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Forecasting System of Wind Speed and Direction by Neural Network 9

Forecasting System of Wind Speed and Direction by Neural Net...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Sari, Anggraini Puspita Yasuno, Takashi Prasetya, Dwi Arman Al Haromainy, Muhammad Muharrom Universitas Pembangunan Nasional 'Veteran' Jawa Timur Informatics Dept. Surabaya Indonesia Tokushima University Electrical and Electronic Engineering Dept Tokushima Japan Universitas Pembangunan Nasional 'Veteran' Jawa Timur Data Science Dept. Surabaya Indonesia

ISBN: (纸本)9798350306835

This paper discusses the neural network that is used to forecast wind speed and direction by combining methods of three-dimensional convolutional neural networks (3D-CNN), convolutional long short-term memory (CLSTM), and two-dimensional CNN (2D-CNN). Its method is called 3CN-CLSTM-2CN. Combining the three methods is to diminish the forecasted error and training time compared to the 3CN-CLSTM method. The input of the forecast system is the wind vector in the form of image data that indicates the image of time-sequential which is given by the Automated Meteorological data Acquisition System (AMeDAS), Japan. Evaluate the forecast system used by the root mean square error (RMSE) between observed data and forecast data. For verification, the usefulness of the proposed forecasted system is compared with the persistent system. The suggested forecasting system's objective is to increase prediction accuracy for each season, which is typical of Japan. The proposed forecasted system by 3CN-CLSTM-2CN for each season in one-year data can increase around 16.11-26.47% to wind speed and around 20.69-23.34% to wind direction over the persistent system, which has a greater accuracy than the 3CN-CLSTM system. So, it was suitable that the 3CN-CLSTM-2CN system be the most robust forecasting system. © 2023 IEEE.

关键词： Long short-term memory

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Dementia Detection Using Machine Learning in Males and Females

Dementia Detection Using Machine Learning in Males and Femal...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Iyer, Aishwarya Hariharan Vekkot, Susmitha Gupta, Deepa Gopalakrishnan, E.A. School of Computing National University of Singapore Singapore Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Bangalore India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798350379716

In this study, we train different Machine Learning classifiers on male and female samples from The ADReSS Challenge dataset. After testing them on data consisting of both male and female samples as well as on data with samples of only the corresponding sex, we use SHapley Additive exPlanations on the models to identify the most important features. Then, we present the results of training the models with only the most important features identified. Finally, we present a comparative analysis based on the observations and insights gathered through these experiments. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluating Machine Learning Methods for Bangla Text Emotion Analysis

Evaluating Machine Learning Methods for Bangla Text Emotion ...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Tahmina Akter Mst. Sharmin Akter Tanjim Mahmud Dilshad Islam Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Physical and Mathematical Sciences Chattogram Veterinary and Animal Sciences University Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Text-based emotion identification goes beyond simple sentiment analysis by capturing emotions in a more nuanced way, akin to shades of gray rather than just positive or negative sentiments. This paper details our experiments with emotion analysis on Bangla text. We collected a corpus of user comments from various social media groups discussing socioeconomic and political topics to identify six emotions: sadness, disgust, surprise, fear, anger, and joy. We evaluated the performance of four widely used machine learning algorithms—RF, DT, k-NN, and SVM—alongside three popular deep learning algorithms—CNNs, LSTM, and Transformer Learning—using TF-IDF feature extraction and word embedding techniques. The results showed that among the machine learning algorithms, DT, RF, k-NN, and SVM achieved accuracy scores of 82%, 84%, 73%, and 83%, respectively. In contrast, the deep learning models CNN and LSTM both achieved higher performance with an accuracy of 85% and 86% respectively. These findings highlight the effectiveness of traditional ML and DL approaches in detecting emotions from Bangla social media texts, indicating significant potential for further advancements in this area.

关键词： Deep learning Support vector machines Technological innovation Sentiment analysis Machine learning algorithms Accuracy Social networking (online)

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Assessing the Efficacy of Distributed Ledger Technology for Immutable data Logging and Fraud Prevention in IoT Retail Applications

Assessing the Efficacy of Distributed Ledger Technology for ...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Ponnrajakumari, M. Elanangai, V. Ramprasad, S.R. Velpula, Vijaya Kumar Bhagyalakshmi, L. Prabha, A.G. Velammal Engineering College Dept. of Ece Chennai India St. Peter's Institute of Higher Education and Research. Avadi Department of Electrical and Electronics Engineering Chennai India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Mlr Institute of Technology Dept of Ece Hyderabad India Rajalakshmi Engineering College Dept. of Ece Chennai India

ISBN: (纸本)9798350388800

This paper presents a comprehensive framework for assessing the efficacy of Distributed Ledger Technology (DLT) in IoT retail applications. The framework integrates five key algorithms: data Validation Algorithm (DVA), Consensus Mechanism Algorithm (CMA), Fraud Detection Algorithm (FDA), Privacy-Preserving Algorithm (PPA), and Dynamic Smart Contract Algorithm (DSCA). Each algorithm addresses specific challenges, ensuring data integrity, achieving consensus, preventing fraud, preserving privacy, and enhancing smart contract adaptability. The ablation study evaluates the individual contributions of each algorithm, highlighting their significance in the overall framework. Comparative performance evaluations demonstrate the proposed framework's superiority, with higher scores across key parameters compared to existing assessment methods. The framework offers a robust solution for enhancing the security, integrity, and efficiency of DLT in IoT retail applications, addressing multifaceted challenges and requirements. © 2024 IEEE.

关键词： Distributed ledger

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Montgomery Modular Multiplication via Single-Base Residue Number Systems

Montgomery Modular Multiplication via Single-Base Residue Nu...

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computer Arithmetic (ARITH)

作者： Zabihollah Ahmadpour Ghassem Jaberipur Jeong-A Lee Dept. of Computer Science and Engineering Shahid Beheshti University Tehran Iran Dept. of Computer Engineering Chosun University Gwangju Republic of Korea

ISBN: (数字)9798350384321

ISBN: (纸本)9798350384338

Montgomery modular multiplication (MMM) in residue number systems (RNS) uses a base extension (BE) technique. This is to avoid division, which is hard, slow and costly in RNS. It is somewhat less costly and faster than the reverse conversion, via Chinese remainder theorem (CRT) and reduction factor method. However, it is used one after the other, for each of the equally large bases. In this work, we modify the conventional RNS-MMM algorithm via replacing the two unparalleled BE undertakings with three parallel CRT-like operations with the same complexity, as BE. As for the reduction factors, we use a special case of the Kawamura’s algorithm that leads to definitive result. The proposed RNS-MMM method allows for squaring the working dynamic range, or halving the bit-width of the balanced residue channels. Moreover, the common practice of dynamically changing the working moduli set in security and crypto applications is less critical due to doubled size of the pool of available moduli. The proposed circuits are simulated, tested and synthesized via Synopsys Design Compiler on the TSMC 65-nm technology, to show 69% less delay and 28% less area-time-product at the cost of 14% more energy consumption, with respect to the most relevant reference work.

关键词： Energy consumption Costs Heuristic algorithms Circuits Dynamic range Cathode ray tubes Digital arithmetic

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Smart Vehicle Parking System on Fog Computing for Effective Resource Management 1

Smart Vehicle Parking System on Fog Computing for Effective ...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Kumara, Shubham Prasad, Nimmala Hari Monika, M. Tuli, Harshit Supreeth, S. Rohith, S. REVA University School of CSE Bengaluru India REVA University School of Computer Science and Engineering Bengaluru India Nagarjuna College of Engineering and Technology Dept. of ECE Bengaluru India

ISBN: (纸本)9798350323795

The rapid advance in the number of vehicles on the road led to a corresponding growth in demand for parking spaces. However, conventional car parking systems are inefficient and time-consuming. Smart Parking systems also require immediate response, and low latency, which may be impacted by data transfer delays to and from the cloud. Cloud computing can provide infrastructure-level services. Also, the smart parking system is a sensitive application that can lead to disaster if an immediate decision has not been taken. To address these challenges, this research proposes the smart car parking system using fog computing that reduces latency. The suggested smart vehicle parking system uses a combination of sensors, cameras, and edge devices to collect and process parking-related data. This connection generates an enormous bundle of data that is required to be saved and processed. Fog computing widens cloud services to the end of the network to cut down latency and congestion. The major challenge in implementing Fog computing is managing the resources, which involves applications to determine each edge gadget effectively, which reduces latency and increases throughput. The proposed simulation that portrays IoT and Fog nodes and evaluates the consequences of resource management approaches in terms of QoS parameters. The proposed hybrid markov system have been utilized in various parking scenarios, including shopping centres airports, and residential areas. The paper contributes to the development of a smart vehicle parking system by proposing a computing-based approach using fog that optimizes resource utilization and provides real-time processing for parking management. © 2023 IEEE.

关键词： Internet of things

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Online Signature Recognition using Transformer Networks: An Overview 6

Online Signature Recognition using Transformer Networks: An ...

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6th IEEE International Conference on Advances in science and Technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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An Overview of Real-Time Electricity Market and Green Energy Trading in India

An Overview of Real-Time Electricity Market and Green Energy...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Zacharias, Annu Mary Dikaiakos, Christos Anto, Robins University of London School of Mathematics Computer Science and Engineering City United Kingdom Indian Institute of Technology Dept. of Hydro and Renewable Energy Roorkee India

ISBN: (纸本)9798350331790

To manage the high load fluctuations arising from renewable energy integration, the Ministry of Power Govt. of India has launched real-time trading in the energy market. In this paper, we discuss the real-time market process, regulatory authorities, system balancing, prerequisites of RTM of India, and one of the prominent real-time energy markets in the world. The market process is based on bidding, and Indian market has a zone wised bidding system. In India, the energy is traded in two market platforms named Energy Exchange of India and Power Exchange of India Ltd. The energy market operations are unavoidably connected with the load dispatch centers as the market has to be cleared by considering the transmission corridor availability within the system. The power exchanges of India and the load dispatch centers are monitored and guided by the central electricity regulatory authority and Power Grid which is formerly known as the Power system operation corporation. The real-time power trading is done for 30 minutes, and the auction is open for 15 minutes, followed by market clearing after gate closure. In addition to the real-time market, this paper gives a brief overview of the green energy market window in Indian energy exchanges. © 2023 IEEE.

关键词： Renewable energy resources

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Performance Analysis of Load Balancing Mechanism in Cloud Computing

Performance Analysis of Load Balancing Mechanism in Cloud Co...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： Annaiah Halappa A. Rajesh Dept. of Computer Science & Engineering Government Engineering College Hassan India Dept. of Computer Science & Engineering Jain Technological University Bengaluru India

The advancement of Information Technology has made cloud computing technology an innovative model for offering its consumers services on a rental basis at any time or location. Numerous firms converted to cloud technology by establishing new data centers because of the flexibility of cloud services. However, it has become necessary to ensure successful job execution and effective resource usage. Load balancing (LB) in cloud computing remains a complex challenge, specifically in the Infrastructure as a Service (IaaS) cloud architecture. A server being overloaded or underloaded is a problem that mustn’t happen in the process of cloud access because it would slow down processing or causes a system crash. Hence, to ignore these problems, a suitable resource schedule must be taken, so that system can load balance tasks over all accessible assists. This research suggests effective load-balancing approaches by analyzing the advantages, applications, and disadvantages of conventional LB techniques. The conclusions show that this research provides an exceptional path for researchers to overcome major drawbacks of existing LB techniques and achieves greater efficiency based on makespan, execution and response time, resource usage, efficiency, load balancing and throughput.

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