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Error Density-dependent Empirical Risk Minimization

Expert Systems with Applications 2024年 254卷

作者： Chen, Hong Zhang, Xuelin Gong, Tieliang Gu, Bin Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen518000 China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518000 China School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Artificial Intelligence Jilin University Jilin130012 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Empirical Risk Minimization (ERM) with the squared loss has become one of the most popular principles for designing learning algorithms. However, the existing mean regression models under ERM usually suffer from poor generalization performance due to their sensitivity to atypical observations (e.g., outliers). For alleviating this sensitivity, some strategies have been proposed by utilizing the quantitative relationship of error values with different observations to form robust learning objectives. Instead of focusing on error values, this paper considers the error density to uncover the structure information of observations and proposes a new learning objective, called Error Density-dependent Empirical Risk Minimization (EDERM), for robust regression under complex data environments. Property characterizations and experimental analysis validate the robustness and competitiveness of the proposed EDERM-based learning models. The implemented codes can be found at https://***/zhangxuelincode/EDERM. © 2024 Elsevier Ltd

关键词： Learning algorithms

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Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model

Advancements in Medical Imaging: Detecting Kidney Stones in ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sudarsan, M.S.V.D. Sapra, Puneet Suganthi, D. Jeevitha, S. Ankit, R. Chauhan, Amit Department of Mathematics V.R. Siddhartha Engineering Vijayawada India Rayat Bahra University Department of Cse Punjab Mohali India Saveetha University Saveetha College of Arts and Sciences Department of Computational Intelligence Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Lovely Professional University School of Physical Education Punjab Phagwara India Christ University Department of Life Sciences Bengaluru India

ISBN: (纸本)9798350360660

Kidney stones have been more common in recent years, leading many to believe that the condition is common. The condition's strong relationship with other terrible diseases makes it a major threat to public health. The development of instruments and procedures that facilitate the diagnosis and treatment of this ailment has the potential to enhance the effectiveness and efficiency of health care. Preprocessing, feature extraction, level set segmentation, and model training are the four steps that make up this approach. Part of the preprocessing includes eliminating the skeletal skeleton and soft-organs. Level set segmentation is commonly used for object tracking, motion segmentation, and image segmentation. An extremely effective feature extraction method called Gray level co-occurrence matrix (GLCM) is suggested for extracting the necessary characteristics from the segmented image. That ELM-I-AdaBoost-RT was used all during training. This cutting-edge technique achieves an average accuracy of 95.83%, surpassing both ELM and AdaBoost. © 2024 IEEE.

关键词： Image segmentation

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ADAPT: Action-aware Driving Caption Transformer

ADAPT: Action-aware Driving Caption Transformer

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bu Jin Xinyu Liu Yupeng Zheng Pengfei Li Hao Zhao Tong Zhang Yuhang Zheng Guyue Zhou Jingjing Liu Chinese Academy of Sciences Institute of Automation China Institute for AI Industry Research (AIR) Tsinghua University China Xidian University China Department of Computer Science and Technology Tsinghua University China Southern University of Science and Technology China School of Mechanical Engineering and Automation Beihang University China

End-to-end autonomous driving has great potential in the transportation industry. However, the lack of transparency and interpretability of the automatic decision-making process hinders its industrial adoption in practice. There have been some early attempts to use attention maps or cost volume for better model explainability which is difficult for ordinary passengers to understand. To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action. ADAPT jointly trains both the driving caption task and the vehicular control prediction task, through a shared video representation. Experiments on BDD-X (Berkeley DeepDrive eXplanation) dataset demonstrate state-of-the-art performance of the ADAPT framework on both automatic metrics and human evaluation. To illustrate the feasibility of the proposed framework in real-world applications, we build a novel deployable system that takes raw car videos as input and outputs the action narrations and reasoning in real time. The code, models and data are available at https://***/jxbbb/ADAPT.

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Dynamic Monitoring Urban Expansion of Huizhou from 2000 to 2020 Using the Google Earth Engine

Dynamic Monitoring Urban Expansion of Huizhou from 2000 to 2...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Peng, Shuhong Qian, Jing Wang, Zhen Liu, Haibin Wei, Shujie Sun, Jiayu Wu, Zhipan School of Computer Science and Engineering Huizhou University Huizhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Management University of Montpellier Montpellier France

ISBN: (纸本)9781665400114

Huizhou is located in the Pearl River Delta of Guangdong Province, bordering Shenzhen and Hong Kong. It is one of the rapidly developing cities in the Guangdong-Hong Kong-Macao Greater Bay Area. With the accelerating process of urbanization, the land use change of built-up areas in Huizhou is also increasing. This paper uses Google Earth engine (GEE) cloud computing platform, selects multiple periods Landsat remote sensing data, uses CART data classifier to discriminate urban land, vegetation land and water body, and implements dynamic monitoring of Huizhou urban expansion from 2000 to 2020. The results show that the urban area changes to an increasing trend from 2000 to 2020, with a total increased area of 350.13 km2. Based on the extracted urban built-up area of Huizhou in 2000, 2005, 2010, 2015 and 2020, the growth rates of urban area in recent years are: 10.63% in 2000-2005, 3.84% in 2005-2010, 5.82% in 2010-2015 and 8.24%in 2015-2020, respectively. This study will provide a data basis for the analysis of urban future planning in Huizhou. © 2021 IEEE.

关键词： Engines

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An Advanced Analysis of Disease Prediction and Prevention Using Machine Learning

An Advanced Analysis of Disease Prediction and Prevention Us...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Karuppasamy M M.Jansi Rani Mahesh Kotha Suma S T. Subburaj Haji Begum K. Suthendran Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil India Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi Virudhunagar India Department of CSE (AI&ML) CMR Technical Campus Hyderabad Telangana India Department of CSE CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering RajaRajeshwari College of Engineering Bangalore India Department of ECE Shadan Women's college of Engineering and Technology Hyderabad Telangana India Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil India

On demand of overgrowing population crop production should be increased to satisfy the daily needs. Nowadays, diseases in crops are increasing. The various disease lead to decrease in the yield of crop production. In the proposed work machine learning techniques are used to identify the disease in crops and take precautionary measures to avoid the disease spread in crops. Based on the investigation, the parameters like rainfall, soil type, temperature leads to the spreading of diseases in crops. Kclosest Neighbor concept is used for analysis of various soil groups. For enhancement of the work support vector machine for crop characterization, straight relapse and ID3algorithm is used for disease prediction.

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Registration-Free Hybrid Learning Empowers Simple Multimodal Imaging System for High-quality Fusion Detection

arXiv

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arXiv 2023年

作者： Guan, Yinghan Dai, Haoran Yu, Zekuan Wang, Shouyu Gu, Yuanjie School of Electronics and Information Engineering Wuxi University Wuxi214105 China Metropolitan College Department of Computer Science Boston University Boston02215 United States Academy for Engineering and Technology Fudan University Shanghai200433 China

Multimodal fusion detection always places high demands on the imaging system and image pre-processing, while either a high-quality pre-registration system or image registration processing is costly. Unfortunately, the existing fusion methods are designed for registered source images, and the fusion of inhomogeneous features, which denotes a pair of features at the same spatial location that expresses different semantic information, cannot achieve satisfactory performance via these methods. As a result, we propose IA-VFDnet, a CNN-Transformer hybrid learning framework with a unified high-quality multimodal feature matching module (AKM) and a fusion module (WDAF), in which AKM and DWDAF work in synergy to perform high-quality infrared-aware visible fusion detection, which can be applied to smoke and wildfire detection. Furthermore, experiments on the M3FD dataset validate the superiority of the proposed method, with IA-VFDnet achieving the best detection performance than other state-of-the-art methods under conventional registered conditions. In addition, the first unregistered multimodal smoke and wildfire detection benchmark is openly available in this letter. Copyright © 2023, The Authors. All rights reserved.

关键词： Imaging systems

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Fast and Secure Coding for Wireless Networks

Fast and Secure Coding for Wireless Networks

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International Conference on Computing and Networking technology (ICCNT)

作者： Seema Sambargi Y.V. Dongre N. V. Balaji Saniya Khurana Sowmiya S Sidhant Das School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka comp Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Speedy and comfy coding for wireless networks is an essential problem in the trendy computing landscape. Wi-fi networks have become increasingly commonplace in recent years, and they present specific demanding situations for code builders. To ensure certain cozy, reliable, and efficient overall performance of network applications, developers must be able to quickly and appropriately generate code for wireless structures. The way to meet these requirements, builders have to be capable of selecting the most appropriate coding approaches to apply if they want to limit errors and maximize security. The main venture to developing easy and reliable code for wireless structures is the complexity of the underlying structures. The ever-increasing complexity of underlying community technologies renders conventional coding techniques inadequate. The elevated demands for real-time records and connection transmission exacerbate this. To address this venture, rapid and at-ease coding strategies need to be carried out. Those strategies can encompass, however are not constrained to, principles including comfy coding, exceptional coding practices, and diverse safety protocols. Principals of comfy coding confer with a method that emphasizes comfortable practices at some point in the improvement and protection of the codebase. This consists of proper protection practices along with authentication, encryption, enter validation, error dealing, and malicious code prevention. This technique allows make certain that the code is written in a strong, comfortable, and efficient manner.

关键词： Codes Protocols Wireless networks Encoding Complexity theory Security Reliability Communication system security Protection Wireless fidelity

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Enhancing Acceptance of Automated Vehicles Through Gamification: Insights from a Driving Simulator Study

SSRN

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SSRN 2025年

作者： Li, Chenchang Yin, Rao Yang, Bo Guan, Muhua Xie, Kun Nakano, Kimihiko Institute of Industrial Science University of Tokyo Tokyo153-8505 Japan School of Design & Innovation China Academy of Art Hangzhou311100 China School of Computer Science & System Engineering Kyushu Institute of Technology Fukuoka820-8502 Japan Department of Civil and Environmental Engineering Transportation Informatics Lab Old Dominion University NorfolkVA23529 United States

Automated vehicles hold great potential to revolutionize transportation, but subjective user acceptance remains a major obstacle to their widespread adoption and the realization of anticipated safety benefits. Recognizing this challenge, this study explores the role of gamification in enhancing user acceptance of automated vehicles and promoting safer driver engagement. By designing a gamified interaction framework and implementing it in a driving simulator experiment, the research addresses three core questions: Does gamification work? Why does it work? How can it be effectively implemented? In the experiment, participants experienced automated driving scenarios with integrated gamified interactions during overtaking tasks. Data on users' attitudes, behaviors, and eye movements were collected and analyzed through qualitative and quantitative methods. Furthermore, the study incorporated design research techniques, including interviews and user journey mapping, into the scientific framework to derive meaningful and actionable insights. The results reveal that gamification significantly improves user acceptance by enhancing Perceived Ease of Use and strengthening Behavioral Intentions. At a deeper mechanistic level, gamification enhances users' situational awareness and trust by increasing their perception efficiency during interactions and raising their risk tolerance, thereby shifting the balance between safety and reward. Based on these mechanisms and further findings, the study proposes three design strategies to maximize the benefits of gamification: designing system transparency, tailoring experiences to individual user characteristics, and leveraging incentive mechanisms to engage and motivate users effectively. This study's findings provide theoretical foundations and practical design directions for leveraging gamification to enhance human-vehicle interactions, utimately contributing to the development of safer and more efficient automated driving systems. © 2

关键词： Eye movements

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Use of Edge Computing and IoT To Create A Smart and Efficient Irrigation System

Use of Edge Computing and IoT To Create A Smart and Efficien...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neha Swaroop Mohanty Deepak Das D. Anandhasilambarasan T. Yawanikha Swati Bula Patil School of Management CV Raman Global University Bhubaneswar India Management Cv Raman Global University Bhubaneswar India Agricultural Engineering Karunya Institute of Science and Technology Coimbatore India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Karpagam Institute of Technology Coimbatore India IT Vishwakarma Institute of Information Technology Pune India

Ingenious frugal and cost-effective irrigation techniques have developed to meet the world's largest population's precious water needs. In other respects, water usage must be moderate sufficiently to focus on saving the limited supply of sparkling flavored. Due to improper watering techniques, a large amount of freshwater was squandered. We used a clever strategy that was qualified to use taxonomy to determine 50% of the choice, and the remaining 50% was dependent on the inertial sensor's values. The ultimate ruling, known as the output of a computer intelligence method, is derived from the ontological judgement as well as the measured values combined (KNN). Besides that, a Microsoft's new server is added among the secondary GSM as well as the primary IoT server. subsystem. This approach is going to lessen latencies but also prevent the IoT server from being overloaded with data acquisition. This method combines the IoT devices with a wide range of devices to nimbly track all incoming data, evaluate it from the end device, transport only a select few data sets towards the primary Wireless sensor computer, and then broadcast the outcome on such any android phone.

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Forgery Detection Scheme of Deep Video Frame-rate Up-conversion Based on Dual-stream Multi-scale Spatial-temporal Representation

Forgery Detection Scheme of Deep Video Frame-rate Up-convers...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qing Gu Xiangling Ding Dengyong Zhang Ce Yang The School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan P. R. China Hunan University of Science and Technology The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Zhengzhou Xinda Institute of Advanced Technology Guangdong Key Laboratory of Information Security Technology The School of Computer and Communication Engineering Changsha University of Science and Technology Changsha P. R. China

Video Frame-Rate Up-Conversion (FRUC) is originally designed to produce the high frame-rate video by periodically inserting new frames between two adjacent frames. However, it can also be utilized to synthesize the faked high frame-rate videos or spliced videos for malicious intents. The existing FRUC detection methods can efficiently identify its occurrence by exploring the blurring effects or deformed structures left over from the traditional FRUC methods. But the video FRUC has been substantially improved in the past years, especially in this deep learning era. Deep learning-based video FRUC (Deep FRUC) weakens the visual traces of the traditional ones such that it is challenging for the current FRUC detectors. In this paper, we propose a forensics algorithm based on dual-stream multi-scale spatial-temporal representation for Deep FRUC. Specifically, we develop a multi-scale receptive field strategy, and attention scheme to learn hidden tampered trace and spatial-temporal representation, respectively. Besides, frame residual and noise residual attention streams are complementary learning from the spatial-temporal dimension, in which the former can capture contrast differences and tampered abnormal boundaries, while the latter can explode noise inconsistencies between original and tampered frames. Experimental results show that the proposed algorithm can effectively achieve the best detection accuracy compared with the existing FRUC forensics works under the Deep FRUC video datasets.

关键词： Location awareness Deep learning Visualization Privacy Forensics Computational modeling Detectors

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