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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha China College of Engineering and Design Hunan Normal University Changsha China

Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. IEEE

关键词： Semantics

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Design and Verification of Parallel Hip Exoskeleton Considering Output Torque Anisotropy

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Journal of Bionic engineering 2024年第3期21卷 1305-1320页

作者： Jilong Xu Yunzhan Niu Fucai Liu Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Heibei Province Yanshan UniversityQinhuangdao066004China College of Mechanical Engineering Yanshan UniversityQinhuangdao066004China

In rehabilitation training,it is crucial to consider the compatibility between exoskeletons and human legs in ***,most exoskeletons today adopt an anthropomorphic serial structure,which results in rotational centers that are not precisely aligned with the center of the hip *** address this issue,we introduce a novel exoskeleton called the Parallel Hip Exoskeleton(PH-Exo)in this ***-Exo is meticulously designed based on the anisotropic law of output *** the friction of the drive components,a dynamic model of the human-machine complex is *** analysis demonstrates that PH-Exo not only exhibits outstanding torque performance but also achieves high controllability in both flexion/extension and adduction/abduction ***,a robust controller is designed to address model uncertainty,friction,and external *** experiments indicate that under the control of the robust controller,each motor achieves excellent tracking performance.

关键词： Hip exoskeleton Parallel mechanism Misalignment Anisotropy Output torque

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in engineering & Sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Blockchain based prophecy of cardiovascular disease using modified XGBoost

Blockchain based prophecy of cardiovascular disease using mo...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Srivastava, Vibha Singh, Ashutosh Kumar Dwivedi, Vijay Kumar Department of Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Naini Prayagraj India

ISBN: (纸本)9781032669663

One of the global and complex disease known as Heart Disease (HD) plays a significant mantle in cardiology and healthcare. In recent times, the blockchain technology is widely used in medical sector for storing the medical records. Still there is a need for enhancing security range and the studies have failed to focus on both the security and HD prediction. Therefore, the proposed study introduced a secured Electronic Health Record (EHR) system over the blockchain environment by implementing encryption and decryption process using Advanced Encryption Standard (AES) algorithm and the HD prediction model is implemented with a Modified Empirical Loss (MEL) Function Extreme Gradient Boosting (XGBoost) classifier. The experimental evaluation is done by using Cleveland Dataset. The efficiency of preferred exemplary is determined with regard to performance metrics namely accuracy, encryption time, precision, decryption time, recall, and F1-score. © 2024 The Author(s).

关键词： Cryptography

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Modified attention based cryptocurrency price presage with convolutional Bi-LSTM

Modified attention based cryptocurrency price presage with c...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Srivastava, Vibha Singh, Ashutosh Kumar Dwivedi, Vijay Kumar Department of Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Naini Prayagraj India

ISBN: (纸本)9781032669663

Predicting stock price is important for identifying the fluctuations in stock market which leads the investors to make profit. As predicting the future price tends to be less accurate in the existing studies, this study considers feature extraction process through the proposed Modified Attention with Convolutional Bi-LSTM for predicting the market value of next day. The stock datasets of Bitcoin and Ethereum are loaded and feature scaling method performs the data pre-processing operations. Further, pre-processed data are considered for feature extraction which is given as input into the classifiers of train and test split method. The comparison of suggested with the prevailing datasets are performed with the estimation of Mean Absolute Error, Mean Squared Error and Root Mean Squared Error and the outcome shows efficacy of suggested mechanism with reference to accuracy. © 2024 The Author(s).

关键词： Feature extraction

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Crime Hotspots Mapping and FIR Data Interface

Crime Hotspots Mapping and FIR Data Interface

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Lilhare, Shweta G. Kumavat, Yash Banait, Gunwant Kurkelli, Aditya JSPM's Imperial College of Engineering and Research Department of Computer Scince and Engineering Bharat Pune India

ISBN: (纸本)9798350319019

The Paper presents a comprehensive crime analysis and prediction system that combines data integration, preprocessing and interactive visualization to support law enforcement agencies in understanding and combating criminal activities effectively. The system aggregates diverse data sources, including crime records, historical crime trends, employing rigorous data preprocessing techniques to ensure data quality. Spatial analysis methodologies are applied to detect crime hotspots, with a keen focus on temporal factors such as date, time, and seasonality, offering an intricate spatiotemporal understanding of criminal patterns. These insights are presented through an interactive mapping interface with color-coded representations, providing a visually intuitive platform for end-users. The user interface is meticulously designed, offering a user-friendly experience for law enforcement professionals and analysts. Robust security measures are in place to protect sensitive data and ensure compliance with privacy regulations. Continuous refinement is a core aspect of the system, with feedback mechanisms enabling iterative improvements. This system empowers law enforcement agencies with data-driven decision-making capabilities, efficient resource allocation, and proactive crime prevention, ultimately fostering safer communities. © 2024 IEEE.

关键词： Mapping

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Deep Learning Approach to Identify Coronary Heart Disease in Its Early Stages 3rd

Deep Learning Approach to Identify Coronary Heart Disease in...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Sinthia, P. Albert, Anitha Juliette Gurumoorthy, G. Nalini, M. Rathore, Vijay Singh Saveetha Engineering College Chennai India Loyola-ICAM College of Engineering and Technology Chennai India SA Engineering College Chennai India Department of Computer Science Engineering Jaipur Engineering College and Research Centre Jaipur India

ISBN: (纸本)9789819737444

One of the causes of coronary artery disease is atherosclerosis. Having plaque form within your arteries is called atherosclerosis. In time, plaque builds up and narrows the interior of the conduits, which might prompt a fractional or complete blockage of blood flow. Three hundred sixty-nine thousand individuals will lose their lives to coronary heart disease this year. Of people aged 20 and over, around 18.2 million (or 6.7% of the population) have CAD. Approximately half of all coronary artery disease (CAD) fatalities occur in persons less than 65 years old. In order to aid doctors in making observation-based decisions and to diagnose diseases, image processing finds extensive usage in the medical subject. The use of cardiovascular magnetic resonance imaging (MRI) has expanded beyond its original specialty to become an integral part of cardiovascular decision-making. Cardiovascular magnetic resonance imaging (MRI) currently provides many alternatives for the detection of ischemia in individuals with known or suspected coronary artery disease (CAD). Recent years have seen a proliferation of data supporting the use of cardiovascular magnetic resonance imaging (MRI) for the diagnosis of coronary artery disease (CAD) and heart failure. For the purpose of processing MRI images of the coronary artery taken from the back plane, we provide an image processing workflow. Image segmentation is used to draw attention to the anomaly in the coronary artery. Individuals with coronary artery disease and those who are healthy are marked differently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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A Comparative Study of Ensemble Learning Models for Accurate Solar Irradiance Forecasting 8

A Comparative Study of Ensemble Learning Models for Accurate...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Chaudhari, Tejaram Joshi, Aditya Bharti, Devkumar Chavan, Pratibha Deshmukh, M.K. Bhalgat, Pritam Trinity College of Engineering & Research Department of Electronics & Telecommunication Engineering Pune India Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering Goa India SCTR's Pune Institute of Computer Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798331505462

Accurate estimation of solar radiation is very important in energy planning and the integration of renewable energy infrastructures into power grids for most places where the data may be sparse. This paper investigates advanced machine learning technologies specifically, ensemble methods such as XGBoost, LightGBM, CatBoost, NGBoost, AdaBoost, Gradient Boosting, Random Forest, and SGD Boost - to improve the prediction of solar radiation. It shows the performance of the above-mentioned models to forecast solar irradiance from meteorological factors using data from the Hawaii Space Exploration Analog and Simulation (HI-SEAS) weather station. It has been shown that ensemble learning boosts the forecast accuracy considerably, especially for ensemble learners optimized for categorical data and probabilistic predictions. This shows how machine learning can be potential and support the global shift to sustainable energy with reliable forecasts of solar radiation in data-scarce regions and offering valuable insights for optimizing renewable energy deployment. © 2024 IEEE.

关键词： Solar irradiance

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