Survey on SVM and their application in image classification
MA Chandra, SS Bedi - International Journal of Information Technology, 2021 - Springer
Life of any living being is impossible if it does not have the ability to differentiate between
various things, objects, smell, taste, colors, etc. Human being is a good ability to classify the …
various things, objects, smell, taste, colors, etc. Human being is a good ability to classify the …
A review on image-based approaches for breast cancer detection, segmentation, and classification
Z Rezaei - Expert Systems with Applications, 2021 - Elsevier
The breast cancer as the most life-threatening disease among the woman has emerged in
the worldwide. It is supposed that the early testing and treatment for breast cancer detection …
the worldwide. It is supposed that the early testing and treatment for breast cancer detection …
A framework for breast cancer classification using multi-DCNNs
Background Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep
convolutional neural networks (DCNN) are currently the main tool used for image analysis …
convolutional neural networks (DCNN) are currently the main tool used for image analysis …
Maize leaf disease classification using deep convolutional neural networks
Crop diseases are a major threat to food security. Identifying the diseases rapidly is still a
difficult task in many parts of the world due to the lack of the necessary infrastructure. The …
difficult task in many parts of the world due to the lack of the necessary infrastructure. The …
Multi-classifier information fusion in risk analysis
This paper develops a novel multi-classifier information fusion approach that integrates the
probabilistic support vector machine (SVM) and the improved Dempster-Shafer (DS) …
probabilistic support vector machine (SVM) and the improved Dempster-Shafer (DS) …
Forecasting the mechanical properties of plastic concrete employing experimental data using machine learning algorithms: DT, MLPNN, SVM, and RF
Increased population necessitates an expansion of infrastructure and urbanization, resulting
in growth in the construction industry. A rise in population also results in an increased plastic …
in growth in the construction industry. A rise in population also results in an increased plastic …
[HTML][HTML] A machine learning model for early detection of diabetic foot using thermogram images
A Khandakar, MEH Chowdhury, MBI Reaz… - Computers in biology …, 2021 - Elsevier
Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The
prevention of DFU may be achieved by the identification of patients at risk of DFU and the …
prevention of DFU may be achieved by the identification of patients at risk of DFU and the …
[HTML][HTML] Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning
Z Pei, D Zhang, Y Zhi, T Yang, L Jin, D Fu, X Cheng… - Corrosion science, 2020 - Elsevier
The atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic
corrosion sensor for 34 days. Using a random forest (RF)-based machine learning …
corrosion sensor for 34 days. Using a random forest (RF)-based machine learning …
Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
YJ Wong, Y Shimizu, A Kamiya, L Maneechot… - Environmental …, 2021 - Springer
Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six
parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD) …
parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD) …
Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM
AD Dolatabadi, SEZ Khadem, BM Asl - Computer methods and programs …, 2017 - Elsevier
Abstract Background and objective Currently Coronary Artery Disease (CAD) is one of the
most prevalent diseases, and also can lead to death, disability and economic loss in patients …
most prevalent diseases, and also can lead to death, disability and economic loss in patients …