What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…While confirming that the latest research tendency is deep learning, regression is still a viable option for studies with limited effort in parameter tuning and method selection, especially considering its computational efficiency and simplicity. …”
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Plant Leaf Identification Using Feature Fusion of Wavelet Scattering Network and CNN With PCA Classifier by S. Gowthaman, Abhishek Das

“…Deep learning models, particularly Convolutional Neural Networks (CNNs), are pivotal in enabling botanists to efficiently identify plant species, which is essential for applications in medicine, agriculture, and the food industry. …”
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Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
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Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model by A. Shamila Ebenezer, S. Deepa Kanmani, V. Sheela, K. Ramalakshmi, V. Chandran, M. G. Sumithra, B. Elakkiya, Bharani Murugesan

“…This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. …”
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Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring by Haitao Bian, Xiaohan Luo, Zhichao Zhu, Xiaowei Zang, Yu Tian

“…To address this issue, this study developed a fixed-power fire source simulation device to establish a reliable small-scale experimental platform incorporating various environmental influences for generating anomalous temperature data. We employed deep learning autoencoders (AEs) to integrate spatiotemporal data, aiming to minimize the impact of outdoor conditions on detection performance. …”
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The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. by Quan Duy Vo, Yukihiro Saito, Toshihiro Ida, Kazufumi Nakamura, Shinsuke Yuasa

“…The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. …”
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Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection by Ângela Semitela, Miguel Pereira, António Completo, Nuno Lau, José P. Santos

“…To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. …”
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EEG-RegNet: Regressive Emotion Recognition in Continuous VAD Space Using EEG Signals by Hyo Jin Jon, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, Eun Yi Kim

“…Electroencephalogram (EEG)-based emotion recognition has garnered significant attention in brain–computer interface research and healthcare applications. While deep learning models have been extensively studied, most are designed for classification tasks and struggle to accurately predict continuous emotional scores in regression settings. …”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. …”
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A Lightweight Convolutional Neural Network for Real-time Detection of Aircraft Engine Blade Damage by Wenzhe Wang, Hua Su, Xinliang Liu, Jawad Munir, Jingqiu Wang

“…To address the large number of parameters and the computational complexity of deep learning models in the field of borescope detection, we propose a lightweight blade damage detection model LSSD using a knowledge distillation algorithm. …”
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Polarity-JaM: an image analysis toolbox for cell polarity, junction and morphology quantification by Wolfgang Giese, Jan Philipp Albrecht, Olya Oppenheim, Emir Bora Akmeriç, Julia Kraxner, Deborah Schmidt, Kyle Harrington, Holger Gerhardt

“…Advances in fluorescence microscopy and deep learning algorithms open up a wealth of unprecedented opportunities to characterise various aspects of cell polarity, but also create new challenges for comprehensible and interpretable image data analysis workflows to fully exploit these new opportunities. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Innovative methods such as Model-Based Deep Learning (MBDL) and Self-Attention Generative Adversarial Networks (SAGAN) demonstrate efficient reconstruction capabilities. …”
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The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector by Jonathan Oluwapelumi Mobayo, Ayooluwa Femi Aribisala, Saheed Olanrewaju Yusuf, Usman Belgore

“…However, regarding the awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had high awareness level. …”
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Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network by Hang Thi Phuong Nguyen, Hieyong Jeong

“…This study proposes a non-invasive methodology for detecting nail-fold capillary morphology by integrating an object detection model for improvement within a deep learning framework. We conducted an ablation study to enhance YOLOv8’s performance in detecting nail-fold capillaries and classifying their morphology. …”
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An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty by Nanziba Basnin, Tanjim Mahmud, Raihan Ul Islam, Karl Andersson

“…<b>Methods:</b> To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. …”
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Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm by Abdallah Al-Husban, Mustafa Mahdi Abdulridha, A. A. Hamad Mohamad, Abdelrahman Mohamed Ibrahim

“…The most recent strategy, which was developed using deep learning and can forecast the severity of Parkinson’s disease, is the one described here. …”
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Key frame extraction algorithm for surveillance videos using an evolutionary approach by Manjusha Rajan, Latha Parameswaran

“…Compared to Differential Evolution (DE) techniques and Deep Learning (DL) models from the literature, this recommended algorithm demonstrates superior efficiency, as verified through quantitative and qualitative evaluation metrics. …”
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A Review of CNN Applications in Smart Agriculture Using Multimodal Data by Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari, Josiane Mothe

“…A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. …”
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Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool by Maria Galanty, Dieuwertje Luitse, Sijm H. Noteboom, Philip Croon, Alexander P. Vlaar, Thomas Poell, Clara I. Sanchez, Tobias Blanke, Ivana Išgum

“…Yet biases in these datasets on which deep-learning models are trained can compromise reliability. …”
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From Data to Decisions: The Power of Machine Learning in Business Recommendations by Kapilya Gangadharan, Anoop Purandaran, K. Malathi, Barathi Subramanian, Rathinaraja Jeyaraj, Soon Ki Jung

“…Future research includes exploring advances in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. …”
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