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An Efficient Scalp Inspection and Diagnosis System Using Multiple Deep Learning-Based Modules un système efficace d'inspection et de diagnostic du cuir chevelure utilisant plusieurs modules basés sur l'apprentissage profond

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and computer engineering 2024年第1期47卷 22-35页

作者： Chen, Liang-Bi Chang, Wan-Jung Chiu, Yi-Chan Huang, Xiang-Rui National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan Southern Taiwan University of Science and Technology Department of Electronic Engineering Tainan710 Taiwan

The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccuracies and misjudgments. To address these shortcomings, this article proposes a novel multimodal deep learning-based scalp inspection and diagnosis system. The proposed system employs various artificial intelligence (AI) object recognition modules, such as single-shot multibox detector (SSD)-MobileNetV2, SSD-InceptionV2, Faster region-based convolutional neural network (R-CNN)-InceptionV2, and Faster R-CNN-Inception-ResNetV2-Atrous (2). These modules form a diverse scalp symptom recognition module integrated into an AI recognition server. This study included nine scalp symptoms, encompassing four primary conditions (dandruff, hair loss, gray hair, and oily hair), as well as five special conditions (folliculitis, chemical residue, mold, fungi, fungus, and psoriasis). The efficiency of the proposed system is evaluated through experiments, and adjustments are made to the neural network architecture to achieve optimal performance across diverse symptoms. The experimental results showed that Faster-R-CNN-Inception-ResNetV2-Atrous (2) excels in recognizing chemical residue and oily hair symptoms (accuracies of 89.33% and 87.75%, respectively);Faster-R-CNN-Inception-ResNetV2-Atrous (4) outperforms in recognizing dandruff, folliculitis, fungal, and psoriasis symptoms (accuracies ranging from 88.77% to 99.72%);and Faster-R-CNN-Inception-ResNetV2-Atrous (4) is the best-performing method overall. © 2021 IEEE.

关键词： Inspection

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Machine Learning Based Diabetes Detection Model for False Negative Reduction

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Biomedical Materials and Devices 2024年第1期2卷 427-443页

作者： Uddin, Md. Ashraf Islam, Md. Manowarul Talukder, Md. Alamin Hossain, Md. Al Amin Akhter, Arnisha Aryal, Sunil Muntaha, Maisha School of Information Technology Deakin University Department of Computer Science and Engineering Jagannath University

Diabetes is a chronic disease characterized by the inability of the pancreas to produce enough insulin or the body’s inability to use insulin efficiently. This disease is becoming increasingly prevalent worldwide and can result in severe complications such as blindness, kidney failure, and stroke. Early detection of diabetes can potentially save millions of lives globally, making it a crucial focus of research. In this study, we propose a machine learning model to aid in predicting diabetes. The model comprises several machine learning methods: Linear Regression (LnR), Logistic Regression (LR), k-nearest neighbor (KNN), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Prior to feeding the pre-processed data into the machine learning model for evaluation, we conducted several pre-processing steps, such as removing null values, standardizing data using normalization, and labeling data using the label encoding process. Imbalanced datasets can adversely affect the accuracy of machine learning algorithms, and we address this problem by balancing the datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. We assessed the model’s performance on two datasets and found that the random forest algorithm produced optimal results, with 97% accuracy on the diabetes dataset 2019 and 80% accuracy on the Pima Indian dataset. However, using a balanced dataset, we can significantly reduce the number of false-negative detections. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： Balance data Features selection Machine learning Pre-processed data Random forest SMOTE

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

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Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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An Offline Biotic Stress Recognition Tool for Rice Plants Through Domain Shift

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SN computer science 2024年第5期5卷 1-18页

作者： Pal, Chiranjit Chatterji, Sanjay Pratihar, Sanjoy Computer Science and Engineering Indian Institute of Information Technology Kalyani Kalyani 741235 India

Researchers are investigating deep learning techniques with their automatic feature learning capabilities for automated rice disease recognition from images. The current study has developed an ensamble model exploring hybrid features i.e., hand crafted and deep features. The proposed approach utilizes a laboratory image dataset comprising 2370 rice leaf images sourced from PlantVillage, augmented with another rice disease dataset featuring the same classes for training. These classes include BrownSpot, Leaf Blast, Hispa, and Healthy images in the dataset. The model achieves an accuracy of 97.9% through k-fold cross-validation. Considering the domain shift concept, we have tested the model’s accuracy on our real field rice leaf images containing BrownSpot, Leaf Blast, and Healthy leaves. The model achieves an accuracy of 93.7% on our dataset. To give the access of automatically identifying rice disease to the village farmers having poor internet connectivity, the current work introduces “easy to use” mobile application, RiceDiseaseRec. This research paves the way for automated rice disease recognition which leads to improving food security and mitigating crop yield losses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crop segmentation Deep learning Domain shift Rice disease detection

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A topical VAEGAN-IHMM approach for automatic story segmentation

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Mathematical Biosciences and engineering 2024年第7期21卷 6608-6630页

作者： Yu, Jia Peng, Huiling Wang, Guoqiang Shi, Nianfeng School of Computer and Information Engineering Luoyang Institute of Science and Technology China Software Research Institute Technological University of Shannon Ireland

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. © 2024 the Author(s).

关键词： Hidden Markov models

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A comprehensive survey of visible infrared person re-identification from an application perspective

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Multimedia Tools and Applications 2024年第42期83卷 90243-90270页

作者： Chang, Hua Xu, Xin Liu, Wei Lu, Lingyi Li, Weigang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V→IR), retrieval of visible images using infrared images (IR→V), and joint retrieval of infrared and visible images (V→IR and IR→V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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DCL-depth: monocular depth estimation network based on iam and depth consistency loss

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Multimedia Tools and Applications 2025年第8期84卷 4773-4787页

作者： Han, Chenggong Lv, Chen Kou, Qiqi Jiang, He Cheng, Deqiang The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised depth estimation methods often suffer from edge blurring in complex textured regions and the loss of depth information in pixels within weakly-textured areas. To enhance the perception ability of the deep network for complex textured areas and the accuracy of depth estimation in weakly-textured regions, the following methods are proposed in this paper. First of all, the image activity measure (IAM) is used to segment the image features. Based on the multi-directional distribution of image contours, the network's perception ability has been improved, resulting in effective enhancement of depth estimation in complex regions. Furthermore, a new loss function called depth consistency loss (DCL) is proposed, which is based on recursive recurrent networks. The DCL aims to measure the similarity between the output images of the first-order network and the second-order network, and the network's constraint on weak-texture regions has been strengthened. By employing this approach, the accuracy of estimating depth information in weakly-textured regions can be enhanced. Through adequate experimentation on the public indoor datasets, the results show that our network outperforms the compared algorithms in terms of accuracy and visualization of predicted depth. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Complex networks

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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

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