Journal of Advancements in Robotics (JoARB) ISSN: 2455-1872 Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 6.05 Journal of Advancements in Robotics (JoARB) is a journal focused towards the rapid publication of fundamental research papers on all areas of Robotics and its advancements. It's a triannual journal, started in 2014. Journal DOI no.: 10.37591/ JoARB Focus and Scope Cover Human Robot Interaction and Social Robotics Sensor Integration Robot Vision.- Robot Programming Medical Robotics Humanoid Robots Autonomous Helicopters Dynamics and kinematics of robot Kinematics, dynamics, and simulation of robots and autonomous intelligent systems. Design of robotic mechanisms. Man-machine interface and integration. Robotics-related computer hardware, software, and architectures. Active sensory processing and control. Machine learning and artificial intelligence for robotics. Medical and Assistive Robotics. Bio-mimetic and Bio-inspired Robotic Systems. Readership: Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies. Indexing: The Journal is index in DRJI, Google Scholar Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email info@stmjournals.com or nikita@stmjournals.com Abbreviation: JoARB Frequency: Three issues per year Peer Reviewed Policy Editorial Board Instructions to Authors Publisher: STM Journals A division of: Consortium eLearning Network Private Ltd Address: A-118, 1st Floor, Sector-63, Noida, Uttar Pradesh-201301, India Phone no.: 0120-4746-214/ Email: nikita@stmjournals.com
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Olfactory Biometric Technique: An Emerging Technology
Now-a-days authentication becomes a part of our daily life. Biometric authentication or, simply, biometrics refers to the method of using the physiological or behavioral characteristics to determine or verify one’s identity. Many of the systems require reliable personal recognition approaches to either identify or determine an individual by requesting their services. The purpose of such approaches is to ensure that whether the rendered services are accessed only by a legitimate user or anyone else. These methods applied for secure access of buildings, computer systems, laptops, cellular phones and ATMs. There are different types of biometric techniques exists in our literature based on the physical characteristics( such as eye’s features (Iris, retina), facial features, hand geometry, ear shape, finger prints, wrist/hand veins, DNA, chemical composition of body odor) and the personal characteristics (such as handwritten signature, keystrokes/typing patterns, voiceprint). In this paper, we presented a perspective scheme i.e., olfactory biometric technique (based on body odor) which is still under development. Odor, as a biometric technique, has some important characteristics, mainly, it is faster and easier since users will be not involved with unfamiliar interfaces such as typing password, signing or even deliberate exposing some part of the body. Keywords: Authentication, biometrics, biometric technology, olfactory biometric technique
Providing security using Signature-Based Broadcast Authentication in Sensor Networks
As per recent studies it is feasible to perform public key cryptographic operations on resource-constrained sensor platforms. But the problem is energy consumption introduced by public key operations makes any public key-based protocol an easy target of Denial-of-Service attacks. For example, if digital signature schemes like Elliptic Curve Digital Signature Algorithm are used directly for broadcast authentication without further protection, an attacker can simply broadcast fake messages and force the receiving nodes to perform a huge number of unnecessary signature verifications, and can consume their battery power. This paper shows how to mitigate such Denial-of-Service attacks when digital signatures are used for broadcast authentication in sensor networks. Specifically, this paper first presents two filtering techniques, the group-based filter and the key chain-based filter, to handle the Denial-of-Service attacks against signature verification. Both methods can reduce the number of unnecessary signature verifications when a sensor node is under Denial-of-Service attack. This paper then combines these two filters and proposes a hybrid solution to further improve the performance. Keywords: Sensor networks, security, broadcast authentication, Denial-of-Service attacks, digital signature.
On Reconnection of Broken Ridges and Binarization for Fingerprint Images
With the increase in security threats, use of biometrics for person identification and authentication has been increased. One of the oldest and most widely used forms of biometric is, Fingerprint. Since it is mainly used in forensic science, accuracy in the fingerprint identification is highly important. Most of the fingerprint identification systems are based on minutiae matching and a critical step in correct matching of fingerprint is extracting minutiae reliably from adequate quality images. However, fingerprint quality may be degraded and corrupted due to variations in skin, pressure and impression conditions. One of these degradations is due to the presence of unwanted breaks or creases on the surface of the image. Moreover most of the feature extraction algorithms work on binary images instead of the gray scale image and results of the feature extraction depends on the quality of binary image used. Keeping these points in mind, an enhancement technique for reconnection of broken ridges and binarization are proposed in this paper. Both of these pre-processing are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations and there by expect to get a robust matching performance. Keywords: Fingerprint recognition, minutiae, enhancement, binarization
Attribute based Level Adaptive Thresholding Algorithm for Object Extraction
Image processing plays a vital role in the computer vision because most of the scenarios require object extraction and recognition. In order to utilize these images for the procedural analysis of the environmental entities in computational means, it must be a noiseless one. However, the images are affected through noises caused by the various acquisition techniques and hence an effective technique for denoising and segmentation is necessary in Computational Tomography. In order to achieve this objective we propose an effective denoising technique which confiscates the advantages offered in thresholding methods. Keywords: Computer Vision, Image Segmentation, Image denoising
A Novel Approach for Confidence Estimation using Support Vector Machines for More Accurate Value Prediction
Data dependencies create hurdles in exploiting instruction-level parallelism (ILP) among instructions. To overcome them, data value predictors are used which guess instructions’ result before it is actually executed. Thus, future instructions which depend on the outcome of that instruction executes sooner. But, since value prediction accuracy is very crucial in determining the amount of parallelism that can be exploited, confidence estimation is used along with it to lessen the value prediction misprediction penalty by guessing whether or not to use a value prediction result. Previous confidence estimators were based on perceptrons which had the limitation of learning only linearly separable functions [1, 2]. But sometimes linear inseparability may arise when a correct prediction on a past instruction causes the current instruction to predict incorrectly [3]. As Support Vector Machines (SVMs) belong to a family of generalized linear classifier and can be interpreted as extension of perceptron, they are both linear and nonlinear classifiers and are computationally more efficient than perceptrons. Thus, we propose a confidence estimator using SVMs in which the prediction accuracy of previous instructions is used to estimate the confidence of current prediction and decide based on its results whether or not the prediction is likely to be correct. The classification algorithm of SVM is implemented using MATLAB platform, and its novel learning methods have been applied on different data sets having two classes. Keywords: value prediction, confidence estimation, SVM
Facial Expression Recognition using Convolutional Neural Network
Machine learning is a subset of Artificial intelligence. Machine learning enables self-learning from past data and predicts the future. Machine learning is used in many applications such as self-driving cars, Google translate etc. Deep learning is a subset of machine learning. CNN is a convolutional neural network. The CNN approach is very effective in image classification. The target of this exploration is to assist a look acknowledgment framework dependent on convolutional neural organization. The conventional CNN network module is used to extract primary expressional vector (EV). The expressional vector (EV) is generated by tracking down relevant facial points of importance.In the proposed solution, gray scale image in Kaggle. This methodology empowers to order seven fundamental feelings which comprise of furious, disdain, dread, glad, unbiased, dismal and shock from Kaggle dataset. TensorFlow framework is used in facial expression with the accuracy of 92%.
Technology has played an indispensable role in survival during pandemic situations. From the initial stages of the COVID till the invention of the vaccine, technology has provided various means to live in such a pandemic situation. Technology has availed us with highly proficient resources to detect the COVID symptoms. It has also played a key role in WFH in various companies, industries, Business and also in education and learning. Many precautionary steps have been taken with the help of new technology. Various Contact tracing applications have been developed in different companies to have a check on the data like number of infectants, treatments, mortality etc. Misinformation also became a survival threat during the pandemic. Telecom services have also made efforts to stop the spread of COVID. AI, Internet, 5G, Cloud etc. were the main contributing domains of the technology. Mostly every domain of the Technology has performed a vital role in survival during COVID. As we all know ‘Necessity is the mother of all inventions’ and here in this scenario our necessity was to make preventive measures for survival during the pandemic. And innovation in the field of Tech to provide needed conditions. Thus, the increase in needs of the population is the Advancement of Technology.
Self Driving Car Simulation Using Deep Learning
Every year, traffic accidents account for 2.2% of worldwide deaths. That accumulates to a few millions a year. On prime of this, some individuals are seriously lacerate in auto-related accidents annually in large numbers mostly due to human error. From distracted driving to drunk driving to reckless driving to careless driving, one poor or inattentive call may be the distinction between a typical drive and a critical state of affairs. However what if we tend to might neutralize human error from the equation?The proposed system of ‘Self Driving Autonomous Car’ is designed using Deep Learning and CNN and Computer Vision. Imagine stepping into your car and however speaking a location into your vehicle’s interface, then lease it drive you to your destination whereas you read a book, surf the net, or nap without fear concerning something. Computer Vision techniques via Open-CV are applied to establish lane lines for a self-driving car. CNN is trained to spot numerous traffic signs using Keras (open-source platform).To generalize the behavior of cars on different tracks, it is not possible to collect and process a huge amount of data, that is why augmentation is done which generalizes data on new tracks. Augmentation can be done using various techniques like crop, zooming, Flipping, and changing brightness
Food Recipe Finder Web Application Based on Similarity of Materials
This project report is entitled “Food recipe finder web application based on similarity of materials”. The main objective of the study is to solve the problems faced by many people regarding cooking. Cooking has been a huge burden for many people, mostly bachelors. So, to conquer this problem, our team is developing a web application to make cooking easier for these people. This application is an online platform especially for the people facing cooking issues. These people mostly suffer three scenarios. Different modules included in this project will help people facing different scenarios. Except for home and login page, there will be total three modules included in this project. In the first module, the user will input all the ingredients he/she have at the moment and what and how can he/she prepare using those ingredients, will appear on their screen. In the second module, the user will input the main dish and all the ingredients will appear on his/her screen. Inthe third module, the user just has to upload a picture of the prepared dish along with the ingredients and the exact or similar dish will appear on the user’s screen. First two modules will take data set and third module will take machine learning algorithm. The study recommends to solving cooking issues and to make people a great cook oneday.
Automate Voice Controlled Robot Using Raspberry Pi 3b+
As, robotics and artificial intelligence (AI) is a subfield of engineering dedicated to research and development. There are a lot of researchers working on to enhance the connection between robots and humans through AI. The proposed system presents the research of the designing and development of a voice controlled talking robot using AI and IOT device. The proposed system is based on microcontroller Raspberry Pi. The client will utilize an android worked PDA to provide voice order. The command can be fetched using a Flutter based app which will convert the voice command into text. The phone will be connected with microcontroller using a Bluetooth module. After conversation of the voice command into text by Flutter based app, it will send necessary data to the microcontroller using Bluetooth of the phone and microcontroller will receive the data using Bluetooth module. As per the order, the robot will push ahead, in reverse, left, right or completely self-ruling. Development of the robot and it will work with the microcontroller to control two distinctive engines of left and right by controlling the course of turn of engines. An ultrasonic sensor will be interfaced to distinguish snag and assist robot with working.
Review: Detection of Motor bike Accident to provide Emergency Assistance Using Narrowband-Internet of Things (NB-IoT) System
Abstract An accident is an unpredicted and unintentional event. Considering the alarming increase in the number of motor bike riders and the number of accidents happening in our country, this system ensures to make the two-wheeler driving safer than before for the rider. The lack of treatment in proper time is the major reason for half of the deaths in road accidents. Many Accident detection systems is based on 3G and 4G wireless communication technology, which requires broad network resources and large amounts of available electricity, was designed for high-speed Internet usage. Most narrowband-Internet of Things (NB-IoT) devices do not require high-speed Internet because most IoTs work by sending commands to and receiving commands from a server only. NB-IoT is a low-power wide area (LPWA) technology that was developed to accommodate a broad variety of new IoT devices. Our newly developed motor bike accident detection system recognize a fall of bike and heartbeats of Victim during the accident, If heartbeats are not in normal conditions then automatically alert up to 4 contacts about the accident . This system aims at providing early detection of accidents and communicating the information immediately to the emergency responses on time to provide quick assistance to the injured person. Keywords: low-power wide area (LPWA), Narrowband-Internet of Things (NB-IoT)
2:1 Multiplexer Using Different Design Styles: Comparative Analysis
Abstract This paper presents a comparative analysis of 2:1 multiplexer using different logic styles (transmission gate, pass transistor and CMOS logic), with three different technologies i.e. 45nm, 32nm and 16nm. Simulation is done using Synopsys HSPICE tool at 1V power supply. As a result, it is found that the least power is consumed by 2:1 multiplexer implemented using TGL. It consumes 99.7% less power than pass transistor logic and PTL consumes 99% more power than CMOS. Since multiplexer implemented by PTL utilizes minimum number of transistors, i.e., 2 ,therefore it is the area efficient logic circuit for 2:1 MUX but its performance is low as its output is somewhat distorted. Abstract This paper presents a comparative analysis of 2:1 multiplexer using different logic styles (transmission gate, pass transistor and CMOS logic), with three different technologies i.e. 45nm, 32nm and 16nm. Simulation is done using Synopsys HSPICE tool at 1V power supply. As a result, it is found that the least power is consumed by 2:1 multiplexer implemented using TGL. It consumes 99.7% less power than pass transistor logic and PTL consumes 99% more power than CMOS.Since multiplexer implemented by PTL utilizes minimum number of transistors, i.e., 2 ,therefore it is the area efficient logic circuit for 2:1 MUX but its performance is low as its output is somewhat distorted.
A Comparative Study on Emotion and Gender Prediction using OpenCV Library and Deep Learning
Abstract In this paper, we are going to study and develop a convolutional neural network (CNN) using EmoPy python Library for training our dataset, which performs human facial recognition as well as their gender prediction with real-time emotions. This model will work with OpenCV, Keras 2.3.1 and EmoPy. A fully connected layer of convolutional neural network which identifies the gender as well as the art of emotion of the human for which lots of data training has been done along with our own images.
Half Adder Using Different Design Styles: A Review on Comparative Study
Abstract A half adder is a digital logic circuit that performs addition of two single bit binary numbers. Generally, in various types of processors, adders are used to perform arithmetic and logical operations .In this paper, working of half adder is analysed by designing it by using three different logic styles, i.e., CMOS logic, Transmission Gate Logic (TGL) and Pass Transistor Logic (PTL). The comparison has been made between the circuits on the basis of their power consumption and transistor count. Simulation of the circuits is carried out using HSPICE tool at 45 nm, 32 nm and 16 nm technologies at 1V power supply. After simulation it is observed that power consumption is lowest when the adder is implemented by transmission gate as compared to CMOS and PTL design styles whereas transistor count is minimum in case of PTL design style. It is also inferred that CMOS gives the best performance out of the three design styles.
A Simulation Analysis of Ripple Correlation Control MPPT for Low Power PV Systems
Abstract In this work, a simulation study of Maximum Power Point Tracking (MPPT) with Ripple Correlation Control (RCC) technique has been done. Photovoltaic (PV) array along with the DC/DC buck converter and its Maximum Power Point Tracking (MPPT) control strategy has been implemented with RCC for various solar radiations at 25oC, and simulations results are presented. RCC with low pass filter and tuned with Proportional Controller (PI) has been implemented. Simulation results show RCC removes all ripples from voltage, current and power outputs, which was the major concern of conventional MPPT techniques.
Decode Human Immune System for Blood Disease Detection using Machine Learning Algorithms
Quick and accurate medical diagnosis are crucial for the successful treatment of diseases. Using machine learning algorithm and based on laboratory blood test result, we have built a model to predict hematology diseases. It is a predictive model which uses various symptoms causing the disease and a few blood parameters to detect the disease. Blood analysis is an essential indicator for many diseases. It contains several parameters which are sign for various specific blood diseases. For predicting the disease according to blood analysis, symptoms that leads to identifying the disease precisely should be recognized. Our model uses machine learning which is the field responsible for building models for predicting the output based on previous data. Accuracy of the machine learning algorithm is based on quality of data collected for learning process. Keywords: Disease, Symptoms, Blood Parameters, technology, human immune system, algorithm. Cite this Article: Ria Vijay Nagaonkar, Suraj Rajaram Jare, Rishi Vinod Pandey, Suhasini Parvatikar. Decode Human Immune System for Blood Disease Detection using Machine Learning Algorithms. Journal of Advancements in Robotics. 2020; 7(2): 9–14p.
Artificial Neural Network and Its Application to Diagnosis
In this paper, we are giving an excellent idea for the designing of the standard diagnosis key diagnosis systems, which security is based on the well known number theoretic problem, i.e., elliptic curve discrete logarithm problem in multiplicative group of the finite field. But now we are moving in the revolutionary idea, “The Automatic groups G converted into the multiplicative groups G’ of the finite field Zp* of the order p-1, where p is the prime number, If G and G’ are one-to-one and onto isomorphic (Theorem).” By using this theorem, we design the new diagnosis key diagnosis system, whose security or encoding scheme is strictly different as compare to the other traditional encoding scheme. Keywords: Diagnosis, Automatic Groups, Diagnosis, Finite Field, Groups, Encoding Scheme. Cite this Article: Sunil Kumar Kashyap, Deepshikha Sharma. Artificial Neural Network and Its Application to Diagnosis. Journal of Advancements in Robotics. 2020; 7(2): 15–22p.
In the current age, the robots and electronic gadgets are getting more sophisticated, reliable and miniaturized. That makes robotic systems increasingly suitable for spying and law enforcement purposes. The need for flexi-controlled smart robot, equipped with appropriate sensors for movement, is increasing day by day due to growing risks and the concern for human safety. Spy-robots can be used for a wide range of applications, from collecting sensitive intelligence data to completing covert missions. This paper propose a spy robot with suitable sensors and cameras to perform different tasks, which can be operated remotely for reconnaissance or patrolling tasks, where it can relay the videos and images captured back to the operator, through wireless communication. Keywords: Spider, robot, image processing, spying, technology. Cite this Article: Ragini Sharma, Monish Adhikari, Sanket Deshmuk, Chetan Mhatre. Spider Robot. Journal of Advancements in Robotics. 2020; 7(2): 29–34p.
Zadeh’s theory of fuzzy for grading the objects is reviewed in this paper under the context to develop the cryptosystem. This paper proposes a cryptosystem whose security comprises with the membership function of fuzzy. The security lies with the linguistic and numeric representation of information and its transformation by fuzzy. The one way hash function is substituted to membership function. The key generation and encryption comprises with fuzzy set and fuzzy logic respectively. Thus, the proposed system provides the security against all attacks by linguistic transformation rather than numeric representation. This is also an efficient cryptosystem by grading. Keywords: Fuzzy Set, Fuzzy Logic, Cryptosystem, Membership Function, Encryption. Cite this Article: Sunil Kumar Kashyap, Ashutosh Pandey. Fuzzy and Cryptosystem. Journal of Advancements in Robotics. 2020; 7(2): 1–8p.
Unmanned Aerial Vehicles, also known as drones, are the aircrafts that can be operated without a human pilot on board of the aerial vehicle. Drones can be navigated from the ground, using a GPS tracking system. Drones are traditionally operated using remote controllers, joysticks, mobile applications, and embedded computers. Some important problems previously faced with these approaches are that drone controlling is limited to the range of electromagnetic radiation and is susceptible to interference noise. In this, the use of hand gestures is considered as another technology to control drones. Hand gestures are used to control the movement of the drone and an onboard camera is used for streaming an aerial view of the surrounding. This UAV (Unmanned aerial vehicles) are becoming very popular and are widely used for photogrammetry and remote sensing applications. This paper basically describes about how the surveillance drone is made and then discusses about its-components and it’s working. The main objective of this project is to provide convenient and efficient surveillance at an affordable price, so that the drones can be used for both private and government purposes. Keywords: Drone, Hand gesture, Quadcopter, Aerial view, Gesture controllers, Aerial surveillance. Cite this Article: Abhinav Vilas Dhanawade, Ninad Dwarkanath Patil, Mukesh Sanjay Dhanure, Rina Bora. Hand Gesture Controlled Drone. Journal of Advancements in Robotics. 2020; 7(2): 23–28p.
In social interaction with other human beings which delivers information by using emotion and facial expression play about their mood that is important part in communication. The “Blue Eyes Technology” creates sensory abilities like those of human beings which enable the computer to gather facts about humans and interact with others. This paper implements the detection of feelings or emotion like fear, surprised, happy, sad and anger; it defines by human eye expressions and by using an emotion. Blue Eyes Technology aims allow people to interact with computers in a more natural manner. This technology BLUE define for Bluetooth, which enables reliable wireless communication and EYES define the movement of the eye that enables us to see lot of interesting and important information. It objective at creating computational machines that have perceptual and sensory ability. In this technology, emotions and action can be identify by using camcorder. The technologies used for manual and artificial intelligent speech recognition, gaze input cascaded, recognition, simple user interest tracker, the eye movement sensor. Its main applications are automobile industry, video games, medical diagnosis and lie detector tests. It is an emerging technology and in future it is expected to reduce the gap between electronic and physical world. The emotion mouse obtains physiological data and emotional state of a person through the single touch of mouse having different sensors. Keywords: Emotion mouse, emotion recognition, eye expressions, support vector machine (SVM), Hidden Markov Model (HMM), blue eyes, images, magic pointing, image processing, sense Cite this Article Sushama Sainwar, Shalini Kulshrestha, Anil Kumar Sharma. Blue Eye Technology. Journal of Advancements in Robotics. 2020; 7(1): 15–18p.
Unsupervised Learning Techniques in Neural Network for Face Recognition
This paper represents the review of systematic comparison between of unsupervised learning algorithms for face recognition. In neural network, different algorithms are used for face recognition. Each algorithm has different accuracy factors. Commonly these methods asset a set of base images and serve faces as linear combo of images. In our proposed review, firstly we discuss about the face detection using neural network along with the concept of digital image processing. In this paper, different era algorithms: K-means, C-means, PCA (principle component analysis), single SVM (support vector machine), ensemble SVM, RBF (redial basis function), and LVQ (learning vector quantization) are compared on the basis of recognition rate, error rate and identify the best algorithm. Keywords: Face recognition (FR), recognition rate (RR), error rate (ER), principle component analysis (PCA), support vector machine (SVM), radial basis function (RBF), learning vector quantization (LVQ), K-means, C-means Cite this Article Prince Verma, Jagriti, Manpreet Kaur. Unsupervised Learning Techniques in Neural Network for Face Recognition. Journal of Advancements in Robotics. 2020; 7(1): 25–29p.
Design of Low Power Preamplifier for Hearing Aid Applications
A microphone preamplifier having low power, low noise and high phase margin for use in hearing aid applications is proposed. The preamplifier uses two-stage topology utilizing self cascode transistors for improving the gain by decreasing the channel length modulation effect and increasing the output impedance. Simulations have been performed in Cadence Virtuoso using SCL 180 nm technology parameters and comparative analysis with other state of art preamplifier design is performed. Simulation result shows that the proposed preamplifier achieves a gain of 20 dB with a bandwidth of 19.2 kHz. The power reported is 9.8 µW which is lower than most of the reported designs which further demonstrates the effectiveness of the proposed design. Keywords: Preamplifier, low power, high gain, low noise, microphone applications, low frequency Cite this Article Richa Neog, Naushad Manzoor Laskar, Koushik Guha, Saurav Nath, K.L. Baishnab. Design of Low Power Preamplifier for Hearing Aid Applications. Journal of Advancements in Robotics. 2020; 7(1): 1–7p.
Event Detection and Analysis of Online Social Image Measurement by Using SNMF
Conventional sites were driven by human-altered occasions which lead to immense web search traffic. This paper is an overview led for distinguishing the different occasion recognition strategies which are helpful for occasion mining. We picked picture examine log as the advantage for event mining, as chase logs can genuinely reflect person's inclinations. Late investigations have demonstrated that an observable level of web search traffic is about get-togethers. We picked picture look log as the asset for occasion mining, as search logs can legitimately mirror individuals' inclinations. Besides, time factor is considered as a significant component for occasion recognition as various occasions create at various times. What is more, to give an outwardly engaging storyboard, every occasion is mapped with a lot of pertinent pictures orchestrated along a timetable. We utilize acclaimed individuals as our test territory, which takes a broad degree of picture look for bargains. Investigations involving web look traffic on 200 acclaimed individuals, for a period of a half year, show amazingly encouraging results differentiated and top-notch production storyboards. Keywords: SNMF, photo selection, event detection, NMF, crawling Cite this Article Nalli Vinaya Kumari, Gouri Arun Lahare, Naresh Kumar Budugu, Vamshi Krishna Kallem. Event Detection and Analysis of Online Social Image Measurement by Using SNMF. Journal of Advancements in Robotics. 2020; 7(1): 19–24p.
Application of Machine Learning Techniques for Tongue Diagnosis in Ayurveda
The analysis of tongue image is a very crucial approach in order to evaluate human health in Ayurveda medication. As a result of the modification in tongue color might counsel physical or mental disorders. Many tongue color quantification strategies for tongue diagnosis are published by many researchers in Chinese medication. However, reliable tongue color analysis algorithms are limited for Ayurveda medicine. The main objective of this paper is to apply advanced techniques and algorithms of digital image processing and Machine learning to quantify and verify clinical knowledge of tongue color identification by characterizing variations in tongue features. Tongue images are captured from good quality camera with sufficient lighting conditions, and collected about 60 tongue images. Active contour segmentation algorithm based on edge information is applied on input image to segment the tongue area, and then apply clustering technique using K-means. Clustering approach is applied to separate tongue-body and coating area. The result of segmenting tongue body and coating is very good in CIELAB color space. Keywords: K-means clustering, CIELAB, tongue diagnosis, adaptive segmentation, tongue images Cite this Article Sumanth N.S., N. Satish Kumar, Harshvardhan Tiwari, Balaji S., Prabhanjan S., Meenakshi Malhothra, Pallavi C.V. Application of Machine Learning Techniques for Tongue Diagnosis in Ayurveda. Journal of Advancements in Robotics. 2020; 7(1): 8–14p.