Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

Brain Sciences ◽

10.3390/brainsci11070885 ◽

2021 ◽

Vol 11 (7) ◽

pp. 885

Author(s):

Maher Abujelala ◽

Rohith Karthikeyan ◽

Oshin Tyagi ◽

Jing Du ◽

Ranjana K. Mehta

Keyword(s):

Machine Learning ◽

Environmental Conditions ◽

Brain Activity ◽

Memory Task ◽

Classification Problem ◽

Brain Regions ◽

Training Data ◽

Information Encoding ◽

Machine Learning Approach ◽

Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

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Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10070436 ◽

2021 ◽

Vol 10 (7) ◽

pp. 436

Author(s):

Amerah Alghanim ◽

Musfira Jilani ◽

Michela Bertolotto ◽

Gavin McArdle

Keyword(s):

Machine Learning ◽

Spatial Data ◽

Classification Accuracy ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Data Set ◽

Semantic Inference ◽

Road Type ◽

The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

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Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

Applied Sciences ◽

10.3390/app11136237 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6237

Author(s):

Azharul Islam ◽

KyungHi Chang

Keyword(s):

Machine Learning ◽

Decision Making ◽

Random Forest ◽

Support System ◽

Classification Accuracy ◽

Short Term Memory ◽

Learning Algorithm ◽

Unstructured Data ◽

Stochastic Gradient Descent ◽

Decision Making Support

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

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The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽

10.15282/mekatronika.v2i2.6683 ◽

2020 ◽

Vol 2 (2) ◽

pp. 1-12

Author(s):

Muhammad Nur Aiman Shapiee ◽

Muhammad Ar Rahim Ibrahim ◽

Muhammad Amirul Abdullah ◽

Rabiu Muazu Musa ◽

Noor Azuan Abu Osman ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Nearest Neighbor ◽

Olympic Games ◽

Learning Approach ◽

K Nearest Neighbor ◽

Test Dataset ◽

Machine Learning Approach ◽

Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

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Machine learning for psychiatric patient triaging: an investigation of cascading classifiers

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy109 ◽

2018 ◽

Vol 25 (11) ◽

pp. 1481-1487 ◽

Cited By ~ 1

Author(s):

Vivek Kumar Singh ◽

Utkarsh Shrivastava ◽

Lina Bouayad ◽

Balaji Padmanabhan ◽

Anna Ialynytchev ◽

...

Keyword(s):

Machine Learning ◽

Psychiatric Patient ◽

Classification Accuracy ◽

Psychiatric Patients ◽

Patient Records ◽

Classification Technique ◽

Machine Learning Approach ◽

The One ◽

Unique Dataset

Abstract Objective Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability. Materials and Methods The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by “I2B2 challenge,” a recent competition in the medical informatics community. Results The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes—the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy. Discussion The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition. Conclusion The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system’s accuracy.

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Enhanced Backpropagation Approach for Identifying Genetic Disease

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.622.75 ◽

2014 ◽

Vol 622 ◽

pp. 75-80

Author(s):

Baskar Nisha ◽

B. Madasamy ◽

J.Jebamalar Tamilselvi

Keyword(s):

Machine Learning ◽

Genetic Disease ◽

Classification Accuracy ◽

Gene Selection ◽

New Classification ◽

Backpropagation Algorithm ◽

High Classification Accuracy ◽

Disease Associations ◽

Disease Analysis

Classification of data on genetic disease is a useful application in microarray analysis. The genetic disease data analysis has the potential for discovering the diseased genes which may be the signature of certain diseases. Machine learning methodologies and data mining techniques are used to predict genetic disease associations of bio informatics data. Among numerous existing methods for gene selection, Backpropagation algorithm has become one of the leading methods and it gives less classification accuracy. It aims to develop a new classification algorithm (Enhanced Backpropagation Algorithm) for genetic disease analysis. Knowledge derived by the Enhanced Backpropagation Algorithm has high classification accuracy with the ability to identify the most significant genes.

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Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9058.109119 ◽

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.

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Use of machine learning to identify autism with natural social gaze behavior (Preprint)

10.2196/preprints.29328 ◽

2021 ◽

Author(s):

Zhong Zhao ◽

Haiming Tang ◽

Xiaobin Zhang ◽

Xingda Qu ◽

Jianping Lu

Keyword(s):

Machine Learning ◽

Eye Tracking ◽

Classification Accuracy ◽

Autism Spectrum ◽

Support Vector ◽

Gaze Behavior ◽

Linear Discriminant ◽

Face To Face ◽

Area Of Interest ◽

Social Gaze

BACKGROUND Abnormal gaze behavior is a prominent feature of the autism spectrum disorder (ASD). Previous eye tracking studies had participants watch images (i.e., picture, video and webpage), and the application of machine learning (ML) on these data showed promising results in identify ASD individuals. Given the fact that gaze behavior differs in face-to-face interaction from image viewing tasks, no study has investigated whether natural social gaze behavior could accurately identify ASD. OBJECTIVE The objective of this study was to examine whether and what area of interest (AOI)-based features extracted from the natural social gaze behavior could identify ASD. METHODS Both children with ASD and typical development (TD) were eye-tracked when they were engaged in a face-to-face conversation with an interviewer. Four ML classifiers (support vector machine, SVM; linear discriminant analysis, LDA; decision tree, DT; and random forest, RF) were used to determine the maximum classification accuracy and the corresponding features. RESULTS A maximum classification accuracy of 84.62% were achieved with three classifiers (LDA, DT and RF). Results showed that the mouth, but not the eyes AOI, was a powerful feature in detecting ASD. CONCLUSIONS Natural gaze behavior could be leveraged to identify ASD, suggesting that ASD might be objectively screened with eye tracking technology in everyday social interaction. In addition, the comparison between our and previous findings suggests that eye tracking features that could identify ASD might be culture dependent and context sensitive.

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RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

10.36106/paripex/9308196 ◽

2020 ◽

pp. 1-2

Author(s):

Zhang- sensen

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Brain Network ◽

Classification Model ◽

Comparative Experiment ◽

Machine Learning Methods ◽

Healthy Elderly ◽

Healthy Elderly People ◽

The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

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