Keywords : Deep Learning

Big Data Analytics in MapReduce: Literature Review

Janani S; Chris D F X

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 9S, Pages 38-42
DOI: 10.47392/irjash.2021.247

Big Data comprises both structured and unstructured data collected from various sources. For collecting, managing, storing and analyzing the large dataset, an efficient tool is required. Hadoop is an open source framework which processes large dataset and MapReduce in Hadoop is an effective programming model reduces the computation time of large scale database in a distributed architecture. A machine and deep learning algorithm based on MapReduce implemented in huge dataset will reduce processing time. This paper aims to study various MapReduce based model and algorithms to analyze huge data. Also, predicts the way of implementing algorithms in MapReduce to reduce the computing time.

Diagnosing Mental Disorders based on EEG Signal using Deep Convolutional Neural Network

Ranjani M; Supraja P

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 7S, Pages 132-137
DOI: 10.47392/irjash.2021.222

Suicides are on the rise all across the world, and depression is a prevalent cause. As a result, effective diagnosis and therapy are required to lessen the symptoms of depression and anxiety. An electroencephalogram (EEG) is a device that measures and records electrical activity from the brain. It can be used to generate a precise assessment on the severity of depression and anxiety. Previous research has shown that EEG data and deep learning (DL) models can be used to diagnose various psychiatric disorders. As a result, this paper offers DeepNet, a DL-based convolutional neural network (CNN) for identifying EEG data from depressed, anxiety and healthy people. This study examines DeepNet's performance in two trials, namely the subject wise split and the record wise split. DeepNet's results have an accuracy of 0.9837, and when record wise split data is used, the area under the receiver operating characteristic curve (AUC) is 0.989.

Survey on Plant Diseases Prediction using Machine learning for better Crop Yield

Toomula Srilatha; Jyothi Sree C.

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 6S, Pages 1-5
DOI: 10.47392/irjash.2021.156

Agriculture is a process of growing crops, soil cultivating, it provides food and fabric and helps for growing country's economy. In India more than 50% of people directly and indirectly depend on the agriculture. For developing agriculture, main interceptions are weather hazards and the crop diseases. Weather hazards cannot be prevented, but the loss that occurs due to crop diseases can be reduced. This can be achieved by identifying the crop disease as early as possible and it is also important to identify the type of crop diseases for preventing the spreading of the disease. In India, we have 160 million hectares of arable land and it is second largest country after the United States. Identifying the crop diseases by human action manually is practically difficult and it is hard to identify the type of crop diseases. So, many researchers involved in to identify the crop diseases based on the image processing for helping the real-time gadgets which can be used to identify the crop disease and its types. This survey focuses on the investigation on the different surveys carried out and work related to the crop disease identification and detection based on the image processing. Computer vision and image processing-based work will help to detect of crop diseases along with many practical based applications like drones, IoT based devices etc. In recent studies most of the works are depending on the machine learning and deep learning-based image processing on various studies of predictions. After analyzing the related work on crop detection based on image processing, most of the works achieved better results based on deep learning algorithms compared to the machine learning algorithms.

Medicinal Plant Identification Using Deep Learning

Geerthana R.; Nandhini P.; Suriyakala R.

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICITCA-2021 5S, Pages 48-53
DOI: 10.47392/irjash.2021.139

In this paper, our main aim is to create a Medicinal plant identification system using Deep Learning concept. This system will classify the medicinal plant species with high accuracy. Identification and classification of medicinal plants are essential for better treatment. In this system we are going to use five different Indian medicinal plant species namely Pungai, Jamun (Naval), Jatropha curcas, kuppaimeni and Basil. We utilize dataset contains 58,280 images, includes approximately 10,000 images for each species. We use leaf texture, shape, and color, physiological or morphological as the features set of the data. The data are collected by us. We use CNN architecture to train our data and develop the system with high accuracy. Several model architectures were trained, with the best performance reaching a 96.67% success rate in identifying the corresponding medicinal plant. The significantly high success rate makes the model a very useful advisory or early warning tool.

Drug Analyser Using Neural Networks with the Use of Transfer Learning Techniques

Swaminathan A; Sakthivel K

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICIES-2021 4S, Pages 22-25
DOI: 10.47392/irjash.2021.105

Resnet architecture was used to create a user-friendly drug analyser web application. This architecture is a transfer learning method that was used as a convolutional neural network in this case, and it will be trained on a collection of images that contain labels for each drug individually. The activation functions used within these neural networks are ReLU (Rectified Linear Unit) and softmax activation functions, as well as categorical cross-entropy as a loss function. Stochastic gradient descent (adam optimizer) was used to change the weights for each input on each epoch. Finally, after receiving a traditional model, it was merged with a web application API such as flask in Python. After that, the web application was deployed to cloud platforms such as Heroku.

Plant Disease Detection Using Deep Learning

Kowshik B; Savitha V; Nimosh madhav M; Karpagam G; Sangeetha K

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICARD-2021 3S, Pages 30-33
DOI: 10.47392/irjash.2021.057

Agriculture is extremely important in human life. Almost 60% of the population is engaged in some kind of agriculture, either directly or indirectly. There are no technologies in the traditional system to detect diseases in various crops in an agricultural environment, which is why farmers are not interested in increasing their agricultural productivity day by day. Crop diseases have an impact on the growth of their respective species, so early detection is critical. Many Machine Learning (ML) models have been used to detect and classify crop diseases, but with recent advances in a subset of ML, Deep Learning (DL), this area of research appears to have a lot of promise in terms of improved accuracy. The proposed method uses a convolutional neural network and a Deep Neural Network to identify and recognise crop disease symptoms effectively and accurately. Furthermore, multiple efficiency metrics are used to assess these strategies. This article offers a thorough description of the DL models that are used to visualise crop diseases. Furthermore, several research gaps are identified from which greater transparency for detecting diseases in plants can be obtained, even before symptoms occur. The proposed methodology aims to develop a convolution neural network-based strategy for detecting plant leaf disease.

Driver Alert System Using Deep Learning and Machine Learning

Krithika G K; Karthik S; Kowsalya R; Alfred Daniel J; Sangeetha K

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICARD-2021 3S, Pages 120-123
DOI: 10.47392/irjash.2021.078

The rising number of road injuries is one of the most pressing challenges facing the world today. One of the main causes of traffic accidents were unsafe and inattentive driving. Drowsiness or a loss of focus on the part of the driver is believed to be a significant factor in such incidents. Driver drowsiness tracking research can assist in the reduction of road accidents.This journal presents a good approach for applying a driver's sleepiness alarm system  that uses Machine Learning and Deep Learning techniques to identify and track the driver's yawning and sleepiness. For face detection and recognition, the device utilises the Histogram Centred Gradient (HOG) function descriptor, which is widely used in image processing. The SVM is then used to determine if the image being identified is a face or not. It also checks the driver's Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) up to a certain countable frames to see whether he or she is sleepy or yawning. Since the driver's drowsiness or fatigue is proportional to the number of hours spent behind the wheel, an extra element for changing theface and mouth reference frames have been added. This increases the sensitivity to detect drowsiness. This also necessitates the introduction of face recognition so that each driver can be tracked individually.This Project aims to provide a Driver Alert System consisting of three sections Face Recognition to unlock vehicle, traffic light detection and Drowsiness alert system.

Analysis of Student Academic Performance through Expert systems

Kandula Neha; S Jahangeer Sidiq

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICIES 9S, Pages 48-54
DOI: 10.47392/irjash.2020.158

Predicting the performance of students is one of the most important topics required for learning contexts such as colleges and universities, as it helps to design successful mechanisms that boost tutorial outcomes and prevent dropouts among various items. These are benefited by automating the many processes involved in the activities of usual students which handle huge volumes of information collected from package tools for technology-enhanced learning. Thus, the careful analysis and interpretation of these information would provide us with valuable data regarding the data of the students and therefore the relationship between them and hence the tutorial tasks. This data is the supply which feeds promising algorithms and methods able to estimate the success of the students. During this analysis, virtually many papers were analysed to show radically different trendy techniques widely applied to predict the success of students, along with the goals they need to achieve in this area. These computing-related techniques and approaches are mainly machine learning techniques, deep learning techniques, Artificial Neural Networks & Neural Networks Convolution, etc. This paper demonstrates the analysis and their comparisons of various methods used to forecast Student Academic success.