Convolution Based Neural Network Model to Prevent Diseases Classification and Prediction Using Deep Learning

The farmer is a backbone to nation, but majority of the cultivated crops in india affecting by various diseases at various stages of its cultivation. Recent research works shows that diseases are not providing accurate results and few identifying but not providing optimized solutions to the system. In proposed work, the recent developments of Artificial intelligence through Deep Learning show that AIR (Automatic Image Recognition systems) using CNN algorithm models can be very beneficial in such scenarios. The Rice leaf diseases images related dataset is not easily available to automate , so that we have created our own trained data set which is small in size hence we have used transfer learning to develop our Proposed model which supports deep learning models. The Proposed CNN architecture illustrated based on VGG-16 model and it is trained, tested on given dataset collected from rice fields and the internet. The accuracy of the proposed model is moderately accurate with 92.46%.

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A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1035 ◽

2021 ◽

Vol 11 (3) ◽

pp. 194-201

Author(s):

Van-Tu Nguyen ◽

◽

Anh-Cuong Le ◽

Ha-Nam Nguyen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Question Answering ◽

Learning Models ◽

Learning Methods ◽

Data Set ◽

External Knowledge ◽

Community Question Answering ◽

Proposed Model

Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.

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Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models

Advances and Applications in Deep Learning ◽

10.5772/intechopen.92172 ◽

2020 ◽

Author(s):

Evren Dağlarli

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Black Box ◽

Learning Models ◽

Learning Methods ◽

Data Set ◽

Box Models ◽

Explainable Artificial Intelligence ◽

Artificial Neural ◽

Black Box Models

The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.

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Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period

Business Systems Research Journal ◽

10.2478/bsrj-2020-0014 ◽

2020 ◽

Vol 11 (2) ◽

pp. 36-50

Author(s):

Aljaž Ferencek ◽

Davorin Kofjač ◽

Andrej Škraba ◽

Blaž Sašek ◽

Mirjana Kljajić Borštnar

Keyword(s):

Neural Network ◽

Deep Learning ◽

Predictive Models ◽

Discrete Event ◽

Quality Data ◽

Call Rate ◽

Learning Models ◽

Data Set ◽

Warranty Period ◽

And Performance

AbstractBackground: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models.Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate.Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance.Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.

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An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

Electronics ◽

10.3390/electronics9091514 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1514

Author(s):

Ali Aljofey ◽

Qingshan Jiang ◽

Qiang Qu ◽

Mingqing Huang ◽

Jean-Pierre Niyigena

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Third Party ◽

Learning Models ◽

Phone Calls ◽

Learning Techniques ◽

Proposed Model ◽

Phishing Detection

Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

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A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study (Preprint)

10.2196/preprints.27806 ◽

2021 ◽

Author(s):

Cheng-Sheng Yu ◽

Shy-Shin Chang ◽

Tzu-Hao Chang ◽

Jenny L Wu ◽

Yu-Jiun Lin ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Deep Learning ◽

Forecast Accuracy ◽

Series Data ◽

Systems Science ◽

Data Set ◽

Heat Map ◽

Mlp Neural Network ◽

Policy Measures

BACKGROUND More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures. OBJECTIVE We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

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A Convolutional Neural Network based System to Detect Plant Disease

Webology ◽

10.14704/web/v18si04/web18175 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 944-962

Author(s):

K. Niha ◽

Dr.S. Amutha ◽

Dr. Aisha Banu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Neural Network Model ◽

Plant Disease ◽

Performance Metrics ◽

Plant Diseases ◽

Detection Accuracy ◽

Learning Models ◽

Proposed Model

Disease in plants are a great challenge in the advancement of agriculture which affects farmers yield and the plants. In this modern research deep learning models got a spot light by increasing plant detection accuracy and classification. The proposed CNN (Convolutional Neural Network) model detect seven plant diseases out of healthy leaf, where the dataset considered in this work contain 8685 leaf images from Plant Village Dataset. The proposed modals performance are evaluated with respect to the performance metrics (F1 score, Precision and Recall) and are compared with SVM and ANN. Where the proposed CNN model outperforms the rest with the accuracy of 96.2% and the F1 score greater than 95%. The feasibility of the proposed model in plant detection and classification may provide a solution to the problem faced by farmers.

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Deep Learning Assisted Cognitive Diagnosis for the D-Riska Application

10.21203/rs.3.rs-942402/v1 ◽

2021 ◽

Author(s):

Cristian Cuerda ◽

Alejandro Zornoza ◽

Ricardo Tesoriero ◽

Jose A. Gallud ◽

Dulce Romero-Ayuso

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Deep Learning ◽

Brain Injury ◽

Diagnostic Tests ◽

Acquired Brain Injury ◽

Cognitive Diagnosis ◽

Neural Network Classifier ◽

Proposed Model ◽

Final Score

Abstract In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class. Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 \% of Accuracy, 94 \% of Precision, 91 \%, of Recall and 92\% of F1-Score.

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High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL ◽

10.14201/adcaij202110297122 ◽

2021 ◽

Vol 10 (2) ◽

Author(s):

Yaser AbdulAali Jasim

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

High Performance ◽

Plant Disease ◽

Detection System ◽

Open Data ◽

Data Set ◽

Performance Rate

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers. In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

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Deep learning assisted cognitive diagnosis for the D-Riska application

Soft Computing ◽

10.1007/s00500-021-06510-w ◽

2021 ◽

Author(s):

Cristian Cuerda ◽

Alejandro Zornoza ◽

Jose A. Gallud ◽

Ricardo Tesoriero ◽

Dulce Romero Ayuso

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Deep Learning ◽

Brain Injury ◽

Diagnostic Tests ◽

Acquired Brain Injury ◽

Cognitive Diagnosis ◽

Neural Network Classifier ◽

Proposed Model ◽

Final Score

AbstractIn this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 % of Accuracy, 94 % of Precision, 91 %, of Recall and 92% of F1-Score.

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Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert

Journal of Artificial Intelligence and Capsule Networks - September 2019 ◽

10.36548/jaicn.2021.2.003 ◽

2021 ◽

Vol 3 (2) ◽

pp. 101-112

Author(s):

Joy Iong-Zong Chen ◽

Kong-Long Lai

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Credit Card ◽

Fraud Detection ◽

Financial Fraud ◽

Detection Accuracy ◽

Learning Models ◽

Proposed Model ◽

Deep Convolution Neural Network

With the exponential increase in the usage of the internet, numerous organisations, including the financial industry, have operationalized online services. The massive financial losses occur as a result of the global growth in financial fraud. Henceforth, devising advanced financial fraud detection systems can actively detect the risks such as illegal transactions and irregular attacks. Over the recent years, these issues are tackled to a larger extent by means of data mining and machine learning techniques. However, in terms of unknown attack pattern identification, big data analytics and speed computation, several improvements must be performed in these techniques. The Deep Convolution Neural Network (DCNN) scheme based financial fraud detection scheme using deep learning algorithm is proposed in this paper. When large volume of data is involved, the detection accuracy can be enhanced by using this technique. The existing machine learning models, auto-encoder model and other deep learning models are compared with the proposed model to evaluate the performance by using a real-time credit card fraud dataset. Over a time duration of 45 seconds, a detection accuracy of 99% has been obtained by using the proposed model as observed in the experimental results.

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