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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Analytical review and study on various course recommendation systems

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 160-170页

作者： Anupama, V. Elayidom, M. Sudheep Division of Computer Science School of Engineering Cochin University of Science and Technology Kerala Kochi India

In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learningbased approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics. © 2023 Inderscience Enterprises Ltd.

关键词： Collaborative filtering

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A systematic review on recent methods on deep learning for automatic detection of Alzheimer's disease

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Medicine in Novel technology and Devices 2025年第1期25卷 1-12页

作者： Radhakrishna Chamakuri Hyma Janapana Department of Computer Science Engineering GITAM School of TechnologyVisakhapatnamAndhra Pradesh530045India

Alzheimer's disease(AD)is the most frequent cause of dementia,however,and it is caused by a number of different *** regard to the elderly population all over the world,Alzheimer's disease is the seventh largest cause of mortality,disability,and ***,social isolation,inactivity,alcohol,smoking,obesity,diabetes,high blood pressure,and age are all variables that can increase the likelihood of getting *** risk factors include social isolation,depression,and smoking.A diagnosis of Alzheimer's disease at an earlier stage may improve the odds of receiving care and *** professionals often diagnose AD based on a limited number of *** the other hand,it is now possible to identify and categorize Alzheimer's disease(AD)because of technological advancements such as artificial intelligence(AI).However,to identify the current AI-enabled approaches,we must conduct an investigation into the state of the *** breakthrough in diagnosis methodologies will enable the development of the Clinical Decision Support System(CDSS),capable of automatically diagnosing Alzheimer's disease(AD)without human *** this publication,we conduct a systematic review of sixty research articles previously reviewed by other *** systematic review sheds light on the synthesis of new knowledge and *** study discusses the current approaches for machine learning,deep learning methods,ensemble models,transfer learning,and methods used for early Alzheimer's disease *** paper provides answers to a large number of research issues and synthesizes fresh information that is helpful to the reader on many elements of AI-enabled approaches for Alzheimer's disease *** addition,it has the potential to stimulate additional research into more effective methods of computer-based intelligent identification of Alzheimer's disease.

关键词： Alzheimer disease diagnosis Artificial intelligence Deep learning Ensemble learning Machine learning

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Analysis of Convolutional Neural Networks on Indian food detection and estimation of calories

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Materials Today: Proceedings 2022年 62卷 4665-4670页

作者： Sathish, Suriyakrishnan Ashwin, S. Abdul Quadir, Md. Pavithra, L.K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Indian Cuisine has a peculiar aroma and flavour distinct from other cuisines. On the other hand, Obesity, Diabetes, and Hypercholesterolemia are severe problems in the Republic of India. This research aims to develop and analyze a Deep Learning model based on OpenCV for identifying Indian cuisine and determining their calories. Indian Cuisine dataset was built by extracting images from the internet and preprocessing them to fine quality. From the dataset, the classification of Indian Cuisine was done by Convolutional Neural Network, and with the help of image processing techniques, calories of the classified food were calculated. After an enormous amount of analysis, the developed model detects Indian food cuisine with an accuracy of 99.19% on training data and 95.30% on testing data;also, the estimation of calories was the accuracy with an error variation of ±10 calories to the actual food. © 2022

关键词： Convolution

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MGOD: Multi-Granular Outlier Detection with Clustlier Analysis

MGOD: Multi-Granular Outlier Detection with Clustlier Analys...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Qingsheng Zhao, Mingjie Ji, Yuzhu Luo, Xiaopeng Zhang, Yiqun Zhang, Yue Guangdong University of Technology School of Computer Science and Technology China Guangzhou Huali College School of Computer Engineering China Guangdong Polytechnic Normal University School of Computer Science China

ISBN: (纸本)9798350386226

Unsupervised Outlier Detection (UOD) is crucial for the analysis of biomedical and health data with undesirable outliers. However, the complex distribution of real data often brings difficulties to UOD where the "masking effect", i.e., only a small number of densely distributed outliers (also called clustliers) can collectively mask themselves from being detected, is particularly challenging. Another difficulty derived from this is how to distinguish clustliers from small clusters. Therefore, we propose a novel Multi-Granular Outlier Detector (MGOD). It first partitions the dataset into subsets with natural neighbor topological relationships to circumvent the non-trivial neighbor range setting. Then it effectively detects both clustliers and isolated samples (also called scatliers) based on a newly designed anomaly score. The score comprehensively takes into account the density and connectivity of samples to reflect different extents and types of abnormality. It turns out that MGOD is accurate and highly interpretable. The performance of MGOD is also robust to the involved hyper-parameters, which are easy to set. Comprehensive evaluations have been conducted to compare seven counterparts on 15 datasets, most of which are biomedical datasets. The results of significance tests confirm the effectiveness and superiority of MGOD. The source code is opened at https://***/r/MGOD-C531. © 2024 IEEE.

关键词： Unsupervised learning

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From Perception to Action:Brain-to-Brain Information Transmission of Pigeons

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Journal of Bionic engineering 2024年第6期21卷 2913-2923页

作者： Lifang Yang Long Yang Haofeng Wang Mengmeng Li Zhigang Shang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001China

Along with the flourishing of brain-computer interface technology,the brain-to-brain information transmission between different organisms has received high attention in recent ***,specific information transmission mode and implementation technology need to be further *** this paper,we constructed a brain-to-brain information transmission system between pigeons based on the neural information decoding and electrical stimulation encoding *** system consists of three parts:(1)the“perception pigeon”learns to distinguish different visual stimuli with two discrepant frequencies,(2)the computer decodes the stimuli based on the neural signals recorded from the“perception pigeon”through a frequency identification algorithm(neural information decoding)and encodes them into different kinds of electrical pulses,(3)the“action pigeon”receives the Intracortical Microstimulation(ICMS)and executes corresponding key-pecking actions through discriminative learning(electrical stimulation encoding).The experimental results show that our brain-to-brain system achieves information transmission from perception to action between two pigeons with the average accuracy of about 72%.Our study verifies the feasibility of information transmission between inter-brain based on neural information decoding and ICMS encoding,providing important technical methods and experimental program references for the development of brain-to-brain communication technology.

关键词： Pigeon Neural Information Decoding Electrical Stimulation Encoding Intracortical Microstimulation Brain-to-brain Information Transmission

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Damaged apple detection with a hybrid YOLOv3 algorithm

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Information Processing in Agriculture 2024年第2期11卷 163-171页

作者： Meng Zhang Huazhao Liang Zhongju Wang Long Wang Chao Huang Xiong Luo Schoolof Computer and Communication Engineering University of Science and Technoogy BejingBejing 1083China lShunde Innovation SchoolUniversity of Science and Technology BeijingFoshan 528399China

This paper proposes an improved You Only Look Once(YOLOv3)algorithm for automatically detecting damaged apples to promote the automation of the fruit processing *** the proposed method,a clustering method based on Rao-1 algorithm is introduced to optimize anchor box *** clustering method uses the intersection over the union to form the objective function and the most representative anchor boxes are generated for normal and damaged apple *** verify the feasibility and effectiveness of the proposed method,real apple images collected from the Internet are *** with the generic YOLOv3 and Fast Region-based Convolutional Neural Network(Fast R-CNN)algorithms,the proposed method yields the highest mean average precision value for the test ***,it is practical to apply the proposed method for intelligent apple detection and classification tasks.

关键词： Rao algorithm Apple detection Clustering Smart agriculture

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Event-triggered tracking control for a class of nonholonomic systems in chained form

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Science China(Information Sciences) 2023年第7期66卷 147-161页

作者： Liang XU Youfeng SU He CAI Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University College of Computer and Data Science Fuzhou University School of Automation Science and Engineering South China University of Technology

In this study, the event-triggered asymptotic tracking control problem is considered for a class of nonholonomic systems in chained form for the time-varying reference input. First, to eliminate the ripple phenomenon caused by the imprecise compensation of the time-varying reference input, a novel time-varying event-triggered piecewise continuous control law and a triggering mechanism with a time-varying triggering function are developed. Second, an explicit integral input-to-state stable Lyapunov function is constructed for the time-varying closed-loop system regarding the sampling error as the external input. The origin of the closed-loop system is shown to be uniformly globally asymptotically stable for any global exponential decaying threshold signals, which in turn rules out the Zeno behavior. Moreover, infinitely fast sampling can be avoided by appropriately tuning the exponential convergence rate of the threshold signal. A numerical simulation example is provided to illustrate the proposed control approach.

关键词： event-triggered nonholonomic systems strict Lyapunov function tracking integral input-to-state stable

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

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Chinese Physics B 2023年第11期32卷 401-407页

作者：马璐任彦霖何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free ***, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

关键词： epilepsy EEG signal horizontal visibility graph complex network

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