A Systematic Review on Application of Multimodal Learning and Explainable AI in Tuberculosis Detection by Barbara Nansamba, Joyce Nakatumba-Nabende, Andrew Katumba, David Patrick Kateete

“…Traditionally, the application of machine learning and deep learning in detecting TB has focused more on using single modes of data. …”
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AI powered detection and assessment of onychomycosis: A spotlight on yellow and deep learning by C. Agostini, R. Ranjan, M. Molnarova, A. Hadzic, O. Kubesch, V. Schnidar, H. Schnidar

“…Considering a total of 1687 images from 440 subjects, the research explores various degrees of onychomycosis and evaluates the infection extent in the toenails detected. We developed an advanced machine learning algorithm for precise segmentation and classification of onychomycosis‐affected toenails, utilizing expert annotations and advanced post‐processing techniques. …”
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Automatic melanoma detection using an optimized five-stream convolutional neural network by Vida Esmaeili, Mahmood Mohassel Feghhi, Hadi Seyedarabi

“…To address these issues in this paper, we have done pre-processing such as hair removal, balancing the skin lesion images using a generative adversarial network (GAN)-based method, denoising using a CNN-based method, and image enhancement. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.…”
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A programmable magnetic digital microfluidic platform integrated with electrochemical detection system by Yong Zhao, Shuyue Jiang, Gaozhe Cai, Lihua Wang, Jianlong Zhao, Shilun Feng

“…In this study, a programmable magnetic digital microfluidic (PMDMF) platform integrated with electrochemical detection system was proposed. It enables non-contact, flexible droplet manipulation without complex processes and high voltages, meeting the requirements of automated electrochemical detection. …”
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Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction by Ashwini Sawant, Sujata Kulkarni, Milind Sawant

“…Ultrasound pictures can be processed using methods including denoising, enhancement, segmentation, and feature extraction to get around these restrictions and boost their quality. …”
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Research and application of vibration loop triggering system for channel wave seismic detection by Hengcong WEN

“…The vibration loop triggering system includes multiple improved processes such as the vibration circuit triggering detection network, multi-sensitivity vibration switch, and quasi-synchronous triggering method. …”
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Machine learning applications for early detection of esophageal cancer: a systematic review by Farhang Hosseini, Farkhondeh Asadi, Hassan Emami, Rayan Ebnali Harari

“…Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. …”
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Object detection with afordable robustness for UAV aerial imagery: model and providing method by Viacheslav Moskalenko, Artem Korobov, Yuriy Moskalenko

“…Neural network object detectors are increasingly being used for aerial video analysis, with a growing demand for onboard processing on UAVs and other limited resources. However, the vulnerability of neural networks to adversarial noise, out-of-distribution data, and fault injections reduces the functionality and reliability of these solutions. …”
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CIDNet: A Maritime Ship Detection Model Based on ISAR Remote Sensing by Fei Liu, Boyang Liu, Hang Zhou, Song Han, Kunlin Zou, Wenjie Lv, Chang Liu

“…In addition, CIDNet achieves a detection frame rate of 63, demonstrating its fine real-time processing capabilities.…”
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RGB and RGNIR image dataset for machine learning in plastic waste detectionZENODO by Owen Tamin, Ervin Gubin Moung, Jamal Ahmad Dargham, Samsul Ariffin Abdul Karim, Ashraf Osman Ibrahim, Nada Adam, Hadia Abdelgader Osman

“…Both datasets underwent further pre-processing to ensure proper labelling and annotations to prepare them for training machine learning models. …”
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Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…COVID-19, the disease induced by the virus, causes severe respiratory complications, necessitating advanced diagnostic tools for early detection. Recent research indicates the potential of radiographic imaging in unravelling critical insights into the characteristics of this formidable pathogen. …”
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Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction by Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu, Long Qi

“…Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a black-box nature in the field, so this paper proposes a soil porosity detection method based on ultrasound and multi-scale CNN-LSTM. …”
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Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images by Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla

“…In this study, we present a deep learning-based technique for breast cancer lesion detection, namely blob detection, which automatically detects hidden and inaccessible cancerous lesions in unsupervised human breast histology images. …”
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A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection by Shijie Zhang, Xu Yang, Chao Geng, Xinyang Li

“…In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. …”
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GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults by Proma Dutta, Kanchon Kanti Podder, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury, Amith Khandakar, Nasser Al-Emadi, Moajjem Hossain Chowdhury, M. Murugappan, Mohamed Arselene Ayari, Sakib Mahmud, S. M. Muyeen

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HybridFusionNet: Deep Learning for Multi-Stage Diabetic Retinopathy Detection by Amar Shukla, Shamik Tiwari, Anurag Jain

“…Diabetic retinopathy (DR) is one of the most common causes of visual impairment worldwide and requires reliable automated detection methods. Numerous research efforts have developed various conventional methods for early detection of DR. …”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. …”
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An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection by Adam Kadi, Aymene Selamnia, Zakaria Abou El Houda, Hajar Moudoud, Bouziane Brik, Lyes Khoukhi

“…In today’s rapidly evolving cyber landscape, the growing sophistication of attacks, including the rise of zero-day exploits, poses critical challenges for network intrusion detection. Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. …”
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Impact of Viral Co-Detection on the Within-Host Viral Diversity of Influenza Patients by Su Myat Han, Yoshiano Kubo, Alexis Robert, Marc Baguelin, Koya Ariyoshi

“…The most frequent viral co-detection was IAV–RV, identified in 48 out of the 96 co-detection cases. …”
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