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Interpretable multi-horizon time series forecasting of cryptocurrencies by leverage temporal fusion transformer

Heliyon 2024年第22期10卷 e40142页

作者： Farooq, Arslan Irfan Uddin, M. Adnan, Muhammad Alarood, Ala Abdulsalam Alsolami, Eesa Habibullah, Safa Institute of Computing Kohat University of Science and Technology KP Kohat 26000 Pakistan College of Computer Science and Engineering University of Jeddah Jeddah 21959 Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah 21959 Saudi Arabia

This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices. © 2024 The Author(s)

关键词： Artificial intelligence Block chain Cryptocurrency Deep learning Sentiment analysis Temporal data Transformers

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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

关键词： Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization

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Reduced biquaternion tensors and applications in color video processing

arXiv

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arXiv 2024年

作者： Yu, Cui-E Liu, Xin Luo, Hui Zhang, Yang Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China Department of Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada

In this paper, we introduce the applications of third-order reduced biquaternion tensors in color video processing. We first develop an algorithm for computing the singular value decomposition (SVD) of a third-order reduced biquaternion tensor via a new Ht-product. As theoretical applications, we define the Moore-Penrose inverse of a third-order reduced biquaternion tensor and consider its characterizations via its SVD. Using Moore-Penrose inverses, we mainly discuss the general (or Hermitian) solutions to reduced biquaternion tensor equation A ∗Ht X = B as well as its least-squares solutions. Finally, we develop two algorithms and apply them in color video compression and deblurring, both of which perform better than the compared algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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A robust optimization approach for enabling flexibility, self-sufficiency, and environmental sustainability in a local multi-carrier energy community

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Applied Energy 2025年 392卷

作者： Dorahaki, Sobhan MollahassaniPour, Mojgan Rashidinejad, Masoud Muyeen, S.M. Siano, Pierluigi Shafie-Khah, Miadreza Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Electrical and Computer Engineering University of Sistan and Baluchestan Zahedan Iran Department of Electrical Engineering Shahid Bahonar University of Kerman Kerman Iran Department of Management & Innovation Systems University of Salerno Fisciano Italy Department of Power Systems National University of Science and Technology POLITEHNICA Bucharest Splaiul Independenței Street no. 313 Bucharest060042 Romania Research and Innovation Division Nowocert Dublin Ireland

Managing Local Multi-Carrier Energy Communities (LMCECs) has become increasingly complex due to the need to balance sustainability, flexibility, and economic performance in modern energy systems. This challenge is further compounded by uncertainties in energy supply and demand, necessitating advanced optimization approaches. To address this, a robust optimization model has been developed to enable LMCECs to effectively participate in programs emphasizing flexibility, self-sufficiency, and environmental sustainability. The model incorporates electrical flexibility constraints to enhance practical applicability and allows the LMCEC manager to adopt emissions limits recommended by upstream energy networks, promoting environmentally conscious operations. By prioritizing self-sufficiency, the model not only strengthens the resilience of LMCECs but also improves their operational efficiency. Results demonstrate the model's effectiveness in handling uncertainties while minimizing operational costs, achieving an average optimal self-sufficiency rate of 76.36 %. This represents a significant step forward in advancing sustainable and resilient energy management practices. Moreover, a comparison between the robust optimization approach and both the deterministic and Distributionally Robust Chance-Constrained (DRCC) methods highlights the superior performance of the proposed robust optimization under worst-case scenarios. © 2025 The Authors

关键词： Sustainable development

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An Enhanced Diagnostic System using Deep Learning for Early Prediction of Pancreatic Cancer

An Enhanced Diagnostic System using Deep Learning for Early ...

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International Conference on Electrical Energy systems (ICEES)

作者： Kumbham Bhargavi M Lakshmi Prasad Arun G Pundru Chandra Shaker Reddy S Yuvalatha M Naga Triveni Department of CSE (AI & ML) Keshav Memorial Institute of Technology Hyderabad Telangana India Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Computer Science and Business Systems Anna University Bannari Amman Institute of Technology Tamil Nadu India Department of CSE (AI&ML) St. Martin's Engineering College Hyderabad Telangana India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

With a dismal 5-year survival-rate of approximately 7%, pancreatic-cancer(PC) remains one of the world's most lethal tumors. In order to improve patient survival rates, early detection of PC is essential. computerized-tomography(CT), MRI combined with MRCP, or a biopsy is necessary for the diagnosis of PC. The steps involved in the suggested CAD design process are as follows: picture preprocessing, segmentation, feature-extraction & classificatiion. Color conversion and the isotropic diffusion filter method are utilized for preprocessing. The next step is the segmentation processes' usage of the suggested Fuzzy K-NN Equality code. A classification tool that makes use of Deep Learning is feature extraction. Using the characteristics gathered from the pancreatic sample, tumor cells are categorized. The image classification criterion includes both the train values and the testing datasets. To identify pancreatic cancer, an algorithm called DCNN_DBN is employed, which combines Deep-Convolutional Neural-Networks with Deep-Belief-Networks. The results of the experiments show that the present CAD system has great promise and is safe for the automated diagnosis of benign & malignant tumors, with an accuracy rate of 99.8 percent. The use of this classifier greatly reduces the computational complexity. More anomalies in pancreatic cancer cells could be detected with an improved version of the proposed method.

关键词： Deep learning Image segmentation Accuracy Sensitivity Image color analysis Databases Roads Pancreatic cancer Classification algorithms Testing

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Dynamic Thermal Line Rating Considering Cooling Impact on Conductor's Heating of Transmission Power Loss

Dynamic Thermal Line Rating Considering Cooling Impact on Co...

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2022 International Conference on Informatics Electrical and Electronics, ICIEE 2022

作者： Afandi, A.N. Wibawa, Aji Prasetya Rahmawati, Yuni Sunaryono Kobayashi, Makiko Smart Power and Advanced Energy Systems Center Center of Advanced Material for Renewable Energy Universitas Negeri Malang Electrical Engineering Department Malang Indonesia Universitas Negeri Malang Electrical Engineering Department Malang Indonesia Universitas Negeri Malang Department of Physics Malang Indonesia Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

ISBN: (纸本)9781665486224

The power system is made up of electricity generating facilities, transmission networks, and distribution networks. Utility companies are also final energy users in these systems. Transmission lines are used in the construction of the trans-mission system to link all networks and transport generated power to load requirements. Electricity loss on transmission lines during power supply is transformed into line heating. In this study, the Thunderstorm Algorithm (TA) is used to optimize transmission conductor heating caused by power losses. By taking into consideration dynamic power losses, conductors, measured external temperature, solar radiation, and wind speed, Transmission Conductor Heating (TCH) is used to determine the temperature of bare overhead lines. Technical parameters are used to apply TA to the TCH as well. The transmission conductor heating is approached using a thermal model for the end temperature, and the results demonstrate that power losses result in thermal performances. Additionally, due to the current dynamic losses, the temperature deviation changes with each operating hour. At the temperature threshold, which represents the ambient operating temperature, the optimization produces various outcomes. © 2022 IEEE.

关键词： Electric power transmission networks

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Transformer-Based Multi-Modal Probabilistic Pedestrian Prediction for Risk-Aware Autonomous Vehicle Navigation

Transformer-Based Multi-Modal Probabilistic Pedestrian Predi...

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International Conference on Advanced Robotics (ICAR)

作者： Murad Mebrahtu Awet Araia Abiel Ghebreslasie Jorge Dias Majid Khonji Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Center for Autonomous Robotic Systems (KUCARS) Abu Dhabi UAE

ISBN: (数字)9798350342291

ISBN: (纸本)9798350342307

Over the past decade, the field of assisted and autonomous driving has experienced significant advancements. However, autonomous driving systems are still challenged by the complexities of dynamic urban environments, especially when it comes to predicting and responding to the often stochastic behavior of pedestrians. Current approaches largely concentrate on most likely predictions but tend to ignore their inherent probabilistic nature. Our research introduces a novel Transformer-based multimodal probabilistic prediction model that utilizes a Gaussian Mixture Model (GMM). This approach is simpler than its predecessors, yet it maintains competitive performance, capable of inferring prediction uncertainties using GMM parameters. Additionally, we demonstrate how our prediction model can be incorporated into a risk-aware behavior planner, based on the Chance-Constrained Stochastic Shortest Path (CC-SSP) framework. This planner uses probabilistic trajectory predictions as a Markov transition function to modulate the speed of the autonomous vehicle, effectively keeping the probability of collision below a defined threshold. Our implementation is available at https://***/Murdism/Probabilistic_Pedestrian_Trajectory_***.

关键词： Pedestrians Stochastic processes Predictive models Probabilistic logic Transformers Behavioral sciences Autonomous vehicles

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： G. Revathy P. Uma Maheswari S. Madhavi Vijay Anand P R. Vajubunnisa Begum H. Jasmin Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer science and Business systems K. Ramakrishnan college of Engineering Trichy India Department of Information Technology Vel tech Multi tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach.

关键词： Deep learning Training Accuracy Histopathology Transfer learning Observers Prediction algorithms Partitioning algorithms Convolutional neural networks Ovarian cancer

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Emerging Trends in AI-Powered IoT: Innovations, Key Challenges, and Future Directions

Emerging Trends in AI-Powered IoT: Innovations, Key Challeng...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Abdelraouf, Hussien Abouyoussef, Mahmoud Fouda, Mostafa M. Fadlullah, Zubair Md Ibrahem, Mohamed I. School of Computer and Cyber Sciences Augusta University AugustaGA30912 United States North Carlina A&T State University Department of Computer Systems Technology GreensboroNC27411 United States Idaho State University Department of Electrical and Computer Engineering PocatelloID83209 United States Western University Department of Computer Science LondonON Canada

ISBN: (纸本)9798331504847

This survey provides a comprehensive overview of recent emerging technologies in artificial intelligence (AI) applied to the Internet of Things (IoT), highlighting their significance and applications across various domains. Our study covers key advancements, including Concept Drift, Transformers, TinyML, Explainable AI (XAI), and Federated Learning (FL). Concept Drift addresses changes in data patterns to maintain the accuracy of machine learning models in dynamic environments. Transformers revolutionize Natural language processing (NLP) by efficiently capturing long-range dependencies. TinyML enables smart IoT applications on low-power devices, while XAI promotes transparency in AI decisions. FL facilitates decentralized model training while preserving data privacy. Our discussion also covers challenges, limitations, and future research directions to improve AI and IoT integration. © 2025 IEEE.

关键词： Federated learning

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