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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Yulan Zou, Beiji Kui, Xiaoyan Li, Qinsong Zhao, Wei Liu, Jun López, Miguel Bordallo Central South University School of Computer Science and Engineering Changsha China Central South University Big Data Institute Changsha China Second Xiangya Hospital Department of Radiology Changsha China University of Oulu Center for Machine Vision and Signal Analysis Oulu Finland

ISBN: (纸本)9798350386226

Vision-language foundational models have achieved commendable results on related tasks. However, their application to medical tasks is still limited due to issues arising from data biases. Currently, leveraging existing foundational models to improve medical tasks remains a challenge. To this end, this paper proposes a strong multimodal representation learning method based on cross-domain distillation handling structural Magnetic Resonance Imaging (sMRI), Positron Emission Computed Tomograph (PET) images, and mini-mental state examination (MMSE) score for Alzheimer's disease (AD) classification. Specifically, we establish a text-to-image cross-domain distillation learning framework, enabling a text encoder pre-trained on general visual recognition tasks to guide the training of sMRI and PET image feature extractors. Simultaneously, positional encoding is used to extract the magnitude features of MMSE scores. Based on the multimodal representations extracted from sMRI, PET images, and MMSE scores, we perform a self-attention operation equipped with a gating mechanism for multimodal feature fusion. This mechanism controls the contribution of each modality representation to the classification decision, dynamically strengthening or weakening specific modality representations and helping construct stronger fused features for AD classification. Our method undergoes 5-fold cross-validation on the widely used ADNI dataset, and comparative experimental results demonstrate that our method achieves advanced performance in two AD-related binary classification tasks. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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An updated classification of cyanobacterial orders and families based on phylogenomic and polyphasic analysis (vol 59, pg 12, 2023)

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JOURNAL OF PHYCOLOGY 2023年第3期59卷 635-635页

作者： Strunecky, O. Ivanova, A. P. Mares, J. Faculty of Fisheries and Protection of Waters CENAKVA Institute of Aquaculture and Protection of Waters University of South Bohemia in České Budějovice Na Sádkách 1780 370 05 České Budějovice Czech Republic Contribution: Conceptualization (equal) Formal analysis (lead) Funding acquisition (supporting) Methodology (lead) Visualization (equal) Writing - original draft (equal) Contribution: Data curation (supporting) Formal analysis (supporting) Visualization (supporting) Biology Centre of the CAS Institute of Hydrobiology Na Sádkách 702/7 370 05 České Budějovice Czech Republic Faculty of Science Department of Botany University of South Bohemia Branišovská 1760 370 05 České Budějovice Czech Republic Contribution: Conceptualization (equal) Formal analysis (supporting) Funding acquisition (lead) Methodology (supporting) Writing - original draft (equal)

Cyanobacterial taxonomy is facing a period of rapid changes thanks to the ease of 16S rRNA gene sequencing and established workflows for description of new taxa. Since the last comprehensive review of the cyanobacterial system in 2014 until 2021, at least 273 species in 140 genera were newly described. These taxa were mainly placed into previously defined orders and families although several new families were proposed. However, the classification of most taxa still relied on hierarchical relationships inherited from the classical morphological taxonomy. Similarly, the obviously polyphyletic orders such as Synechococcales and Oscillatoriales were left unchanged. In this study, the rising number of genomic sequences of cyanobacteria and well-described reference strains allowed us to reconstruct a robust phylogenomic tree for taxonomic purposes. A less robust but better sampled 16S rRNA gene phylogeny was mapped to the phylogenomic backbone. Based on both these phylogenies, a polyphasic classification throughout the whole phylum of Cyanobacteria was created, with ten new orders and fifteen new families. The proposed system of cyanobacterial orders and families relied on a phylogenomic tree but still employed phenotypic apomorphies where possible to make it useful for professionals in the field. It was, however, confirmed that morphological convergence of phylogenetically distant taxa was a frequent phenomenon in cyanobacteria. Moreover, the limited phylogenetic informativeness of the 16S rRNA gene, resulting in ambiguous phylogenies above the genus level, emphasized the integration of genomic data as a prerequisite for the conclusive taxonomic placement of a vast number of cyanobacterial genera in the future.

关键词： 16S rRNA Cyanophyceae multilocus phylogenomic polyphasic approach taxonomy Vampirovibriophyceae

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

arXiv

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arXiv 2024年

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman The Biomedical data analysis and visualization lab School of Computer Science The University of Sydney NSW Australia The Department of Molecular Imaging Royal Prince Alfred Hospital NSW Australia The Institute of Translational Medicine Shanghai Jiao Tong University Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Kumar, Ankit Singh, Sushil Kumar Bhardwaj, Indu Singh, Prakash Kumar Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Department of Electronics and Communication Engineering Galgotias University U.P. Greater Noida India Rajkiya Engineering College U.P Mainpuri India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology (GGSIPU) New Delhi India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA United States

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial Networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, im

关键词： Audio spectrogram Convolutional neural network (CNN) Emotion recognition Generative adversarial networks (GAN) Harmonic to noise rate (HNR) Mel-frequency cepstral coefficient (MFCC) Zero crossing rate (ZCR)

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Fusion of DRL and CNN for Effective Face Recognition 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Kumar, Ankit Singh, Sushil Kumar Ravikumar, R.N. Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Computer Science and Engineering Department Maharaja Agrasen Institute of Technology Delhi India Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St FremontCA94555 United States

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

Digital Authentication combined with IOT networks is one of the most innovative practices in innovations. IoT devices such as surveillance cameras have acquired real-time video data in the natural environment. Facial Biometrics for System authentication is considered to be a sophisticated technology used in various devices. The correct processing of digital information in a natural environment is one of the challenging tasks. Noise effect inducts random variations on the subject in the natural environment that may hinder the recognition efficiency of the model. The proposed system recognizes facial biometric features under the natural environment that may contain non-symmetrical randomness of variations. The system's objective is to mitigate the false recognition rates under adverse environmental conditions. It combines digital information with a person’s real-time environment. The detection of the facial region of interest form frames in a real-time dataset has been taken through the Viola-Jones algorithm. Feature extrication using video frames has been developed through Deep Reinforcement Learning (DRL) algorithm, which aims to generate binary trees containing feature vectors. Further, the system uses a convolutional neural network (CNN) model to establish the correlation of feature vectors belonging to the facial identity. The model's objective is to retain high recognition of facial biometric trait feature units under various randomness in a natural environment. The proposed system is also tested under various attacks to test the robustness of the proposed model. The model can secure an average accuracy of 98.85%. Digital forensics, crime investigation, online attendance, etc., are the few applications of Facial data authentication from dynamic input. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep reinforcement learning

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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PresRecCD: A Novel Herbal Prescription Recommendation Framework with Cross-Domain Learning and Neural Collaborative Filtering

PresRecCD: A Novel Herbal Prescription Recommendation Framew...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wansong Dong, Xin Yang, Kuo Zhang, Lei Wang, Xinyu Huang, Rouye Zhang, Runshun Zhou, Xuezhong Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing Jiaotong University Institute of Medical Intelligence Department of Artificial Intelligence Beijing100044 China China Academy of Chinese Medical Sciences National Data Center of Traditional Chinese Medicine Beijing China Guang'Anmen Hospital China Academy of Chinese Medical Sciences Beijing China

ISBN: (纸本)9798350386226

Herbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling. In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, alleviating the sparsity of data, and neural collaborative filtering is utilized to carry out prescription recommendations. Comprehensive experiments demonstrate the superiority of the proposed PresRecCD model over the SOTA model. This study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment. © 2024 IEEE.

关键词： Medicinal chemistry

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TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction

TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescr...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Xingzhi Dong, Xin Li, Chunhao Bai, Yuning Xu, Yulong Cheung, Ka Chun Se, Simon Song, Xinpeng Zhang, Runshun Zho, Xuezhong Zhang, Nevin L. Hong Kong University of Science and Technology Department of Computer Science and Technology Hong Kong Hong Kong Beijing Jiaotong University Institute of Medical Intelligence Department of Artificial Intelligence Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing100044 China China Academy of Chinese Medical Sciences Guang'anmen Hospital Beijing China Henan University of Chinese Medicine School of Information Technology China Nvidia Nvaitc Hong Kong Hong Kong Nvidia Nvaitc Singapore

ISBN: (纸本)9798350386226

Traditional Chinese medicine (TCM) has relied on specific combinations of herbs in prescriptions to treat various symptoms and signs for thousands of years. Predicting TCM prescriptions poses a fascinating technical challenge with significant practical implications. However, this task faces limitations due to the scarcity of high-quality clinical datasets and the complex relationship between symptoms and herbs. To address these issues, we introduce DigestDS, a novel dataset comprising practical medical records from experienced experts in digestive system diseases. We also propose a method, TCM-FTP (TCM Fine-Tuning Pre-trained), to leverage pre-trained large language models (LLMs) via supervised fine-tuning on DigestDS. Additionally, we enhance computational efficiency using a low-rank adaptation technique. Moreover, TCM-FTP incorporates data augmentation by permuting herbs within prescriptions, exploiting their order-agnostic nature. Impressively, TCM-FTP achieves an F1-score of 0.8031, significantly outperforming previous methods. Furthermore, it demonstrates remarkable accuracy in dosage prediction, achieving a normalized mean square error of 0.0604. In contrast, LLMs without fine-tuning exhibit poor performance. Although LLMs have demonstrated wide-ranging capabilities, our work underscores the necessity of fine-tuning for TCM prescription prediction and presents an effective way to accomplish this. © 2024 IEEE.

关键词： Medicinal chemistry

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