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IEEE Signal Processing and Communications Applications (SIU)

作者： İnanç Arın Zeynep Işık Seçilay Kutal Somaiyeh Dehghan Arzucan Özgür Berrin Yanikoğlu Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Center of Excellence in Data Analytics (VERIM) Sabanci University Istanbul Turkey Department of Computer Engineering Bogazici University Turkey

Hate speech spread on social media can strongly affect people and societies, especially when it targets a specific group of people in terms of religion, culture, or a specific social situation, such as refugees. For this reason, the detection and elimination of hate speech in social networks have attracted the attention of natural language processing researchers in recent years. A competition is organized to benchmark progress in Turkish hate speech recognition, within SIU2023 Computational Social Sciences special session, with four different tasks. A total of 20 teams registered for the competition, while eight teams submitted results at the end. The details of the winning models and their results are explained in this paper.

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Parking Behaviour Analysis of Shared E-Bike Users Based on a Real-World dataset - A Case Study in Dublin, Ireland

arXiv

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arXiv 2022年

作者： Yan, Sen Liu, Mingming O'Connor, Noel E. The SFI Insight Centre for Data Analytics The School of Electronic Engineering Dublin City University Dublin Ireland

In recent years, an increasing number of shared E-bikes have been rolling out rapidly in our cities. It therefore becomes important to understand new behaviour patterns of the cyclists in using these E-bikes as a foundation for the novel design of shared micromobility services as part of the realisation for next generation intelligent transportation systems. In this paper, we deeply investigate the users' behaviour of shared E-bikes in a case study by using the real-world dataset collected from the shared E-bike company, MOBY, which currently operates in Dublin, Ireland. More specifically, we look into the parking behaviours of users as we know that inappropriate parking of these bikes can not only increase the management costs of the company but also result in other users' inconveniences, especially in situations of battery shortage, which inevitably reduces the overall operational efficacy of these shared E-bikes. Our work has conducted analysis at both bike station and individual level in a fully anonymous and GDPR-Compliant manner, and our results have shown that up to 12.9% of shared E-bike users did not park their bikes properly at the designated stands. Different visualisation tools have been applied to better illustrate our obtained results. Copyright © 2022, The Authors. All rights reserved.

关键词： Bicycles

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Real-Time Pothole Detection at night to prevent road accidents using YOLOV5

Real-Time Pothole Detection at night to prevent road acciden...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： Gouranga Mandal Anurag De Arindam Dey Sudeepthi Gangarapu Sharmila Rani Chekuru Chekuri Nandini School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh India School of Computer Science and Engineering With Specialization in Data Analytics Vellore Institute of Technology Andhra Pradesh India

This article proposes a technique to create a pothole detection system that can operate both during the day and at night. This model was developed using the real-time object identification YOLO (You Only Look Once) technique. The potholes are located using a pre-trained model with YOLO v5. In its creation, the PyTorch framework is utilized. The first YOLO model created using the PyTorch framework is YOLOv5, which is a lot more user-friendly and lightweight. In a manner similar to real-time object detection systems, the proposed method displays potholes in real time and emphasizes them with boundary boxes for accurate identification. The accuracy achieved with this method is 97% during the day and 92% at night which is found to be satisfactory.

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RespoBot: Chatbot used for the prediction of diseases using Machine Learning and Deep Learning with respect to Covid-19

RespoBot: Chatbot used for the prediction of diseases using ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B. Sreedevi L. Antony Rosewelt Neela Ramesh Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India Big Data Analytics Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India

Abstract-Resembling man’s evolution over time the life threatening diseases have also evolved with him a disease is basically a disorder in structure or functioning of an organism’s body. There are a several kinds of diseases but the most dangerous ones are the infectious diseases. Infectious diseases occur when a foreign body such as bacteria, fungi, and virus enter the body and attack the immune system. That being the smaller problem of infectious diseases as the main problem it’s a communicable disease that spreads when a uninfected person comes in contact with an infected person they are highly likely to contract the same. The prediction is performed using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent SGD), Gradient Boosting (GD), Decision Trees (DT), Naive Bayes Classifier (NBC) and Voting Classifier (VC) ensemble technique. After the prediction a comparative analysis in terms of accuracy is performed. A Chatbot using Natural Learning Processing on a Neural Network is employed. The user is asked to upload a Chest X-ray if in possession as a second step confirmation for Tuberculosis, Pneumonia, Covid-19 and Healthy lung. In terms of highest accuracy for Covid-19 the GBC, VC, 56 tops the charts and for other diseases also GBC, VC, ANN top the charts.

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Rahadian, Helmy Burohman, Azka Muji Widyotriatmo, Augie Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Bandung Indonesia Universitas Dian Nuswantoro Electrical Engineering Study Program Semarang Indonesia Research Center for Photonics Tangerang Selatan Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia University Center of Excellence Ai on Vision Nlp & Big Data Analytics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309225

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed. © 2023 IEEE.

关键词： Heuristic methods

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A Review on AI Algorithms for Energy Management in E-Mobility Services

A Review on AI Algorithms for Energy Management in E-Mobilit...

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Conference on Vehicle Control and Intelligence (CVCI)

作者： Sen Yan Maqsood Hussain Shah Ji Li Noel O’Connor Mingming Liu School of Electronic Engineering and SFI Insight Centre for Data Analytics Dublin City University Ireland Nanjing University of Aeronautics and Astronautics China Department of Mechanical Engineering University of Birmingham UK

E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector. The depletion of fossil fuels, escalating greenhouse gas emissions, and the imperative to combat climate change underscore the significance of transitioning to electric vehicles (EVs). This paper seeks to explore the potential of artificial intelligence (AI) in addressing various challenges related to effective energy management in e-mobility systems (EMS). These challenges encompass critical factors such as range anxiety, charge rate optimization, and the longevity of energy storage in EVs. By analyzing existing literature, we delve into the role that AI can play in tackling these challenges and enabling efficient energy management in EMS. Our objectives are twofold: to provide an overview of the current state-of-the-art in this research domain and propose effective avenues for future investigations. Through this analysis, we aim to contribute to the advancement of sustainable and efficient e-mobility solutions, shaping a greener and more sustainable future for transportation.

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data Security System in Maintaining the Medical Records Using Wireless Network Cryptography Management Integrated with the Artificial Intelligence 3

Data Security System in Maintaining the Medical Records Usin...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bhatt, Rahul Salman, Hayder Mahmood Kaduskar, Vikas P. Jakka, Geethamanikanta Aich, Shibani C Verma, Devvret Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India Al-TurathUniversiy College Computer Science Department Baghdad Iraq College of Engineering Pune India University of the Cumberlands Data Analytics Information Technology United States Amrita Vishwa Vidyapeetham Department of English Amritapuri India Graphic Era Deemed to be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Therefore, we will study here. One of the technological fields in the communication industry that is actively developing and increasing the fastest is wireless technology. Interconnection is the ability to communicate without the usage of cables, wires, or maybe another removable drives. This network architecture, which focused on offering timely action versus numerous problems, is able to evaluate the info and practically through and cellular networking and it will protect the health records from possible threat by keeping it. In order to protect material from possible harm, encrypted methods have been employed extensively throughout the years. Using communication devices may help increase the information is available protection. In this way, only authorized persons may view the information. Ensuring that now the data is secure from threats, and no one else can. A variety of sensors are used in wireless sensor systems to measure environmental factors and transmit information to a central site. Originally designed for defense use, these sensor nodes are now extensively used in a wide range of business and commercial situations in addition to cutting-edge industries like medicine. Wireless sensor networks for remote health monitoring certainly improve the standard of care today. There are two frequent security flaws in healthcare applications: dropping and impersonating. © 2023 IEEE.

关键词： Sensor nodes

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Estimation of the pig's limb orientation and gripping points based on the pose estimation deep neural networks 10

Estimation of the pig's limb orientation and gripping points...

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Manko, Maksym Smolkin, Oleh De Medeiros Esper, Ian Popov, Anton Mason, Alex Data&Analytics Ciklum Kyiv Ukraine Electronic Engineering Department Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Faculty of Applied Sciences Ukrainian Catholic University Lviv Ukraine Ås Norway Animalia AS Oslo Norway

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

The robotization of the pig carcass slaughtering process requires the possibility of automatic identification of the pig's body position and orientation of its individual parts for further gripping and manipulation of the limbs. This paper presents a method for locating the gripping points on the pig limbs based on pose estimation of a pig carcass fixed in a meat factory cell from RGB-D images of carcasses taken from 6 different views. A deep learning model based on U-Net architecture was proposed to solve the problem of keypoint detection to estimate the pose and gripping points of pig carcasses. The proposed method demonstrates high precision and robustness in estimating the gripping points of pig limbs: Norwegian style gripping points - mAP(0.5...0.95) = 0.9504, mAR(0.5...0.95) = 0.9688, distance error is within 15 mm;Danish style gripping points - mAP(0.5...0.95) = 0.9831, mAR(0.5...0.95) = 0.9937, distance error is within 15 mm. © 2022 IEEE.

关键词： Deep neural networks

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PFD-Net: Pyramid Fourier Deformable Network for medical image segmentation

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Computers in Biology and Medicine 2024年 172卷 108302-108302页

作者： Yang, Chaorong Zhang, Zhaohui College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Hebei Normal University Shijiazhuang050024 China

Medical image segmentation is crucial for accurately locating lesion regions and assisting doctors in diagnosis. However, most existing methods fail to effectively utilize both local details and global semantic information in medical image segmentation, resulting in the inability to effectively capture fine-grained content such as small targets and irregular boundaries. To address this issue, we propose a novel Pyramid Fourier Deformable Network (PFD-Net) for medical image segmentation, which leverages the strengths of CNN and Transformer. The PFD-Net first utilizes PVTv2-based Transformer as the primary encoder to capture global information and further enhances both local and global feature representations with the Fast Fourier Convolution Residual (FFCR) module. Moreover, PFD-Net further proposes the Dilated Deformable Refinement (DDR) module to enhance the model's capacity to comprehend global semantic structures of shape-diverse targets and their irregular boundaries. Lastly, Cross-Level Fusion Block with deformable convolution (CLFB) is proposed to combine the decoded feature maps from the final Residual Decoder Block (DDR) with local features from the CNN auxiliary encoder branch, improving the network's ability to perceive targets resembling the surrounding structures. Extensive experiments were conducted on nine publicly medical image datasets for five types of segmentation tasks including polyp, abdominal, cardiac, gland cells and nuclei. The qualitative and quantitative results demonstrate that PFD-Net outperforms existing state-of-the-art methods in various evaluation metrics, and achieves the highest performance of mDice with the value of 0.826 on the most challenging dataset (ETIS), which is 1.8% improvement compared to the previous best-performing HSNet and 3.6% improvement compared to the next-best PVT-CASCADE. Codes are available at https://***/ChaorongYang/PFD-Net. © 2024 Elsevier Ltd

关键词： Convolution

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Abstractive Text Summarization for Bangla Language Using NLP and Machine Learning Approaches

arXiv

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arXiv 2025年

作者： Miazee, Asif Ahammad Roy, Tonmoy Islam, Md Robiul Safat, Yeamin Maharishi International University Iowa United States Data Analytics & Information Systems Utah State University Utah United States William & Mary Virginia United States Mymensingh Engineering College Dhaka Bangladesh

Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a brief summary that effectively conveys the main points of the original text. We spend a significant amount of time each day reading the newspaper to stay informed about current events both domestically and internationally. While reading newspapers enriches our knowledge, we sometimes come across unnecessary content that isn’t particularly relevant to our lives. In this paper, we introduce a neural network model designed to summarize Bangla text into concise and straightforward paragraphs, aiming for greater stability and efficiency. © 2025, CC BY.

关键词： Newsprint

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