WHO/INRUD prescribing indicators with a focus on antibiotics utilization patterns at outpatient department of Adigrat general hospital, Tigrai, Ethiopia: a retrospective cross-sect... by Gebretekle Gebremichael Hailesilase, Brhane Gebrehiwot Welegebrial, Mezgebe Gidey Weres, Senait Abera Gebrewahd

“…Methods A retrospective cross-sectional study was done to evaluate prescription patterns. A systematic random sampling technique was used to select 600 prescriptions and the prescriptions were reviewed using WHO/International Network of Rational Use of Drugs prescribing indicators. …”
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Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability by Santiago Buitrago-Osorio, Julian Gil-González, Andrés Marino Álvarez-Meza, David Cardenas-Peña, Alvaro Orozco-Gutierrez

“…To address these limitations, we propose a threefold DL-based framework for coding EEG-based pain detection patterns. (i) We employ the Kernel Cross-Spectral Gaussian Functional Connectivity Network (KCS-FCnet) to code pairwise channel dependencies for pain detection. …”
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BTCP: Binary Temporal Convolutional Network-Based Data Prefetcher for Low Inference Latency and Storage Overhead by Chang Ho Ryu, Tae Hee Han

“…BTCP aids in detecting memory patterns by processing addresses and program counters through bitwise XOR operations and feeding them into a neural network. …”
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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. …”
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Multidimensional bioinformatics analysis of chondrosarcoma subtypes and TGF-β signaling networks using big data approaches by Shengke Li, Junteng Chen, Fuping He, Maosheng Wang, Jun Liu, Hui Xie

“…Pseudotime analysis charted differentiation trajectories, while machine learning models evaluated the classification accuracy of gene expression patterns. …”
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Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy by Jesús Jaime Moreno Escobar, Oswaldo Morales Matamoros, Erika Yolanda Aguilar del Villar, Hugo Quintana Espinosa, Liliana Chanona Hernández

“…Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. …”
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Hide and seek in transaction networks: a multi-agent framework for simulating and detecting money laundering activities by Qianyu Wang, Wei-Tek Tsai, Tianyu Shi, Wang Tang, Bowen Du

“…Abstract Detecting money laundering within financial networks presents a complex challenge due to the elusive behavior patterns of laundering agents, often resulting in data gaps. …”
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Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network by Nisa Vuran Sarı, Mehmet Acı, Çiğdem İnan Acı

“…Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. …”
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Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics by Thawirasm Jungrungrueang, Sawrawit Chairat, Kasidach Rasitanon, Praopim Limsakul, Krit Charupanit

“…This study investigated dynamic features of resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns of dementia subtypes, such as Alzheimer’s disease (AD) and frontotemporal dementia (FD), and to evaluate their potential as biomarkers. …”
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Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education by Yuhao You

“…Existing action recognition models are often computationally intensive and struggle to generalize across diverse skeletal patterns. To address this, we propose a lightweight graph convolutional network (GCN) that integrates an improved Ghost module with multi-attention mechanisms, including a global attention mechanism (GAM) and a channel attention mechanism (CAM), to enhance spatial and temporal feature extraction. …”
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The Settlement Structure Is Reflected in Personal Investments: Distance-Dependent Network Modularity-Based Measurement of Regional Attractiveness by Laszlo Gadar, Zsolt T. Kosztyan, Janos Abonyi

“…How can the structure of investment patterns characterize the attractiveness and development of economic regions? …”
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Efficacy of antimalarial drugs for treatment of uncomplicated falciparum malaria in Asian region: A network meta-analysis. by Cho Naing, Maxine A Whittaker, Norah Htet Htet, Saint Nway Aye, Joon Wah Mak

“…Moreover, one drug regimen is better than another is only if current drug-resistance patterns are at play. For example, the AL might be better than DHP in areas where both artemisinin and piperaquine resistance patterns are prevalent. …”
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Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection by Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li

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Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures by Fábio Felix Dias, Moacir Antonelli Ponti, Rosane Minghim

“…With that, we propose guidelines for using neural networks to classify species based on their sound patterns, even for a small dataset. …”
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Northern goshawk optimization for optimal reactive power compensation in photovoltaic low-voltage radial distribution networks by Shaikh Sohail Mohiyodin, Rajesh Maharudra Patil, Dr MS Nagaraj

“…Considering the PVs and CBs of the network, a modified IEEE 33-bus test system was used to evaluate the effectiveness of the suggested methodology.…”
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Hybrid Contrastive Learning With Attention-Based Neural Networks for Robust Fraud Detection in Digital Payment Systems by Md Shahin Alam Mozumder, Mohammad Balayet Hossain Sakil, Md Rokibul Hasan, Md Amit Hasan, K. M Nafiur Rahman Fuad, M. F. Mridha, Md Rashedul Islam, Yutaka Watanobe

“…This article proposes a novel Hybrid Contrastive Learning framework integrating Siamese Networks with Attention-Based Neural Networks to effectively distinguish fraudulent from legitimate transactions. …”
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Probabilistic Forecasting of Crude Oil Prices Using Conditional Generative Adversarial Network Model with Lévy Process by Mohammed Alruqimi, Luca Di Persio

“…Recent research highlights Generative Adversarial Networks (GANs) as a promising alternative approach for capturing intricate patterns in time series data, leveraging the adversarial learning framework. …”
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The use of artificial neural network algorithms to enhance tourism economic efficiency under information and communication technology by Yun Tian, Xiaojie Tang

“…This work proposes an analysis method based on the artificial neural network to predict tourist behavior patterns through big data analysis, thereby optimizing the allocation of tourism resources. …”
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Individual-level cortical morphological network analysis in idiopathic normal pressure hydrocephalus: diagnostic and prognostic insights by Yifeng Yang, Meijing Yan, Lianxi Sun, Xiao Liu, Xuhao Fang, Shihong Li, Guangwu Lin

“…This study investigates individual-level cortical morphological network patterns in iNPH, explores their diagnostic utility and prognostic value for postoperative outcomes. …”
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Classification of Short-Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network by Md Hassanuzzaman, Samit Kumar Ghosh, Mohammad Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I. Ahmed, Raqibul Mostafa, Ahsan H. Khandoker

“…Phonocardiograms (PCGs), as a non-invasive and cost-effective diagnostic modality, capture vital acoustic signals that reflect the mechanical activity of the heart and can reveal pathological patterns associated with various CHD types. This study investigates the minimum signal duration required for accurate automatic classification of heart sounds and evaluates signal quality using the root mean square of successive differences (RMSSD) and the zero-crossing rate (ZCR). …”
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