American Journal of Neural Networks and Applications最新文献

{"title":"Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision","authors":"Benjamin Jacob, Heather McDonald, Joe Bohn","doi":"10.11648/j.ajnna.20241001.11","DOIUrl":"https://doi.org/10.11648/j.ajnna.20241001.11","url":null,"abstract":"Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline. Within existing R-CNN literature, the evolution exhibited by R-CNN is most profound in terms of computational efficiency integrating each training stage to reduce test time and improvement in mean average precision (mAP), which can be infused into an artificially intelligent (AI), machine learning (ML), real-time, interactive, recovery capital application (app). This article introduces a Region Proposal Network (RPN) that shares full-image convolutional features with a real-time detection AI-ML infused network in an interactive, continuously self-learning wrist-wearable real-time recovery capital app for enabling cost-free region proposals (e.g., instantaneous body physiological responses, mapped connections to emergency services, sponsor, counselor, peer support, links to local and specific recovery capital assets, etc.). A fully merged RPN and Faster R-CNN deep convolutional unified network in the app can simultaneously train to aggregate and predict object bounds and objectness scores for implementing recovery capital real-time solutions (e.g., baseball card scoring dashboards, token-based incentive programs, etc.) A continuous training scheme alternates between fine-tuning RPN tasks (e.g., logging and updating personal client information, gamification orientation) and fine-tuning the detection (e.g., real-time biometric monitoring client’s behavior for self-awareness of when to connect with an addiction specialist or family member, quick response (QR) code registration for a 12-step program, advanced security encryption, etc.) in the interactive app. The very deep VGG-16 model detection system has a frame rate of 5fps within a graphic processing unit (GPU) while accomplishing sophisticated object detection accuracy on PASCAL Visual Object Classification Challenge (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets. This is achieved with only 300 proposals per real-time retrieved data capture point, information bit or image. The app has real-time, infused cartographic and statistical tracking tools to generate Python Codes, which can enable a gamified addiction recovery-oriented digital conscience. Faster R-CNN and RPN can be the foundations of an interactive real-time recovery capital app that can be adaptable to multiple recovery pathways based on participant recovery plans and actions. This paper discusses some of the critical attributes and features to include in the design of a future app to support and close current gaps in needed recovery capital to help those who are dealing with many different forms of addiction recovery.\u0000","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"36 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260371","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}

{"title":"Modelling and Forecasting Inflation Rates in Kenya Using ARIMA-ANN Hybrid Model","authors":"Barry Agingu Jagero, Thomas Mageto, S. Mwalili","doi":"10.11648/j.ajnna.20230901.12","DOIUrl":"https://doi.org/10.11648/j.ajnna.20230901.12","url":null,"abstract":"","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139311960","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}

{"title":"AI-Driven Security: How Machine Learning Will Shape the Future of Cybersecurity and Web 3","authors":"Jasmin Praful Bharadiya","doi":"10.11648/j.ajnna.20230901.11","DOIUrl":"https://doi.org/10.11648/j.ajnna.20230901.11","url":null,"abstract":"","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370377","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}

{"title":"Forecasting Foodgrains Production Using Arima Model and Neural Network","authors":"Veluchamy Kasthuri, Selvakumar S","doi":"10.11648/J.AJNNA.20210702.12","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20210702.12","url":null,"abstract":"The time series is a set of values arranged in a specific order of time. Prediction and analysis of food grain is a vital role in agriculture statistics. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, irrigation, land holdings, agricultural prices and market intelligence, livestock, fisheries, forestry etc. Agricultural credit and subsidies also consider important supporting factors for agricultural growth. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on production of food grains in India using time series data ranging from 1990- 91 to 2018-19. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for predicting foodgrains of India were compared. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were compared. The results were displayed numerically and graphically.","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114321896","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}

{"title":"Modelling the Canopy Conductance of Cocoa Tree Using a Recurrent Neural Network","authors":"O. Sajo, P. Oguntunde, J. Fasinmirin, A. Akinnagbe, A. Olufayo, S. Agele","doi":"10.11648/J.AJNNA.20210702.11","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20210702.11","url":null,"abstract":"Direct measurement of crop water use is difficult and labour intensive. In some cases, the technicalities involved can only be exploited by well-trained researchers. Therefore, estimating this important crop parameter from readily available climatic data by way of modelling will ease the burden of direct measurement. The aim of the study is to parameterize models of canopy conductance of rain-fed cocoa tree, suitable for inclusion in physically-based model for predicting water use of cocoa trees. To do this, Sap flow density was monitored in three cocoa trees (Forestaro cultivar group) at the eight (8) year old cocoa plantation of the Federal University of Technology, Akure, Nigeria (7° 18' 15.9\"N, 5° 07' 32.3\"E), from 8th March 2018 to 7th March 2019, covering the two seasons of the region. Cocoa tree transpiration was determined from the measured sap flow and fitted into a physically based model (PM) to derive canopy conductance used for modelling. To choose the best model that predicts canopy conductance (the stomata control of water transport) in cocoa trees, Vector Autoregressive Models (VAR), a multivariate time series model, and Long Short-Term Memory (LSTM) network, an Artificial Intelligence (AI) model were employed. The prediction power of the VAR model was assessed and visualized using the vars R package, while the LSTM model, a Recurrent Neural Network (RNN) algorithm was implemented using Python programming within Google COLAB jupyter notebook. Before modelling, data were tested for stationarity using the Augmented Dickey-Fuller test. While two-thirds of the data were used to train the models, the remaining one-third of the data were used to test the trained model. As VAR models were evaluated using R-squared and Root Mean Squared Error (RMSE), LSTM was evaluated by comparing the train loss and test loss, and also RMSE. VAR (with Adjusted R-Squared=0.11) is found not to be suitable to model the complex relationship between canopy conductance and climatic variables. Further iteration to exclude insignificant climatic variables from the VAR model did not also improve the model. However, LSTM with RMSE of 0.026 and having the test loss not dropping below the training loss was observed to perform better in modelling the canopy conductance of Cocoa. The result of the research further revealed that temporal dynamics of transpiration is complex and difficult to be defined by traditional regression. LSTM with a prediction accuracy of 97.4% could therefore be used for the prediction of cocoa canopy conductance.","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130799437","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}

{"title":"Chaotic Recurrent Neural Networks for Financial Forecast","authors":"Jeff Wang, Raymond S. T. Lee","doi":"10.11648/J.AJNNA.20210701.12","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20210701.12","url":null,"abstract":"In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"41 4-5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127678095","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}

{"title":"New Neural Network Corresponding to the Evolution Process of the Brain","authors":"S. Yanagawa","doi":"10.11648/J.AJNNA.20210701.11","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20210701.11","url":null,"abstract":"In this paper, the logic is developed assuming that all parts of the brain are composed of a combination of modules that basically have the same structure. The fundamental function is the feeding behavior searching for food while avoiding the dangers. This is most necessary function of animals in the early stages of evolution and the basis of time series data processing. The module is presented by a neural network with learning capabilities based on Hebb's law and is called the basic unit. The basic units are placed on layers and the information between the layers is bidirectional. This new neural network is an extension of the traditional neural network that evolved from pattern recognition. The biggest feature is that in the process of processing time series data, the activated part in the neural network changes according to the context structure of the data. Predicts events from the context of learned behavior and selects best way. It is important to incorporate higher levels of intelligence such as learning, imitation functions furthermore long-term memory and object symbolization. A new neural network that deals the \"descriptive world\" that expresses past and future events to the neural network that deals the \"real world\" related to the familiar events is added. The scheme of neural network's function is shown using concept of category theory","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131232260","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}

{"title":"Each Role of Short-term and Long-term Memory in Neural Networks","authors":"S. Yanagawa","doi":"10.11648/J.AJNNA.20200601.12","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20200601.12","url":null,"abstract":"Based on known functions of neuroscience the neural network that performs serial parallel conversion and its inverse transformation is presented. By hierarchy connecting the neural networks, the upper neural network that can process general time sequence data is constructed. The activity of the upper neural networks changes in response to the context structure inherent in the time series data and have both function of accepting and generating of general time series data. Eating behavior in animals in the early stages of evolution is also processing time series data, and it is possible to predict behavior although be limited short term by learning the contextual structure inherent in time series data. This function is the behavior of so-called short-term memory. Transition of the activation portion in this type of operation is illustrated. Although status of nervous system of the animal change according to the recognition by sensory organ and to the manipulation of the object by muscle in the vicinity of the animal itself, the evolved animals have in addition another nervous system so-called long-term memory or episodic memory being involved experience and prediction. The nervous system of long-term memory behaves freely but keeping consistency of the change in the environment. By the workings of long-term memory, lot of information are exchanged between fellows, and lot of time series data are conserved by characters in human society. In this paper, the model of the transfer of data between different nervous systems is shown using the concept of category theory.","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127986732","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}

{"title":"Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction","authors":"M. Elbisy, F. A. Osra","doi":"10.11648/J.AJNNA.20190502.12","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20190502.12","url":null,"abstract":"The estimation of wave parameters is of great importance in coastal activities such as design studies for harbor, inshore and offshore structures, coastal erosion, sediment transport, and wave energy estimation. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, numerical-based approaches, and soft computing. In this study, the group method of data handling type neural network (GMDH-NN) was presented for significant wave height prediction in an attempt to suggest a new model with superior explanatory power and stability. The GMDH-NN results were compared with the field data and with a multilayer perceptron neural networks (MLPNN) model. The results indicate that the prediction accuracy and avoidance of over-fitting of the GMDH-NN method were superior to those of the MLPNN method. The percentage improvement in the root mean square error and the mean absolute percentage error of the GMDH-NN model over the MLPNN model were 72.92% and 81.02%, respectively. Also, according to the indices, the GMDH-NN model performs the best for predicting the Hs of all of the wave height ranges. That is, the GMDH-NN model is capable of predicting wave heights for different ranges. The results of the analysis suggest that the GMDH-NN-based modeling is effective in predicting significant wave height.","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123963276","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}

{"title":"Medical Images Classification and Diagnostics Using Fuzzy Neural Networks","authors":"Y. Zaychenko, Aghaei Agh Ghamish Ovi Nafas","doi":"10.11648/J.AJNNA.20190502.11","DOIUrl":"https://doi.org/10.11648/J.AJNNA.20190502.11","url":null,"abstract":"The problem of medical images of cervix epithelium classification for express diagnostics is considered…The following states of cervix epithelium are to be recognized and classified: normal state - columnar epithelium; squamous epithelium (normal state); metaplasia-benign changes of cervix uterus epithelium; CIN1-displasia of light degree, CIN 2-displasia of middle degree, CIN 3-displasia of high degree- intra-epithelium cancer: For its solution the application of fuzzy neural network (FNN NEFClass M) is suggested. The application of FNN is grounded by its following properties: it may work with fuzzy and qualitative information; it has accelerated convergence as compared with crisp classification methods; it enables to attain better classification accuracy than conventional classifiers. The structure of FNN NEFClass and its model description are presented. Training algorithm stochastic gradient descent for membership functions of fuzzy sets is considered and implemented. Data set of medical images of cervix epithelium which was obtained by special device colposcope is described and some images are presented. The experimental investigations of FNN NEFClass application for medical images recognition on real data are carried out, the results are presented. The comparison with NN Back Propagation, RBF NN and cascade RBF NN was made and estimation of efficiency of the suggested approach was performed. The problem of reduction of features number in classification tasks using principal component method (PCM) method is considered and implemented.","PeriodicalId":325288,"journal":{"name":"American Journal of Neural Networks and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124650061","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}