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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Moumita Pramanik Hiranmay Samanta Sudipta Basu Pal Konika Das Bhattacharya Chandan Kumar Chanda Hiranmay Saha Department of Electrical Engineering(EE) Indian Institute of Engineering Science and Technology (IIEST) Shibpur Howrah India Green Energy and Sensor Systems(SAMGESS) School of Advanced Materials Indian Institute of Engineering Science and Technology(IIEST) Shibpur Howrah India Department of Computer Science Technology and Computer Science Information Technology Engineering University of Engineering and Management(UEM) Kolkata Kolkata India

The need for energy conservation and environmental protection have triggered the development and deployment of electric vehicles. Light electric vehicles and fossil fuel based light vehicles are popular means of transport for short distances in not only remote rural areas but also in places of tourist attractions. For Islands in the tropical belt like the Sunderbans in India and other pristine sea beaches in South East Asia where nature is luxuriantly present, green transportation is proposed in this work. The objective is to develop environment friendly transport for these natural havens and islands that have ample renewable resources especially in the form of solar power. In this work, a model of a light electric vehicle is developed that utilizes the solar energy during the daytime for charging its batteries. Battery charge controller inbuilt with boost based MPPT (Maximum Power Point Tracking) of solar irradiation have especially been designed to meet the requirements. The efficiency of the designed MPPT is studied. As accumulation of dust and sand reduces the solar module efficiency, an automated self-cleaning mechanism has also been designed that automatically cleans the solar panel regularly. The life cycle cost and the reduction in carbon emissions coupled with the Cost of Capital Investment and operating expenses of the developed model of green transportation far outweighs the similar values obtained from other modes of remote transportation.

关键词： Maximum power point trackers Renewable energy sources Radiation effects Costs Solar energy Fossil fuels Green transportation

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Editorial: Recent advances in image fusion and quality improvement for cyber-physical systems, volume II

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Frontiers in Neurorobotics 2024年 18卷 1422982页

作者： Jin, Xin Lee, Shin-Jye Wozniak, Michal Jiang, Qian School of Software Yunnan University Yunnan Kunming China Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland Institute of Management of Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Multi-source visual information fusion and quality improvement can help the robotic system to perceive the real world. Image fusion is a computational technique fusing multisource images from multiple sensors into a synthesized image that provides a comprehensive or reliable description. Quality improvement techniques can be used to address the challenge of low-quality image analysis tasks [1][2][3][4][5][6]. At present, a lot of brain-inspired algorithm methods (or models) are aggressively proposed to accomplish these two tasks, and the artificial neural network has become one of the most popular techniques in processing image fusion and quality improvement techniques in this decade, especially deep convolutional neural networks [4][5][6][7][8]. This is an exciting research field for the research community of image fusion, and many interesting issues remain to be explored, such as deep few-shot learning, unsupervised learning, application of embodied neural systems, and industrial *** to develop a sound biological neural network and embedded system to extract the multiple features of source images are two key questions that need to be addressed in the fields of image fusion and quality improvement. Hence, studies in this field can be divided into two aspects: new end-to-end neural network models for merging constituent parts during the image fusion process and the embodiment of artificial neural networks for image processing systems. In addition, current booming techniques, including deep neural systems and embodied artificial intelligence systems, are considered potential future trends for reinforcing image fusion performance and quality *** paper of Zhang et al. introduces a palmprint recognition method based on a gating mechanism and adaptive feature fusion. They propose a new network structure, GLGAnet, for extracting local and global features of palmprints. The method incorporates a gating mechanism to control features extracted by dee

关键词： artificial neural networks embedded learning system feature extraction image quality improvement image fusion robot vision

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Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering

arXiv

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arXiv 2023年

作者： Hou, Fei Chen, Xuhui Wang, Wencheng Qin, Hong He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Computer Science Stony Brook University New York United States School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter r (a small positive real number) as input and extracts an iso-surface with an iso-value r using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the r-offset volume of the target zero level-set S, which is an orientable manifold, regardless of the topology of S. Next, the algorithm computes a covering map to project the boundary mesh onto S, preserving the mesh's topology and avoiding folding. If S is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://***/jjjkkyz/DCUDF. © 2023, CC BY-NC-ND.

关键词： Topology

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Exploring the relationship between performance metrics and cost saving potential of defect prediction models

arXiv

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arXiv 2021年

作者： Tunkel, Steffen Herbold, Steffen Institute of Computer Science University of Goettingen Germany Institute of Software and Systems Engineering TU Clausthal Germany

Context: Performance metrics are a core component of the evaluation of any machine learning model and used to compare models and estimate their usefulness. Recent work started to question the validity of many performance metrics for this purpose in the context of software defect prediction. Objective: Within this study, we explore the relationship between performance metrics and the cost saving potential of defect prediction models. We study whether performance metrics are suitable proxies to evaluate the cost saving capabilities and derive a theory for the relationship between performance metrics and cost saving potential. Methods: We measure performance metrics and cost saving potential in defect prediction experiments. We use a multinomial logit model, decision, and random forest to model the relationship between the metrics and the cost savings. Results: We could not find a stable relationship between cost savings and performance metrics. We attribute the lack of the relationship to the inability of performance metrics to account for the property that a small proportion of very large software artifacts are the main driver of the costs. fact that performance metrics are incapable of accurately considering the costs associate with individual artifacts, which is required due to the exponential distribution of artifact sizes. Conclusion: Any defect prediction study interested in finding the best prediction model, must consider cost savings directly, because no reasonable claims regarding the economic benefits of defect prediction can be made otherwise. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision trees

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E3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images

arXiv

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arXiv 2024年

作者： Qi, Yunshan Li, Jia Zhao, Yifan Zhang, Yu Zhu, Lin The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The School of Computer Science Beijing Institute of Technology Beijing100081 China The SenseTime Research and Tetras.AI China

Neural Radiance Fields (NeRF) achieve impressive rendering performance by learning volumetric 3D representation from several images of different views. However, it is difficult to reconstruct a sharp NeRF from blurry input as it often occurs in the wild. To solve this problem, we propose a novel Efficient Event-Enhanced NeRF (E3NeRF) by utilizing the combination of RGB images and event streams. To effectively introduce event streams into the neural volumetric representation learning process, we propose an event-enhanced blur rendering loss and an event rendering loss, which guide the network via modeling the real blur process and event generation process, respectively. Specifically, we leverage spatial-temporal information from the event stream to evenly distribute learning attention over temporal blur while simultaneously focusing on blurry texture through the spatial attention. Moreover, a camera pose estimation framework for real-world data is built with the guidance of the events to generalize the method to practical applications. Compared to previous image-based or event-based NeRF, our framework makes more profound use of the internal relationship between events and images. Extensive experiments on both synthetic data and real-world data demonstrate that E3NeRF can effectively learn a sharp NeRF from blurry images, especially in non-uniform motion and low-light scenes. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Hybrid Makes Better: Hybrid Differential Privacy Medical Image Classification Based on Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Jiang, Shunrong Liu, Yingjie Wang, Zhi Li, Yonggang Chi, Haotian Du, Xiaojiang China University of Mining and Technology School of Computer Science Xuzhou221116 China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

Machine learning has the potential to revolutionize medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will compromise patient privacy. Federated learning is a promising technique that protects patient privacy and improves the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving federal learning scheme via hybrid differential privacy for medical image classification (FHDM). Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm combines Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 9.60% compared to previous differential privacy schemes with the same medical image dataset and privacy budget. © 2004-2012 IEEE.

关键词： Federated learning

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Sepsis Risk Assessment with XGBoost: A Data-Intensive Approach

Sepsis Risk Assessment with XGBoost: A Data-Intensive Approa...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： M. Gayathri V. Kavitha Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of DataScience and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

In sepsis, immunological response to infection fails, causing systemic inflammation and organ failure. Sepsis progresses quickly and is commonly misdiagnosed, making early discovery and treatment crucial for patient outcomes. Augmented patient care and earlier intervention are possible using machine learning sepsis prediction systems. This research investigates if the well-known gradient boosting technique XGBoost can predict sepsis. Our goal is to construct a viable predictive model that can identify sepsis patients at risk of deterioration utilizing a huge dataset of EHRs from various healthcare facilities. The information includes vitals, test results, and patient details. Methodology includes "data preprocessing," "feature engineering," and model development". Train the XGBoost model to predict sepsis onset and then build a threshold-based system to alert patients when their risk surpasses a certain level. Some indicators of model efficacy include sensitivity, specificity, and AUC-ROC. Sepsis prediction appears promising using the XGBoost model. If doctors can detect at-risk patients early and administer early treatments, fatality rates and patient outcomes may decrease. The study emphasizes the need of machine learning in healthcare to improve clinical decision support systems and assist critical care physicians.

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AI-based Prediction of Catheter-related Thrombosis Risk for Cancer Patients 24

AI-based Prediction of Catheter-related Thrombosis Risk for ...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Guo, Yaoqi Ma, Yun Shao, Zijian Bi, Weichen Wang, Yanfeng School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Department of Comprehensive Oncology National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China

ISBN: (纸本)9798400701726

Cancer patients face a heightened risk of venous thromboembolism (VTE), emerging as the second most prevalent cause of death within this population. Central venous catheterization (CVC), a routine procedure in cancer care, amplifies the VTE risk, leading to catheter-related thrombosis (CRT). Although traditional risk-assessment models and certain AI methods exist for VTE prediction, their capability and application in CRT risk prediciton for cancer patients remains limited. This paper addresses the shortcomings of current models (RAMs) by crafting a dedicated AI model to predict CRT risks for cancer patients. Leveraging a dataset encompassing 10,512 cancer patients undergoing catheterization over a decade, we meticulously select nine specific features for model construction, resulting in an impressive 0.794 AUROC in prediction, 54.9% higher than baseline. Furthermore, we estimate CRT-free probability using the Kaplan–Meier method. We also develop a WeChat Mini Program designed for efficient data collection and risk prediction, enhancing the efficiency of CRT risk detection for both doctors and patients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Machine learning

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A Cloud-Based UV Monitoring System for Remote Real-Time UV Exposure Tracking 4

A Cloud-Based UV Monitoring System for Remote Real-Time UV E...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Dankan Gowda, V. Prasad, V. Nuthan Prasad, K.D.V. Prasad, V.K. Satya Mahajan, Yogesh Suneetha, Sampathirao Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India M S R i T Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Pune India Symbiosis International University Symbiosis Centre for Management and Human Resource Development Maharashtra Pune India Andhra University College of Engineering Andhra University Department of Computer Science and Systems Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350300888

Due to the widespread nature of sun-related health risks, sophisticated UV monitoring systems are urgently needed. This research study introduces a cloud-based UV monitoring system with the intent of remotely tracking and analyzing UV exposure in real time. The system incorporates a network of UV sensors deployed in various locations, continuously collecting UV radiation data. These sensors are linked to a cloud system, which allows for easy sharing and processing of collected data. As a result, we're working to perfect a cutting-edge device that can monitor vitamin D absorption, measure cumulative UV exposure, and warn of impending danger. Extensive studies have demonstrated the benefits of UV exposure for both general health and protection against the newly discovered coronavirus. However, finding a happy medium is important because too much sun exposure can cause cancer, lupus flares, and cataracts. The proposed model gives users the ability to set their own daily UV exposure limits, taking into account clinically validated thresholds for various skin tones. The developed sensor module is able to measure the UV index with high precision even in bright sunlight because it closely mimics the skin erythema spectrum. © 2023 IEEE.

关键词： Ultraviolet radiation

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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International Conference on Emerging Technologies, ICET

作者： Hassan Bin Khalid Abu Bakar Siddique Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain.

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