KERATOCONUS DETECTION USING DEEP LEARNING by Younis Abbosh, Shatha Ali, Dia Ali, Iman Jasim

“…The eye is considered as one of the most complicated organs of the human body, with various ailments that can impact it. …”
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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images by Vatsala Anand, Prabhnoor Bachhal, Deepika Koundal, Arvind Dhaka

“…The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. …”
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DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

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Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms by Julius Griškevičius, Kristina Daunoravičienė, Liudvikas Petrauskas, Andrius Apšega, Vidmantas Alekna

“…The raw signals from accelerometers and gyroscopes were converted into time–frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. …”
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AI-Based Architecture and Distributed Processing for the Detection and Mitigation of Spoofing Attacks in IoT Networks by William Villegas, Iván Ortiz-Garcés, Jaime Govea

“…The exponential increase in IoT devices within industrial networks has heightened their exposure to cyberattacks, with spoofing attacks posing one of the most critical threats. These attacks exploit communication vulnerabilities, enabling malicious entities to manipulate network traffic and impersonate legitimate devices, compromising system integrity and security. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Identifying Bias in Deep Neural Networks Using Image Transforms by Sai Teja Erukude, Akhil Joshi, Lior Shamir

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Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images by Onur Sevli

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Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest by Gabriel R. Pardini, Paulo M. Tasinaffo, Elcio H. Shiguemori, Tahisa N. Kuck, Marcos R. O. A. Maximo, William R. Gyotoku

“…The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. …”
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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review by Wei Chen, Monisha Dhawan, Jonathan Liu, Damie Ing, Kruti Mehta, Daniel Tran, Daniel Lawrence, Max Ganhewa, Nicola Cirillo

“…Results Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). …”
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EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification by Zeshan Aslam Khan, Muhammad Waqar, Khalid Mehmood Cheema, Ali Abu Bakar Mahmood, Quratul Ain, Naveed Ishtiaq Chaudhary, Abdullah Alshehri, Sultan S. Alshamrani, Muhammad Asif Zahoor Raja

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Application of a modified set of GoogLeNet and ResNet-18 convolutional neural networks towards the identification of environmentally derived-MPLs in the Yadkin-pee dee river basin by Wesley Allen Williams, Anirudh Arunprasad, Shyam Aravamudhan

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PD Recognition for Typical Cardboard Insulation Defect with CNN by ZHAO Jing-he, XIU Da-yuan, WANG Jin-long, CHI Ming-he

“…The convolutional neural network， which is constructed and optimized， obtains the correct rate of about 96.5% and 89.9% respectively in the training set and the test set, showing that convolutional neural networks are suitable for local discharge recognition based on PRPD images.…”
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Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonan... by Shaik Basheera, M Satya Sai Ram

“…Neuroimaging and computer‐aided diagnosis techniques are used for classification of AD by physicians in the early stage. Most of the previous machine learning techniques work on handpicked features. …”
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CNN-based vane-type vortex generator modelling by Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta, Roberto Garcia-Fernandez, Xabier Uralde-Guinea

“…The simplicity and accuracy of Computational Fluid Dynamics (CFD) tools have made them the most widely used method for solving fluid dynamics problems. …”
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Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants by H. M. S. S. Herath, S. L. P. Yasakethu, Nuwan Madusanka, Myunggi Yi, Byeong-Il Lee

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Method of multilayer object sectioning based on a light scattering model by S.D. Bazhitov, A.V. Larichev, A.V. Razgulin, T.E. Romanenko

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A Method of Fatigue Driving State Detection Based on Deep Learning by XIONG Qunfang, LIN Jun, YUE Wei

“…Current domestic and overseas fatigue recognition algorithms are implemented using fatigue features which are mostly singular and man-made. Most of those algorithms have complex structure, low efficiency and weak adaptability in face of driver’s individual behavior habit. …”
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Turkish Lira Banknote Classification using Transfer Learning and Deep Learning by Mirsat Yeşiltepe, Harun Elkıran, Jawad Rasheed

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RACNet: risk assessment Net of cervical lesions in colposcopic images by Tianxiang Xu, Peizhong Liu, Ping Li, Xiaoxia Wang, Huifeng Xue, JingMing Guo, Binhua Dong, Pengming Sun

“…The RACNet was compared with the most advanced methods and colposcopists under the same condition. …”
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