AI-Assisted Forecasting of a Mitigated Multiple Steam Generator Tube Rupture Scenario in a Typical Nuclear Power Plant by Sonia Spisak, Aya Diab

“…These simulations generated a comprehensive dataset, which served as the foundation for training three ML architectures: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional LSTM (CNN+LSTM). Among these models, the CNN+LSTM hybrid configuration demonstrated superior performance, excelling in both predictive accuracy and computational efficiency. …”
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مقارنة بعض نماذج الضوضاء الصوتية وتأثيرها على التشخيص الصوتي لبصمات برامج التواصل الاجتماعي by Sarah Taha Ali, Wleed Abdalaa Araheemah, Mohammed Ahmed Taiye

“… تزايدت اهمية المحافظة على البصمات الصوتية فضلاً عن التحقق من عائديتها خاصة وأن الاعتماد عليها في مدة كورونا جعل الكثير من المستخدمين يعتمدونها في أعمالهم في توجيه الاوامر الإدارية نتيجة لذلك جاء هذا البحث في محاولة لتوظيف خوارزميات عدة لشبكات عصبونية للتحقق من البصمات الصوتية لـــ(50 – 100) شخص ولكل شخص تم اخذ (10 – 20) عينة اظهرت النتائج تأثر التحويل المويجي بكل من (عدد الاشخاص، عدد البصمات الصوتية، الضوضاء ) وقد تم اخذها من احد اشهر برامج التواصل الاجتماعي وهو برنامج (whatsApp)، وقد اظهرت النتائج تأثر التصميم لكل من نوع الضوضاء ونوع المرشح المعتمد لتقليل تأثير الضوضاء وقد بلغ اعلى تصنيف (99,2%) وهو يعود الى الشبكة العصبونية التلافيقية CNN (Band pass filter) في حين بلغ اسوء تصنيف 95.5% وهو يعود الى حالة شبكة التلافيقية CNN (AWGN) كما أظهرت النتائج قدرة بعض المرشحات على زيادة دقة التصنيف العائدة الى الشبكة العصبونية التلافيقية وتقليل أثر الضوضاء. …”
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Classification and Analysis of <em>Agaricus bisporus</em> Diseases with Pre-Trained Deep Learning Models by Umit Albayrak, Adem Golcuk, Sinan Aktas, Ugur Coruh, Sakir Tasdemir, Omer Kaan Baykan

“…This research evaluates 20 advanced convolutional neural network (CNN) architectures for classifying mushroom diseases in <i>Agaricus bisporus</i>, utilizing a custom dataset of 3195 images (2464 infected and 731 healthy mushrooms) captured under uniform white-light conditions. …”
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Electricity Theft Detection in Power Grids with Deep Learning and Random Forests by Shuan Li, Yinghua Han, Xu Yao, Song Yingchen, Jinkuan Wang, Qiang Zhao

“…In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. …”
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On the assessment and reliability of political and ideological education in colleges using deep learning methods by Yongsheng Ma, Xianhui Sun, Aiqun Ma

“…Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…This evidence indicates a possibility of using the NIQE as a new option in evaluating denoised quality to find improvement or compare the quality between CNN models.…”
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Movie scene segmentation using object detection and set theory by Ijaz Ul Haq, Khan Muhammad, Tanveer Hussain, Soonil Kwon, Maleerat Sodanil, Sung Wook Baik, Mi Young Lee

“…Texture and shape features are fused for shots segmentation, and each shot is represented by a set of detected objects acquired from a light-weight CNN model. Finally, we apply set theory with the sliding window–based approach to integrate the same shots to decide scene boundaries. …”
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Automatic Algorithm for Fractal Plant Art Image Similarity Feature Generation by Zhizhe Liu, Luo Sun

“…The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. The security and performance of the proposed scheme are theoretically analyzed and experimentally verified. …”
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Investigation of multi-input convolutional neural networks for the prediction of particleboard mechanical properties by Shuoye Chen, Shunsuke Sakai, Miyuki Matsuo-Ueda, Kenji Umemura

“…Two types of convolutional neural networks (CNNs) were designed: a single-input CNN processing one image and a multi-input CNN capable of analyzing multiple images simultaneously. …”
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YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image by Nur Iriawan, Anindya A. Pravitasari, Ulfa S. Nuraini, Nur I. Nirmalasari, Taufik Azmi, Muhammad Nasrudin, Adam F. Fandisyah, Kartika Fithriasari, Santi W. Purnami, null Irhamah, Widiana Ferriastuti

“…The fundamental finding is to detect and localize the tumor area with YOLO-CNN and segment it with the FCN-UNet architecture. …”
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A New Multiple-Distribution GAN Model to Solve Complexity in End-to-End Chromosome Karyotyping by Yirui Wu, Xiao Tan, Tong Lu

“…Afterwards, we fine-tune CNN for chromosome classification task by involving generated and sufficient training images. …”
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Classification of Invoice Images By Using Convolutional Neural Networks by Sait Ali Uymaz, Ömer Arslan

“…The Invoice classification system developed using Convolutional Neural Networks (CNN) architectures named LeNet-5, VGG-19, and MobileNetV2 was trained on three different data sets. …”
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Integrating principal component analysis, fuzzy inference systems, and advanced neural networks for enhanced estuarine water quality assessment by Richard O. Usang, Bamidele I. Olu-Owolabi, Kayode O. Adebowale

“…New hydrological insights for the region: The hybrid LSTM-CNN model demonstrated superior predictive performance, achieving RMSE values lower than 10 % and R² values exceeding 0.90 across various predictive tasks, indicating high accuracy in forecasting water quality parameters. …”
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A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain–Computer Interfaces to Enhance Motor Imagery Classification by Souheyl Mallat, Emna Hkiri, Abdullah M. Albarrak, Borhen Louhichi

“…In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. …”
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Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media by Irwan Budiman, Mohammad Reza Faisal, Astina Faridhah, Andi Farmadi, Muhammad Itqan Mazdadi, Triando Hamonangan Saragih, Friska Abadi

“…However, CNN has limitations in understanding global context, while LSTM focuses more on understanding word-by-word sequences. …”
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A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique by T. Bharath Kumar, Deepak Prashar, Gayatri Vaidya, Vipin Kumar, S. Deva Kumar, F. Sammy

“…The suggested CNN model outperforms the current methods in terms of performance.…”
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3D Face Modeling Algorithm for Film and Television Animation Based on Lightweight Convolutional Neural Network by Cheng Di, Jing Peng, Yihua Di, Siwei Wu

“…Through the analysis of facial feature extraction technology, this paper designs a lightweight convolutional neural network (LW-CNN). The LW-CNN model adopts a separable convolution structure, which can propose more accurate features with fewer parameters and can extract 3D feature points of a human face. …”
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Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences by A. M. Mutawa

“…<b>Results</b>: The results indicate that the Convolutional neural network (CNN) with Bidirectional long short-term memory (BLSTM) with attention layer (CNN-BLSTM-Att) achieved an accuracy of 99.99%, which outperformed the other models. …”
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Edge and texture aware image denoising using median noise residue U-net with hand-crafted features by Soniya S., Sriharipriya K. C.

“…Although fully convolution neural networks (CNN) are capable of removing the noise using kernel filters and automatic extraction of features, it has failed to reconstruct the images for higher values of noise standard deviation. …”
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Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning by Shuai Liang

“…This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. …”
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