Machine Learning: A Review of the Algorithms and Its Applications

Dhall, Devanshi; Kaur, Ravinder; Juneja, Mamta

doi:10.1007/978-3-030-29407-6_5

Cited by 120 publications

(91 citation statements)

References 11 publications

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“…SL jointly employs pre-labeled data, e.g., MCI versus healthy subjects, and additional features derived from clinical or neuroimaging sources to determine which feature predicts the pre-labeled data the most (Dwyer et al, 2018;Graham et al, 2020). SL operates with probabilistic and non-probabilistic classifiers (Naïve Bayes and Support Vector Machine, respectively), as well as with decision tree, linear, and logistic regression (Dhall and Kaur, 2020). UL techniques, instead, sets unlabeled and unstructured data, e.g., clinical notes, as a starting point to seek relationships or patterns and to learn general representations that enable the automatic extraction of information when building predictors (Miotto et al, 2017;Dwyer et al, 2018;Graham et al, 2020).…”

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

“…UL techniques, instead, sets unlabeled and unstructured data, e.g., clinical notes, as a starting point to seek relationships or patterns and to learn general representations that enable the automatic extraction of information when building predictors (Miotto et al, 2017;Dwyer et al, 2018;Graham et al, 2020). The algorithms employed by UL include K-means clustering, PCA, and Artificial Neural Networks (ANN) (Dhall and Kaur, 2020).…”

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

“…The ability to process raw data, the need for manual engineering of features, and the extensive expertise needed to perform the analyses represent the main limitations of conventional (shallow) ML techniques ( Lecun et al, 2015 ; Vieira et al, 2017 ; Zhang et al, 2020 ). This has led to the dissemination of deep learning (DL) algorithms, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) ( Dhall and Kaur, 2020 ). DL outperforms ML in many ways, showing best-in-class performance and increased complexity in the computed function and addressing problems in multiple domains such as language and speech ( Zhang et al, 2020 ).…”

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Cavedoni

Chirico

Pedroli

et al. 2020

Front. Hum. Neurosci.

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Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative's behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures-underlying a categorical model of diagnosis-could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients' daily instrumental activities (IADL) usually take place. Clinicians can record patients' kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people's neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.

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Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Cavedoni

Chirico

Pedroli

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…This section provides a non-comprehensive introduction on the topics of artificial intelligence, machine learning and deep learning, whereas a theoretically more substantial and elaborated description of AI and its sub-classes can be found in (LeCun et al 2015;Binkhonain and Zhao 2019;Dhall et al 2020;Goodfellow et al 2016;Frochte 2019;Wolfgang 2017;Rebala et al 2019;Chowdhary 2020). Furthermore, (Goulet 2020) gives in particular a textbook-like introduction to AI topics with a focus on civil engineering.…”

Section: Basics On Ai Machine Learning and Deep Learningmentioning

confidence: 99%

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

Kraus

Drass

2020

Glass Struct Eng

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’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.

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“…71 ML can be considered a type of AI for which the task is designed to process data without computers being explicitly programmed; in this context, we consider ML a method of data analysis, or algorithm. 72,73 There are three types of "learning" generally utilized in ML methods: supervised, unsupervised, and reinforcement; the two methods relevant to articular cartilage pathology classification are supervised and unsupervised learning. Supervised learning differs from unsupervised methods as the input and outputs, or independent and outcome variables, for the analysis are specified or defined.…”

Section: A Way Forwardmentioning

confidence: 99%

Classification, Categorization, and Algorithms for Articular Cartilage Defects

2020

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There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside–bench–bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.

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Machine Learning: A Review of the Algorithms and Its Applications

Cited by 120 publications

References 11 publications

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

Classification, Categorization, and Algorithms for Articular Cartilage Defects

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