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

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.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

scholarly journals Design of a Fracture Detection System based on Deep Program in a Convolutional Neural Network

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 509-518
Author(s):  
Payman Hussein Hussan ◽  
Syefy Mohammed Mangj Al-Razoky ◽  
Hasanain Mohammed Manji Al-Rzoky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bone Fractures ◽  
Detection System ◽  
Training Data ◽  
Learning Models ◽  
Fracture Detection ◽  
Data Set ◽  
Final Fracture

This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.

scholarly journals Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data

2019 ◽  
Vol 8 (2) ◽  
pp. 5073-5081
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Student Performance ◽  
Activation Function ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fine Tuning ◽  
Academic Excellence ◽  
Data Set

Prediction of student performance is the significant part in processing the educational data. Machine learning algorithms are leading the role in this process. Deep learning is one of the important concepts of machine learning algorithm. In this paper, we applied the deep learning technique for prediction of the academic excellence of the students using R Programming. Keras and Tensorflow libraries utilized for making the model using neural network on the Kaggle dataset. The data is separated into testing data training data set. Plot the neural network model using neuralnet method and created the Deep Learning model using two hidden layers using ReLu activation function and one output layer using softmax activation function. After fine tuning process until the stable changes; this model produced accuracy as 85%.

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

2021 ◽  
Vol 10 (8) ◽  
pp. 46-50
Author(s):  
D J Samatha Naidu ◽  
M.Gurivi Reddy
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Internet ◽  
Learning Models ◽  
Data Set ◽  
Proposed Model ◽  
Recent Developments ◽  
Automatic Image Recognition ◽  
Recognition Systems

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%.

scholarly journals Aspect Based Sentimen Analysis Opini Publik Pada Instagram dengan Convolutional Neural Network

2019 ◽  
Vol 1 (2) ◽  
pp. 50-57
Author(s):  
Muhammad Arief Rahman ◽  
Herman Budianto ◽  
Esther Irawati Setiawan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network

Internet sebagai sarana informasi dan komunikasi sudah sangat dikenal di kalangan masyarakat dalam menawarkan kemudahan dan fleksibilitas yang cukup memadai ketika menjadi media. Oleh karena itu opini publik terhadap Operator Telekomunikasi merupakan hal yang sangat penting untuk dijadikan patokan. Namun, untuk mengevaluasi umpan balik online itu, bukan masalah sederhana. Kadang-kadang ketika menganalisis ulasan online yang berkembang pesat ini, menjadi sulit untuk mengkategorikan apakah opini pelanggan puas atau tidak puas terhadap produk dan layanan. Selain itu, sebagai bagian dari peningkatan kualitas mereka, organisasi seperti jasa ini perlu mengklasifikasikan aspek produk dan layanan yang paling disukai pelanggan. Deep Learning adalah area baru dalam penelitian Machine Learning, yang telah diperkenalkan dengan tujuan menggerakkan Machine Learning lebih dekat dengan salah satu tujuan aslinya yaitu Artificial Intelligence. Deep Learning adalah tentang belajar beberapa tingkat representasi dan abstraksi yang membantu untuk memahami data seperti gambar, suara, dan teks. Convolutional Neural Network adalah salah satu contoh metode Deep Learning. Metode Convolutional Neural Network diharapkan dapat digunakan dalam pengimplementasian opini publik untuk keperluan data training yang dikumpulkan dari beragam data yang dianotasikan kelas sentimennya secara otomatis. Hasil dari penelitian menunjukkan dari 4 aspek dan 3 sentimen maka didapatkan nilai rata-rata precision, recall, dan f1-score adalah precision  97.6%, recall 84%, f1-score 90.3%. Bisa disimpulkan score representation ini dapat digunakan untuk klasifikasi sentimen.

scholarly journals Survey for the Prediction of Chronic Kidney Disease using Machine Learning

2019 ◽  
pp. 154-158
Author(s):  
Pooja Sharma ◽  
Saket J Swarndeep
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Network System ◽  
Data Set ◽  
Neural Network System

According the 2010 global burden of disease study, Chronic Kidney Diseases (CKD) was ranked 18th in the list of causes of total no. of deaths worldwide. 10% of the population worldwide is affected by CKD. The prediction of CKD can become a boon for the population to predict the health. Various method and techniques are undergoing the research phase for developing the most accurate CKD prediction system. Using Machine Learning techniques is the most promising one in this area due to its computing function and Machine Learning rules. Existing Systems are working well in predicting the accurate result but still more attributes of data and complicity of health parameter make the root layer for the innovation of new approaches. This study focuses on a novel approach for improving the prediction of CKD. In recent time Neural network system has discovered its use in disease diagnoses, which is depended upon prediction from symptoms data set. Chronic kidney disease detection system using neural network is shown here. This system of neural network accepts disease-symptoms as input and it is trained according to various training algorithms. After neural network is trained using back propagation algorithms, this trained neural network system is used for detection of kidney disease in the human body.

scholarly journals Micro Clustering Methodology for Document Objects using Deep Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 1546-1552
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Large Data ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Volume Data ◽  
Data Set ◽  
Clustering And Classification

Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

scholarly journals Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 352
Author(s):  
Anyou Wang ◽  
Rong Hai ◽  
Paul J. Rider ◽  
Qianchuan He
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cancer Screening ◽  
Detection System ◽  
Population Level ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Data Set ◽  
Cancer Types ◽  
Deep Learning Neural Network

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types. We integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions.: This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

scholarly journals Brand Detection System using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 497-500
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Visual Representation ◽  
Cosmetic Products ◽  
Medical Object

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

An Application of Machine Learning in Plant Leaves Disease Detection and Classification

2020 ◽  
Vol 17 (6) ◽  
pp. 2645-2652
Author(s):  
Sachin Dahiya ◽  
Tarun Gulati
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Automatic System ◽  
Crop Production ◽  
Plant Disease ◽  
Data Augmentation ◽  
Machine Intelligence ◽  
Disease Detection ◽  
Initial Stage

Plant disease severely affects the crop production. Food security is always a challenge because the population of the world is increasing at a rapid rate. Diseases in plants can be controlled at the initial stage with the help of automatic system that can be able to detect the wide variety of diseases before its spreading to the whole cultivation area. With the development of various machine learning and deep learning algorithms it is now possible to design such an automatic system. Deep neural network like convolution neural network are able to detect the plant disease with high accuracy. In this paper we have discussed about the deep learning techniques, CNN and its parameters, data augmentation, transfer learning and various factor that affects the performance of DL model. Recent studies that apply the machine intelligence in plant leaf disease detection are also discussed.

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