Optical Memory and Neural Networks最新文献_第2页

Sign Language Video Generation from Text Using Generative Adversarial Networks 使用生成对抗网络从文本生成手语视频

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700851

R. Sreemathy, Param Chordiya, Soumya Khurana, Mousami Turuk

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Advanced Attention-Based Pre-Trained Transfer Learning Model for Accurate Brain Tumor Detection and Classification from MRI Images 基于注意力的高级预训练迁移学习模型用于MRI图像中脑肿瘤的准确检测和分类

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700863

A. Priya, V. Vasudevan

{"title":"Advanced Attention-Based Pre-Trained Transfer Learning Model for Accurate Brain Tumor Detection and Classification from MRI Images","authors":"A. Priya, V. Vasudevan","doi":"10.3103/S1060992X24700863","DOIUrl":"10.3103/S1060992X24700863","url":null,"abstract":"Brain tumor identification using MRI images involves the detailed examination of brain tissues to detect and characterize tumors. Conventional ML and DL algorithms sometimes encounter difficulties due to a lack of labelled data, resulting in inferior performance and poor generalization. To address these issues, this study introduces an Advanced Attention-based Pre-trained Transfer Learning (TL) model that enhances accuracy and resilience in identifying and categorizing brain tumors using MRI images. The methodology starts with pre-processing, which includes image scaling and noise reduction with an adaptive median filter. After pre-processing, the images are fed into a CNN-based framework called Pre-trained Attention-fused Image SpectraNet. This framework comprises of five convolutional layers, after which Rectified Linear Unit (ReLU) activation and pooling layers are added to learn progressively more complex features. A novel self-attention layer is implemented to capture deep features that reveal aberrant tissue patterns, hence increasing model interpretability and accuracy. A globally average pooling layer is employed to reduce computational complexity, and it is accompanied by a fully connected layer with batch normalization to assure stability and convergence during training. The last layer uses softmax to categorize normal, pituitary, glioma, and meningioma. Utilizing the Adam optimizer, the suggested approach enhances performance, yielding excellent metrics such as 98.33% accuracy, 98.35% precision, 98.28% recall, and a 98.31% F1-score. These measures show considerable increases over existing ML and DL methods, demonstrating the system’s ability to improve brain tumor detection accuracy. The advancement of these treatments has significant implications for medical professionals who specialize in the timely identification of brain tumors.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 4","pages":"477 - 491"},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IFDRF: Advancing Anomaly Detection with a Hybrid Machine Learning Model IFDRF：利用混合机器学习模型推进异常检测

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700474

Hariharan Ramesh, Faridoddin Shariaty, Sanjiban Sekhar Roy

{"title":"IFDRF: Advancing Anomaly Detection with a Hybrid Machine Learning Model","authors":"Hariharan Ramesh, Faridoddin Shariaty, Sanjiban Sekhar Roy","doi":"10.3103/S1060992X24700474","DOIUrl":"10.3103/S1060992X24700474","url":null,"abstract":"Anomaly detection is the identification of aberrations in the dataset using statistical methods or machine learning algorithms. It is widely performed using unsupervised learning algorithms because labelling the data manually can be expensive. While unsupervised anomaly detection is sufficient for data cleaning, this is not the case in real-world applications, where accuracy is of the utmost importance. For example, it would be unacceptable to misdiagnose someone as not having breast cancer and not provide them with treatment because our model failed to recognize it as an anomaly. In this paper, we propose an optimized model—IFDRF (Isolation Forest, DBSCAN, and Random Forest) that has incorporated feedback (corrections) into the unsupervised detection model. IFDRF is a novel hybrid model combining an unsupervised learning model at the first layer followed by a clustering model at the second layer and a supervised learning model at the end. The proposed model tunes the unsupervised learning model followed by a model fitting with the help of the feedback mechanism. It obviates the need to label the entire dataset and thus increases the scope of anomaly detection applications. We have compared our proposed model to the existing state-of-the-art anomaly detection baseline models to show its efficacy. The proposed model performed significantly ((P{text{-value}} < 2.2 times {{10}^{{ - 16}}})) better than the other algorithms, with an AUC score of 0.875.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 4","pages":"385 - 400"},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Tracking and Computation of Characteristics of the Movement of People in Groups on Video Using Convolutional Neural Networks 基于卷积神经网络的视频人群运动特征跟踪与计算

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700802

Huafeng Chen, A. Krytsky, Shiping Ye, Rykhard Bohush, S. Ablameyko

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Hybrid Network Model for Cardiac Image Segmentation Using MRI Images 基于MRI图像分割心脏图像的混合网络模型

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700498

A. Rasmi

{"title":"Hybrid Network Model for Cardiac Image Segmentation Using MRI Images","authors":"A. Rasmi","doi":"10.3103/S1060992X24700498","DOIUrl":"10.3103/S1060992X24700498","url":null,"abstract":"Cardiac magnetic resonance imaging (MRI) commonly yields numerous images per scan, and manually delineating structures from these images is a laborious and time-intensive task. The automation of this process is highly desirable as it would enable the generation of crucial clinical measurements like ejection fraction and stroke volume. However, due to variations in scanning settings and patient characteristics, automated segmentation faces several challenges that lead to a high degree of variability in picture statistics and quality. Our study presents a neural network approach that utilizes the UNet and ResNet-50 architectures to efficiently partition the left and right ventricles' endocardial and epicardial boundaries. The Dice metric is used as the loss function in our strategy to maximize the trainable parameters in the network. Additionally, in the neural network’s predicted binary picture, we employed a preprocessing step to save just the segmentation labels' most connected component. Using datasets from the Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge, the suggested method was learned. The test set of 160 that had been reserved for testing was used by the challenge organizers to evaluate the approach.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 4","pages":"447 - 454"},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Abnormal Sound Event Detection Method Based on Time-Spectrum Information Fusion 基于时谱信息融合的异常声事件检测方法

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700814

Changgeng Yu, Chaowen He, Dashi Lin

{"title":"Abnormal Sound Event Detection Method Based on Time-Spectrum Information Fusion","authors":"Changgeng Yu, Chaowen He, Dashi Lin","doi":"10.3103/S1060992X24700814","DOIUrl":"10.3103/S1060992X24700814","url":null,"abstract":"In this paper, we propose an abnormal sound event detection method based on Time-Frequency Spectral Information Fusion Neural Network (TFSIFNN), addressing the problem that the time structure and frequency information of sound events in real environment are widely varied, resulting in poor performance of abnormal sound event detection. First, we construct a TCN-BiLSTM network based on Temporal Convolutional Networks (TCN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to extract the temporal context information from sound events. Next, we enhance the feature learning capability of the MobileNetV3 network through Efficient Channel Attention (ECA), culminating in the design of an ECA-MobileNetV3 network to capture the spectral information within sound events. Finally, a TFSIFNN model was established based on TCN-BiLSTM and ECA-MobileNetV3 to improve the performance of abnormal sound event detection. The experimental results, conducted on the Urbansound8K and TUT Rare Sound Events 2017 datasets, demonstrate that our TFSIFNN model achieved notable performance improvements. Specifically, it reached an accuracy of 93.93% and an F1-Score of 94.15% on the Urbansound8K dataset. On the TUT Rare Sound Events 2017 dataset, compared to the baseline method, the error rate on the evaluation set decreased by 0.55, and the F1-Score improved by 29.69%.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 4","pages":"411 - 421"},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computer Analysis of EPR Spectra of 31P Atom Quantum Pair Embedded in Spinless Isotope 28Si Substrate 嵌入在无自旋同位素28Si衬底中的31P原子量子对EPR谱的计算机分析

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700826

S. N. Dobryakov, V. V. Privezentsev

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Development and Implementation of Neuromorphic Elements of the Information and Control System of a Mobile Robot 移动机器人信息与控制系统神经形态元件的开发与实现

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700784

A. Korsakov, V. Ivanova, A. Demcheva, R. Eidelman, I. Fomin, A. Bakhshiev

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Combined Use of Dynamic Inversion and Reinforcement Learning for Motion Control of an Supersonic Transport Aircraft 动态反演与强化学习在超音速运输机运动控制中的联合应用

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X2470067X

Gaurav Dhiman, Yu. V. Tiumentsev, R. A. Tskhai

{"title":"Combined Use of Dynamic Inversion and Reinforcement Learning for Motion Control of an Supersonic Transport Aircraft","authors":"Gaurav Dhiman, Yu. V. Tiumentsev, R. A. Tskhai","doi":"10.3103/S1060992X2470067X","DOIUrl":"10.3103/S1060992X2470067X","url":null,"abstract":"The task of aircraft motion control has to be solved under conditions of numerous heterogeneous uncertainties both in the aircraft motion model and in the environment in which the aircraft is flying. These uncertainties, in particular, are caused by the fact that in the flight of the aircraft can occur various kinds of abnormal situations caused by failures of equipment and systems of the aircraft, damage to the airframe and propulsion system of the aircraft. Some of these failures and damages have a direct impact on the dynamic characteristics of the aircraft as a control object. In this regard, the problem arises of such an adjustment of aircraft control algorithms that would provide the ability to adapt to the changed dynamics of the aircraft. It is extremely difficult, and in some cases impossible, to foresee in advance all possible damages, failures and their combinations. Hence, it is necessary to implement adaptive flight control algorithms that are able to adjust to the changing situation. One of the effective tools for solving such problems is reinforcement learning in the Approximate Dynamic Programming (ADP) variant, in combination with artificial neural networks. In the last decade, a family of methods known as Adaptive Critic Design (ACD) has been actively developed within the ADP approach to control the behavior of complex dynamic systems. In our paper we consider the application of one of the variants of the ACD approach, namely SNAC (Single Network Adaptive Critic) and its development through its joint use with the method of dynamic inversion. The effectiveness of this approach is demonstrated on the example of longitudinal motion control of a supersonic transport airplane.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S399 - S413"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive Weight Decomposition Analysis of Modern Parameter-Efficient Methods 现代参数高效方法的综合权重分解分析

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Optical Memory and Neural Networks Pub Date : 2025-01-23 DOI: 10.3103/S1060992X24700796

A. V. Demidovskij, I. G. Salnikov, A. M. Tugaryov, A. I. Trutnev, I. A. Novikova

{"title":"Comprehensive Weight Decomposition Analysis of Modern Parameter-Efficient Methods","authors":"A. V. Demidovskij, I. G. Salnikov, A. M. Tugaryov, A. I. Trutnev, I. A. Novikova","doi":"10.3103/S1060992X24700796","DOIUrl":"10.3103/S1060992X24700796","url":null,"abstract":"Large Language Models fine-tuning is an essential part of modern artificial intelligent systems that solve numerous tasks, such as natural language processing and computer vision. Among the various fine-tuning strategies, the most prominent approach for Large Language Model fine-tuning is Parameter-Efficient Fine-Tuning (PEFT), as it allows to achieve state-of-the-art performance on multiple tasks while minimizing computational resources and training time. Recently, an increasing number of PEFT methodologies have been developed, each asserting superiority based on performance metrics. However, a critical evaluation of how these methods align with the tuning dynamic of the full fine-tuning (FT) remains largely unexplored. This study focuses on bridging this gap by analyzing the learning behavior of such PEFT approaches as LoRA, LoRA+, AdaLoRA, DoRA, VeRA, PiSSA, LoKr and LoHa in comparison to FT. This work provides a comprehensive comparative analysis aimed at identifying which PEFT methods diverge significantly in weights update dynamic from the FT standard. The findings reveal insights into the underlying causes of these discrepancies, offering a deeper understanding of each method’s behavior and efficacy.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3 supplement","pages":"S513 - S522"},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0