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arXiv 2023年

作者： Cheng, Xin Cao, Yuzhou Wang, Haobo Wei, Hongxin An, Bo Feng, Lei College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Homophily Within and Across Groups

arXiv

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arXiv 2024年

作者： Rizi, Abbas K. Michielan, Riccardo Stegehuis, Clara Kivelä, Mikko DTU Compute Technical University of Denmark Kongens Lyngby2800 Denmark Copenhagen Center for Social Data Science University of Copenhagen Denmark Department of Computer Science School of Science Aalto University FI-00076 Finland Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Traditional social network analysis often models homophily—the tendency of similar individuals to form connections—using a single parameter, overlooking finer biases within and across groups. We present an exponential family model that integrates both local and global homophily, distinguishing between strong homophily within tightly knit cliques and weak homophily spanning broader community interactions. By modeling these forms of homophily through a maximum entropy approach and deriving the network behavior under percolation, we show how higher-order assortative mixing influences network dynamics. Our framework is useful for decomposing homophily into finer levels and studying the spread of information and diseases, influence dynamics, and innovation diffusion. We demonstrate that the interaction between different levels of homophily results in complex percolation thresholds. We tested our model on various datasets with distinct homophily patterns, showcasing its applicability. These homophilic connections significantly affect the effectiveness of intervention and mitigation strategies. Hence, our findings have important implications for improving public health measures, understanding information dissemination on social media, and optimizing intervention strategies. © 2024, CC BY.

关键词： Percolation (computer storage)

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IRS-enabled Wireless Power Transfer and data Collection in UAV-assisted IoT

IRS-enabled Wireless Power Transfer and Data Collection in U...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Wenwen Xie Geng Sun Jiahui Li Xue Wang Jiacheng Wang Hongyang Du Dusit Niyato College of Computer Science and Technology Jilin University Changchun China College of Computing and Data Science Nanyang Technological University Singapore College of Communication Engineering Jilin University Changchun China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

An intelligent reflecting surface (IRS)-enabled wireless power transfer (WPT) and data collection scheme for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) network is investigated in this paper. Specifically, IoT devices (IoTDs) first harvest energy from the UAV and then upload the sensed data by applying time-division multiple access (TDMA), where an IRS is deployed to improve the transmission quality. We aim to minimize the age of information (AoI) and energy consumption of the UAV. For achieving this, we formulate an optimization problem by jointly optimizing the UAV trajectory, IRS phase shits, charging time allocation, and binary IoTD scheduling, which is a mixed-integer non-convex optimization problem. To address the issue, we first formulate our problem into a Markov decision process (MDP) and then propose an alternating optimization-double parameterized deep Q-network (AO-DPDQN) approach to solve the optimization problem. Specifically, an AO-based method is adopted to optimize the phase shifts of IRS to simplify the action space of MDP, and then double parameterized deep Q-network (DPDQN) is employed to optimize UAV trajectory, charging time allocation, and IoTD scheduling. Simulation results demonstrate the effectiveness and superiority of the proposed approach compared to various baselines.

关键词： Energy consumption Wireless sensor networks Wireless power transfer data collection Autonomous aerial vehicles Information age Trajectory Internet of Things Resource management Optimization

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Regression with cost-based rejection 23

Regression with cost-based rejection

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xin Cheng Yuzhou Cao Haobo Wang Hongxin Wei Bo An Lei Feng College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

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Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Priyadarshini Chatterjee Surajit Das Shadab Siddiqui Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telengana India Department of Artificial Intelligence and Data Science B V Raju Institute of Technology Narsapur Medak Telengana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Masking, in the context of image processing and computer vision, refers to the process of isolating specific regions or objects of interest within an image while suppressing or removing irrelevant information. It involves the creation of a mask, which is essentially a binary image that defines which pixels in the original image belong to the region of interest and which do not. The mask typically consists of black and white pixels, where white pixels represent the areas to be preserved or highlighted, and black pixels represent the areas to be ignored or masked out. This paper presents a novel approach aimed at enhancing the accuracy of masking algorithms for colposcopy images, a crucial component in the diagnosis of cervical cancer among women. While previous research has demonstrated promising outcomes in this domain, our study introduces a novel masking algorithm tailored specifically to improve segmentation accuracy. The colposcopy dataset utilized in this research is sourced from the International Agency for Research on Cancer in France.

关键词： Training Technological innovation Accuracy Convolution Object detection computer architecture Streaming media

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Agile Ontology: A Dynamic Framework for E-Business Evolution

Agile Ontology: A Dynamic Framework for E-Business Evolution

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohammad Mustafa Taye Rawan Abulail Belal Al-Ifan Software Engineering Department Philadelphia University Amman Jordan Computer Science Department Philadelphia University Amman Jordan Data Science and Artificial Intelligence Department Philadelphia University Amman Jordan

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

In this work, a progressed version of Extreme Programming (XP) for e-business is provided. In an effort to efficiently address the precise troubles of this dynamic industry, the strategy promotes iterative procedures and more functions. By including new elements that guide growth, independence, and fine manipulation, Enhanced XP for E-business (EXPe) builds upon the fundamental XP concepts of continuous feedback and collaboration. The e-business enterprise faces challenges such as rapidly evolving marketplace needs and the want for scalable, adaptive solutions. EXPe targets to cope with these by using selling iterative development and incorporating more suitable functionalities to help non-stop adaptation and scalability. The layout prioritizes user standards, adds more scalability mechanisms, and employs iterative improvement techniques to help development teams adapt quickly and efficaciously to changing E-enterprise desires. This study indicates that EXPe is a great choice for quickly and efficaciously growing E-business solutions due to its many benefits and capabilities. This technique aims to provide a strong foundation for engaging in non-intrusive studies on agile methods. Enhanced XP for E-Business (EXPe) uniquely integrates ontology-unique requirements and semantic research into the agile framework, extensively enhancing the adaptability and scalability of e-commercial enterprise solutions. Furthermore, listed for this purpose are applicable files and XP artifacts, including user stories.

关键词： Scalability Layout User centered design Semantics Collaboration Ontologies Programming

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Big data-Intelligence Analytics for Energy Optimization in IoT-Enabled Smart Home Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Yihong Song, Qiang Zhaoqing University Big Data Engineering Technology Center China Zhaoqing University School of Computer Science and Software China

This article explores the integration of Artificial Intelligence (AI) and Big data Analytics to optimize energy consumption in IoT-enabled smart home devices. It presents a robust analytical framework that leverages Variational Autoencoders (VAEs) for feature extraction and Differential Evolution (DE) for optimizing energy management parameters. data was gathered from various IoT devices, including energy usage patterns, occupancy data, and environmental conditions. The results show a notable 40% reduction in energy consumption, leading to annual cost savings of up to $300 per household. Moreover, user satisfaction increased by 25%, with participants reporting heightened awareness and engagement in energy conservation. The study highlights how the proposed framework efficiently identifies common usage patterns and optimizes energy distribution while preserving user comfort. These findings reinforce the potential of AI-driven analytics in improving energy efficiency in smart homes, demonstrating that advanced algorithms not only support energy conservation but also promote active user participation in sustainability efforts. © 2025 IEEE.

关键词： Energy utilization

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Federated Learning in Big data with IoT for Intrusion Detection 6

Federated Learning in Big Data with IoT for Intrusion Detect...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Suganya, A. Jayasakthi, K. Rajkumar, N. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of Artificial Intelligence and Data Science Anand Institute of Higher Technology Kazhipattur Tamil Nadu Chengalpattu603103 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India

ISBN: (纸本)9798331522667

Artificial Intelligence (AI) part of Federated Learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features. © 2025 IEEE.

关键词： Federated learning

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning 3

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper. © 2023 IEEE.

关键词： Support vector machines

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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