Review on anomalous gait behavior detection using machine learning algorithms

Razak, Hana’ Abd; Tahir, Nooritawati Md

doi:10.11591/eei.v9i5.2255

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…The legal system's advancement via the usage of the machine learning algorithm is crucial in reducing the workload of legal professions and saves the time to settle pending cases during the Covid-19 pandemic [21], [31]- [33]. Therefore, this study aimed to investigate the existing machine learning method developed to predict judicial decisions.…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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<span>Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.</span>

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Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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“…Deep learning algorithms have proven effective in a variety of computer vision tasks, such as object classification [9], [17], object detection [12], [18], and action recognition [19], [20], including anomaly detection in video surveillance. As already introduced in the previous section, the approaches that have been proposed to tackle this challenge can be grouped into four categories: reconstruction error, future frame prediction, classifiers, and scoring.…”

Section: Deep Learning-based Methods For Anomaly Detectionmentioning

confidence: 99%

Deep learning-based methods for anomaly detection in video surveillance: a review

et al. 2023

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Detecting anomalous events in videos is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various approaches have been proposed to address the anomaly detection problems. These approaches vary from hand-crafted to deep learning. Many researchers have gone into determining the best approach for effectively detecting anomalies in video streams while maintaining a low false alarm rate. The results proved that approaches based on deep learning offer very interesting results in this field. In this paper, we review a family of video anomaly detection approaches based on deep learning techniques, which are compared in terms of their algorithms and models. Moreover, we have grouped state-of-the-art methods into different categories based on the approach adopted to differentiate between normal and abnormal events, and the underlying assumptions. Furthermore, we also present publicly available datasets and evaluation metrics used in existing works. Finally, we provide a comparison and discussion on the results of various approaches according to different datasets. This paper can be a good starting point for such researchers to understand this field and review existing work related to this topic.

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“…Note that transfer learning is indeed valuable for handling insufficient data for a new domain in the neural network, and there is a big pre-existing data pool that can be transferred to the problem to be solved. The benefit of this approach is that much less data is needed, which significantly reduces the computational time (Razak et al, 2020a;Razak et al, 2020b;Wahdan et al, 2020;Yang et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Handwriting Classification using Deep Transfer Learning Techniques

Almisreb¹,

Tahir²,

Turaev³

et al. 2022

JST

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Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.

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Review on anomalous gait behavior detection using machine learning algorithms

Cited by 4 publications

References 39 publications

A systematic literature review of machine learning methods in predicting court decisions

A systematic literature review of machine learning methods in predicting court decisions

Deep learning-based methods for anomaly detection in video surveillance: a review

Arabic Handwriting Classification using Deep Transfer Learning Techniques

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