Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images by Navya K. T, Seemitr Verma, Keerthana Prasad, Brij Mohan Kumar Singh

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Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model by Rajesh Doss, Jayabrabu Ramakrishnan, S. Kavitha, S. Ramkumar, G. Charlyn Pushpa Latha, Kiran Ramaswamy

“…For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. …”
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Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance by Komi Mensah Agboka, Elfatih M. Abdel-Rahman, Daisy Salifu, Brian Kanji, Frank T. Ndjomatchoua, Ritter A.Y. Guimapi, Sunday Ekesi, Landmann Tobias

Subjects: “…Combining Self-Organizing Maps (SOM) and Convolutional Neural Network (CNN) for improving model accuracy…”
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Deteksi Dan Klasifikasi Hama Potato Beetle Pada Tanaman Kentang Menggunakan YOLOV8 by Daniel Geoffrey Manurung, Mohammad Ryan Pinasthika, Muhammad Azka Obila Vasya, Rania Aprilia Dwi Setya Putri, Agustinus Parasian Tampubolon, Rakan Fadhil Prayata, Septia Khoirin Nisa, Novanto Yudistira

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A Hardware Accelerator for the Inference of a Convolutional Neural network by Edwin González, Walter D. Villamizar Luna, Carlos Augusto Fajardo Ariza

Subjects: “…CNN…”
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Evaluation of Efficacy of Artificial Intelligence in Orthopantomogram in Detecting and Classifying Radiolucent Lesions by Sheetal Singar, Ajay Parihar, Prashanthi Reddy

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Automatic MRI Image Classification Using Attention and Residual CNNs With Enhanced Image Denoising Filters by Suganya Athisayamani, A. Robert Singh, Faten Khalid Karim, Samih M. Mostafa

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Control of a Mobile Line-Following Robot Using Neural Networks by Hugo M. Leal, Ramiro S. Barbosa, Isabel S. Jesus

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Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification by Agus Andreansyah, Julian Supardi

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Application research of convolutional neural network and its optimization in lightning electric field waveform recognition by Caixia Wang, Xiaoyi Zhang, Hui Yang, Jinyuan Guo, Jia Xu, Zhuling Sun

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Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases by K. A. Muthukumar, Dhruva Nandi, Priya Ranjan, Krithika Ramachandran, Shiny PJ, Anirban Ghosh, Ashwini M, Aiswaryah Radhakrishnan, V. E. Dhandapani, Rajiv Janardhanan

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Classification of Dyslexia Among School Students Using Deep Learning by Alia Hussein, Ahmed Talib Abdulameer, Ali Abdulkarim, Husniza Husni, Dalia Al-Ubaidi

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Face Recognition using Deep Learning and TensorFlow framework by Makrem Beldi

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Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques by Marwa K. Elteir

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Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

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Research on Concrete Beam Damage Detection Using Convolutional Neural Networks and Vibrations from ABAQUS Models and Computer Vision by Xin Bai, Zi Zhang

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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning by Waqas Ali, Wesam Alsabban, Muhammad Shahbaz, Ali Al-Laith, Bassam Almogadwy

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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…This study aims to compare the performance of six classification algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in detecting fall incidents using wearable sensor data such as accelerometers and gyroscopes. …”
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A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction by Kuo Xin, Fu Jia, Byoungik Choi, Geesoo Lee

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Deep Learning-Based Dzongkha Handwritten Digit Classification by Yonten Jamtsho, Pema Yangden, Sonam Wangmo, Nima Dema

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