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A multi-modal approach for mixed-frequency time series forecasting

Neural computing and Applications 2024年第34期36卷 21581-21605页

作者： Filho, Leopoldo Lusquino de Oliveira Werneck, Rafael Castro, Manuel Ribeiro Mendes Júnior, Pedro Lustosa, Augusto Zampieri, Marcelo Linares, Oscar Moura, Renato Morais, Elayne Amaral, Murilo Salavati, Soroor Loomba, Ashish Esmin, Ahmed Gonçalves, Maiara Schiozer, Denis José Ferreira, Alexandre Davólio, Alessandra Rocha, Anderson Institute of Computing Universidade Estadual de Campinas - UNICAMP SP Campinas13083-852 Brazil Institute of Science and Technology São Paulo State University - UNESP SP Sorocaba18087-180 Brazil Universidade Estadual de Campinas - UNICAMP SP Campinas13083-970 Brazil Department of Computer Science Federal University of Lavras - UFLA MG Centro Lavras37200-000 Brazil School of Mechanical Engineering Universidade Estadual de Campinas - UNICAMP SP Campinas13083-970 Brazil

This study proposes a novel multimodal approach for mixed-frequency time series forecasting in the oil industry, enabling the use of high-frequency (HF) data in their original frequency. We specifically address the challenge of integrating HF data streams, such as pressure and temperature measurements, with daily time series without introducing noise. Our approach was compared with existing econometric regression model mixed-data sampling (MIDAS) and with the data-driven models N-HiTS and a GRU-based network, across short-, medium-, and long-term prediction horizons. Additionally, we validated the proposed method on datasets from other domains beyond the oil industry. The experimental results indicate that our multimodal approach significantly improves long-term prediction accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Petroleum industry

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Performance evaluation using execution time aware bio-inspired virtual machine placement strategy 10

Performance evaluation using execution time aware bio-inspir...

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10th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2021

作者： Rawat, Pradeep Singh Bhadoria, Robin Singh Department of Computer Science & Engineering Uttarakhand Technical University Dehradun India Department of Computer Science & Engineering Birla Institute of Applied Sciences Bhimtal Nainital India

ISBN: (纸本)9780738105239

In the generation of computing, technology, and communication, cloud computing plays a prominent role. The utility computing paradigm which provides resources as a service is known as cloud computing. Cloud computing provides efficient use of cloud resources using a scalable simulation and modeling process. This work focuses on the performance evaluation of task assignments on virtual machine execution using a bio-inspired strategy. The quality of service is evaluated using average waiting time and average completion time (ms) of tasks assigned to the virtual machines using heavy-duty virtualization. An integrated architecture is proposed which inculcates the features of the bio-inspired technique and Kubernetes-based orchestration of container or lightweight virtual machine. The presented results are compared against static approaches, i.e., time shared and first come first serve techniques. © 2021 IEEE.

关键词： Virtual machine

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Nanophotonic-structured front contact for high-performance perovskite solar cells

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science China Materials 2022年第7期65卷 1727-1740页

作者： Md.Akhtaruzzaman Mohammad Ismail Hossain Mohammad Aminul Islam Md.Shahiduzzaman Ghulam Muhammad A.K.Mahmud Hasan Yuen Hong Tsang Kamaruzzaman Sopian Solar Energy Research Institute(SERI) Universiti Kebangsaan Malaysia43600 BangiSelangorMalaysia Department of Applied Physics Hong Kong Polytechnic University(PolyU)KowloonHong KongChina Centre for Integrated Systems Engineering and Advanced Technologies(Integra) Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia(UKM)43600 BangiSelangorMalaysia Graduate School of Pure and Applied Sciences University of TsukubaTsukubaIbaraki 305-8573Japan Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaJalan Universiti50603 Kuala LumpurSelangorMalaysia Nanomaterials Research Institute(NanoMaRi) Kanazawa UniversityKakumaKanazawa 920-1192Japan Department of Computer Engineering College of Computer and Information SciencesKing Saud University11543 RiyadhSaudi Arabia Department of Electrical and Computer Engineering University of CaliforniaDavisCA 95616USA Institute of Sustainable Energy Universiti Tenaga Nasional(@The National Energy University)Jalan IKRAM-UNITEN43000 KajangSelangorMalaysia

We report the design of a nanophotonic metaloxide front contact aimed at perovskite solar cells(PSCs)to enhance optoelectronic properties and device stability in the presence of ultraviolet(UV)***-quality Cr-doped ZnO film was prepared by industrially feasible magnetron sputter deposition for the electron transport layer of *** a means,the influence of the Cr content on the film and device was systematically ***-depth device optics and electrical effects were studied using advanced three-dimensional opto-electrical multiphysics rigorous simulations,optimizing the front contact for realizing high *** numerical simulation was validated by fabricating PSCs optimized to reach high performance,energy conversion efficiency(ECE)=17.3%,open-circuit voltage(V_(OC))=1.08 V,short-circuit current density(J_(SC))=21.1 mA cm^(-2),and fillfactor(FF)=76%.Finally,a realistic front contact of nanophotonic architecture was proposed while improving broadband light absorption of the solar spectrum and light harvesting,resulting in enhanced quantum efficiency(QE).The nanophotonic PSC enables J_(SC)improvement by~17%while reducing the reflection by 12%,resulting in an estimated conversion efficiency over 23%.It is further demonstrated how the PSCs’UV-stability can be improved without considerably sacrificing optoelectronic *** of nanophotonic designed ZnO:Cr front contact,PSCs device,and fabrication process are described.

关键词： ZnO:Cr front contact magnetron sputtering perovskite solar cells UV stability optics and electrical effects

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Advanced Receiver Operating Characteristic Curve Analysis to Identify Outliers in Binary Machine Learning Classifications for Precision Medicine

Advanced Receiver Operating Characteristic Curve Analysis to...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Namdar, Khashayar Khalvati, Farzad Department of Diagnostic & Interventional Radiology The Hospital for Sick Children TorontoON Canada SickKids Research Institute Neurosciences & Mental Health Research Program TorontoON Canada Department of Medical Imaging University of Toronto TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada University of Toronto Department of Computer Science TorontoON Canada Department of Mechanical and Industrial Engineering University of Toronto TorontoON Canada Vector Institute TorontoON Canada

ISBN: (纸本)9798350351552

The Receiver Operating Characteristic (ROC) curve is a critical tool for binary classification analysis in medicine, with the Area Under the ROC Curve (AUROC) serving as a widely accepted metric to evaluate the performance of binary classifiers. This study conducts a comprehensive review of the ROC curve with a focus on its utility in outlier identification. We introduce a novel scoring method to rank actual positives and actual negatives within a test set, according to their impact on AUROC degradation. We bridge the scoring system with the ROC curve analysis to quantify each data point's contribution to AUROC loss. Furthermore, we introduce the IMICS ROC Analyzer, a graphical user interface-based software, embedded with our innovative algorithms. Through the use of an open-source prostate cancer dataset, we illustrate the application of our algorithms for practical outlier detection in binary classification tasks. The IMICS ROC Analyzer enhances the field of precision medicine by allowing for measuring an individual's contributions (be it patients, lesions, or samples) to the overall AUROC, thus facilitating confidence measurement of Machine Learning (ML) classifiers for individual cases of interest in a cohort. © 2024 IEEE.

关键词： Adversarial machine learning

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Velocity-Free Containment Control with Binary Relative Measurements Only

SSRN

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SSRN 2024年

作者： Shukla, Ashish Dunna, Vijaya Kumar Sen, Arijit Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Madhya Pradesh Bhopal462066 India

This paper introduces a containment control law for a multi agent system (MAS) of double integrator agents using only relative position information. Unlike existing methods, this approach eliminates the need for velocity or exact position measurements as the feedback, reducing communication and sensor requirements. Using the non-smooth Lyapunov analysis, we prove the convergence of the proposed control law under any detailed-balanced network with any positive gains. Moreover, the controller inherently guarantees bounded control effort and finite-time or fixed-time convergence without needing any additional update law. Finally, the numerical simulations demonstrate the effectiveness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Early Diagnosis of Diabetes and Prediction Using Machine Learning Algorithm

Early Diagnosis of Diabetes and Prediction Using Machine Lea...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Pavithra S Ravikumar S Sreesubha S Babu R M. Kalaiselvi School of Computer Science and Engineering Vellore Institute of Technology Chennai Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Alagappa chettiar government college of engineering and technology

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This study utilizes machine learning techniques to predict the presence of diabetes at an early stage. Diabetic complications, including kidney and heart disease, can worsen over time if not detected at an earlier stage. Making the correct changes to one's lifestyle at the right time is crucial for preventing diabetes and its complications. So, there is an increasing need for a tool that can help doctors to detect this potentially deadly disease. This study has proposed machine learning based early stage diabetes detection model. The proposed model's performance is evaluated by analyzing how well the random forest classifier, a model known for its high accuracy, and resistance to overfitting performs. Random forest is a great option for feature selection because they deal with feature-intensive datasets, even in high-dimensional settings, and give useful information regarding the importance of features.

关键词： Resistance Analytical models Accuracy Machine learning algorithms Medical services Robustness Diabetes Random forests Tuning Diseases

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Recurrent Neural for Time Series Prediction in Finance

Recurrent Neural for Time Series Prediction in Finance

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Tina Babu S Kishore Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications name of organization Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Overcoming risk is crucial when predicting financial time series, such as stock prices, due to market volatility influenced by factors like economic reports, political events, and investor sentiment. Linear models like ARIMA handle linear dependencies well but fail to capture nonlinear relationships, which are key in financial forecasting. While Long Short-Term Memory (LSTM) models excel at capturing temporal dependencies, they struggle with identifying significant market events, potentially contaminating predictions. This paper introduces an Attention RNN with Sentiment Integration, which combines Recurrent Neural Networks (RNN) with an attention mechanism for sentiment analysis of financial news and social media. The model considers relevant time steps and market sentiment, improving context-awareness. Results demonstrate that this hybrid approach outperforms traditional methods by providing a more accurate and flexible forecasting model, addressing the complexities of financial markets. Additionally, sentiment data helps the model capture both stock-specific and overall market sentiment, particularly during periods of heightened uncertainty or volatility, offering a more comprehensive approach to stock price prediction. Keywords: Risk, Financial Time Series, ARIMA, LSTM, Attention RNN, Sentiment Analysis, Market Sentiment, Stock Prices, Forecasting, Volatility.

关键词： Sentiment analysis Technological innovation Recurrent neural networks Attention mechanisms Uncertainty Biological system modeling Time series analysis Predictive models Forecasting Long short term memory

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Strain-balanced AlScN/GaN HEMTs with fT/fMAX of 173/321 GHz

Strain-balanced AlScN/GaN HEMTs with fT/fMAX of 173/321 GHz

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Nguyen, T.S. Nomoto, K. Zhao, W. Savant, C. Xing, H.G. Jena, D. Department of Materials Science and Engineering Cornell University IthacaNY14853 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

ISBN: (纸本)9798350365429

We report the effect of scaling the gate length from 0.8 µm to 40 nm on the performance of novel strain-balanced AlScN/GaN high electron mobility transistors (HEMTs) on SiC substrates. A new strain-balanced heterostructure is introduced where the tensile strain of the AlN interlayer is balanced by the compressive strain in the AlScN barrier layer. MBE regrown source/drain ohmics with ultralow contact resistance of 0.09 Ω·mm allow a low HEMT on resistance of 0.83 Ω·mm. The shortest gate length AlScN/GaN HEMTs exhibit maximum drain currents of 2.8 A/mm, peak transconductance of 0.55 S/mm, and speed characterized by fT/fMAX of 173/321 GHz. The highest fMAX obtained here is ∼2X higher than previous AlScN HEMTs. © 2024 IEEE.

关键词： Gallium nitride

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Next-Gen Waste Management: AI as a Catalyst for Sustainability 2

Next-Gen Waste Management: AI as a Catalyst for Sustainabili...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sheikh, Nishad Kumar, Praveen Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of computer science and medical engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331505745

Traditional waste management techniques frequently be afflicted by inefficiencies, high operational fees, and environmental drawbacks. However, latest improvements in Artificial Intelligence, such as computer vision and Internet of Things, are remodeling waste management right into an automated and optimized procedure. This research study explores how AI-pushed technology enhance waste series, sorting, and recycling methods. Machine learning algorithms enhance waste category accuracy, computer vision permits unique fabric separation, and IoT-powered smart containers optimize collection schedules, decreasing needless trips and gasoline consumption. Additionally, AI-pushed direction optimization algorithms Significantly reduce emissions by means of designing efficient series pathways. These advancements Contribute. Not only to improve operational efficiency but also to the broader dreams of decreasing environmental impact and promoting a Circular economy. Despite its transformative potential, AI integration in waste control affords monetary, ethical, and technological challenges that require cautious attention. This examine provides a comprehensive evaluation of AI packages, their blessings, boundaries, and destiny capacity in revolutionizing waste management. With AI solutions, the enterprise can transition towards a more adaptive, efficient, and sustainable waste management ecosystem, addressing both urban and environmental demanding situations in the process. © 2025 IEEE.

关键词： Waste treatment

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Towards Accelerating k-NN with MPI and Near-Memory Processing

Towards Accelerating k-NN with MPI and Near-Memory Processin...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Hooyoung Ahn Seonyoung Kim Yoomi Park Woojong Han Nick Contini Bharath Ramesh Mustafa Abduljabbar Dhabaleswar K. Panda Supercomputing Technology Research Center Electronics and Telecommunications Research Institute Department of Computer Science and Engineering The Ohio State University

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Message Passing Interface (MPI) is a common parallel programming model in High-Performance computing fields. Recently, it has been widely used in Artificial Intelligence (AI) applications. However, the performance of those applications is limited by the memory wall problem, which is the performance gap between the processor and memory. To address these problems, we propose a novel computing architecture for accelerating k-NN query, which is the key operation of AI applications. The proposed computing architecture involves two or more computing nodes within a rack using the memory of an NMP device as a communication buffer and the accelerator of the NMP device for MPI collective communication. The superiority of the proposed method is that it reduces data copying during local computation and eliminates the network cost of gathering local computation results for global computation. Furthermore, it can enhance performance by processing data in the device's memory through near-memory processing. However, since these NMP devices are still in development, with no commercially available products currently, we have undertaken the task of developing them ourselves. Despite these circumstances, to demonstrate the feasibility of our proposed approach, we have implemented it in a single-node environment without making any unreasonable assumptions and have compared it with conventional approaches. Although we are using a single-node environment, we have designed a more sophisticated architecture, analyzed the time complexity, and compared the estimated performance of both the conventional and proposed approaches. We can conclude that the proposed computing architecture is feasible, and we anticipate an even more performance enhancement in a multi-node environment.

关键词： Performance evaluation Costs Parallel programming Message passing High performance computing Conferences computer architecture

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