Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network by Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Dost Muhammad Khan, Mickäel Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Hizbullah Khattak, Sikandar Ali, Ali Samad

“…We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. …”
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Exploring Aggregated wav2vec 2.0 Features and Dual-Stream TDNN for Efficient Spoken Dialect Identification by Ananya Angra, H. Muralikrishna, Dileep Aroor Dinesh, Veena Thenkanidiyoor

“…Followed by this, we explore the usage of recently proposed global-aware filter (GAF) layer based dual-stream time delay neural network (DS-TDNN) for DID. The GAF layer employs a set of learnable transform-domain filters between a 1D discrete Fourier transform and its inverse transform to capture global context along with dynamic filtering and sparse regularization. …”
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Exploration of Arrhenius activation energy and thermal radiation on MHD double-diffusive convection of ternary hybrid nanofluid flow over a vertical annulus with discrete heating by Shilpa B, V. Leela, Irfan Anjum Badruddin, Sarfaraz Kamangar, P. Ganesan, Abdul Azeem Khan

“…Also, the heat and mass transfer characteristics are forecasted and analyzed by considering the Levenberg–Marquardt backpropagating artificial neural network technique.…”
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Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation by O. Lamminpää, J. Susiluoto, J. Hobbs, J. McDuffie, A. Braverman, H. Owhadi

“…In contrast with artificial neural network (ANN)-based methods, it is interpretable, and its efficiency is based on learning a kernel in an engineered and expressive family of kernels.…”
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A CNN-RF Hybrid Approach for Rice Paddy Fields Mapping in Indramayu Using Sentinel-1 and Sentinel-2 Data by Dodi Sudiana, Mia Rizkinia, Rahmat Arief, Tiara De Arifani, Anugrah Indah Lestari, Dony Kushardono, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo

“…This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. …”
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Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise by Hongwei Fan, Ceyi Xue, Xuhui Zhang, Xiangang Cao, Shuoqi Gao, Sijie Shao

“…In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. …”
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Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases by Mosleh Hmoud Al-Adhaileh, Ebrahim Mohammed Senan, Waselallah Alsaade, Theyazn H. H Aldhyani, Nizar Alsharif, Ahmed Abdullah Alqarni, M. Irfan Uddin, Mohammed Y. Alzahrani, Elham D. Alzain, Mukti E. Jadhav

“…In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. …”
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Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection by Zishuo Guo, Baopeng Zhang, Jack Fan, Zhu Teng, Jianping Fan

“…In this work, we propose a novel unified neural network named MGDL-Net (Modal-Guided Domain Learning Network), which contains a spatial branch, a temporal branch, and a frequency branch. …”
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Predicting the Botanical Origin of Honeys with Chemometric Analysis According to Their Antioxidant and Physicochemical Properties by Anna Maria Kaczmarek, Małgorzata Muzolf-Panek, Jolanta Tomaszewska-Gras, Piotr Konieczny

“…The aim of this study was to develop models based on Linear Discriminant Analysis (LDA), Classification and Regression Trees (C&RT), and Artificial Neural Network (ANN) for the prediction of the botanical origin of honeys using their physicochemical parameters as well as their antioxidative and thermal properties. …”
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Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease by Wei Zheng, Xiaoyan Qin, Ronghua Mu, Peng Yang, Bingqin Huang, Bingqin Huang, Zhixuan Song, Xiqi Zhu, Xiqi Zhu

“…An Unet-based deep learning neural network model was developed to automate the segmentation of the hippocampus. …”
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A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identifi... by Nourelhouda Groun, María Villalba-Orero, Lucía Casado-Martín, Enrique Lara-Pezzi, Eusebio Valero, Jesús Garicano-Mena, Soledad Le Clainche

“…In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. …”
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Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. …”
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Adaptive algorithms for change point detection in financial time series by Alexander Musaev, Dmitry Grigoriev, Maxim Kolosov

“…The work highlights the potential for future advancements in neural network applications and multi-expert decision systems, further enhancing predictive accuracy in volatile environments.…”
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An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition by Xiaoli Ma, Hongyan Xu, Xiaoqian Zhang, Haoyong Wang

“…The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. …”
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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar, Abdullatif Albaseer, Muhammad Asif Khan, Mohamed Abdallah, Hamid Menouar, Saif Al-Kuwari, Ala Al-Fuqaha

“…Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. …”
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A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity by Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao

“…Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. …”
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Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time by Silvana Petruseva, Valentina Zileska-Pancovska, Diana Car-Pušić

“…Five hybrid models have been developed, and the most accurate one was the BTC-GRNN model, which uses Bromilow’s time-cost (BTC) model as a process-based model and the general regression neural network (GRNN) as a data-driven model. For evaluating the quality of the models, the 10-fold cross-validation method has been used. …”
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DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews by Yuanfang Dong, Xiaofei Li, Meiling He, Jun Li

“…The algorithm constructs two channels, transforming text into both character and word vectors and inputting them into Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network (CNN) models. The combination of these channels facilitates a more comprehensive feature extraction from reviews. …”
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Image segmentation and CNN-based deep learning architectures for the modelling on particulate matter formation during solid fuels combustion by Yanchi Jiang, Lanting Zhuo, Xiaojiang Wu, Zhongxiao Zhang, Xinwei Guo, Wei Wang, Cunjiang Fan

“…Six convolutional neural network models were developed, and three transfer learning strategies were studied. …”
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Analysis and synthesis of images of environmentally oriented innovative technologies of construction production by V. I. Telichenko, A. A. Lapidus, M. Yu. Slesarev

“…To test this hypothesis, the authors propose to test the integration of human machine and living intelligence using artificial intelligence, using the example of the GPT neural network developed by Microsoft and OpenAI.Materials and methods. …”
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