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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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Local Auxiliary Spatial-Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14784-14803页

作者： Chen, Qiusheng Fang, Zhuoqun Li, Zhaokui Du, Qian Deng, Shizhuo Jia, Tong Chen, Dongyue Northeastern University College of Information Science and Engineering Shenyang 110819 China Shenyang Aerospace University College of Artificial Intelligence Shenyang 110136 China Shenyang Aerospace University School of Computer Science Shenyang 110136 China Mississippi State University Department of Electrical and Computer Engineering Starkville 39762 MS United States Northeastern University Foshan Graduate School of Innovation Guangdong Foshan 528311 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Liaoning Shenyang 110819 China

The feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial and spectral global information shared by both the source and target domains. To overcome this issue, we propose a local auxiliary spatial-spectral decoupling transformer (LASDT) network to ease the learning of global domain-invariant information. The spatial and spectral global features of HSIs are extracted through a dual-branch design, preventing the feature coupling of different dimensions. In order to compress the model's parameter search space, a local auxiliary global feature extraction strategy is devised. First, local prior constraints are introduced by extracting primitive features using convolutional intra-token embedding. Next, the extraction of global spatial and spectral information from these primitive features is effectively achieved using the self-attention mechanism. Finally, a dynamic feature fusion mechanism is devised that enables the model to focus on features more conducive to transfer while suppressing irrelevant features. By using only standard adversarial domain alignment, LASDT achieves the state-of-the-art performance, demonstrating the model's superior capability in UDA for HSI classification. © 2008-2012 IEEE.

关键词： Hyperspectral image classification transformer unsupervised domain adaptation

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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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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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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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Mining fuzzy local periodic activity pattern for Smart home applications

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Knowledge-Based Systems 2024年 293卷

作者： Iqbal, Mohammad Prayitno, Adila Sekarrati Dwi Pao, Hsing-Kuo Mukhlash, Imam Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category. © 2024 Elsevier B.V.

关键词： Automation

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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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Investigating The Impact of Sampling Techniques on an Imbalanced Classification Problem

Investigating The Impact of Sampling Techniques on an Imbala...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Dina Shehada Suadad Muammar Maryam Ali Amour Ahmed Bouridane Department of Electrical Engineering University of Sharjah Sharjah UAE College of Engineering and IT University of Dubai Dubai United Arab Emirates Centre for Data Analytics and Cybersecurity University of Sharjah Sharjah UAE

Classification with imbalanced datasets can be quite challenging as it can make classifiers biased towards the majority class. This can cause imbalanced classification, which indicates poor predictive performance, particularly with the minority class. This paper is concerned with a comparative study to investigate the impact of the different sampling techniques to address the imbalanced classification problem of an extremely imbalanced binary dataset with 284,807 instances. In the dataset, 99.83% of the samples belong to one class and the remaining 0.17% belong to the other class. The impact of the imbalanced dataset was studied on three classification algorithms: Logistic Regression, K-Nearest Neighbor, and Random Forest. Moreover, Over Sampling, Under Sampling, and Hybrid Sampling, were applied to tackle the problem of imbalanced classification. A detailed evaluation of the performance of each algorithm, before and after applying the sampling techniques is provided. The performance is evaluated based on six metrics which are: Confusion Matrix, Accuracy, Recall, Precision, F1 Score, and Area Under Precision-Recall Curve. Results showed that Logistic Regression was heavily impacted by the imbalanced dataset. The best classification results were achieved by K-Nearest Neighbor and Random Forest classifiers when applying the Random Over Sampling technique to balance the dataset.

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Enhancing Medication Recommendation with Hierarchical Network and Patient Visit Histories 46

Enhancing Medication Recommendation with Hierarchical Networ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chairat, Sawrawit Sae-Ang, Apichat Suvirat, Kerdkiat Ingviya, Thammasin Chaichulee, Sitthichok Prince of Songkla University Faculty of Medicine Department of Biomedical Sciences and Biomedical Engineering Songkhla Thailand Prince of Songkla University Division of Information Technology Faculty of Medicine Songkhla Thailand Prince of Songkla University Division of Digital Innovation and Data Analytics Faculty of Medicine Songkhla Thailand Prince of Songkla University Faculty of Medicine Department of Family Medicine and Preventive Medicine Songkhla Thailand

ISBN: (纸本)9798350371499

Prescribing medications is an essential part of patient care and requires precision and personalization in selection. Our study introduces a hierarchical medication recommendation system that aims to improve the prescribing process. We use FastText to embed medical contexts and employ a hierarchical attention-based model to manage the hierarchical structure of medication codes. The system takes input data from the current visit and the three previous visits to make recommendations. We trained and evaluated our model on 99,417 anonymized primary care outpatient visits. Our model achieved a mean average precision (mean AP) of 0.8724, 0.7419, 0.6805, and 0.6184 at the first, second, third, and fourth levels of the ATC system, respectively. We demonstrate that incorporating patient visit histories can improve predictions. Our results provide a solution to improve medication prescribing and suggest possible extensions for more comprehensive recommendations. © 2024 IEEE.

关键词： Electronic health record

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Privacy-preserving blockchain-based federated learning for brain tumor segmentation

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Computers in Biology and Medicine 2024年 177卷 108646-108646页

作者： Kumar, Rajesh Bernard, Cobbinah M. Ullah, Aman Khan, Riaz Ullah Kumar, Jay Kulevome, Delanyo K.B. Yunbo, Rao Zeng, Shaoning University of Electronic Science and Technology of China Huzhou313001 China Institute for Big Data Analytics Dalhousie University Halifax NS Canada Department of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan Shangdong250200 China

Improved data sharing between healthcare providers can lead to a higher probability of accurate diagnosis, more effective treatments, and enhanced capabilities of healthcare organizations. One critical area of focus is brain tumor segmentation, a complex task due to the heterogeneous appearance, irregular shape, and variable location of tumors. Accurate segmentation is essential for proper diagnosis and effective treatment planning, yet current techniques often fall short due to these complexities. However, the sensitive nature of health data often prohibits its sharing. Moreover, the healthcare industry faces significant issues, including preserving the privacy of the model and instilling trust in the model. This paper proposes a framework to address these privacy and trust issues by introducing a mechanism for training the global model using federated learning and sharing the encrypted learned parameters via a permissioned blockchain. The blockchain-federated learning algorithm we designed aggregates gradients in the permissioned blockchain to decentralize the global model, while the introduced masking approach retains the privacy of the model parameters. Unlike traditional raw data sharing, this approach enables hospitals or medical research centers to contribute to a globally learned model, thereby enhancing the performance of the central model for all participating medical entities. As a result, the global model can learn about several specific diseases and benefit each contributor with new disease diagnosis tasks, leading to improved treatment options. The proposed algorithm ensures the quality of model data when aggregating the local model, using an asynchronous federated learning procedure to evaluate the shared model's quality. The experimental results demonstrate the efficacy of the proposed scheme for the critical and challenging task of brain tumor segmentation. Specifically, our method achieved a 1.99% improvement in Dice similarity coefficient for enha

关键词： Tumors

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