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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?

Low-Resource Named Entity Recognition: Can One-vs-All AUC Ma...

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IEEE International Conference on data Mining (ICDM)

作者： Ngoc Dang Nguyen Wei Tan Lan Du Wray Buntine Richard Beare Changyou Chen Department of Data Science and AI Monash University Melbourne Australia College of Engineering and Computer Science VinUniversity Hanoi Vietnam Faculty of Medicine Peninsula Clinical School Melbourne Australia Department of CS and Engineering University at Buffalo New York USA

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings.

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Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield Prediction

Mobile Application Implementation as Agriculture 4.0 Strateg...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Hangger Gahara Mawandha Reza Rahutomo Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology Institut Pertanian Stiper Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350304084

ISBN: (纸本)9798350304091

Information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application’s interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar

关键词： Productivity Operating systems Estimation Production facilities Raw materials Mobile applications Recording

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A Hybrid Stock optimization Approach for Inventory Management 3

A Hybrid Stock optimization Approach for Inventory Managemen...

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3rd International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2021

作者： Cimen, Egemen Berki Kurban, Ilknur Selmanoglu, Ozgur Sahin, Murat Kilinc, Deniz Data Science Eldor Corporation Izmir Turkey Software Development Eldor Corporation Izmir Turkey Technology Eldor Corporation Izmir Turkey Data Science Kalybe.AI Izmir Turkey Bakirçay University Computer Engineering Department of Faculty of Engineering and Architecture Izmir Turkey

ISBN: (纸本)9781665440585

Modern world is rapidly evolving around knowledge and business that know how to use knowledge shows superiority. Thus, smart decision making becomes vital for the modern business world to achieve sustainability in life and business. Especially, with a world of scarce resources, utilizing knowledge would play critical not only today but also for future. Moreover, using knowledge is inevitable in supply chain and inventory management must be supported with smart algorithms and modern heuristics to avoid excessive inventory while fighting with stockout. Therefore, this study explores the opportunity for inventory planning with heuristics and tailored techniques as well as how to hybridize modern heuristics and tailored techniques. In this study, inventory optimization experiments are proposed to model spare part inventory management and find the best way to determine reorder amount to deal with shortage and excessive inventory. Four heuristics which are i) basic golden ratio, ii) simulated annealing, iii) statistical rule-based heuristic and iv) hybrid algorithm of simulated annealing and statistical rule-based heuristic are evaluated on existing spare part dataset with an example from real-life part supplier named Eldor. In this test case, 400 products are analyzed, and best reorder points and amounts are selected with the help of heuristics. Heuristics' main structures and parameters are adjusted for the problem's need and improvement on quality of results. Parameters are determined according to trial and error with experts' guidance on heuristics. The best result suggests 8% improvement on cost and 37% improvement on inventory load could achieve with the help of heuristics. These solutions are usable against even hard constraints like shortage. © 2021 IEEE.

关键词： Simulated annealing

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DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting

arXiv

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arXiv 2023年

作者： Ye, Maoyuan Zhang, Jing Zhao, Shanshan Liu, Juhua Liu, Tongliang Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science Faculty of Engineering The University of Sydney Australia JD Explore Academy *** China College of Computing & Data Science Nanyang Technological University Singapore

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. Besides, they overlook the exploring on multilingual text spotting which requires an extra script identification task. In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection, recognition, and script identification simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, our method not only performs well in English scenes but also masters the transcription with complex font structure and a thousand-level character classes, such as Chinese. On the other hand, our DeepSolo++ achieves better performance on the additionally introduced script identification task with a simpler training pipeline compared with previous methods. Extensive experiments on public benchmarks demonstrate that our simple approach achieves better training efficiency compared with Transformer-based models and outperforms the previous state-of-the-art. For example, on ICDAR 2019 ReCTS for Chinese text, our method boosts the 1-NED metric to a new record of 78.3%. On ICDAR 2019 MLT, DeepSolo++ achieves absolute 5.5% H-mean and 8.0% AP improvements on joint detection an

关键词： Semantics

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Informing Innovation Management: Linking Leading R&D Firms and Emerging Technologies

arXiv

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arXiv 2023年

作者： Gong, Xian McFarland, Claire McCarthy, Paul Griffith, Colin Rizoiu, Marian-Andrei Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Beesness Pty Ltd Australia The Data Science Institute University of Technology SydneyNSW Australia School of Computer Science and Engineering UNSW Sydney NSW Australia Australia

Understanding the relationship between emerging technology and research and development has long been of interest to companies, policy makers and researchers. In this paper new sources of data and tools are combined with a novel technique to construct a model linking a defined set of emerging technologies with the global leading R&D spending companies. The result is a new map of this landscape. This map reveals the proximity of technologies and companies in the knowledge embedded in their corresponding Wikipedia profiles, enabling analysis of the closest associations between the companies and emerging technologies. A significant positive correlation for a related set of patent data validates the approach. Finally, a set of Circular Economy Emerging Technologies are matched to their closest leading R&D spending company, prompting future research ideas in broader or narrower application of the model to specific technology themes, company competitor landscapes and national interest concerns. © 2023, CC BY-NC-SA.

关键词： Patents and inventions

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Role of Machine Learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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Multilayer Feature Fusion and Joint Loss Optimization for Emotion Recognition in Conversations

Multilayer Feature Fusion and Joint Loss Optimization for Em...

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International Conference on computer Supported Cooperative Work in Design

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The goal of Emotion Recognition in Conversations (ERC) is to accurately identify the emotions expressed in each utterance within a dialogue. Despite advancements made by current ERC methods, particularly those using RNN-based and GCN-based models to capture emotional dynamics and model speaker relationships, there remain two primary limitations: first, an insufficient integration of multiple feature representations and commonsense knowledge, which hampers the model's ability for deep emotional understanding; and second, the reliance on a single cross-entropy loss for classification optimization, which restricts the accuracy and robustness of emotion recognition. To address these issues, we propose a method for ERC using Multilayer Feature Fusion and Joint Loss Optimization (MFFJL). This approach combines contextual information, speaker dependency, and commonsense knowledge features by extracting feature vectors through RoBERTa and COMET, utilizing bidirectional LSTM and attention mechanisms to capture conversational context, and applying a cross-fusion module to deeply integrate various features, thus enhancing comprehension of complex emotional expressions. Additionally, the feature classification module incorporates joint cross-entropy and KL divergence optimization, further improving classification accuracy and consistency. Experimental results demonstrate the effectiveness of our method, as evidenced by superior performance on the IEMOCAP and MELD datasets. Our code is available at https://***/r/MFFJL-D9D8.

关键词： Emotion recognition Accuracy Federated learning Computational modeling Feature extraction Nonhomogeneous media Vectors Robustness Optimization Commonsense reasoning

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Prediction of Heart Failure by using Machine Learning and Feature Selection

Prediction of Heart Failure by using Machine Learning and Fe...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Aslam Syed Fawad Hussain Machine Learning and Data Science (MDS) Lab Faculty of Computer Science & Engg. GIK Institute of Engg. Sciences & Tech. Khyber Pakhtunkhwa Pakistan

ISBN: (纸本)9781665459938

Heart attacks are one of the foremost causes of death in the world. While doctors can carry out multiple tests to diagnose it, it may go undetected for a long time which can prove fatal. However, it is possible to predict the potential risk of a heart attack using Machine Learning (ML) algorithms. This is done using patients' historical data and has found some success in the literature. However, not all features are equally important in such data which may result in low accuracy of prediction. In this study, we use feature selection techniques to select a subset of features with contribution to improve upon the prediction accuracy of ML models. Inforation theory based models are used for feature selection in combination with popular ML classifiers including Random forest (RF), Decision Trees (DTs), Logistic Regression (LR), k-nearest neighbors (kNN), Gradient Boosting, Bagging, Ensemble, Neural Network (NN) and Support Vector Machines (SVM). The model is tested the real-world and publicly available Framingham dataset. Results show that the proposed approach improves the prediction of heart disease compared to baseline models.

关键词： Support vector machines Heart Machine learning algorithms Predictive models Feature extraction Regression tree analysis Random forests

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