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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Shi, Hongyang Lu, Ming Wang, Hongjun Zhang, Yonghong Dong, Zengshou Shenyang Institute of computing technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Infrastructure & Cloud Service SSG Lenovo Beijing China R.C of Big Data and Artificial Intelligence Technology Shandong University Jinan China Beijing Zhongke Zhihe Digital Technology Co. Ltd Beijing China School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)9783800757398

In recent years, tower crane safety accidents have occurred frequently, resulting in casualties and significant losses of construction site property. In order to reduce the probability of tower crane accidents, quality supervisor needs a method that can cause anomaly detection of alarm surges in a certain time period of the tower crane. Due to the lack of generalization capability of existing methods for new equipment and the large calculation overhead and weak interpretability, quality supervisor is often unable to effectively detect the risks brought about by the improper operation of special operators. This paper adopts the unsupervised anomaly detection method based on copula function, estimates the empirical copula function according to the empirical cumulative distribution of input data through nonparametric method, and then estimates the tail probability of joint distribution in all dimensions to evaluate the anomaly. In order to evaluate the effect of the model, this paper calculates the sudden increase point by comparing with each time series value and historical value, and uses it as an anomaly label. By comparing other different classes of anomaly detection algorithms horizontally, the copula function-based anomaly detection method for the scenario in this paper has better generalization and generalization ability, and the overall performance is better than other algorithms. In addition, compared with other methods, this method has the characteristics of low calculation consumption, scalability and high interpretability. For the anomaly detection of the total amount of tower crane alarm, this method has high application value. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Function evaluation

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A Lightweight Feature Selection Method Based on Rankability

SSRN

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SSRN 2023年

作者： Kong, Lingping Silva, Juan Domingo Velasquez Perfilieva, Irina Pant, Millie Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile University Hospital Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Instituto Sistemas Complejos de Ingeniería Santiago Chile Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

Feature selection, as one of the essential dimensionality reduction techniques, hasbecome one popular yet challenging area in the field, such as data mining andmachine learning. Unlike feature extraction (such as principle component analysis andnon-negative matrix factorization), preserving the entire information but losing thefeature relevance. data processing in feature selection will lose information, leading tothe need to develop lightweight, efficient, and practical methods that preserve the datainformation as much as possible while performing dimensional reduction. In this paper,we propose a rankability-based feature selection method. The rankability concept wasproposed in 2019, similar to the entropy concept, and has not been studied widely *** proposed method is lightweight in terms of complexity, which requires no iterativeoptimization or auxiliary estimator *** experimented with sixteen datasets and compared our results with four otheralgorithms. The results show that our rankability-based feature selection methodoutperforms the fuzzy entropy-based method on five datasets in eight, and the averageaccuracy increased by 0.1482, 0.1078, and 0.1157, respectively. Then, in the varieddimension-reducing experiments, the proposed method shows superiority on fourdatasets out of eight and is competitive with others on two datasets out of eight. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Bipartite Containment Control of Nonuniform Delayed Fractional-Order Multi-Agent Systems Over Signed Networks

Bipartite Containment Control of Nonuniform Delayed Fraction...

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IEEE Conference on Decision and Control

作者： Weihao Li Kaiyu Qin Jinliang Shao Lei Shi Mengji Shi Wei Xing Zheng School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Artificial Intelligence Henan University Zhengzhou China School of Computer Data and Mathematica Sciences Western Sydney University Sydney NSW Australia

In this study, the bipartite containment control problem of fractional-order multi-agent systems with nonuni-form time delays is addressed. An in-depth analysis of the system stability and bipartite containment control performance from a delay margin perspective is provided. Theoretically, the corresponding delay margin (maximum allowable time delay) over undirected and directed signed networks is obtained in the presence of nonuniform time delays, respectively. In addition, numerical relationships between the delay margin and the control coefficients, fractional order, and topology parameters are established, thus enabling easy and direct calculation of the maximum allowable time delay and facilitating distributed controller design and controller parameter tuning. Finally, some simulation examples are given to verify the effectiveness of the proposed bipartite containment controller and the obtained delay margin.

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artificial intelligence in Cancer Immunotherapy:Navigating Challenges and Unlocking Opportunities

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工程（英文） 2025年第1期44卷 12-16页

作者： Wei Xiang Lu Yu Xiaoyuan Chen Marco J.Herold Australian Centre for Artificial Intelligence in Medical Innovation La Trobe UniversityMelbourneVIC 3086Australia School of Computing Engineeringand Mathematical SciencesLa Trobe UniversityMelbourneVIC 3086Australia Department of Diagnostic Radiology Yong Loo Lin School of MedicineNational University of SingaporeSingapore 119074Singapore Department of Chemical and Biomolecular Engineering College of Design and EngineeringNational University of SingaporeSingapore 117575Singapore Department of Biomedical Engineering College of Design and EngineeringNational University of SingaporeSingapore 117575Singapore Department of Pharmacy and Pharmaceutical Sciences Faculty of ScienceNational University of SingaporeSingapore 117544Singapore Clinical Imaging Research Centre Centre for Translational MedicineYong Loo Lin School of MedicineNational University of SingaporeSingapore 117599Singapore Nanomedicine Translational Research Program Yong Loo Lin School of MedicineNational University of SingaporeSingapore 117597Singapore Theranostics Center of Excellence(TCE) Yong Loo Lin School of MedicineNational University of SingaporeSingapore 138667Singapore Institute of Molecular and Cell Biology Agency for ScienceTechnologyand Research(A*STAR)Singapore 138673Singapore Olivia Newton-John Cancer Research Institute HeidelbergVIC 3084Australia School of Cancer Medicine La Trobe UniversityMelbourneVIC 3086Australia The Walter and Eliza Hall Institute of Medical Research ParkvilleVIC 3052Australia Department of Medical Biology University of MelbourneParkvilleVIC 3010Australia

*** Cancer continues to be a major cause of global mortality rates,with conventional treatments such as chemotherapy and radio-therapy exhibiting inconsistent efficacy,high costs,and consider-able side *** the past decade,a promising alternative has emerged:cancer immunotherapy,which leverages the body's immune system to identify and eradicate cancer cells[1].

关键词： intelligence immunotherapy cancer artificial challenges navigating opportunities unlocking

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges

Penetration of Deep Learning in Human Health Care and Pharma...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Ramakrishnan Raman Radha. H. R T M Inbamalar D. Abdus Subhahan Ashok Kumar S. Bathrinath Swagata Sarkar Symbiosis Institute of Business Management Pune & Symbiosis International Pune Maharashtra Department of Chemistry K. S. School of Engineering and Management Bangalore Karnataka Department of Electronics and Communication R.M.K. College of Engineering Thiruvallur Tamil Nadu Department of Computer Science B V Raju Institute of Technology Narsapur Telangana Department of Computer Science Banasthali Vidyapith Rajasthan Department of Mechanical Engineering Kalasalingam Academy of Research Krishnankoil Tamil Nadu Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Tamil Nadu India

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties.

关键词： Deep learning Drugs Genomics Medical services Production Feature extraction Biology

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Emotion Identification and Detection from Multichannel EEG Signals Using artificial Neural Network

Emotion Identification and Detection from Multichannel EEG S...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Cuddapah Anitha E.P John. K. Kathiresan N.J Krishnakumar. V. Karthik J. Sasidevi Department of Computer Science and Engineering Sree Vidyaniketan Engineering College Mohan Babu University Sri Sainath Nagar A Rangampeta Tirupati College of Management SRM Institute of Science and Technology Chennai TamilNadu India Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore TamilNadu India Department of Electronics and Communication Engineering Gopalan College of Engineering and Management Seetharamapalaya Basava Nagar Bangalore Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore TamilNadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Samayapuram Trichy TamilNadu

Modified steady inclination affirmation considering multi-station EEG signals is shaping into a basic PC upheld technique for feeling issue finding in sensory system science and psychiatry as a problematic model affirmation task. In light of expansive space data, standard machine learning approaches call for arranging and eliminating various features from a lone or various channels. Along these lines, these strategies could acquaint a test with individuals who need point dominance. Then again, huge learning frameworks have been truly applied in various persistent created endeavors to see consolidates and sort different kinds of information. In this overview, standard signals are considered, and a direct yet strong pre-managing methodology is proposed to expand confirmation accuracy. In the interim, by truly learning compositional spatial-standard depiction of unpalatable EEG streams, a cross mix brain network that joins "Convolution Brain Organization (CNN)" and "Long Brain Organization (RNN)" has been used to portray human tendency states. The CNN module changes the chain-like EEG gathering into a 2D packaging improvement to mine the between channel relationship among genuinely lining EEG ***, harshness, and wrath are several the various sentiments that people experience reliably. Electroencephalography (EEG) data should in this way have a convincing inclination recognizing confirmation structure to unequivocally reflect feeling. in the present. In spite of the way that new assessment on this issue can offer great execution estimations, they are at this point lacking for the execution of a full inclination affirmation system.

关键词： Standards organizations Organizations artificial neural networks Machine learning Packaging Electroencephalography Reliability

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

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

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

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.

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Ibrahim Abd Elatif Gomaa Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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