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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of ai and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

Few-shot Learning for Named Entity Recognition Based on BERT and Two-level Model Fusion

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Data Intelligence 2021年第4期3卷 568-577页

作者： Yuan Gong Lu Mao Changliang Li ai lab King Soft CorporationBeijing 100086China

Currently,as a basic task of military document information extraction,Named Entity Recognition(NER)for military documents has received great *** 2020,China Conference on Knowledge Graph and Semantic Computing(CCKS)and System Engineering Research Institute of Academy of Military Sciences(AMS)issued the NER task for test evaluation,which requires the recognition of four types of entities including Test Elements(TE),Performance Indicators(PI),System Components(SC)and Task Scenarios(TS).Due to the particularity and confidentiality of the military field,only 400 items of annotated data are provided by the *** this paper,the task is regarded as a few-shot learning problem for NER,and a method based on BERT and two-level model fusion is ***,the proposed method is based on several basic models fine tuned by BERT on the training ***,a two-level fusion strategy applied to the prediction results of multiple basic models is proposed to alleviate the over-fitting ***,the labeling errors are eliminated by *** method achieves F1 score of 0.7203 on the test set of the evaluation task.

关键词： Few-shot learning Named entity recognition BERT Two-level model fusion

Secretome of polarized macrophages:potential for targeting inflammatory dynamics in spinal cord injury

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Neural Regeneration Research 2025年第11期20卷 3231-3232页

作者： Andreia Monteiro Susana Monteiro Nuno A.Silva Life and Health Sciences Research Institute(ICVS) School of MedicineUniversity of MinhoBragaPortugal ICVS/3B’s Associate lab PT Government Associated LabBraga/GuimaraesPortugal

Spinal cord injury(SCI)involves an initial traumatic phase,followed by secondary events such as ischemia,increased blood-spinal cord barrier permeability,ionic disruption,glutamate excitotoxicity,and metabolic alterations.A pe rsistent and exagge rated inflammato ry response within the spinal cord accompanies these events(Lima et al.,2022).The complexity and interplay of these mechanisms exacerbate the initial injury,leading to a degenerative process at the injury *** the initial trauma is unavoidable,the secondary injury begins within minutes and can last for months,creating an optimal window for therapeutic intervention.

关键词： alterations injury companies

Syntax-guided text generation via graph neural network

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Science China(Information Sciences) 2021年第5期64卷 67-76页

作者： Qipeng GUO Xipeng QIU Xiangyang XUE Zheng ZHANG Shanghai Key laboratory of Intelligent Information Processing School of Computer ScienceFudan University NYU Shanghai and AWS Shanghai ai lab

Text generation is a fundamental and important task in natural language processing. Most of the existing models generate text in a sequential manner and have difficulty modeling complex dependency structures. In this paper, we treat the text generation task as a graph generation problem exploiting both syntactic and word-ordering relationships. Leveraging the framework of the graph neural network, we propose the word graph model. During the process, the model builds a sentence incrementally and maintains syntactic integrity via a syntax-driven, top-down, breadth-first generation process. Experimental results on both synthetic and real text generation tasks show the efficacy of our approach.

关键词： text generation deep learning graph neural network dependency parsing

Newton design:designing CNNs with the family of Newton's methods

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Science China(Information Sciences) 2023年第6期66卷 122-137页

作者： Zhengyang SHEN Yibo YANG Qi SHE Changhu WANG Jinwen MA Zhouchen LIN School of Mathematical Sciences Peking University Bytedance ai lab JD Explore Academy Key laboratory of Machine Perception School of Intelligence Science and TechnologyPeking University Pazhou lab

Nowadays, convolutional neural networks(CNNs)have led the developments of machine ***, most CNN architectures are obtained by manual design, which is empirical, time-consuming, and non-transparent. In this paper, we aim at offering better insight into CNN models from the perspective of optimization theory. We propose a unified framework for understanding and designing CNN architectures with the family of Newton’s methods, which is referred to as Newton design. Specifically, we observe that the standard feedforward CNN model(PlainNet)solves an optimization problem via a kind of quasi-Newton method. Interestingly, residual network(ResNet)can also be derived if we use a more general quasi-Newton method to solve this problem. Based on the above observations, we solve this problem via a better method,the Newton-conjugate-gradient(Newton-CG)method, which inspires Newton-CGNet. In the network design,we translate binary-value terms in the optimization schemes to dropout layers, so dropout modules naturally appear in the derived CNN structures with specific locations, rather than being an empirical training *** experiments on image classification and text categorization tasks verify that Newton-CGNets perform very competitively. Particularly, Newton-CGNets surpass their counterparts ResNets by over 4% on CIFAR-10 and over 10% on CIFAR-100, respectively.

关键词： CNN dropout optimization method network design Newton's method

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UNIFORM DECAY IN WEAKLY DISSIPATIVE TIMOSHENKO SYSTEM WITH INTERNAL DISTRIBUTED DELAY FEEDBACKS

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Acta Mathematica Scientia 2016年第3期36卷 815-830页

作者： Tijani A. APALARA Department of Mathematics University of Hafr AI-Batin (UoHB) Hafr AI-Batin 31991 Saudi Arabia

In this paper we consider one-dimensional Timoshenko system with linear fric- tional damping and a distributed delay acting on the displacement equation. Under suitable assumptions on the weight of the delay and the wave speeds, we establish the well-posedness of the system and show that the dissipation through the frictional damping is strong enough to uniformly stabilize the system even in the presence of delay.

关键词： Timoshenko system well-posedness uniform stability distributed delay

Past review,current progress,and challenges ahead on the cocktail party problem

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Frontiers of Information Technology & Electronic Engineering 2018年第1期19卷 40-63页

作者： Yan-min QIAN Chao WENG Xuan-kai CHANG Shuai WANG Dong YU Tencent ai lab Tencent Bellevue 98004 USA Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China

The cocktail party problem,i.e.,tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously,is one of the critical problems yet to be solved to enable the wide application of automatic speech recognition(ASR) *** this overview paper,we review the techniques proposed in the last two decades in attacking this *** focus our discussions on the speech separation problem given its central role in the cocktail party environment,and describe the conventional single-channel techniques such as computational auditory scene analysis(CASA),non-negative matrix factorization(NMF) and generative models,the conventional multi-channel techniques such as beamforming and multi-channel blind source separation,and the newly developed deep learning-based techniques,such as deep clustering(DPCL),the deep attractor network(DANet),and permutation invariant training(PIT).We also present techniques developed to improve ASR accuracy and speaker identification in the cocktail party *** argue effectively exploiting information in the microphone array,the acoustic training set,and the language itself using a more powerful *** optimization ob jective and techniques will be the approach to solving the cocktail party problem.

关键词： Cocktail party problem Computational auditory scene analysis Non-negative matrix factorization Permutation invariant training Multi-talker speech processing

Intent-Slot Correlation Modeling for Joint Intent Prediction and Slot Filling

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Journal of Computer Science & Technology 2022年第2期37卷 309-319页

作者： Jun-Feng Fan Mei-Ling Wang Chang-Liang Li Zi-Qiang Zhu Lu Mao ai laboratory KingSoft CorporationBeijing 100190China

Slot filling and intent prediction are basic tasks in capturing semantic frame of human *** and intent have strong correlation for semantic frame *** each utterance,a specific intent type is generally determined with the indication information of words having slot tags(called as slot words),and in reverse the intent type decides that words of certain categories should be used to fill as ***,the Intent-Slot correlation is rarely modeled explicitly in existing studies,and hence may be not fully *** this paper,we model Intent-Slot correlation explicitly and propose a new framework for joint intent prediction and slot ***,we explore the effects of slot words on intent by differentiating them from the other words,and we recognize slot words by solving a sequence labeling task with the bi-directional long short-term memory(BiLSTM)***,slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic *** addition,we integrate the Slot-Gated mechanism into slot filling to model dependency of slots on ***,we obtain slot recognition,intent prediction and slot filling by training with joint *** results on the benchmark air-line Travel Information System(ATIS)and Snips datasets show that our Intent-Slot correlation model achieves state-of-the-art semantic frame performance with a lightweight structure.

关键词： spoken language understanding slot filling intent prediction Intent-Slot correlation slot recognition

ChemDFM-X: towards large multimodal model for chemistry

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Science China(Information Sciences) 2024年第12期67卷 99-100页

作者： Zihan ZHAO Bo CHEN Jingpiao LI Lu CHEN Liyang WEN Pengyu WANG Zichen ZHU Danyang ZHANG Yansi LI Zhongyang Dai Xin CHEN Kai YU X-LANCE lab Department of Computer Science and EngineeringMoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Suzhou laboratory

Chemistry, as a naturally multimodal discipline, plays a crucial role in various vital fields such as pharmaceutical research and material manufacturing. Therefore, research on artificial intelligence(ai) for chemistry has garnered increasing attention. Despite the rapid development, most of the chemical ai models today mainly focus on single tasks with unimodal input [1].

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