Optical Memory and Neural Networks最新文献_第9页

MCYP-DeepNet: Nutrition and Temperature Based Season Wise Multi Crop Yield Prediction Using DeepNet 230 Classifier MCYP-DeepNet：使用 DeepNet 230 分类器进行基于营养和温度的季节性多作物产量预测

IF 1

Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700115

Nilesh U Sambhe, Manjusha Deshmukh, L. Ashok Kumar, Sandip Chavan, Nidhi P. Ranjan

{"title":"MCYP-DeepNet: Nutrition and Temperature Based Season Wise Multi Crop Yield Prediction Using DeepNet 230 Classifier","authors":"Nilesh U Sambhe, Manjusha Deshmukh, L. Ashok Kumar, Sandip Chavan, Nidhi P. Ranjan","doi":"10.3103/S1060992X24700115","DOIUrl":"10.3103/S1060992X24700115","url":null,"abstract":"A vital source of nutrition and a major contributor to the nation’s economic expansion is agriculture. Due to numerous complex factors such as environment, humidity, soil nutrients, and soil moisture, multi crop yield forecasting was very challenging. Because crop prediction is a complicated process, improving performance is challenging. To address these problems, an advance deep learning model was developed to predict crop types and its yields in a particular soil. A real time data were created, which contain various parameters such as soil nutrition’s, weather, data, seasons and temperature. The created dataset is pre-processed using outlier detection as well as normalization because it contains unwanted rows and columns. After that, the pre-processed data were given as input for the DeepNet230 model to analyze the input parameters like soil nutrition and temperature to predict the multi crop type and its yield quantity. DeepNet230 have the capacity of automatic feature learning and rapid unstructured process, so it provides an efficient prediction performance of crop yield and its types. The performance analysis of crop prediction for the proposed model are 93.7% accuracy, 93.4% recall, 92.8% precision and 92.9% specificity. Then, the performance of yield prediction for the identified crops are 95.5% accuracy, 91.6% recall, 93% precision and 94.2% specificity. In addition, the developed method was compared with several opposing methods for validation. The observed results show that the suggested method performed significantly better in real time due to its improved predictive capabilities.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"236 - 253"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552132","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

HPO Based Enhanced Elman Spike Neural Network for Detecting Speech of People with Dysarthria 基于 HPO 的增强型 Elman Spike 神经网络用于检测构音障碍者的语音

IF 1

Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700097

Pranav Kumar, Md. Talib Ahmad, Ranjana Kumari

{"title":"HPO Based Enhanced Elman Spike Neural Network for Detecting Speech of People with Dysarthria","authors":"Pranav Kumar, Md. Talib Ahmad, Ranjana Kumari","doi":"10.3103/S1060992X24700097","DOIUrl":"10.3103/S1060992X24700097","url":null,"abstract":"Motor speech condition called dysarthria is caused by a lack of movement in the lips, tongue, vocal cords, and diaphragm are a few of the muscles needed to produce speech. Speech that is slurred, sluggish, or inaccurate might be the initial sign of dysarthria, which varies in severity. Parkinson’s disease, muscular dystrophy, multiple sclerosis, brain tumors, brain damage, and amyotrophic lateral sclerosis are among the health problems that can result from dysarthria. This research develops an efficient method for extracting features and classifying dysarthria affected persons from speech signals. This suggested method uses a speech signal as its source. The supplied speech signal is pre-processed to improve the identification of dysarthria speech. Pre-processing methods like the Butterworth band pass filter and Savitzky Golay digital FIR filter are used to smoothing the raw data. After pre-processing, the signals are input into the feature extraction techniques, such as Yule-Walker Autoregressive modelling, Mel frequency cepstral coefficients and Perceptual Linear Predictive to extract the important features. The dysarthria speech is finally detected using an improved Elman Spike Neural Network (EESNN) algorithm-based classifier. Hunter Prey Optimization (HPO) is used to select the weights of EESNN optimally. The proposed algorithm achieves 94.25% accuracy and 94.26% specificity values. Thus this proposed approach is the best choice for predicting dysarthria disease using speech signal.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"205 - 220"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552133","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

Analytical Calculation of Weights Convolutional Neural Network 权重分析计算卷积神经网络

IF 1

Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700061

P. Sh. Geidarov

{"title":"Analytical Calculation of Weights Convolutional Neural Network","authors":"P. Sh. Geidarov","doi":"10.3103/S1060992X24700061","DOIUrl":"10.3103/S1060992X24700061","url":null,"abstract":"In this paper proposes an algorithm for the analytical calculation of convolutional neural networks without using neural network training algorithms. A description of the algorithm is given, on the basis of which the weights and threshold values of a convolutional neural network are analytically calculated. In this case, to calculate the parameters of the convolutional neural network, only 10 selected samples were used from the MNIST digit database, each of which is an image of one of the recognizable classes of digits from 0 to 9, and was randomly selected from the MNIST digit database. As a result of the operation of this algorithm, the number of channels of the convolutional neural network layers is also determined analytically. Based on the proposed algorithm, a software module was implemented in the Builder environment C++, on the basis of which a number of experiments were carried out with recognition of the MNIST database. The results of the experiments described in the work showed that the computation time of convolutional neural networks is very short and amounts to fractions of a second or a minute. Analytically computed convolutional neural networks were tested on the MNIST digit database, consisting of 1000 images of handwritten digits. The experimental results showed that already using only 10 selected images from the MNIST database, analytically calculated convolutional neural networks are able to recognize more than half of the images of the MNIST database, without application of neural network training algorithms. In general, the study showed that artificial neural networks, and in particular convolutional neural networks, are capable of not only being trained by learning algorithms, but also recognizing images almost instantly, without the use of learning algorithms using preliminary analytical calculation of the values of the neural network’s weights.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2","pages":"157 - 177"},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552119","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

Divergence Parametric Smoothing in Image Compression Algorithms 图像压缩算法中的发散参数平滑法

IF 1

Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700012

M. V. Gashnikov

引用次数: 0

Lasers and Modern Energy 激光与现代能源

IF 1

Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010090

V. E. Privalov, V. G. Shemanin

引用次数: 0

Q-Memory Task Routing to Prevent Deadlocks in Ethernet Control with Memory Crossbar Switching 利用 Q-Memory 任务路由防止以太网控制中的死锁与内存交叉条交换

IF 1

Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010077

Smita Sudhakar Palnitkar, Sudhir Kanade

引用次数: 0

Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model 利用梯度提升回归树和 CNN 学习模型预测 PM2.5 浓度

IF 1

Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010107

A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya, T. J. Swasthika Jain

{"title":"Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model","authors":"A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya, T. J. Swasthika Jain","doi":"10.3103/S1060992X24010107","DOIUrl":"10.3103/S1060992X24010107","url":null,"abstract":"Air pollution imposed by particle matter (PM) made it a public health concern and hazard to humans and the environment. Reduced vision, allergic responses, pneumonia, asthma, cardiovascular disorders, lung cancer, and even mortality can result from prolonged exposure to the concentration of air’s small particulate matter. Air quality prediction can offer reliable information for future air pollution status to operate air pollution control effectively and make preventative plans. Tracking, predicting, and regulating emissions is crucial. Controlling PM2.5 is the key for enhancing air quality, and it can be accomplished by forecasting PM2.5 concentrations. This work develops a methodology for forecasting PM2.5 concentrations using a gradient-boosted regression tree with Convolutional Neural Network (CNN) and fuzzy K-nearest neighbour (fuzzy-KNN). The results of the proposed methodology have been comparatively analysed with multiple linear regression, stacked long short-term memory, bidirectional gated recurrent unit, and gradient-boosted regression tree. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are evaluated, and it shows that the gradient-boosted regression tree model produces a reduced error with improved accuracy in forecasting air quality.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"86 - 96"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300090","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

Multi-Modal Co-Attention Capsule Network for Fake News Detection 用于假新闻检测的多模式协同关注胶囊网络

IF 1

Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010041

Chunyan Yin, Yongheng Chen

引用次数: 0

Review on Improved Machine Learning Techniques for Predicting Chronic Diseases 关于预测慢性疾病的改进型机器学习技术的综述

IF 1

Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010028

L. Abirami, J. Karthikeyan

{"title":"Review on Improved Machine Learning Techniques for Predicting Chronic Diseases","authors":"L. Abirami, J. Karthikeyan","doi":"10.3103/S1060992X24010028","DOIUrl":"10.3103/S1060992X24010028","url":null,"abstract":"Healthcare industry is a stage which is presented with tremendous innovative headways consistently. Parkinson disease (PD) has become a critical overall general clinical issue starting late. To provide the solution for this problem, in this paper, use fusion of machine learning and federated learning techniques for processing electronically collected patients’ health record (PD dataset) in accurate manner. The PD dataset are constantly gathered and sorted out to give a point by point history of patients, their sicknesses and determination plans. The medical PD dataset contains 43 400 electronic records of potential patients which includes normal, Ischemic and Hemorrhagic stroke. Cleaning, finding feature correlation and imputing missing values in the PD has to be performed by preprocessing & normalization approach. For further processing, using Random over sampling (ROS) methods the imbalanced PD dataset will be converted into balanced. From the balanced PD datasets the stroke prediction accuracy will be validated using Decision Tree, Logistic Regression, Random Forest and Improved LSTM (Imp-LSTM) machine learning algorithms. Using distinct experiments of executing performance measurements the accuracy rate from our prediction classifiers for the patient with smokes category will be 62.29, 71.36, 96.51 and 99.56% respectively as like the patient with never smoked category dataset the accuracy will be 70.49, 75.86, 96.49 and 99.58% respectively. The proposed Imp-LSTM algorithm in this research will effectively produce high overall accuracy in both the datasets, which means a successful decrease in the misdiagnosis rate for stroke prediction.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"28 - 46"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300452","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

Lateral Motion Control of a Maneuverable Aircraft Using Reinforcement Learning 利用强化学习实现可操控飞机的横向运动控制

IF 1

Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X2401003X

Yu. V. Tiumentsev, R. A. Zarubin

{"title":"Lateral Motion Control of a Maneuverable Aircraft Using Reinforcement Learning","authors":"Yu. V. Tiumentsev, R. A. Zarubin","doi":"10.3103/S1060992X2401003X","DOIUrl":"10.3103/S1060992X2401003X","url":null,"abstract":"Machine learning is currently one of the most actively developing research areas. Considerable attention in the ongoing research is paid to problems related to dynamical systems. One of the areas in which the application of machine learning technologies is being actively explored is aircraft of various types and purposes. This state of the art is due to the complexity and variety of tasks that are assigned to aircraft. The complicating factor in this case is incomplete and inaccurate knowledge of the properties of the object under study and the conditions in which it operates. In particular, a variety of abnormal situations may occur during flight, such as equipment failures and structural damage, which must be counteracted by reconfiguring the aircraft’s control system and controls. The aircraft control system must be able to operate effectively under these conditions by promptly changing the parameters and/or structure of the control laws used. Adaptive control methods allow to satisfy this requirement. One of the ways to synthesize control laws for dynamic systems, widely used nowadays, is LQR approach. A significant limitation of this approach is the lack of adaptability of the resulting control law, which prevents its use in conditions of incomplete and inaccurate knowledge of the properties of the control object and the environment in which it operates. To overcome this limitation, it was proposed to modify the standard variant of LQR (Linear Quadratic Regulator) based on approximate dynamic programming, a special case of which is the adaptive critic design (ACD) method. For the ACD-LQR combination, the problem of controlling the lateral motion of a maneuvering aircraft is solved. The results obtained demonstrate the promising potential of this approach to controlling the airplane motion under uncertainty conditions.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"1 - 12"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299881","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