Department of Quantitative analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia Electrical and Computer Engineering University of VictoriaVictoriaV9A1B8Canada

Cardiovascular disease is the leading cause of death *** disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as ***,minimizing the death ratio can be achieved by early detection of heart attack(HA)*** the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four ***,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives *** objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their *** address these challenges,we employ deep learning *** have utilized a vision transformer(ViT)to address this ***,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product ***,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more *** response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction *** incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack *** the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original dat

关键词： Image analysis posture of heart attack(PHA)detection hybrid features VGG-16 ResNet-50 vision transformer advance multi-head attention layer

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Fake News Detection Based on Cross-Modal Ambiguity Computation and Multi-Scale Feature Fusion

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computers, Materials & Continua 2025年第5期83卷 2659-2675页

作者： Jianxiang Cao Jinyang Wu Wenqian Shang Chunhua Wang Kang Song Tong Yi Jiajun Cai Haibin Zhu State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China School of Computer Science and Engineering Guangxi Normal UniversityGuilin541004China Department of Computer Science Nipissing UniversityNorth BayON P1B 8L7Canada

With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant *** social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant *** solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)***,the model deeply explores the textual content,image content,and frequency domain ***,it employs a Co-Attention mechanism for cross-modal ***,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news *** verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline *** improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.

关键词： Fake news detection multimodal cross-modal ambiguity computation multi-scale feature fusion

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Implementation of InceptionV3 Model for Skin Disease Detection and Classification 6

Implementation of InceptionV3 Model for Skin Disease Detecti...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Reddy, Kunam Subba Ayyavaraiah, Monelli Lalithadevi, B. Mohanadevi, A. Sagili, Sreenivas Reddy Bharathi, V.C. Department of CSE Rajeev Gandhi Memorial College of Engineering& Technology Nandyal India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of IT Kongunadu College of Engineering and Technology India Department of computer science Rochester Institute Of Technology 1 Lomb Memorial Drive RochesterNY14623 United States School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331522667

Skin disorders represent a significant worldwide health issue, and timely diagnosis and classification are necessary for effective treatment. In this work, we present an improved approach for the identification and recognition of skin illnesses using a DL based system. Using the powerful Inception V3 convolutional neural network architecture, we propose a new method. The novel work specifies in the diagnosis and classification of a broad variety of skin disorders, counting normal skin as well as rashes, melanoma, keloid, measles, chickenpox, monkeypox, and basal cell carcinoma. The Inception V3 model is trained using a dataset that contains annotated samples of diverse skin conditions. Images of skin lesions are then preprocessed to add more features. The pretrained Inception V3 network is transfer learning-tuned to our specific task, enabling it to learn the discriminative characteristics required for skin condition identification. During the training phase, the algorithm learned to extract complicated patterns and information from photographs of skin lesions, which allows it to identify between various skin illnesses with excellent accuracy. We employ techniques like proposed data augmentation to expand the representation capability to simplify while lowering overfitting. Various test datasets are utilized for validation in direction to assess the trained representation accuracy in real-world scenarios. Our tests' results demonstrate how effectively the recommended approach may accurately diagnose and classify a range of skin problems. Among the notable performance measures the algorithm achieves across a range of classes are high accuracy, sensitivity, and specificity. The model is shown to have superior performance and shows potential for clinical applications through cross-validation and comparison with existing methods to evaluate its resilience. © 2025 IEEE.

关键词： Convolutional neural networks

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An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

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Scalable and Reliable Data Framework for Sensor-Enabled Virtual Power Plant Digital Twin

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 108-120页

作者： Singh, Amritpal Demirbaga, Umit Aujla, Gagangeet Singh Jindal, Anish Sun, Hongjian Jiang, Jing Northumbria University Department of Computer and Information Sciences Newcastle Upon TyneNE1 8ST United Kingdom Bartin University Department of Computer Engineering Bartin74110 Turkey Durham University Department of Computer Science DurhamDH1 3LE United Kingdom Durham University Department of Engineering DurhamDH1 3LE United Kingdom Northumbria University Department of Mathematics Physics and Electrical Engineering NewcastleNE1 8ST United Kingdom

Sensor-enabled distributed energy resources (DERs) provide various advantages, including a lower carbon footprint, yet effective management of millions of DERs is still an issue. Virtual power plants (VPP) can integrate several DERs into a unified operational digital twin to enable real-time monitoring, analysis, and control. VPP may utilize advanced solutions to improve operational efficiency by combining substantial measurement data from DERs. However, effectively managing the quantity and complexity of data flows, whether streaming data or high-impact low-frequency data, is essential in maintaining the performance of DERs at sustained levels. The vast amounts of diverse data generated from various DERs pose significant challenges for storage, processing, and resource management. This article proposes a comprehensive framework that employs a distributed big data cluster to ensure scalable and reliable data storage and utilizes a robust message broker system for efficient data queuing. In addition, we present innovative load-balancing strategies within the VPP digital twin system. A decision tree algorithm is implemented to calculate the forthcoming workload collected by various deployed sensors at various DERs. The required resources are identified per workload, and the numbers are forwarded to the orchestrator. The orchestrator scales up and down resources based on resource utilization suggested by the decision tree algorithm when the resources or nodes are insufficient to handle the sensor data, optimizing the utilization of computing resources. The framework also features a failure detection component that performs root cause analysis to provide actionable insights for system optimization. Experimental results show that this framework ensures high efficiency, reliability, and real-time operational capability in VPP digital twin by addressing critical challenges in data storage, streaming data analysis, and load balancing. © 2025 The Authors.

关键词： Carbon footprint

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Multi-food detection using a modified swin-transfomer with recursive feature pyramid network

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Multimedia Tools and Applications 2024年第19期83卷 57731-57757页

作者： Lee, Chao-Yang Khanum, Abida Kumar, Pinninti Praneeth Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Country Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan Department of Aeronautical Engineering National Formosa University Yunlin Country Taiwan

Humans need food, and the food detection system is a fascinating research topic and a complex weight loss mechanism. Eating healthy and balanced is crucial. Over the last few years, studies on food-related tasks have progressed, but the existing system deals only with single- and multi-food items by using convolution neural networks (CNN), and the recent development of Transformers in computer-vision has outperformed famous networks like ResNet 50, VGG 16, etc., but Transformer-based methods are limited in food-related tasks. For this issue, we improve Swin Transformer-based method by taking the dominance of transformer and CNN, we propose a novel multi-food detection utilizing a modified Swin-Transfomer and Recursive Feature Pyramid Network called (MFD-MST) and Swin-Transformer with spatial extraction block (STSE) as backbone to recognize multi-food item in images to improve local and structural information of image and RFP as neck to enhance This feature representation recognizes multi-food items. STSE solves transformer positional encoding, and RFP can look at feature map twice, helping the model build powerful representations. A prominent dataset, UEC-FOOD 100, Indian Food 28, UEC-FOOD 256 was widely tested to construct a detection system that can distinguish various objects in food photos and classify them into food categories. Our model’s evaluation measures vary. MFD-MST outperforms Swin-Transformer by 2.7% at AP[0.50] and 3.3%, 1.4% on three food datasets respectively. Test results suggest that our system accurately detects food items. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Image enhancement

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis

arXiv

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arXiv 2025年

作者： Kargupta, Priyanka Agarwal, Ishika August, Tal Han, Jiawei 1Department of Computer Science University of Illinois at Urbana-Champaign

With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fields. This makes it challenging to assess the significance, novelty, incremental findings, and equivalent ideas between related works, particularly those from different research communities. Large language models (LLMs) have recently demonstrated strong quantitative and qualitative reasoning abilities, and multi-agent LLM debates have shown promise in handling complex reasoning tasks by exploring diverse perspectives and reasoning paths. Inspired by this, we introduce Tree-of-Debate (ToD), a framework which converts scientific papers into LLM personas that debate their respective novelties. To emphasize structured, critical reasoning rather than focusing solely on outcomes, ToD dynamically constructs a debate tree, enabling fine-grained analysis of independent novelty arguments within scholarly articles. Through experiments on scientific literature across various domains, evaluated by expert researchers, we demonstrate that ToD generates informative arguments, effectively contrasts papers, and supports researchers in their literature review. © 2025, CC BY.

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