scholarly journals Face, Expression and Gesture Recognition and Compilation in Database Using Machine Learning

2021 ◽  
pp. 348-352
Author(s):  
Prof. Prashant Wakhare ◽  
Vaishnavi More ◽  
Rutuja Surdi ◽  
Kajal Patil ◽  
Vishwadip Ingale
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Public Places ◽  
Recognition Process ◽  
Video Clips ◽  
Face Expression ◽  
Day By Day ◽  
Face Expressions

In today's scenario, numbers of crimes have increased day by day. At many public places government has placed many CCTV cameras so police can get that CCTV footage to identify the suspects but sometimes it becomes difficult to recognize the criminals So here we have come up with a solution to make this process smooth, easier than the traditional one. The system which automates all the suspect recognition process and provides better solutions to reduce the increasing rate of crimes. We plan to design a system to capture face, expressions and gestures of the targeted people (Criminals) through distributed CCTV System and are maintaining it in a database along with time and location stamp. The compiled database will be used to identify suspects from video clips of crime related CCTV footage captured series of CCTV Systems located on routes and close to scene of crime. This research discusses the various types of methodologies that can be used to identify the suspects which are captured in CCTV footage and convert it into useful information for further analysis of particular crime cases.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

2020 ◽  
Vol 26 (26) ◽  
pp. 3049-3058
Author(s):  
Ting Liu ◽  
Hua Tang
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Future Development ◽  
Malaria Parasite ◽  
Mitochondrial Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Effective Drugs ◽  
Construction Strategies ◽  
Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

HAND GESTURE RECOGNITION IN REAL-TIME

2021 ◽  
Vol 1 (10) ◽  
pp. 346-350
Author(s):  
Priyanshi Gupta ◽  
Amita Goel ◽  
Nidhi Sengar ◽  
Vashudha Bahl
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Research Field ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Normal People ◽  
Human Gestures ◽  
Dynamic Gestures ◽  
Hand Pose

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Tensor Flow, Machine learning, React js, handmark model, media pipeline

scholarly journals Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 1771-1777
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Decision Fusion ◽  
Hand Gesture Recognition ◽  
Machine Learning Techniques ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition

The hand gesture detection problem is one of the most prominent problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the hand gesture recognition. These techniques find applications in sign language recognition, virtual reality, human machine interaction, autonomous vehicles, driver assistive systems etc. In this paper, the goal is to design a system to correctly identify hand gestures from a dataset of hundreds of hand gesture images. In order to incorporate this, decision fusion based system using the transfer learning architectures is proposed to achieve the said task. Two pretrained models namely ‘MobileNet’ and ‘Inception V3’ are used for this purpose. To find the region of interest (ROI) in the image, YOLO (You Only Look Once) architecture is used which also decides the type of model. Edge map images and the spatial images are trained using two separate versions of the MobileNet based transfer learning architecture and then the final probabilities are combined to decide upon the hand sign of the image. The simulation results using classification accuracy indicate the superiority of the approach of this paper against the already researched approaches using different quantitative techniques such as classification accuracy.

scholarly journals Pengenalan Gerak Manusia Menggunakan Algoritma Relevance Vector Machine pada MSRC-12 Dataset

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Vina Ayumi ◽  
Erwin Dwika Putra
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Statistical Learning ◽  
Gesture Recognition ◽  
Learning Theory ◽  
Relevance Vector Machine ◽  
Motion Model ◽  
Communication Tools ◽  
Machine Learning Technique ◽  
Learning Technique

Relevance vector machine is a popular machine learning technique that is motivated by statistical learning theory. RVM can be used for gesture recognition which is one of the communication tools used by humans. This study proposes an experiment using the Relevance Vector Machine (RVM) algorithm on gesture data from Microsoft Research Cambridge-12 (MSRC-12) as a proposed solution to overcome unbalanced problems in data processing. The results of the study are the accuracy for 1-person motion model reaches 100% and the lowest accuracy with 5 people the motion model reaches 96%. Graphically, the more people or models, the lower the algorithm's accuracy.

scholarly journals Sentiment Analysis through Machine Learning

2021 ◽  
Vol 56 (3) ◽  
pp. 384-393
Author(s):  
Md. Abbas Ali Khan ◽  
Ali-Emran ◽  
Md. Alamgir Kabir ◽  
Mohammad Hanif Ali ◽  
A. K. M. Fazlul Haque
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Distance ◽  
The Internet ◽  
Text Document ◽  
Ride Sharing ◽  
Wheeled Vehicle ◽  
The People ◽  
Day By Day ◽  
Rainy Days

In recent years, App-Based Transportation System (ABTS) like Ride Sharing (Uber, Patho) has become popular day by day. For our daily life, a rickshaw (a 3-wheeled vehicle usually for one or two passengers that one man pulls) is most important for a short distance. If we add this vehicle to our ABTS system, it will be very much helpful for us, specifically for the rainy season in Bangladesh. On heavy rainy days, in our city Dhaka, other vehicles like CNG, cars, and bikes become unused because roads go underwater. However, the man who pulled the rickshaw can serve this condition. It is more important than the conventional rickshaw is unable to provide such service properly. In this regard, we are proposing an App-Based Rickshaw (ABR), which is convenient to get over distance through the internet. To do this, we have collected data through close questionnaires’ from several types of people. In contrast, collected data are based on a text document. So our aim is to Sentiment Analysis (SA) of the people through machine learning and checks the feasibility of applicability in the real world.

scholarly journals On the Methods for Detecting Brain Tumor from MRI images

2020 ◽  
Vol 9 (9s) ◽  
pp. 37-44
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Imaging ◽  
Tumor Detection ◽  
Gray Level ◽  
Wide Spread ◽  
Emerging Trends ◽  
Day By Day ◽  
High Diversity

Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples. Automatic detection has got wide spread acclaim because the manual detection by experts is time consuming and prone to error in judgment. Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. Here we present a review of few methods from simple thresholding to advanced deep learning methods for segmentation of tumor from MRI data. The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. The results of various methods are compared to find the best methods available. As medical imaging methods have improving day by day this review will help to understand emerging trends in brain tumor detection.

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