Search Results - "machine learning and deep learning algorithms"

Performance evaluation of DHRR-RIS based HP design using machine learning algorithms

Authors: Girish Kumar N G Sree Ranga Raju M N

Source: Intelligent and Converged Networks, Vol 4, Iss 3, Pp 237-260 (2023)

Subject Terms: dynamic hybrid relay reflecting (dhrr)-ris, machine learning and deep learning algorithms, reconfigurable intelligent surfaces (ris), 0103 physical sciences, channel state estimation, Telecommunication, 0202 electrical engineering, electronic engineering, information engineering, TK5101-6720, 02 engineering and technology, hybrid precoder, 01 natural sciences, 7. Clean energy, multi user multiple input multiple output (mu-mimo)

Access URL: https://doaj.org/article/543f942f32e946da9d2cc9ed99680213

Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa

Authors: Senthil Krishnamurthy Oludamilare Bode Adewuyi Emmanuel Luwaca et al.

Source: Energy Conversion and Management: X, Vol 24, Iss , Pp 100772- (2024)

Subject Terms: Energy resource management, Load and generation forecasting, Artificial intelligence-based predictive analytics, Machine learning and deep learning algorithms, Demand-side management, Engineering (General). Civil engineering (General), TA1-2040

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2590174524002502; https://doaj.org/toc/2590-1745

Access URL: https://doaj.org/article/b446e4022a454226835b1e3545884181

Compendium Juxtapose of Algorithmic Ingress to Evaluate Performance of Brain Signals in Seizure Detection

Authors: L. Mary Gladence, Bharanidharan. G, Syed Jamalullah. R

Source: International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 3 (2024); 2197-2206

Subject Terms: EEG Signal Processing, Seizure Detection, Machine Learning Algorithms, Deep Learning Algorithms, Signal Processing, Preprocessing, Signal Data Analytics

File Description: application/pdf

Access URL: https://www.ijisae.org/index.php/IJISAE/article/view/5689

Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas

Authors: Lakshmi Babu Saheer Ajay Bhasy Mahdi Maktabdar et al.

Source: Frontiers in Big Data, Vol 5 (2022)

Subject Terms: urban air quality, climate change mitigation, urban vegetation detection, regression based prediction algorithms, machine learning and deep learning algorithms, aerial view image recognition, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fdata.2022.822573/full; https://doaj.org/toc/2624-909X

Access URL: https://doaj.org/article/f1371990b3674b088a3566a4dbdd6eb2

Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective

Authors: Serafeim Moustakidis Eirini Christodoulou Elpiniki Papageorgiou et al.

Subject Terms: Osteoarthritis is the most common form of arthritis in the knee that comes with a variation in symptoms' intensity, frequency and pattern. Knee OA (KOA) is often diagnosed using invasive and expensive methods that can measure changes in joint morphology and function. Early and accurate identification of significant risk factors in clinical data is of vital importance in diagnosing KOA. A machine intelligence approach is proposed here to enable automated, non-invasive identification of risk factors from self-reported clinical data about joint symptoms, disability, function and general health. The proposed methodology was applied to recognize participants with symptomatic KOA or being at high risk of developing KOA in at least one knee. Different machine learning and deep learning algorithms were tested and compared in terms of multiple criteria e.g. accuracy, per class accuracy and execution time. Deep learning was proved to be the most effective in terms of accuracy with classification accuracies up to 86.95%, evaluated on data from the osteoarthritis initiative study. Insights about ten different feature subsets and their effect on classification accuracy are provided. The proposed methodology was also demonstrated in subgroups defined by gender and age. The results suggest that machine intelligence and especially deep learning may facilitate clinical evaluation, monitoring and even prediction of knee osteoarthritis. Apart from the classical implementation of the proposed methodology, a quantum perspective is also discussed highlighting the future application of quantum computers in OA diagnosis

Relation: https://zenodo.org/communities/eu/; https://zenodo.org/records/3613568; oai:zenodo.org:3613568

Availability: https://doi.org/10.1007/s42484-019-00008-3
https://zenodo.org/records/3613568

China’s annual forest age dataset at a 30-m spatial resolution from 1986 to 2022

Authors: Rong Shang Xudong Lin Jingming Chen et al.

Subject Terms: Forest ecosystems, Global change biology, Photogrammetry and remote sensing, forest age composition, Dynamic Mapping, Change detection (CD), forest disturbance data, machine learning and deep learning algorithms, Landsat data, GEDI

Availability: https://doi.org/10.6084/m9.figshare.24464170.v3
https://figshare.com/articles/dataset/China_s_annual_forest_age_dataset_at_30-m_spatial_resolution_from_1986_to_2022/24464170

CTDN (Convolutional Temporal Based Deep- Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction.

Authors: Singh DP Kaushik B

Source: Computational biology and chemistry [Comput Biol Chem] 2023 Aug; Vol. 105, pp. 107868. Date of Electronic Publication: 2023 Apr 07.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: England NLM ID: 101157394 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-928X (Electronic) Linking ISSN: 14769271 NLM ISO Abbreviation: Comput Biol Chem Subsets: MEDLINE

MeSH Terms: Antineoplastic Agents*/pharmacology , Neoplasms*/drug therapy, Humans ; Neural Networks, Computer ; Algorithms ; Machine Learning

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques.

Authors: Singh DP Kaushik B

Source: Chemical biology & drug design [Chem Biol Drug Des] 2023 Jan; Vol. 101 (1), pp. 175-194. Date of Electronic Publication: 2022 Nov 10.

Publication Type: Systematic Review; Journal Article

Journal Info: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101262549 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1747-0285 (Electronic) Linking ISSN: 17470277 NLM ISO Abbreviation: Chem Biol Drug Des Subsets: MEDLINE

MeSH Terms: Deep Learning*, Humans ; Machine Learning ; Algorithms ; Drug Discovery