Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review by Liu Haixian, Liu Haixian, Pang Shu, Pang Shu, Li Zhao, Li Zhao, Lu Chunfeng, Lu Chunfeng, Li Lun

“…BackgroundWith the rapid advances in artificial intelligence—particularly convolutional neural networks—researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCLC) non-invasively, rapidly and repeatably. …”
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The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review by Radwan Qasrawi, Ghada Issa, Suliman Thwib, Razan AbuGhoush, Malak Amro, Raghad Ayyad, Stephanny Vicuna, Eman Badran, Yousef Khader, Raeda Al Qutob, Faris Al Bakri, Hana Trigui, Elie Sokhn, Emmanuel Musa, Jude Dzevela Kong

“…The findings reveal a predominance of deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieving mean accuracy rates of 96.3 % in pathogen detection from medical imaging. …”
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Fingernail analysis management system using microscopy sensor and blockchain technology by Shih Hsiung Lee, Chu Sing Yang

“…In this work, microscope sensor was used to capture the clear image and segment the lunula and nail plate effectively through image preprocessing. …”
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Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis by Jianhua Sun, Ye Cao, Ying Zhou, Baoqiao Qi

“…This study focuses on the development and evaluation of deep learning models for the classification of knee joint injuries using Magnetic Resonance Imaging (MRI) data. The research aims to provide an efficient and reliable tool for clinicians to aid in the diagnosis of knee joint disorders, particularly focusing on Anterior Cruciate Ligament (ACL) tears.MethodsKneeXNet leverages the power of graph convolutional networks (GCNs) to capture the intricate spatial dependencies and hierarchical features in knee MRI scans. …”
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FLPneXAINet: Federated deep learning and explainable AI for improved pneumonia prediction utilizing GAN-augmented chest X-ray data. by Shuvo Biswas, Rafid Mostafiz, Mohammad Shorif Uddin, Muhammad Shahin Uddin

“…We utilized a benchmark dataset from Kaggle, comprising 8,402 CXR images (3,904 normal and 4,498 pneumonia). The dataset was preprocessed and augmented using a cycle-consistent generative adversarial (CycleGAN) network to increase the volume of training data. …”
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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

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High-Quality Road Detection Using U-Net-Based Semantic Segmentation with High-Resolution Orthophotos and DSM Data in Urban Environments by M. Fawzy, M. Fawzy, A. Juhász, A. Barsi

“…Preliminary results indicate a significant improvement in road detection accuracy when integrating DSM data, highlighting the synergistic value of multi-source data in geospatial analysis. …”
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Leveraging airport CCTV footage through video understanding techniques for visibility prediction by Zeonlung Pun, Xinyu Tian, Shan Gao

“…However, existing visibility prediction methods predominantly rely on single-image analysis, using either traditional image processing techniques or deep learning models, which often fail to fully capture the dynamic and temporal characteristics inherent in video data. …”
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Clouds Detection in Polar Icy Terrains: A Deformable Attention-Based Deep Neural Network for Multispectral Polar Scene Parsing by Shaojin Dong, Cailan Gong, Yong Hu, Long Cheng, Yang Wang, Fuqiang Zheng

“…Cloud detection is crucial for remote sensing image analysis. Presently, deep learning methods are the preferred choice for cloud detection. …”
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ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing by Lei Zhang, Ruoyang Zhang, Yu Wu, Yadong Wang, Yanfeng Zhang, Lijuan Zheng, Chongbin Xu, Xin Zuo, Zeyu Wang

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Robust scale fusion and edge-aware feature attention network for remote sensing UAV road detection under harsh weather by Jialang Liu, Jialei Zhan, Jiehua Zhang, Jiangming Chen, Yan Song, Lixing Tang, Le Zhou, Chengsi Du, Yingmei Wei, Yanming Guo

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How deep learning identifies and learns aspects of plant for classification by Devi V. Sowmya, Pooja Govu, Ramya Sande, Priya Nakka Vishnu

“…To extract optimal features which are missed by the traditional methods to overcome this we took high quality snapshots with advanced image processing methods. Convolutional Neural Network (CNN) of supervised learning algorithm used along with the Keras and Tensorflow the features were analysed and deployed in Streamlit. …”
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Business network accessibility: Innovation capability as a connecting factor between Customer Relationship Management and market orientation on SMEs’ performance by Muhammad Fahmi, Muhammad Andi Prayogi, Muhammad Taufik Lesmana, Maharani Citra Kencana, Syahnita

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FOLU-Net: A novel framework using long short-term memory networks to predict future forestry and other land use by Sanchali Banerjee, Paige T. Williams, Randolph H. Wynne

“…This model had an R2 coefficient of 83.0%. (2) A multi-layer neural network using Keras was trained on the classified points from each image with 5-fold cross-validation, achieving an accuracy of 73.0%. (3) The classification model from step 2 was then applied to the reflectance data generated from the LSTM (step 1) to predict the future land type at each point. …”
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An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…Discussion of Results & Conclusions  In this study, a neural network model was developed based on palaeogeography and evolutionary analysis of brachiopods in North America, and a back-propagation neural network model was created to estimate the location of the Rhynchotrema and Hiscobeccus specimens, based on a set of nine morphometric data (Nouri-Taleghani et al. 2015; Abdizadeh et al. 2017; Farzi et al. 2017) (Fig. 4). …”
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A Non-Invasive and Highly Accurate Multi-Wavelength Light Near-Infrared Glucose Sensor Using A Multilevel Metric Learning–Back Propagation Network by Yuwei Chen, Chenxi Li, Bo Gao, Huangrong Xu, Weixing Yu

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Multimodal integration of homotopic connectivity and transcriptomic signatures in major depressive disorder with sleep disorder comorbidity by Mingyu Li, Yudan Ding, Yangpan Ou, Huabing Li, Feng Liu, Dongsheng Lv, Ping Li, Bing Lang, Wenbin Guo

“…Genetic neuroimaging correlation analysis integrated transcriptomic data from the Allen Human Brain Atlas to identify genes and pathways associated with VMHC alterations. …”
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An Efficient Distributed Forensic System Based on Hadoop:Principle and Method by Songyang Wu, Xizhe Zhang, Xupeng Wang, Xiangxue Li

“…With the development and popularization of information technology and intelligence device, the diversity of different device making forensic analysis of existing equipment cannot meet today's networking and storage technology requirements, and exhibit complex operation, low efficiency, on high speed disk image storage and massive data correlation. …”
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Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution by Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Anitha Bhat Talagini Ashoka, Mayura Gurjar Cheepinahalli Vasudeva, Shudarsan Saravanan, Venkatesh Thirugnana Sambandham, Pavan Tummala, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger

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Comparative Analysis of Machine Learning Algorithms for Antenna Alignments by Mohammad Al Bataineh, Dana I. Abu Abdoun, Mahmoud Al Ahmad

“…This is the first work to propose a data-driven antenna alignment method based solely on S-parameter analysis, offering a cost-effective and scalable alternative to complex imaging or optical systems. …”
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