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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 987-1000页

作者： Jian Pan Congbo Li Ying Tang Wei Li Xiaoou Li the State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing 400044China the Department of Electrical and Computer Engineering Rowan UniversityGlassboroNJ 08028 USA the Computer Science Department The Research and Advanced Studies Centre of the National Polytechnic InstituteMexico City 07360Mexico

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization *** improve the generalization abilities,more and more parameters are acquired for energy prediction *** the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,*** work proposes a framework for energy modeling based on incomplete data to address this ***,some necessary preliminary operations are used for incomplete data ***,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data ***,we test the predictive accuracy of the obtained *** experiments are designed to investigate the performance of the proposed framework with different rates of missing *** results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.

关键词： Energy consumption prediction incomplete data generative adversarial imputation nets(GAIN) gene expression programming(GEP)

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks 13

A Heuristic Sparsification Method to Improve the Performance...

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13th International Conference on Information science and Technology, ICIST 2023

作者： Zhang, Fangyu Wei, Yanjie Lin, Zhenli Pan, Yi Liu, Jin Centre for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350313925

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced. © 2023 IEEE.

关键词： Topology

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia computer science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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Randomly Compiled Quantum Simulation with Exponentially Reduced Circuit Depths

arXiv

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arXiv 2024年

作者： Watson, James D. Joint Center for Quantum Information & Computer Science National Institute of Standards and Technology University of Maryland College Park United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States

The quantum stochastic drift protocol, also known as qDRIFT, has become a popular algorithm for implementing time-evolution of quantum systems. In this work we develop qFLO, a higher order randomised algorithm for time-evolution. To estimate an observable expectation value at time T to precision ∊, we show it is sufficient to use circuit depths of O(T2 log(1/∊)) – an exponential improvement over standard qDRIFT requirements with respect to ∊. The protocol achieves this using O(1/∊2) repeated runs of the standard qDRIFT protocol combined with classical post-processing in the form of Richardson extrapolation. Notably, it requires no ancillary qubits or additional control gates making it especially promising for near-term quantum devices. Furthermore, it is well-conditioned and inherits many desirable properties of randomly compiled simulation methods, including circuit depths that do not explicitly depend on the number of terms in the Hamiltonian, making it an attractive algorithm for problems in quantum chemistry. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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A Robust Design of Real-Time Resilient Smile Recognition System using Hybrid Deep Learning Principles 10

A Robust Design of Real-Time Resilient Smile Recognition Sys...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Cherian, Aswathy K. Devipriya, S. Saoji, Bhushan P. Mallikeswari, B. Thiagarajan, R. Krishnamoorthy, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Jerusalem College of Engineering Department of Cse Chennai India Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navimumbai India JusticeBasheer Ahmed Sayeed College For Women Dept of Computer Chennai India Prathyusha Engineering College Department of It Tamilnadu Chennai India Chennai Institute of Technology Centre For Advanced Wireless Integrated Technology Chennai India

ISBN: (纸本)9798350353068

We often convey our happiness via smiling or making a happy face. Simple, latent feelings like contentment and joy are brought to the surface in this way. In terms of social communication, this is the most difficult and potent task there is. One function of digital cameras is smile recognition, which prevents taking pictures of people unless they are smiling. There are two distinct stages to smile detection. After identifying a face in a video or picture, it watches for a grin. To be more precise, we may state that a motion detector takes a video or image and breaks it down into frames. It then examines the frameworks, such as the balanced level and flash, for the face area. The camera can tell whether someone is laughing or smiling because it looks for certain facial features - a jutted jaw, narrowed eyes, and an elevated cheekbone among others. The components of computer architecture and software advancements are advancing at a rapid pace, which is leading to a progressive increase in customer needs for electric products. A growing number of people are interested in human computer interaction systems that differ from the conventional user interface, which includes the keyboard and mouse. This article depicts the realtime implementation of deep learning algorithms in computer settings with limited resources. In this study, we provide HLSR, a new kind of deep learning model that combines the XGBoost and Convolutional Neural Network (CNN) algorithms for smile recognition. To get a clear picture of how well the suggested technique works, we cross-validate the HLSR model with the current CNN model. © 2024 IEEE.

关键词： Face recognition

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

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Advancement in Technology (ICONAT), International Conference for

作者： Deepika Nayak Rekha R Nair TinaBabu S Kishore Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute Of Professional Education Nitte(Deemed to be University) Mangaluru Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was $\mathbf{9 7. 5 8 \%}$. The system’s application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole.

关键词： Productivity Accuracy Profitability Plants (biology) Crops Agriculture Real-time systems Convolutional neural networks Monitoring Diseases

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Strategic Management of Airline Operations: A Comprehensive Analysis of Flight Delay Causes and Mitigation Strategies

Strategic Management of Airline Operations: A Comprehensive ...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Singh, Shikha Sajjan, Sonali Singh, Teekam Kumari, Nandani Graphic Era Hill University School of Computing Dehradun India G L Bajaj Institute of Management Greater Noida201310 India Greater Noida Institute of Professional Studies Greater Noida201310 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India J S M College Phusro Bokaro India

ISBN: (纸本)9798350382723

Travelers nowadays are often informed of delays, which lowers consumer satisfaction. The delays and other variables cost both tangible and intangible. Thus, airlines and airports must foresee and mitigate flight delays to improve operational efficiency and perhaps apply customer-centric solutions to mitigate their negative effects. Flight delays influence operational efficiency, customer satisfaction, and financial health in the aviation industry, a vital part of global transportation networks. The numerous causes of flight delays in strategic airline operation management are extensively reviewed in this research. It covers external and internal factors including weather, air traffic control, mechanical issues, and administrative initiatives. The study integrates logistics, human resources, and customer service management to understand airline delays' complexity. This study uses Data Mining to anticipate Hartsfield-Jackson international airport arrival delays by identifying the most important characteristics. The study uses the Knowledge Discovery Database (KDD) process, including data gathering, sampling methods (SMOTE and Under Sampling), data division into training and test sets, data preprocessing (addressing missing values and outliers), and data transformation. Define and train Decision Trees, Random Forests, and Multilayer Perceptrons, then evaluate model performance using various metrics. After experimenting, the best model uses departure variables, the Multilayer Perceptron algorithm, SMOTE for data balancing, outlier removal, and Gain Ratio-based selection of ten key variables. © 2024 IEEE.

关键词： Air transportation

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