Online ISSN: 2515-8260

Keywords : Deep Learning


Vision Based Alert System for Road Signs Detection

K. Hemalatha; D.Uma Nandhini; Karthika S

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1872-1877

TOver recent years, there is a huge increase in road accidents which makes us take more surveillance actions to reduce road accidents. In recent due to researches there is a huge improvement in the field of deep learning and computer vision. Our project is mainly focused on developing a vision based alert system for drivers. We built the model with the help of convolution neural networks a sub field of deep learning and computer vision. We have taken road sign data and trained the model to detect 32 different road signs. The data has been collected from German road sign datasets which consists of 20000 images. We developed the learning model with Keras frame- work which is a high-level API. The Keras works on the Tensor Flow backend which is developed by Google. The Keras framework enables us to build a state-of-the-art model to detect the road sign. For developing the model and to pre-processes the image we have used python language which has a vast number of libraries for image computations and to build deep neural networks. The main aim of our project is to develop a vision based alert system for drivers which will help us to improve road safety. Our model will also help new learners to improve the driving experience.

Multi-Stage Classification Technique for Breast Cancer Detection in Histopathology Images using Deep Learning

Nagamani Gonthina; C. Jagadeeswari; Prabhavathi V; Sneha B

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1104-1110

This research paper proposes the past decenary, substantial improvement in computational ability and betterment in algorithms for analysis of Images has gained vast fame in resolving challenges in the area of medical diagnosis. Subsequently, computerized tissue histopathology at present is becoming tractable towards the implementation of digitized analysis of images and deep learning methods. Cancer is a cluster of disorders involving irregular cell maturation with the capability to conquer or proliferate to other organs of the body. Detection of cancer in the earlier stages is a exacting task due to which many people are prone to death. Treatment of cancer benefits from the pace, perfection of Deep Learning-obliged practice of diagnosis. Deep Learning techniques are utilized to diagnose the features of progressed carcinoma with enhanced perfection compared to individual pathologist. This paper suggests a deep convolution neural network for categorizing a tissue as malicious, there after segregate the tissue then ultimately perform multi-class detection and classification of Breast Cancer disease and its stages in histopathology images

Deep Learning in Tuberculosis Diagnosis: A Survey

B. Sandhiya; Dr.R. Punniyamoorthy; Saravanan. B; Vijay Prabhu. R; Subhash. V

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2736-2740

Tuberculosis is a contagious syndrome that leads to death Worldwide. In majority of the developing countries, the access to the diagnostic tool and the test usage is relatively poor. Now the recent advancement in the field of Artificial Intelligence may help them to fill this technology gap. Computer Aided Detection and Diagnosis helps in diagnosing the diseases through some clinical symptoms as well as X-ray images of the patients. Nowadays many strategies are formulated to increase the classification accuracy of tuberculosis diagnosis using AI and Deep Learning approaches. Our survey paper, focus to describe the wide AI and deep learning approaches employed in the diagnosis of tuberculosis.

Identification and Detection of Abnormal Human Activities using Deep Learning Techniques

ASHISH SHARMA; NEERAJ VARSHNEY

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 408-417

In recent years, it is in public to use the surveillance cameras for continuous monitoring of public and private spaces because of increasing crime. Most current surveillance systems need a human operator to constantly watch them and are ineffective as the amount of video data is increasing day by day. Surveillance cameras will be more useful tools if instead of passively recording; they generate warnings or real-time actions when unusual activity is detected. But recognizing and classifying human activity as normal or abnormal from a live video stream is a stimulating job in the pitch of CPU vision. There is a need for a smart surveillance system for the automatic identification of abnormal behaviour of humans for a specific-scene. Presentpaperstretches an overview of different machine learning methods used in recent years to develop such a model. It also gives an exposure to the recent works in the field of anomaly detection in surveillance video and its applications

Assessment of Patient Health Condition based on Speech Emotion Recognition (SER) using Deep Learning Algorithms

Dr. DNVSLS Indira; B. Lakshmi Hari Prasanna; Chunduri Pavani; Ganta Vandana

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1135-1147

Human Emotion detection either through face or speech became a relatively nascent research area. Speech Emotion Acknowledgment concerns the undertaking of perceiving a speaker's feelings from their discourse chronicles. Perceiving feelings from discourse can go far in deciding an individual's physical and mental condition of prosperity. These emotions can be used for further assessment of patient’s status for better diagnosis. This paper aims to categorize emotions in speech into four different categories which are happy, sad, angry and neutral. For this analysis, four different algorithms - the Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Convolutional Neural Network (CNN-1D) are developed. Detection of Emotion through speech of an individual might be a bit hectic, because of the dynamic changes in voice signal of the same person within a very subtle period of time. So, features like mfcc, chroma, tomez contrast and mel were extracted and given to the model in order to detect the emotions. Those features were given as input to the algorithms and the empirical results implicate that Convolutional Neural Network-1D performs well comparatively. RAVDESS database is chosen for the categorization. A good recognition rate of 89% was obtained from CNN-1D.

Improved Convolution Neural Network For Detecting Covid-19 From X-Ray Images

Sankara Sai Sumanth Kota; Anthony Rajesh Reddy; YeruvaRohit Desai; Venubabu Rachapudi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 1221-1230

Coronavirus disease 2019 (COVID-19) is a communicable disease caused by coronavirus 2 (SARS-CoV-2), a severe acute respiratory syndrome. It was first identified in Wuhan, Hubei, China in December 2019 and has contributed to a continuing pandemic. As of early July 2020, more than 10.6 million cases throughout 188 countries around the world were identified culminating in much more than 516,000 deaths. To prevent COVID-19 from spreading among people, an automated detection system needs to be introduced as a fast-alternative diagnosis method. Machine learning algorithms based on radiographic images can be used as mechanism to support decision taking and help radiologists speed up the diagnostic process. This work introduces a new paradigm for automatic detection of COVID-19 using raw X-ray images in the chest. The proposed model with 4 Convolutional Layers, 2 Max Pooling Layers and Drop Outs, is designed to provide reliable diagnostics for binary (COVID vs. No-Findings) and multi-class (COVID vs. No-Findings vs. Pneumonia) diagnosis. Our model provided gives 98.9% Binary Classification accuracy and 85% Multi Classification accuracy.

Human Activity Recognition using SVM and Deep Learning

V. Parameswari; S. Pushpalatha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1984-1990

Human activity recognition is one among the foremost vital rising technology. Principle parts from the body parts territory are utilized for human movement acknowledgment to scale back spatial property. A multi scale delineation human action acknowledgment is done to save the segregate data before spatial property decrease. This paper could be a human action Recognition system for identification of person. It takes input a video of COVID-19 patients and searches for a match within the hold on pictures. This method is predicate d on Gabor options extraction mistreatment Gabor filter. For feature extraction the input image is matching with Gabor filter and further personal sample generation formula is employed to pick out a collection of informative and non redundant Gabor options. DNN (Deep learning Models) is used for matching the input human action image to the hold on pictures. This method is used in hospital management application for detecting the COVID-19 patient activity from surveillance cameras. By using the SVM and deep learning the human activity is recognized using matlab tool.

IOT based urban surveillance using RaspberryPi and Deep learning with Mobile-Net Pre-trained model

Sathya Vignesh R; Vaishnavi.R. G; G. Aravind; G. SreeHarsha; B. HariKrishnaReddy; Yogapriya J

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2473-2477

The object detection is required to have a stronger protection in the surveillance areas. some of the surveillance systems uses cc cameras to monitor the area .It needs someone to check the output in particular area with-out rest. It is a difficult process for people who have to secure distant areas like fields , homes ,roads, restricted areas which cannot be monitored continuously by a person. object detection using raspberry pi and deep learning with pre-trained model can able to secure the place even without the person. It continuously monitors the area and identifies if any unwanted presence is detected and immediately sends an alert message to the respective device. The setup is fed with a lot of sample images like person, dog ,cat etc .The system checks the unwanted object to the sample images using mobile-net single shot detection by determining the accuracy of common features .Thus it helps to detect the unwanted presence with more accuracy than the previous existing systems.

BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD

Dr. Vikas Jain; Dr.S. Kirubakaran; Dr.G. Nalinipriya; Binny. S; Dr.M. Maragatharajan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3294-3301

Brain-computer interface (BCI) decoding connects the human nervous world to the external world.
People's brain signals to commands that computer devices can detect. In-depth study the performance
of brain-computer interface systems has recently increased. In this article, we will systematically
Investigate brain signal types for BCI and explore relevant in-depth study concepts for brain signal
analysis. In this study, we have a comparison of different traditional classification algorithms new
methods of in-depth study. We explore two different types Deep learning methods, i.e., traditional
neural networks Architecture with Long Short term Memory and Repetitive Neural Networks. We
check the classification Accuracy of Recent 5-Class Study-State Visual Evoked Opportunities dataset.
The results demonstrate in-depth expertise learning methods compared to traditional taxonomy
Algorithms.