Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine 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.

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Obfuscated computer virus detection using machine learning algorithm

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v8i4.1584 ◽

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.

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Offline Pashto Characters Dataset for OCR Systems

Security and Communication Networks ◽

10.1155/2021/3543816 ◽

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.

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

MATEC Web of Conferences ◽

10.1051/matecconf/201925506008 ◽

2019 ◽

Vol 255 ◽

pp. 06008 ◽

Cited By ~ 1

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.

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v25.i1.pp481-487 ◽

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>

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

American Behavioral Scientist ◽

10.1177/0002764219878238 ◽

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.

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Turbine recommender: The selection of wind turbine type using one of a machine learning technique

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2022.02.013 ◽

2022 ◽

Vol 9 (2) ◽

pp. 119-127

Author(s):

Alrige et al. ◽

Keyword(s):

Machine Learning ◽

Saudi Arabia ◽

Wind Turbine ◽

Industrial Development ◽

Nearest Neighbor ◽

Wind Farms ◽

K Nearest Neighbor ◽

Machine Learning Technique ◽

Long Run ◽

Learning Technique

This study aims to utilize the machine learning technique to build a model to recommend the suitable wind turbine type based on some variables, such as air speed and air density, as well as visualize the location of the recommended wind turbine selection on a 3D map. Particularly, we applied the K-nearest neighbor model (KNN) to determine the amount of energy produced by a single wind turbine. We applied it on 10 separate wind farms in Saudi Arabia. The results indicate that the model performs very well in predicting the best wind turbine type with the mean accuracy of 88%, where ten wind stations resulted from the optimized model with the suggested turbine type in each station. Adding more wind attributes and other factors may assist in increasing the model mean accuracy. The project’s findings will assist decision-makers in Saudi Arabia to make informed decisions as to what kind of wind turbine is suitable for a specific location. In the long run, this will help to make wind energy-a sustainable source of energy-one of the main goals of the 2030 vision, specifically under National Industrial Development and Logistics Program.

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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