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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yun, Sohyeon Kim, Han-Joon University of Seoul Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350344349

High-quality data is essential to increase the reliability of machine learning-based prediction models. For time series data, anomalies significantly reduce the accuracy of prediction models. In this paper, we propose a novel time series data correction method that converts abnormal values of univariate time series data into normal ones. For anomaly detection and correction, we utilize the LSTM Autoencoder model, where we propose a new weight function that considers temporal distance. Through experiments using the open NAB data, we show that our proposed method is superior to the recent conventional methods. © 2024 IEEE.

关键词： Anomaly detection

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Mutual character dialogue generation with semi-supervised multitask learners and awareness

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

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Cricket Team Selection and Player Analysis using Data Analytics 6

Cricket Team Selection and Player Analysis using Data Analyt...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Raajesh, Sanjay Martin, Noel Jiji, Jyothsna Nair, Aakash Haritha, H. Muthoot Institute of Technology and Science Department of Computer Science and Engineering India

ISBN: (纸本)9798350381689

The inception of the 'Cricket Team Selection and Player Analysis using Data Analytics' project arose from the imperative to introduce precision through data-driven methods in the process of selecting cricket teams. Recognizing the pivotal role of team composition in the sport, our project employs the K-means clustering algorithm to categorize players based on their historical performance data with the strike rate. This approach facilitates the streamlined creation of well-balanced teams, targeting cricket enthusiasts, coaches, and team selectors. The ultimate goal is to enhance their decision-making processes, optimizing team performance and strategy. The project adopts a comprehensive approach, heralding a new era in cricket by leveraging data. It enables the formation of playing elevens derived from historical player performance metrics, ensuring a more resilient and strategic selection process. The project also has a selection of top players based on the attributes provided by the user. The tangible outcome of this project is a tool designed to serve cricket stakeholders, aiding them in making informed decisions for improved performance on the field. Representing a significant stride towards a more data-driven approach to cricket, the 'Cricket Team Selection and Player Analysis using Data Analytics' project is poised to redefine team selection and strategy in the sport. Its applicability extends to a broad audience, promising to be a transformative force in the realm of cricket decision-making. © 2024 IEEE.

关键词： Support vector machines

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Simplified Model Predictive Current Control of a Mono-Inverter Dual Parallel Permanent Magnet Synchronous Motors

Simplified Model Predictive Current Control of a Mono-Invert...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lee, Hyung-Woo Lee, Kyo-Beum Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350376067

This paper proposes the simplified model predictive current control (MPCC) of a mono-inverter dual parallel (MIDP) permanent magnet synchronous motors (PMSMs). In the proposed MPCC method, one of the two parallel PMSMs is selected as the master PMSM to be object of control. The output torque of the slave PMSM is controlled with the master PMSM by utilizing the current relationship between the two motors. The application of the MPCC is operated through the d-axis current of the master PMSM. The candidate voltage vectors are generated within a segmented voltage vector plane for the reduction of computational burden and improvement of torque quality. To reduce the torque ripple, the voltage vectors applied to the inverter are conclusively determined using discrete space vector modulation (DSVM). The validity of the proposed method is verified through simulation results. © 2024 IEEE.

关键词： Dual PMSM drive Model predictive current control Mono-inverter Surface-mounted permanent magnet synchronous motor

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A comprehensive handwritten Indic script recognition system: a tree-based approach

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 943-960页

作者： Singh, Pawan Kumar Sarkar, Ram Bhateja, Vikrant Nasipuri, Mita Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Lucknow India

A noteworthy achievement has been accomplished in developing optical character recognition (OCR) systems for different Indic scripts handwritten document images. But in a multi-script country like India, this cannot serve the entire purpose of document digitization when such multi-script document images need to be converted into machine readable form. But developing a script-invariant OCR engine is almost impossible. Therefore, in any multi-script environment, a complete framework of script identification module is very essential before starting the actual document digitization through OCR engine. Keeping this research need in mind, in this paper, we propose a novel handwritten script recognition model considering all the 12 officially recognized scripts in India. The classification task is performed at word-level using a tree-based approach where the Matra-based scripts are firstly separated from non-Matra scripts using distance-Hough transform (DHT) algorithm. Next, the Matra and non-Matra based scripts are individually identified using modified log-Gabor filter based features applied at multi-scale and multi-orientation. Encouraging outcomes establish the efficacy of the present tree-based approach to the classification of handwritten Indic scripts. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Trees (mathematics)

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High-Accuracy Transfer Learning-Based Power Quality Disturbance Recognition Under A Noisy Environment 28

High-Accuracy Transfer Learning-Based Power Quality Disturba...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Zarei, Mohammad Ebrahim Emadaleslami, Mahdi Abedi, Mohammad Mahdi Baghaee, Hamid Reza Haghifam, Mahmoud-Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350385502

The smart and sustainable development of power grids has brought about numerous power quality disturbances (PQD) that threaten the efficiency and reliability of the smart grids. Detecting and classifying these disturbances is crucial to taking appropriate corrective action to mitigate possible impacts. While machine learning (ML) algorithms are at the center of attention for most PQD detection problems, their performance is limited due to insufficient samples. Therefore, this paper proposes a new model based on continuous wavelet transform (CWT) and transfer learning to overcome the data challenge. In this regard, raw voltage signals are First transformed to 2D contour images using the CWT. Then, converted images are used to fine-tune a pre-trained model on the Imagenet dataset through transfer learning. The simulation results over 29 groups of PQD under different noise levels show its effectiveness over other ML models. Finally, our code is publicly available in a git repository for enthusiastic readers. © 2024 IEEE.

关键词： Smart power grids

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MOOD: Mindfulness fOr sOftware Developers 24

MOOD: Mindfulness fOr sOftware Developers

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18th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2024

作者： Romano, Simone Scanniello, Giuseppe Marchetto, Alessandro Giorgini, Paolo Guidetti, Gloria Converso, Daniela Viotti, Sara Department of Computer Science University of Salerno Italy Department of Information Engineering and Computer Science University of Trento Italy Department of Psychology University of Turin Italy

ISBN: (纸本)9798400710476

Peopleware, which includes anything related to the role of people in Software Development (SD), has been arousing an increasing interest from both the software industry and research community. This interest is due to the current economic system that demands high-quality software products with a short time to market, staying on the budget. This exposes software developers to the risk of experiencing stress, burnout, and reduced motivation, leading, in turn, to reduced job performance, low-quality SD-related artifacts, and increased turnover. Mindfulness represents a promising intervention that might let developers do their best at work, limiting or even preventing the previously mentioned negative outcomes. This paper presents MOOD (Mindfulness fOr sOftware Developers), a research project whose overarching goal is to customize a well-known and validated group-based intervention program, Mindfulness-Based Stress Reduction (MBSR), in the context of SD-related tasks and assess whether it helps developers to improve their well-being and performance, as well as the quality of the SD-related artifacts they produce. © 2024 Owner/Author.

关键词： Budget control

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems 3

Secure and Efficient Deduplication for Multimedia Sharing in...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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A Study of Contrastive Learning Methods for Bengali Social Analysis 6

A Study of Contrastive Learning Methods for Bengali Social A...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Maisha, Jannatim Swarnali, Farhana Hossain Iftikar, M. Saymon Islam Ahammed, Sarkar Bulbul Shah, Faisal Muhammad Ahsanullah University of Science and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Across various domains, Contrastive Learning (CL) has already proven to be a powerful technique but using the Bengali language in the domain of Natural Language Processing (NLP) its' application is still unexplored. In this research, we introduce the implementation of CL in Bengali NLP by presenting a comprehensive benchmark study on two distinct datasets: Bengali Hate Speech, and Rokomari Book Review. The efficiency of the supervised contrastive techniques is emphasized by our methodology. We have implemented a contrastive learning technique through Bangla Bert. The superiority of supervised contrastive learning techniques over traditional Cross Entropy (CE) methods has been showcased by our result. Detailed experiments reveal performance variations across datasets, models, and hyperparameters but ensure the superiority of CL techniques over CE. Our findings show the significance of contrastive learning methods in low-resource settings, contributing to the advancement of Bengali NLP research. © 2024 IEEE.

关键词： Learning systems

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Detection of fungal infection in dogs using deep learning techniques 4th

Detection of fungal infection in dogs using deep learning te...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sunaina Kaur, Sukhpreet Kaur, Sukhdeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Fungal infections at the skin of puppies, including dermatophytosis, are commonplace and pose great health dangers to each the animals and their proprietors. Although these infections do no longer affect human skin immediately, rapid detection remains important for well-timed remedy and spreading prevention. The currently established diagnosis process frequently includes the visual confirmation of the infection by medical professionals and fungal culture and is often referred to as time-intensive and highly dependent on medical professionals’ qualifications. Over the past few years, deep learning has become quite a promising area of research, R&D, and deployment in multiple fields, including medical imaging. The current research develops a deep learning model for fungal infection detection on the skin of dogs based on a variety of architectures and variations of Convolutional Neural Networks to classify image patches as healthy and infected. Transfer learning for the model structuring is reviewed to determine the impact of pre-trained models on the model’s learning efficiency. © 2025 the Author(s).

关键词： Convolutional neural networks

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