Optical Memory and Neural Networks最新文献_第8页

Automatic Lung Cancer Detection Using Computed Tomography Based on Chan Vese Segmentation and SENET 基于 Chan Vese 分段和 SENET 的计算机断层扫描肺癌自动检测技术

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Optical Memory and Neural Networks Pub Date : 2024-09-26 DOI: 10.3103/S1060992X2470022X

C. S. Parvathy, J. P. Jayan

{"title":"Automatic Lung Cancer Detection Using Computed Tomography Based on Chan Vese Segmentation and SENET","authors":"C. S. Parvathy, J. P. Jayan","doi":"10.3103/S1060992X2470022X","DOIUrl":"10.3103/S1060992X2470022X","url":null,"abstract":"Lung cancer is the most common cancer and the primary reason for cancer related fatalities globally. Lung cancer patients have a 14% overall survival rate. If the cancer is found in the early stages, the lives of patients with the disease may be preserved. A variety of conventional machine and deep learning algorithms have been developed for the effective automatic diagnosis of lung cancer. But they still have issues with recognition accuracy and take longer to analyze. To overcome these issues, this paper presents deep learning assisted Squeeze and Excitation Convolutional Neural Networks (SENET) to predict lung cancer on computed tomography images. This paper uses lung CT images for prediction. These raw images are preprocessed using Adaptive Bilateral Filter (ABF) and Reformed Histogram Equalization (RHE) to remove noise and enhance an image’s clarity. To determine the tunable parameters of the RHE approach Tuna Swam optimization algorithm is used in this proposed method. This preprocessed image is then given to the segmentation process to divide the image. This proposed approach uses the Chan vese segmentation model to segment the image. Segmentation output is then fed into the classifier for final classification. SENET classifier is utilized in this proposed approach to final lung cancer prediction. The outcomes of the test assessment demonstrated that the proposed model could identify lung cancer with 99.2% accuracy, 99.1% precision, and 0.8% error. The proposed SENET system predicts CT scanning images of lung cancer successfully.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3","pages":"339 - 354"},"PeriodicalIF":1.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414131","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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Enhancement of Neural Network Performance with the Use of Two Novel Activation Functions: modExp and modExpm 利用两个新激活函数：modExp 和 modExpm 提高神经网络性能

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Optical Memory and Neural Networks Pub Date : 2024-09-26 DOI: 10.3103/S1060992X24700152

Heena Kalim, Anuradha Chug, Amit Prakash Singh

{"title":"Enhancement of Neural Network Performance with the Use of Two Novel Activation Functions: modExp and modExpm","authors":"Heena Kalim, Anuradha Chug, Amit Prakash Singh","doi":"10.3103/S1060992X24700152","DOIUrl":"10.3103/S1060992X24700152","url":null,"abstract":"The paper introduces two novel activation functions known as modExp and modExpm. The activation functions possess several desirable properties, such as being continuously differentiable, bounded, smooth, and non-monotonic. Our studies have shown that modExp and modExpm consistently outperform ReLU and other activation functions across a range of challenging datasets and complex models. Initially, the experiments involve training and classifying using a multi-layer perceptron (MLP) on benchmark data sets like the Diagnostic Wisconsin Breast Cancer and Iris Flower datasets. Both modExp and modExpm demonstrate impressive performance, with modExp achieving 94.15 and 95.56% and modExpm achieving 94.15 and 95.56% respectively, when compared to ReLU, ELU, Tanh, Mish, Softsign, Leaky ReLU, and TanhExp. In addition, a series of experiments were carried out on five different depths of deeper neural networks, ranging from five to eight layers, using MNIST datasets. The modExpm activation function demonstrated superior performance accuracy on various neural network configurations, achieving 95.56, 95.43, 94.72, 95.14, and 95.61% on wider 5 layers, slimmer 5 layers, 6 layers, 7 layers, and 8 layers respectively. The modExp activation function also performed well, achieving the second highest accuracy of 95.42, 94.33, 94.76, 95.06, and 95.37% on the same network configurations, outperforming ReLU, ELU, Tanh, Mish, Softsign, Leaky ReLU, and TanhExp. The results of the statistical feature measures show that both activation functions have the highest mean accuracy, the lowest standard deviation, the lowest Root Mean squared Error, the lowest variance, and the lowest Mean squared Error. According to the experiment, both functions converge more quickly than ReLU, which is a significant advantage in Neural network learning.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 3","pages":"286 - 301"},"PeriodicalIF":1.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414006","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

On Recognition Capacity of a Phase Neural Network 关于相位神经网络的识别能力

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Optical Memory and Neural Networks Pub Date : 2024-09-26 DOI: 10.3103/S1060992X24700188

B. V. Kryzhanovsky

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Numerical Analysis of All-Optical Binary to Gray Code Converter Using Silicon Microring Resonator 使用硅微oring 谐振器的全光二进制到灰码转换器的数值分析

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700085

Manjur Hossain, Kalimuddin Mondal

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DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction DAGM-Mono：用于单目三维重建的可变形注意力引导建模

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X2470005X

Youshaa Murhij, Dmitry Yudin

{"title":"DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction","authors":"Youshaa Murhij, Dmitry Yudin","doi":"10.3103/S1060992X2470005X","DOIUrl":"10.3103/S1060992X2470005X","url":null,"abstract":"Accurate 3D pose estimation and shape reconstruction from monocular images is a challenging task in the field of autonomous driving. Our work introduces a novel approach to solve this task for vehicles called Deformable Attention-Guided Modeling for Monocular 3D Reconstruction (DAGM-Mono). Our proposed solution addresses the challenge of detailed shape reconstruction by leveraging deformable attention mechanisms. Specifically, given 2D primitives, DAGM-Mono reconstructs vehicles shapes using deformable attention-guided modeling, considering the relevance between detected objects and vehicle shape priors. Our method introduces two additional loss functions: Chamfer Distance (CD) and Hierarchical Chamfer Distance to enhance the process of shape reconstruction by additionally capturing fine-grained shape details at different scales. Our bi-contextual deformable attention framework estimates 3D object pose, capturing both inter-object relations and scene context. Experiments on the ApolloCar3D dataset demonstrate that DAGM-Mono achieves state-of-the-art performance and significantly enhances the performance of mature monocular 3D object detectors. Code and data are publicly available at: https://github.com/YoushaaMurhij/DAGM-Mono.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"144 - 156"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552129","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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Stacked BI-LSTM and E-Optimized CNN-A Hybrid Deep Learning Model for Stock Price Prediction 叠加 BI-LSTM 和 E-Optimized CNN--用于股价预测的混合深度学习模型

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700024

Swarnalata Rath, Nilima R. Das, Binod Kumar Pattanayak

{"title":"Stacked BI-LSTM and E-Optimized CNN-A Hybrid Deep Learning Model for Stock Price Prediction","authors":"Swarnalata Rath, Nilima R. Das, Binod Kumar Pattanayak","doi":"10.3103/S1060992X24700024","DOIUrl":"10.3103/S1060992X24700024","url":null,"abstract":"Univariate stocks and multivariate equities are more common due to partnerships. Accurate future stock predictions benefit investors and stakeholders. The study has limitations, but hybrid architectures can outperform single deep learning approach (DL) in price prediction. This study presents a hybrid attention-based optimal DL model that leverages multiple neural networks to enhance stock price prediction accuracy. The model uses strategic optimization of individual model components, extracting crucial insights from stock price time series data. The process involves initial pre-processing, wavelet transform denoising, and min-max normalization, followed by data division into training and test sets. The proposed model integrates stacked Bi-directional Long Short Term Memory (Bi-LSTM), an attention module, and an Equilibrium optimized 1D Convolutional Neural Network (CNN). Stacked Bi-LSTM networks shoot enriched temporal features, while the attention mechanism reduces historical data loss and highlights significant information. A dropout layer with tailored dropout rates is introduced to address overfitting. The Conv1D layer within the 1D CNN detects abrupt data changes using residual features from the dropout layer. The model incorporates Equilibrium Optimization (EO) for training the CNN, allowing the algorithm to select optimal weights based on mean square error. Model efficiency is evaluated through diverse metrics, including Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2), to confirm the model’s predictive performance.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"102 - 120"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552127","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

Latent Semantic Index Based Feature Reduction for Enhanced Severity Prediction of Road Accidents 基于潜语义索引的特征还原用于增强道路事故严重性预测

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700103

Saurabh Jaglan, Sunita Kumari, Praveen Aggarwal

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Transfer Learning Based Face Emotion Recognition Using Meshed Faces and Oval Cropping: A Novel Approach 使用网格化人脸和椭圆形裁剪进行基于迁移学习的人脸情感识别：一种新方法

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700073

Ennaji Fatima Zohra, El Kabtane Hamada

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Aspect Based Suggestion Classification Using Deep Neural Network and Principal Component Analysis with Honey Badger Optimization 利用深度神经网络和主成分分析以及蜜獾优化技术进行基于方面的建议分类

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700036

Nandula Anuradha, Panuganti VijayaPal Reddy

{"title":"Aspect Based Suggestion Classification Using Deep Neural Network and Principal Component Analysis with Honey Badger Optimization","authors":"Nandula Anuradha,  Panuganti VijayaPal Reddy","doi":"10.3103/S1060992X24700036","DOIUrl":"10.3103/S1060992X24700036","url":null,"abstract":"Aspect based suggestion is the process of analyzing the aspect of the review and classifying them as suggestion or non-suggestion comment. Today, online reviews are becoming a more popular way to express suggestions. To manually analyze and extract recommendations from such a large volume of reviews is practically impossible. However, the existing algorithm yields low accuracy with more errors. A deep learning-based DNN (Deep Neural Network) is created to address these problems. Raw data’s are collected and pre-processed to remove the unnecessary contents. After that, a count vectorizer is utilized to convert the words into vectors as well as to extract features from the data. Then, reducing the dimension of the feature vector by applying a hybrid PCA-HBA (Principal Component Analysis-Honey Badger Algorithm). HBA optimization is utilized to select the optimal number of components to enhance the accuracy of the proposed model. Then, the features are classified using two trained deep neural network. One trained model is utilized to identify the aspect of the review, and another trained model is utilized to identify whether the aspect is a suggestion or non-suggestion. The experimental analysis shows that the proposed approach achieves 93% accuracy and 93% specificity for aspect identification as well as 87% accuracy and 66% specificity for the classification of suggestions. Thus, the designed model is the best choice for aspect-based suggestion classification.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"121 - 132"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552161","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

Improved Equilibrium Optimizer for Accurate Training of Feedforward Neural Networks 改进平衡优化器，实现前馈神经网络的精确训练

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700048

Seyed Sina Mohammadi, Mohammadreza Salehirad, Mohammad Mollaie Emamzadeh, Mojtaba Barkhordari Yazdi

{"title":"Improved Equilibrium Optimizer for Accurate Training of Feedforward Neural Networks","authors":"Seyed Sina Mohammadi, Mohammadreza Salehirad, Mohammad Mollaie Emamzadeh, Mojtaba Barkhordari Yazdi","doi":"10.3103/S1060992X24700048","DOIUrl":"10.3103/S1060992X24700048","url":null,"abstract":"One of the most demanding applications of accurate Artificial Neural Networks (ANN) can be found in medical fields, mainly to make critical decisions. To achieve this goal, an efficient optimization and training method is required to tune the parameters of ANN and to reach the global solutions of these parameters. Equilibrium Optimizer (EO) has recently been introduced to solve optimization problems more reliably than other optimization methods which have the ability to escape from the local optima solutions and to reach the global optimum solution. In this paper, to achieve a higher performance, some modifications are applied to the EO algorithm and the Improved Equilibrium Optimizer (IEO) method is presented which have enough accuracy and reliability to be used in crucial and accurate medical applications. Then, this IEO approach is utilized to learn ANN, and IEO-ANN algorithm will be introduced. The proposed IEO-ANN will be implemented to solve real-world medical problems such as breast cancer detection and heart failure prediction. The obtained results of IEO are compared with those of three other well-known approaches: EO, Particle Swarm Optimizer (PSO), Salp Swarm Optimizer (SSO), and Back Propagation (BP). The recorded results have shown that the proposed IEO algorithm has much higher prediction accuracy than others. Therefore, the presented IEO can improve the accuracy and convergence rate of tuning neural networks, so that the proposed IEO-ANN is a suitable classifying and predicting approach for crucial medical decisions where high accuracy is needed.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"133 - 143"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552128","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