RECOGNITION OF HAND-PRINTED LATIN CHARACTERS BASED ON GENERALIZED HOUGH TRANSFORM AND DECISION TREE LEARNING TECHNIQUES

2000 ◽  
Vol 14 (03) ◽  
pp. 369-387 ◽  
Author(s):  
ADNAN AMIN
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Hough Transform ◽  
Large Degree ◽  
New Technique ◽  
Decision Tree Learning ◽  
Generalized Hough Transform ◽  
Learning Techniques ◽  
Base Form ◽  
A New Technique

This paper presents a new technique for the recognition of hand-printed Latin characters using machine learning. Conventional methods have relied on manually constructed dictionaries which are not only tedious to construct but also difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over a large degree of variation between writing styles, and recognition rules can be constructed by example. Characters are scanned into the computer and preprocessing techniques transform the bit-map representation of the characters into a set of primitives which can be represented in an attribute base form. A set of such representations for each character is then input to C4.5 which produces a decision tree for classifying each character.

scholarly journals Implementation of machine learning techniques to control the speed of the vehicles

2018 ◽  
Vol 7 (4.5) ◽  
pp. 654
Author(s):  
M. S. Satyanarayana ◽  
Aruna T.M ◽  
Divyaraj G.N
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
New Technique ◽  
Vehicle Speed ◽  
Learning Techniques ◽  
Traffic Awareness ◽  
Alarm Sound ◽  
The Government ◽  
A New Technique

Accidents have become major issue in Developing countries like India now a day. As per the Surveys 60% of the accidents are happening due to over speed. Though the government has taken so many initiatives like Traffic Awareness & Driving Awareness Week etc.., but still the percentage of accidents are not getting reduced. In this paper a new technique has been introduced to reduce the percentage of accidents. The new technique is implemented using the concept of Machine Learning [1]. The Machine Learning based systems can be implemented in all vehicles to avoid the accidents at low cost [1]. The main objective of this system is to calculate the speed of the vehicle at three various locations based on the place where the vehicle speed must be controlled and if the speed is greater than the designated speed in that road then the vehicle automatically detects the problem and same will be intimated to the driver to control the speed of the vehicle. If the speed is less or equal to the designated speed in that road then the vehicle will be passed without any disturbance. The system will be giving beep sound along with color indication to driver in each and every scenario. The other option implemented in this system is if the driver is driving the vehicle in the night and if he feel drowsy the system detects it immediately and alarm sound will be initiated to wake up the driver. This system though it won’t avoid 100% accidents at least it will reduce the percentage of accidents. This system is not only to avoid accidents it will also intelligently control the speed of the vehicles and creates awareness amongst the drivers.  

scholarly journals Design and Implementation of Medical QA System using Machine Learning Techniques

2021 ◽  
pp. 129-134
Author(s):  
Nisha Patil ◽  
Dr. Kuldeep Sambrekar
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
The State ◽  
Machine Learning Techniques ◽  
New Technique ◽  
Basic Information ◽  
Design And Implementation ◽  
Learning Techniques ◽  
Left And Right ◽  
A New Technique

Nowadays, people rely on Traditional books or Google for every answer to their questions on a day-to-day basis, from basic information to medical queries. Till now, people are facing problems and are unable to find the accurate answer to their questions or fetch relevant results. Also, this technique is time-consuming as people have to go through many books to obtain one relevant answer or search various websites, which is a tedious task and not an efficient way in today's world where time is the top priority, yet the majority of people follow these techniques. So, to overcome this technique and solve the current problems, we have implemented a new technique in this paper. The BERT model, pre-trains deep bidirectional representations from the unlabeled text which conditions on both left and right, as a result, provides accurate answers to the user’s query when compared to the state-of-the-art model. This same model can be further implemented in other domains to obtain accurate results.

scholarly journals Toward Palmprint Recognition Methodology Based Machine Learning Techniques

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
M. M. Ata ◽  
K. M. Elgamily ◽  
M. A. Mohamed
Keyword(s):  
Machine Learning ◽  
Hough Transform ◽  
Recognition Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Palmprint Recognition ◽  
Feature Vectors ◽  
Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

Optimising pin-in-paste technology using gradient boosted decision trees

2018 ◽  
Vol 30 (3) ◽  
pp. 164-170 ◽  
Author(s):  
Péter Martinek ◽  
Oliver Krammer
Keyword(s):  
Decision Tree ◽  
Prediction Method ◽  
Percentage Error ◽  
Solder Paste ◽  
Hole Filling ◽  
Decision Tree Learning ◽  
Stencil Printing ◽  
Content Type ◽  
Learning Techniques ◽  
Through Hole

Purpose This paper aims to present a robust prediction method for estimating the quality of electronic products assembled with pin-in-paste soldering technology. A specific board quality factor was also defined which describes the expected yield of the board assembly. Design/methodology/approach Experiments were performed to obtain the required input data for developing a prediction method based on decision tree learning techniques. A Type 4 lead-free solder paste (particle size 20–38 µm) was deposited by stencil printing with different printing speeds (from 20 mm/s to 70 mm/s) into the through-holes (0.8 mm, 1 mm, 1.1 mm, 1.4 mm) of an FR4 board. Hole-filling was investigated with X-ray analyses. Three test cases were evaluated. Findings The optimal parameters of the algorithm were determined as: subsample is 0.5, learning rate is 0.001, maximum tree depth is 6 and boosting iteration is 10,000. The mean absolute error, root mean square error and mean absolute percentage error resulted in 0.024, 0.03 and 3.5, respectively, on average for the prediction of the hole-filling value, based on the printing speed and hole-diameter after optimisation. Our method is able to predict the hole-filling in pin-in-paste technology for different through-hole diameters. Originality/value No research works are available in current literature regarding machine learning techniques for pin-in-paste technology. Therefore, we decided to develop a method using decision tree learning techniques for supporting the design of the stencil printing process for through-hole components and pin-in-paste technology. The first pass yield of the assembly can be enhanced, and the reflow soldering failures of pin-in-paste technology can be significantly reduced.

Improved argumentative paragraphs detection in academic theses supported with unit segmentation

2021 ◽  
pp. 1-11
Author(s):  
Jesús Miguel García-Gorrostieta ◽  
Aurelio López-López ◽  
Samuel González-López ◽  
Adrián Pastor López-Monroy
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Automatic Detection ◽  
Machine Learning Techniques ◽  
Svm Classifier ◽  
Complex Task ◽  
Decision Tree Classifier ◽  
Learning Techniques ◽  
Tree Classifier ◽  
Academic Author

Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches.

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2012 ◽  
pp. 155-165
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

Machine Learning Techniques Applied to Profile Mobile Banking Users in India

2013 ◽  
Vol 5 (1) ◽  
pp. 82-92 ◽  
Author(s):  
M. Carr ◽  
V. Ravi ◽  
G. Sridharan Reddy ◽  
D. Veranna
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Decision Trees ◽  
Multilayer Perceptron ◽  
Machine Learning Techniques ◽  
Mobile Banking ◽  
Classification Rules ◽  
Learning Techniques ◽  
Potential Customers

This paper profiles mobile banking users using machine learning techniques viz. Decision Tree, Logistic Regression, Multilayer Perceptron, and SVM to test a research model with fourteen independent variables and a dependent variable (adoption). A survey was conducted and the results were analysed using these techniques. Using Decision Trees the profile of the mobile banking adopter’s profile was identified. Comparing different machine learning techniques it was found that Decision Trees outperformed the Logistic Regression and Multilayer Perceptron and SVM. Out of all the techniques, Decision Tree is recommended for profiling studies because apart from obtaining high accurate results, it also yields ‘if–then’ classification rules. The classification rules provided here can be used to target potential customers to adopt mobile banking by offering them appropriate incentives.

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