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A Novel Dual FedAux-based Harvested Energy Prediction for Road Safety Systems

A Novel Dual FedAux-based Harvested Energy Prediction for Ro...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Papa Sidy Mactar Traoré Wael Jaafar Fatoumata Baldé Dept. Computer Science Université Alioune Diop Bambey Sénégal Dept. Software and IT Engineering École de Technologie Supérieure (ÉTS) Montreal Canada

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

In the evolving landscape of road safety, the role of solar-powered Internet-of-Things (IoT) devices, crucial for supporting connected and autonomous vehicles (CAVs) with enhanced computing and storage capabilities, is increasingly significant These devices, often deployed in remote areas such as transcontinental highways, rely heavily on their energy harvesting capabilities for optimal operation. Given their expected widespread deployment, this paper explores the challenge of accurately predicting harvested energy in such IoT devices. To do so, we introduce "Dual FedAux", a novel approach that blends Federated Learning with a dual-model architecture, integrating long short-term memory (LSTM) and Transformer models. This combination, which is enhanced with meta-learning, i.e., stacking ensemble learning, is particularly adapted for time series analysis, such as for energy prediction. Through experiments, we demonstrate that the proposed Dual FedAux significantly outperforms benchmark models in terms of root-mean-square error (RMSE) and convergence, thus underscoring its potential to enhance the efficiency and reliability of large-scale road safety IoT-based systems.

关键词： Computational modeling Time series analysis Solar energy computer architecture Predictive models Transformers Road safety

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Heuristic approaches for hiding sensitive frequent itemsets in uncertain databases

Heuristic approaches for hiding sensitive frequent itemsets ...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Mira Lefkir Farid Nouioua Phillipe Fournier-Viger Dept. of Computer Science University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj BBA Algeria College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Pattern mining is a core objective within data mining, involving the detection of frequent itemsets (collections of values) within databases. This process serves to extract valuable insights from the data, facilitating informed decision-making. In recent times, a range of algorithms has emerged to adapt this task to the realm of uncertain *** has a wide range of applications, such as analyzing medical data to identify correlated symptoms based on their existential probabilities. However, pattern mining algorithms also pose the risk of revealing sensitive information. As far as we know, there is no existing solution that addresses the inadvertent disclosure of sensitive itemsets when pattern mining algorithms are employed on uncertain databases. This paper addresses this problem by proposing three heuristic approaches, which ensure that sensitive expected frequent itemsets are not discovered while minimizing undesirable side effects for other itemsets. The first heuristic approach (called aggregate) deletes some transactions from an uncertain database. The second approach (named disaggregate) removes some selected items from each transaction. And the third approach (called the hybrid approach) combines the two previous approaches, and deletes some selected items from some selected transactions. An experimental evaluation is presented to assess and compare the effectiveness of the three approaches.

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Sankalpa - Interactive Digital Learning Platform to Enrich Students with Special Needs

Sankalpa - Interactive Digital Learning Platform to Enrich S...

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Advancements in Computing (ICAC), International Conference on

作者： A.I Basnayaka E.K.G.D. Epitakaduwa C.P.U Jayawardena Ashan Zoysa Uditha Dharmakeerthi Sanath Jayawardena Dept of Computer Science and Software Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka Dept of Computer Science and Information Technology Sri Lanka Institute Of Information Technology Malabe Sri Lanka Dept of Computer Science and Network Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka

The term special needs mean the special educational needs that the students who suffer from learning disabilities must have due to several reasons. Learning impairment is a condition of the neurological system that impairs one's capacity to efficiently acquire and process knowledge. Failures in diagnosing the people with learning disabilities have been repeatedly highlighted and remain unpopular among people in the developing countries like Sir Lanka. Dyscalculia (difficulties in math), dysgraphia (difficulties in writing) and dyslexia (difficulties in reading) can be considered as some of the major learning disability issues. The ‘mobile application based’ solution “Sankalpa Math Master” has been created to offer a game based all-in-one solution including screening, and intervention to identify one of the above-mentioned learning disabilities called ‘Dyscalculia’. Dyscalculia is generally distinguished by challenges in learning math, including organizing problems, multi-step calculations, transposing numbers, distinguishing directions, and understanding mathematical signs, leading to confusion with basic operations and facts. The provided solution has been accurate with the use of machine learning and deep learning techniques. In the process of screening with the use of Support Vector Machine (SVM) machine learning algorithm 99.77% of accuracy level has been obtained. The skill level prediction process has acquired an accuracy level of 97% with the use of Random Forest machine learning algorithm. Sri Lankan traditional education system will be vastly benefited by the high level of accuracy in screening, skill predicting, and other game and voice chat-based activities. The students with special needs will gain more effective and personalized learning experience which will improve their learning abilities and help to close the technology gap in education.

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Quantitative Trends Analysis on Russian Literature Research in Korean Academia: Insights from KCI Data

Quantitative Trends Analysis on Russian Literature Research ...

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International Conference on Platform Technology and Service (PlatCon)

作者： Ten Alexander Iosifovich Sang-Soo Yeo Young-Ae Jung Dept. of Computer Science Sun Moon University Asan South Korea Dept. of Computer Engineering Mokwon University Daejeon South Kora Dept. of AI Software Technology Sun Moon University Asan South Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

This paper presents a quantitative analysis of trends in Russian literature and Korean-Russian translated literature within Korean academia from 1996 to 2024. By analyzing frequency themes and contexts, the study explores evolving research trends, revealing how sociocultural, political, and artistic exchanges between Korea and Russia have influenced each other. Future research using text mining promises to uncover more specific patterns, offering deeper insights into literary and cultural exchanges. This study contributes to understanding the role of literature in expressing and understanding sociopolitical and cultural values in both countries.

关键词： Text mining Translation Statistical analysis Data visualization Curriculum development Market research Cultural differences Indexes

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Real-Time Traffic Sign Detection for Autonomous Vehicles Using YOLOv11

Real-Time Traffic Sign Detection for Autonomous Vehicles Usi...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Shahabub Alam Al Sani Md Tanvir Islam Ayan Kumar Ghosh Avijit Chowdhury Department of Computer Science Universität Potsdam Potsdam Germany Dept. of Industrial & Production Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Software Sungkyunkwan University Suwon Republic of Korea Department of Mechanical Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Reliable detection of traffic signs plays a critical role in autonomous vehicles and driver-assistance technologies, especially when operating under diverse and complex real-world conditions. This study investigates the effectiveness of various YOLO-based object detection frameworks, ranging from YOLOv5 to the newly proposed YOLOv11, in addressing the task of traffic sign recognition. The dataset includes varying class configurations and challenging conditions such as low-light and noise, enabling a comprehensive analysis of model robustness. YOLOv11 outperformed prior versions, achieving the highest mAP of 0.961, establishing it as a state-of-the-art (SOTA) solution. The study also conducted ablations to analyze the impact of batch size, data augmentation, and adverse conditions on model performance. Notably, YOLOv11 demonstrated strong resilience to noise and low-light scenarios, maintaining high accuracy even under dense conditions. Comparisons with existing models further validated its superiority in terms of precision and consistency. These results highlight YOLOv11’s potential for real-world traffic sign detection tasks, where reliability is critical.

关键词： YOLO Analytical models Adaptation models Electric potential Accuracy Noise Robustness Real-time systems Autonomous vehicles Resilience

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks

A Comparative Study of the Performance of Real time database...

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International Multi-Topic ICT Conference (IMTIC)

作者： Syed Muhammd Nabeel Mustafa Muhammad Umer Farooq Syeda Sundus Zehra Jospeh Peter Terenece Noronha Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics.

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lujain Alshomali Rania Khorma Abdullah Al-Refai Abedalrhman Alkhateeb Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Lakehead University Thunder Bay Canada

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ 2 ) was used to compare categorical data. The Chi-Square "Best first" method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p<0.05). A significant correlation was found between TMB and Race, Neoplasm Histologic Grade, and gender when applying the Best First/Chi-Square method to clinical variables and level of TMB. This enables further investigation and application of the prediction models of the level of TMB, responsiveness to immunotherapy, and prognosis based on the clinical features of the patients.

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Data Augmentation Techniques for Chinese Disease Name Normalization

Data Augmentation Techniques for Chinese Disease Name Normal...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqian Cui Xiangling Fu Shaohui Liu Mingjun Gu Xien Liu Ji Wu Irwin King Dept. Computer Science and Enginnering The Chinese University of Hong Kong Hong Kong China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Dept. Electronic Engineering Tsinghua University Beijing China Dept. Electronic Engineering College AI Tsinghua University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data 1 .

关键词： Training Biological system modeling Training data Medical services Data augmentation Data models Natural language processing Bioinformatics Diseases

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Language Models for Code-switch Detection of te reo Maori and English in a Low-resource Setting

Language Models for Code-switch Detection of te reo Maori an...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： James, Jesin Yogarajan, Vithya Shields, Isabella Watson, Catherine Keegan, Peter J. Jones, Peter-Lucas Mahelona, Keoni Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand Strong AI Lab School of Computer Science The University of Auckland New Zealand Te Puna Wananga The University of Auckland New Zealand Te Hiku Media New Zealand

ISBN: (纸本)9781955917766

Te reo Maori, New Zealand's only indigenous language, is code-switched with English. Maori speakers are atleast bilingual, and the use of Maori is increasing in New Zealand English. Unfortunately, due to the minimal availability of resources, including digital data, Maori is under-represented in technological advances. Cloud-based multilingual systems such as Google and Microsoft Azure support Maori language detection. However, we provide experimental evidence to show that the accuracy of such systems is low when detecting Maori. Hence, with the support of Maori community, we collect Maori and bilingual data to use natural language processing (NLP) to improve Maori language detection. We train bilingual sub-word embeddings and provide evidence to show that our bilingual embeddings improve overall accuracy compared to the publiclyavailable monolingual embeddings. This improvement has been verified for various NLP tasks using three bilingual databases containing formal transcripts and informal social media data. We also show that BiLSTM with pretrained Maori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting Maori language. However, this research uses large models 'as is' for transfer learning, where no further training was done on Maori-English data. The best accuracy of 87% was obtained using BiLSTM with bilingual embeddings to detect Maori-English codeswitching points. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Embeddings

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