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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Han, Shuangshuang Bai, Yongqiang Chen, Yueyun Tellambura, Chintha University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350318470

The current Internet of Vehicles (IoV) data are facing challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. This paper proposes an IoV Information Management System (IIMS) based on parallel intelligence and blockchain to address these challenges. By leveraging the decentralized, immutable, and traceable nature of blockchain, combined with the incentive mechanisms and collaborative models of decentralized autonomous organizations (DAOs), a secure and trustworthy data sharing platform is established for collaborative analysis, business cooperation, and innovative applications of IoV data. The proposed approach utilizes the decentralized nature and data backup capabilities of blockchain to mitigate the single point of failure issues associated with centralized servers. Additionally, the anonymity, immutability, and traceability features of blockchain ensure the privacy, security, and integrity of data sharing. Moreover, the incentive mechanisms and DAO solutions within the blockchain promote data sharing among vehicles while ensuring data quality. © 2023 IEEE.

关键词： Blockchain

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Optimizing Hiring Practices: A Machine Learning Approach for Candidate Selection 3

Optimizing Hiring Practices: A Machine Learning Approach for...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dogiparthy, Naga Datta Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Recruitment is one of the most crucial factors in shaping efficient and high-performing teams within organizations. However, the traditional recruitment processes are often plagued by biases and subjective judgments that lead to inconsistent and suboptimal hiring decisions. This project uses machine learning(ML) to make recruitment more objective, efficient, and data-driven. By taking the age, gender, education level, experience, interview scores, and personality traits of the applicants as key features, the model predicts candidate suitability without bias. The methodology uses robust preprocessing, Chi-Square feature selection, and the SMOTE technique to overcome class imbalance in the dataset. The proposed system uses various different machine learning algorithms like CatBoost, KD-Trees, Random Forest, and Logistic Regression and many more. All these models are trained and fine-tuned with GridSearchCV for hyperparameter optimization. Among them, the best model was CatBoost with 95% accuracy. This work shows how machine learning can change the recruitment game for organizations to make unbiased, data-driven hiring decisions and ultimately increase workforce efficiency and organizational success. © 2025 IEEE.

关键词： Adversarial machine learning

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Platoon-Based Resource Allocation in NOMA-Integrated V2X Networks 23

Platoon-Based Resource Allocation in NOMA-Integrated V2X Net...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Bao, Haizhou Huo, Yiming School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China University of Victoria Department of Electrical and Computer Engineering Victoria Canada

ISBN: (纸本)9798350325959

This paper discusses the application of Cellular Vehicle-to-Everything (C-V2X) communications, specifically focusing on the integration of non-orthogonal multiple access (NOMA) in platoon technology within the V2X network. These systems have the potential to significantly enhance a variety of critical, content-oriented applications that extend beyond 5G and Internet of Things (IoT) infrastructures. Crucially, we argue that optimized communication at the edge of the network can effectively mitigate network congestion, augment delivery efficiency, and optimize energy use. Nonetheless, constraints inherent to vehicular networks, such as limited backhaul bandwidth and ever-changing topology, can compromise the reliability and latency of multimedia services in C-V2X networks, thereby affecting overall user experience. To address the mobility-related challenges of the topology, we introduce an intelligent driver model (IDM)-based platoon paradigm, and subsequently propose a NOMA-based platoon transmission model designed specifically for C-V2X networks. This NOMA-based transmission approach is intended to enhance vehicle-to-vehicle communications within the platoon, with the objective of optimizing throughput. Our simulation outcomes affirm the effectiveness of our proposed model in improving throughput while reducing response latency. © 2023 IEEE.

关键词： 5G Cellular Vehicular-to-Everything (C-V2X) Internet-of-Things (IoT) Non-Orthogonal Multiple Access (NOMA) Platoon

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Forecasting Bitcoin Price Trends: Integrated Machine Learning with Market Trends 3

Forecasting Bitcoin Price Trends: Integrated Machine Learnin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Samir Ahamed, S. Radha, D. Kirthika Devi, V.S. Department of Computer Science And Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Bitcoin price prediction remains a complicated task in financial markets since price fluctuations are characterized by high variability, non-linear and dependent on different trends. The common models of forecasting can be disruptive because they do not readily accommodate the breadth of the data and the dynamics of the marketplace. The paper presents a new approach to predicting Bitcoin price shifts based on state-of-the-art data structures and both machine learning and technical analysis. The formative historical price datasets and large scale features are organized and manipulated in an efficient manner with the aid of data structures as well as the temporal and non-linear trends in prices are captured by ARIMA,Exponential smoothing and Random. Also, trends of moving averages are used to boost the model’s competency to analyze the sentiments of the market. The proposed framework was put through a test on real world Bitcoin price data and it was shown to provide better results as compared to the traditional methods with better accuracy and visualization. Besides that, our method enhances the accuracy of the forecast, while the deployment of the method allows developing a realtime Bitcoin price prediction model that can be useful for traders and investors. The integration of machine learning and technical analysis gives a powerful and reliable tool to forecast the Bitcoins price trends. © 2025 IEEE.

关键词： Prediction models

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A Deep Multi-View Framework for Anomaly Detection on Attributed Networks (Extended Abstract) 39

A Deep Multi-View Framework for Anomaly Detection on Attribu...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Peng, Zhen Luo, Minnan Li, Jundong Xue, Luguo Zheng, Qinghua Xi'an Jiaotong University School of Computer Science and Technology China University of Virginia Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350322279

Many existing anomaly detection methods on attributed networks do not seriously tackle the inherent multi-view property in attribute space but concatenate multiple views into a single feature vector, which inevitably ignores the incompatibility between heterogeneous views caused by their own statistical properties. In practice, the distinct but complementary information brought by multi-view data promises the potential for more effective anomaly detection than the efforts only based on single-view data. Furthermore, abnormal patterns naturally behave diversely in different views, which coincides with people's desire to discover specific abnormalities according to their preferences for views (attributes). Most existing methods cannot adapt to people's requirements as they fail to consider the idiosyncrasy of user preferences. Thus, in this paper, we propose a multi-view framework ALARM to incorporate user preferences into anomaly detection and simultaneously tackle heterogeneous attribute characteristics through multiple graph encoders and a well-designed aggregator that supports self-learning and user-guided learning. Experiments on synthetic and real-world datasets corroborate the desirable performance of ALARM and its effectiveness in supporting user-oriented anomaly detection. © 2023 IEEE.

关键词： Anomaly detection

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Towards a Scalable Reference-Free Evaluation of Generative Models 38

Towards a Scalable Reference-Free Evaluation of Generative M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ospanov, Azim Zhang, Jingwei Jalali, Mohammad Cao, Xuenan Bogdanov, Andrej Farnia, Farzan Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong Department of Cultural and Religious Studies The Chinese University of Hong Kong Hong Kong School of Electrical Engineering and Computer Science University of Ottawa Canada

While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the unavailability of applicable reference datasets. Recently, the reference-free entropy scores, VENDI [1] and RKE [2], have been proposed to evaluate the diversity of generated data. However, estimating these scores from data leads to significant computational costs for large-scale generative models. In this work, we leverage the random Fourier features framework to reduce the computational price and propose the Fourier-based Kernel Entropy Approximation (FKEA) method. We utilize FKEA's approximated eigenspectrum of the kernel matrix to efficiently estimate the mentioned entropy scores. Furthermore, we show the application of FKEA's proxy eigenvectors to reveal the method's identified modes in evaluating the diversity of produced samples. We provide a stochastic implementation of the FKEA assessment algorithm with a complexity O(n) linearly growing with sample size n. We extensively evaluate FKEA's numerical performance in application to standard image, text, and video datasets. Our empirical results indicate the method's scalability and interpretability applied to large-scale generative models. The codebase is available at https://***/aziksh-ospanov/FKEA. © 2024 Neural information processing systems foundation. All rights reserved.

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GNN-Based Data Rate Maximization in Double Intelligent Reflecting Surface (IRS)-Aided Communication System

GNN-Based Data Rate Maximization in Double Intelligent Refle...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Li, Kaixin Peng, Limei Ho, Pin-Han School of Computer Science and Engineering Kyungpook National University Deagu Korea Republic of University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (纸本)9798350327380

This paper explores the utilization of double intelligent reflecting surfaces (IRSs) in wireless communication systems to enhance signal propagation. By dynamically adjusting the reflecting elements of the IRSs, we can efficiently manipulate the signal direction to improve the communication performance such as data rate. Nonetheless, due to the passive nature of the reflecting elements, it is quite challenging to accurately measure the channels directly between the base station (BS), the IRS, and the users based on the conventional channel estimation methods. To address this, we propose to apply a machine learning algorithm that bypasses the traditional channel estimation process and optimizes system parameters by directly extracting information from the received pilot signal. Through extensive simulations, we demonstrate the effectiveness of our proposed method in enhancing the performance of wireless systems incorporating double IRSs. © 2023 IEEE.

关键词： Graph neural networks

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Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification

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IEEE Transactions on Neural Networks and Learning Systems 2024年第2期35卷 2167-2181页

作者： Huang, Yu Yen, Gary G. Tseng, Vincent S. National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU, early treatment contributes to the patient's fast recovery, and early warning could even save lives. Hence, in these cases, it is worthy of trading, to some extent, classification accuracy in favor of earlier decisions when the time series data are collected over time. In this article, we propose a novel deep reinforcement learning-based framework, snippet policy network V2 (SPN-V2), for long and varied-length multi-lead electrocardiogram (ECG) early classification. The proposed SNP-V2 contains two main components: snippet representation learning (SRL) and early classification timing learning (ECTL). The SRL is proposed to encode inner-snippet spatial correlations and inter-snippet temporal correlations into the hidden representations of the subsegment (snippet) of the input ECG. ECTL aims to learn a decision agent to classify the time series early and accurately. To optimize the proposed framework, we design a novel knee-guided neuroevolution algorithm (KGNA) to solve cardiovascular diseases' early classification problem, automatically optimizing the proposed SPN-V2 regarding the tradeoff between accuracy and earliness. In addition, we conduct a series of experiments on two real-world ECG datasets. The experimental results show the superiority of the proposed algorithm over the state-of-the-art competing methods. © 2012 IEEE.

关键词： Electrocardiograms

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Inkjet Printed Flexible High Isolation Patch Antenna for 5.8 GHz Full-Duplex Applications

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Progress In Electromagnetics Research C 2022年 127卷 127-143页

作者： Hossain, Abdul R. Sagar, Md S. I. Tran, Nghi Sekhar, Praveen K. Karacolak, Tutku School of Engineering and Computer Science Washington State University VancouverWA United States Department of Electrical and Computer Engineering University of Akron OH United States

In this paper, a flexible full-duplex antenna is proposed with robust performance and high isolation for 5.8 GHz using foam and PET paper. The patch of the antenna is modified by corner cut and inset feeding, while the defected ground structure is used to improve isolation between transmit and receive ports. Silver nanoparticle ink is used for printing the antenna in an inkjet printer. The fabricated version supports simulated results by showing acceptable performance in desired bandwidth. Bending tests and human body loading experiments are carried out on the fabricated antenna to demonstrate antenna’s effectiveness for wearable applications. To the best of authors’ knowledge, this is the first flexible full-duplex antenna designed, achieving a high isolation level of −50 dB. Moreover, wide bandwidth, improved gain, radiation efficiency, low cost, easy fabrication, and robust performance make it a good option for 5.8 GHz wearable applications. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Radiation efficiency

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Smartphone Price Prediction and Category Classification using Machine Learning 3

Smartphone Price Prediction and Category Classification usin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Agalayil, Helen Terese Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and LightGBM was found to be the best regressor for price prediction. © 2025 IEEE.

关键词： Smartphones

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