Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. …”
Get full text

Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach by Huyong Yan, Asad Khan, Ahsan Jamil, Belkendil Abdeldjalil, Taoufik Saidani, Nazih Y. Rebouh

“…On Google Colab with GPU acceleration, the model is trained and verified and then further improved for computational efficiency on a Mac M1 machine. …”
Get full text

MoSeq based 3D behavioral profiling uncovers neuropathic behavior changes in diabetic mouse model by Akm Ashiquzzaman, Eunbin Lee, Brahnu Fentaw Znaub, An Nazmus Sakib, Geehoon Chung, Sang Seong Kim, Young Ro Kim, Hyuk-Sang Kwon, Euiheon Chung

“…This study employed MoSeq-based 3D behavioral profiling combined with unsupervised machine learning to identify subtle yet significant alterations in nicotinamide (NA)- and streptozotocin (STZ)-induced DN mouse models. …”
Get full text

Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data. by Michael H Schwartz, Andrew G Georgiadis

“…This paper introduces Evidence-Based Gait Analysis Interpretation Tools (EB-GAIT), a novel framework leveraging machine learning to support treatment decisions. The core of EB-GAIT consists of two key components: (1) treatment recommendation models, which are models that estimate the probability of specific surgeries based on historical standard-of-practice (SOP), and (2) treatment outcome models, which predict changes in patient characteristics following treatment or natural history. …”
Get full text

Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method by Wen-Sheng Chen, Chu Zhang, Shengyong Chen

“…Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. …”
Get full text

Thermal and carbonation resistance of tunnel concrete: Experimental evaluation and hybrid ANN–GPR modeling under fire–CO₂ exposure by Amirhossein Fatemi, Ahmad Ganjali, Reza Babaei Semiromi, Pejman Aminian

“…Ultrasonic pulse velocity (UPV) measurements showed strong correlation with both strength loss and carbonation depth, supporting UPV as a reliable non-destructive evaluation method. A hybrid machine learning model combining artificial neural networks (ANN) and Gaussian process regression (GPR) was developed to predict residual compressive strength and carbonation depth based on UPV and exposure parameters. …”
Get full text

FastColitisDetector-XAI: An efficient AI model utilizing sparse Autoencoder with explainable AI for ulcerative colitis diagnosis by Sumedh Vithalrao Dhole, Sangeeta R. Chougule

“…Features so extracted are passed to a machine learning classifier for classification for detection of UC presence. …”
Get full text

On signal encryption at MapReduce and collaborative attribute-based access with ECAs for a preprocessed data set with ML in a privacy-preserving health 4.0 by Arnab Mitra, Anabik Pal

“…To support the cost-effectiveness modeling of data security and privacy in Healthcare 4.0 scenarios, the Privacy-Preserving Health 4.0 (PPH 4.0) framework was proposed by integrating Machine Learning (ML) and Elementary Cellular Automata (ECAs). …”
Get full text

Globalized parameter tuning of microwave passives by dimensionality-reduced surrogates and multi-fidelity simulations by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…This study introduces an alternative approach for rapid global optimization of microwave passive components using artificial intelligence (AI) techniques, specifically, machine learning (ML). The core elements of our methodology include reduction of the problem dimensionality using a rapid global sensitivity analysis, multi-fidelity EM simulations, and a two-stage search process. …”
Get full text

DenseNet-Enabled Intelligent Ship Energy Consumption Prediction Method with Navigation Information and Environmental Factors by Gulashan Tang, Rui Luo, Bin Huang, Xiang Li, Hui Ma

Get full text

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model by Xiaoai Dai, Junying Cheng, Shouheng Guo, Chengchen Wang, Ge Qu, Wenxin Liu, Weile Li, Heng Lu, Youlin Wang, Binyang Zeng, Yunjie Peng, Shuneng Liang

“…Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. …”
Get full text

Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

Get full text

A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. …”
Get full text

A multi-level classification model for corrosion defects in oil and gas pipelines using meta-learner ensemble (MLE) techniques by Adamu Abubakar Sani, Mohamed Mubarak Abdul Wahab, Nasir Shafiq, Kamaludden Usman Danyaro, Nasir Khan, Adamu Tafida, Arsalaan Khan Yousafzai

“…It provides vital information for improving pipeline safety and optimizing predictive maintenance practices by providing an in-depth assessment of various machine learning models, especially when real-time monitoring systems are integrated.…”
Get full text

Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…In the second phase, the weight of the factors affecting OAEs was investigated using deep learning (DL) and support vector machine (SVM) algorithms. …”
Get full text

HiViT-IDS: An Efficient Network Intrusion Detection Method Based on Vision Transformer by Hai Zhou, Haojie Zou, Wei Li, Di Li, Yinchun Kuang

“…Nevertheless, IDS relying on traditional Machine Learning (ML) technologies demonstrate limited efficacy in classifying malicious traffic. …”
Get full text

Spatio-temporal dynamics of urbanization and environmental sustainability: A predictive modelling approach to forecasting land use transitions in Vellore, India by Sai Saraswathi Vijayaraghavalu, Kumaraguru Arumugam, Sakshi Dange

“…The integration of Remote Sensing, Geographical Information System, and machine learning provides a scientifically rigorous framework for monitoring, analysing, and forecasting LULC and climate trends with precision. …”
Get full text

A Systematic Review of Model Predictive Control for Robust and Efficient Energy Management in Electric Vehicle Integration and V2G Applications by Camila Minchala-Ávila, Paul Arévalo, Danny Ochoa-Correa

“…Future research should focus on integrating digital modeling, real-time optimization, and machine learning techniques to improve predictive accuracy and operational resilience. …”
Get full text

Geospatial dataset on deforestation and urban sprawl in Dhaka, Bangladesh: A resource for environmental analysisMendeley Data by Md. Fahad Khan, Md. Rakibul Islam, Shanto Kumar Basak, Ahmed Imtiaz, Abhijit Bhowmik, Dip Nandi, Mashiour Rahman, Debajyoti Karmaker

“…Utilizing the annotations and masks enables the training of machine learning models to identify and forecast vegetation changes, aiding environmental monitoring and conservation initiatives. …”
Get full text

Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic by Md. Abdul Kaium, Md. Sharif Ahmed, Muhammad Habib-Ur-Rahman, Md. Saidul Islam, Yeasmin Akter Ratry, Md Mostofa Uddin Helal, Muhammad Ali Fardoush Siddquy, Most. Moslema Haque, Ahsan Raza, Fatma Mansour, Majed Alotaibi, Ayman El Sabagh, Reimund P. Roetter

“…This study aims to assess yield impacts of extreme temperatures and rainfall variability on wheat, and winter and summer season-planted maize in northwestern Bangladesh. Utilizing a machine learning approach, future yield patterns were predicted for these crops under various climate change scenarios. …”
Get full text