scholarly journals Offline Pashto Characters Dataset for OCR Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Sulaiman Khan ◽  
Habib Ullah Khan ◽  
Shah Nazir
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Faculty Members ◽  
Text Recognition ◽  
Machine Learning Algorithm ◽  
Machine Learning Technique ◽  
Scientific Research Work ◽  
Learning Technique ◽  
Accumulation Phase

In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.

scholarly journals Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3790
Author(s):  
Zachary Choffin ◽  
Nathan Jeong ◽  
Michael Callihan ◽  
Savannah Olmstead ◽  
Edward Sazonov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pressure Sensor ◽  
Learning Algorithm ◽  
Flexible Substrate ◽  
Ankle Injuries ◽  
Sensor System ◽  
Measurement Unit ◽  
K Nearest Neighbor ◽  
Machine Learning Technique ◽  
Learning Technique

Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

scholarly journals Obfuscated computer virus detection using machine learning algorithm

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Tan Hui Xin ◽  
Ismahani Ismail ◽  
Ban Mohammed Khammas
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Virus Detection ◽  
Computer Virus ◽  
Machine Learning Technique ◽  
Memory Space ◽  
Alternative Approach ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
String Feature

Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the traditional signature based detection method and investigated the use of machine learning technique for obfuscated computer virus detection. In this work, text strings are used and have been extracted from virus program codes as the features to generate a suitable classifier model that can correctly classify obfuscated virus files. Text string feature is used as it is informative and potentially only use small amount of memory space. Results show that unknown files can be correctly classified with 99.5% accuracy using SMO classifier model. Thus, it is believed that current computer virus defense can be strengthening through machine learning approach.

scholarly journals A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring

2019 ◽  
Vol 255 ◽  
pp. 06008 ◽  
Author(s):  
Mohd. Dasuki Yusoff ◽  
Ching Sheng Ooi ◽  
Meng Hee Lim ◽  
Mohd. Salman Leong
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Degradation Process ◽  
Gaussian Mixture ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Model Set ◽  
Set Up ◽  
Original Equipment Manufacturers

Industrial practise typically applies pre-set original equipment manufacturers (OEMs) limits to turbomachinery online condition monitoring. However, aforementioned technique which considers sensor readings within range as normal state often get overlooked in the developments of degradation process. Thus, turbomachinery application in dire need of a responsive monitoring analysis in order to avoid machine breakdown before leading to a more disastrous event. A feasible machine learning algorithm consists of k-means and Gaussian Mixture Model (GMM) is proposed to observe the existence of signal trend or anomaly over machine active period. The aim of the unsupervised k-means is to determine the number of clusters, k according to the total trend detected from the processed dataset. Next, the designated k is input into the supervised GMM algorithm to initialize the number of components. Experiment results showed that the k-means-GMM model set up not only capable of statistically define machine state conditions, but also yield a time-dependent clustering image in reflecting degradation severity, as a mean to achieve predictive maintenance.

scholarly journals The general design of the automation for multiple fields using reinforcement learning algorithm

2022 ◽  
Vol 25 (1) ◽  
pp. 481
Author(s):  
Vijaya Kumar Reddy Radha ◽  
Anantha N. Lakshmipathi ◽  
Ravi Kumar Tirandasu ◽  
Paruchuri Ravi Prakash
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Optimization Methods ◽  
Graph Representation ◽  
Machine Learning Technique ◽  
Learning Society ◽  
Learning Technique ◽  
Learning Concept

<p>Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.</p>

scholarly journals Classification of Phishing Email Using Random Forest Machine Learning Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Andronicus A. Akinyelu ◽  
Aderemi O. Adewumi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
False Negative ◽  
Machine Learning Algorithm ◽  
Detection Techniques ◽  
Phishing Attacks ◽  
Learning Technique ◽  
Phishing Detection

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

Machine Cleaning of Online Opinion Spam: Developing a Machine-Learning Algorithm for Detecting Deceptive Comments

2019 ◽  
pp. 000276421987823
Author(s):  
Yu Won Oh ◽  
Chong Hyun Park
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Social Issues ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Opinion Spam ◽  
Automated Machine Learning ◽  
Asian Languages

Humans are not very good at detecting deception. The problem is that there is currently no other particular way to distinguish fake opinions in a comments section than by resorting to poor human judgments. For years, most scholarly and industrial efforts have been directed at detecting fake consumer reviews of products or services. A technique for identifying deceptive opinions on social issues is largely underexplored and undeveloped. Inspired by the need for a reliable deceptive comment detection method, this study aims to develop an automated machine-learning technique capable of determining opinion trustworthiness in a comment section. In the process, we have created the first large-scale ground truth dataset consisting of 866 truthful and 869 deceptive comments on social issues. This is also one of the first attempts to detect comment deception in Asian languages (in Korean, specifically). The proposed machine-learning technique achieves nearly 81% accuracy in detecting untruthful opinions about social issues. This performance is quite consistent across issues and well beyond that of human judges.

Software-Based Testing Kit Using Machine Learning for Diagnosis and Predictive Analytics of COVID-19 Patients

2021 ◽  
Vol 12 (2) ◽  
pp. 39-50
Author(s):  
Vishal Kumar Goar ◽  
Jyoti Prabha
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Prime Factor ◽  
Research Work ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm ◽  
Selection Of

Nowadays, the global community is being affected with COVID-19 disease and integrated infections, which are becoming a menace to the whole world. Research is going on to find out the solution, and still, no particular vaccination or solution has been achieved. This research work is focusing on the analytics of dataset extracted, which has assorted attributes, and these attributes are processed in the machine learning algorithm so that the prime factor can be recognized. In this research manuscript, the usage of COVID-19 dataset is done and trained using supervised learning approach of artificial neural network (ANN) on Levenberg-Marquardt (LM) algorithm so that the predictions of the test patients can be done on the key attributes of age, gender, location, and related parameters. The selection of LM-based implementation with ANN is done as it is the faster approach compared to other functions in neural networks.

scholarly journals Text Recognition Using Silent Speech

2021 ◽  
Vol 9 (VIII) ◽  
pp. 556-559
Author(s):  
Dr. Girish Kumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
English Language ◽  
Learning Algorithm ◽  
Convolution Neural Network ◽  
Text Recognition ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Automatic Information ◽  
Silent Speech

Our objective is to identify the characters from the quite speech of the English language. We tend to focus on the lip region to recognize the characters spoken clearly in the video. Our contribution is: foremost, this model is developed by using a pipeline method form absolutely automatic information assortment from the video. Though this, it generates a data set that is spoken by the individuals. Secondly, it is developed by using the machine learning algorithm Convolution Neural Network (CNN) that learns the lip motion. Thirdly, Convolution network turn out the efficient result by examining the video and also the data set.

scholarly journals A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions

2018 ◽  
Vol 7 (1.9) ◽  
pp. 145
Author(s):  
Bipin Nair B.J ◽  
Lijo Joy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Hot Spot ◽  
Research Work ◽  
Hybrid Approach ◽  
Prediction Algorithm ◽  
Protein Drug ◽  
Machine Learning Algorithm ◽  
Learning Concept

In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.

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