Comment on S Memon, et al. (J Pak Med Assoc. 74: 1163-1166, June 2024) Osmolar gap in hyponatraemia: An exploratory study by Muhammad Ramish Irfan

“… Madam, Your paper about the osmolar gap in hyponatraemiawas much appreciated as it remains a subject shrouded inmisunderstanding.The observations reported in this paper are certainly thoughtprovoking, therefore I would extend a few conceptualclarifications that I believe your readership would benefit fromin gaining deeper insight about the findings reported in thisstudy.The difference between tonicity, osmolarity and osmolality isoften disregarded and appears convoluted however, it is crucialto delineate between these terms nevertheless. …”
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An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…The results confirm that the accuracies of both CNN and ViT models exceed 95% after 100 epochs of training, with no significant difference observed between them for binary classification. …”
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Radar signal recognition exploiting information geometry and support vector machine by Yuqing Cheng, Muran Guo, Limin Guo

“…Specifically, the time‐frequency images of different LPI radar signals are obtained via the Choi‐Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time‐frequency features. …”
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Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System by Ahmet Bozdag, Muhammed Yildirim, Mucahit Karaduman, Hursit Burak Mutlu, Gulsah Karaduman, Aziz Aksoy

“…<b>Results:</b> The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. …”
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A Comprehensive Evaluation of Machine Learning and Deep Learning Models for Churn Prediction by Nabil M. AbdelAziz, Mostafa Bekheet, Ahmad Salah, Nissreen El-Saber, Wafaa T. AbdelMoneim

“…Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. …”
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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion by Chuanjiang Wang, Junhao Ma, Guohui Wei, Xiujuan Sun

“…In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. …”
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Leveraging IoT-Enabled Sensor Networks and Machine Learning for Early Detection and Management of Wheat Rust by Adnan Myasar M., Almoussawi Zainab Abed, Anuradha Kodali

“…Progressive remote sensing technologies, such as the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI), are active in displaying plant health and recognizing primary symbols of disease. …”
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Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling by Shuqin Li, Saisai Li, Hengyang Cao, Kun Meng, Meng Ding

“…Role modeling learns different roles and behaviors by using a convolutional neural network. …”
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Large-scale tobacco identification via a very-high-resolution unmanned aerial vehicle benchmark and a ConvFlow Transformer by Wei Han, Shaohao Chen, Shuanglin Xiao, Yunliang Chen, Huihui Zhao, Jining Yan, Xiaohan Zhang, Sheng Wang

“…Then, a dual-branch ConvFlow Transformer is proposed to address tobacco’s rich diversity and high inter-class similarity among different crops. A novel Convolutional Feature-enhanced Multi-Head Self-attention (CF-MHSA) with a location-free design in the ConvFlow Transformer is developed to replace the value matrix in the standard attention with the convolutional multi-scale features, which effectively achieves feature interaction and fusion from the convolutional and transformer branches. …”
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Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. …”
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Hybrid Deep Learning Models for Sentiment Analysis by Cach N. Dang, María N. Moreno-García, Fernando De la Prieta

“…Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. …”
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Comparison of neural networks for suppression of multiplicative noise in images by V.A. Pavlov, A.A. Belov, V.T. Nguen, N. Jovanovski, A.S. Ovsyannikova

“…It is shown that different architectures require significantly different amount of training data to reach the same noise suppression quality. …”
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WISP: Workframe for Interferogram Signal Phase-Unwrapping by Timofey F. Khirianov, Aleksandra I. Khirianova, Egor V. Parkevich, Ilya Makarov

“…Iterations continue until the difference between the reconstructed and experimental phase distributions reaches an asymptotic minimum. …”
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Infrared object detection for robot vision based on multiple focus diffusion and task interaction alignment by Jixu Zhang, Li Wang, Hung-Wei Li, Meng-Yen Hsieh, Shunxiang Zhang, Hua Wen, Meng Chen

“…However, the small gray-scale difference between the object and the background region in the infrared grayscale image and the single gray-scale information lead to the blurring of the semantic information of the image, which makes the robot unable to detect the object effectively. …”
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Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning by ZHANG Huiyun, ZUO Fangjun, LI Hang, YU Xi

“…Secondly, a dynamic convolutional neural network was employed to dynamically weight and aggregate the masked instances, enabling discriminative feature modeling of different masked instances. …”
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Direction of Arrival Estimation Algorithm for Underwater Distributed Sources Based on Deep Neural Network by Yinian LIANG, Jie LI, Fangjiong CHEN, Fei JI, Hua YU

“…The proposed method is compared with four traditional subspace-based methods and one deep convolutional neural network algorithm, and the results show that the root mean square error of the proposed method under the coherently distributed source case is 0.42° lower than that of other methods under different signal-to-noise ratios(SNRs) and snapshots; under the incoherently distributed source case, the RMSE of the proposed method is 0.04° lower than that of other methods with SNR greater than 0 dB and snapshots greater than 600. …”
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YOLOv8-CBSE: An Enhanced Computer Vision Model for Detecting the Maturity of Chili Pepper in the Natural Environment by Yane Ma, Shujuan Zhang

“…Additionally, SRFD and DRFD modules are introduced to replace the original convolutional layers, effectively capturing features at different scales and enhancing the diversity and adaptability of the model through the feature fusion mechanism. …”
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Caste, Constitution, Court, Equality: The Social Justice Imbroglio in Contemporary India by Ishita Banerjee-Dube

“…This article addresses these issues by revisiting the convoluted trajectory of positive discrimination (termed “reservation”) in India as an illustrative and instructive example. …”
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Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic–Ischemic Encephalopathy Injury by Leah Twomey, Sergi Gomez, Emanuel Popovici, Andriy Temko

“…First, the EEG signal is transformed into an amplitude and frequency modulated audio spectrogram, which enhances its relevant signal properties. The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. …”
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Semantic Fusion-Oriented Bi-Typed Multi-Relational Heterogeneous Graph Neural Network by Yifan Sun, Jing Yan, Lilei Lu, Hongbo Zhang, Yanhong Shang

“…Additionally, it employs relational convolutions to capture relationship features within different types and fuses different relationship features through a relational-level attention mechanism. …”
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