Bayesian optimized deep learning and ensemble classification approach for multiclass plant disease identification by Silpa Padmanabhuni, Pradeepini Gera

“…Bayesian optimization is used to identify and combine optimal activation functions, enhancing the network's capacity to learn complex disease patterns from tomato leaf images. …”
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Classification of Hybrid and Peking Duck DOD Varieties Based on Feather Images Using CNN by Khoironi, I Wayan Rangga Pinastawa

“…In this study, a deep learning approach with the Convolutional Neural Network (CNN) algorithm was applied to classify DOD varieties based on feather images. …”
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Federated Learning for Privacy-Preserving Employee Performance Analytics by Jay Barach

“…This paper introduces HFAN-Priv, a hierarchical federated attention network designed to predict employee resignation risk and evaluate performance trends without sharing raw data across organizations. …”
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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…Convolutional neural network (CNN) architectures were used to classify movement patterns. …”
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An Integrated Spatial-Spectral Denoising Framework for Robust Electrically Evoked Compound Action Potential Enhancement and Auditory Parameter Estimation by Fan-Jie Kung

“…The electrically evoked compound action potential (ECAP) is a crucial physiological signal used by clinicians to evaluate auditory nerve functionality. Clean ECAP recordings help to accurately estimate auditory neural activity patterns and ECAP magnitudes, particularly through the panoramic ECAP (PECAP) framework. …”
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Requirements for Becoming a Startup: A Study with an Economic Sociology Approach (Case Study: Managers of Startups in Isfahan City) by Rahmatollah Torkan, Seyed Samad Beheshty, Ramin Moradi

“…Materials & MethodsThis qualitative research grounded in the interpretive paradigm and utilizing the thematic network approach (Attride-Stirling, 2001) was designed to uncover meaningful patterns within the data. …”
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Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol by Andrew Wister, Kate Smolina, Felicity Clemens, Mike A Irvine, Jennifer Ferris, Boah Kim, Lean K Fiedeldey, Sogol Haji Hosseini

“…We will assess the stability of obtained disease clusters across the research literature to date, in order to evaluate the strength of evidence for specific disease patterns in multimorbidity.Ethics and dissemination This study does not require ethics approval as the work is based on published research studies. …”
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Antimicrobial Susceptibility Profiles of <i>Escherichia coli</i> Isolates from Clinical Cases of Geese in Hungary Between 2022 and 2023 by Ádám Kerek, Ábel Szabó, Ákos Jerzsele

“…Cluster analysis and principal component analysis (PCA) were applied to identify resistance patterns. Co-resistance relationships were examined through network analysis, while Monte Carlo simulations were used to estimate the expected prevalence of MDR strains. …”
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Human mobility and malaria risk in peri-urban and rural communities in the Peruvian Amazon. by Joaquin Gomez, Alessandro Grosso, Mitchel Guzman-Guzman, Stefano Garcia Castillo, Marcia C Castro, Katherine Torres, Joseph M Vinetz, Dionicia Gamboa

“…We used social network analysis (SNA) to obtain weighted and unweighted degrees of connectivity and explore its variability by socio-demographic characteristics. …”
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Estimation of state of health for lithium-ion batteries using advanced data-driven techniques by Smitanjali Rout, Sudhansu Kumar Samal, Demissie Jobir Gelmecha, Satyasis Mishra

“…Advanced machine learning models, including Adaboost, Xgboost, Ridge Regression, Decision Trees, Random Forests, Artificial Neural Networks, and Long Short-Term Memory Networks (LSTM), are employed to analyze battery performance. …”
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An improve fraud detection framework via dynamic representations and adaptive frequency response filter by Juncheng Yang, Shuxia Li, Zijun Huang, Junhang Wu

“…With the sequential network, we capture users’ dynamic behavioral features for LSN input. …”
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Optimization of the construction scheme for coal-based synthetic natural gas export pipelines by Ye ZHANG, Shuiying XU, Yang ZHAO, Fengsheng WANG, Jia YANG, Bojun ZHANG, Liang MING, Weichao YU

“…This method effectively reduces the one-time pipeline investment costs and significantly contributes to optimizing the natural gas pipeline network from a supply chain perspective. Conclusion It is recommended to leverage the strengths of resource supply producers, pipeline network operators, distributors, and end users within a fair and open pipeline network operation mechanism.This approach will establish a continuous and stable supply-demand pattern for the coal-based SNG supply chain, improve the efficiency of resource upload pipelines, and enhance the risk manage ment and control capabilities of the pipeline network.…”
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Comparison and Interpretability Analysis of Deep Learning Models for Classifying the Manufacturing Process of Pigments Used in Cultural Heritage Conservation by Inhee Go, Yu Fu, Xi Ma, Hong Guo

“…Four convolutional neural networks (CNNs) (i.e., AlexNet, GoogLeNet, ResNet, and VGG) and one vision transformer (ViT) were compared on micrograph datasets of various pigments. …”
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Anomaly detection research using Isolation Forest in Machine Learning by A. S. Kechedzhiev, O. L. Tsvetkova

“…To implement this research, the Python programming language and the scikit-learn library were chosen to implement the Isolation Forest, as well as Pandas for working with data.Result. Evaluating the applicability of the Isolation Forest method on unstructured data revealed its potential for identifying anomalous patterns without the need for extensive labeling. …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…Identifying groundwater recharge zones is crucial for sustainable water resource management in water-scarce environments, such as Kurdistan, Iran. This study evaluated four deep learning models for delineating groundwater recharge zones: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and hybrid deep learning Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). …”
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Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques by Hesham Kamal, Maggie Mashaly

“…The Transformer-CNN model focuses on three primary objectives to enhance detection accuracy and performance: (1) reducing false positives and false negatives, (2) enabling real-time intrusion detection in high-speed networks, and (3) detecting zero-day attacks. We evaluate our proposed model, Transformer-CNN, using the NF-UNSW-NB15-v2 and CICIDS2017 benchmark datasets, and assess its performance with metrics such as accuracy, precision, recall, and F1-score. …”
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Cross-lingual hate speech detection using domain-specific word embeddings. by Ayme Arango Monnar, Jorge Perez Rojas, Barbara Polete Labra

“…Hate speech detection in online social networks is a multidimensional problem, dependent on language and cultural factors. …”
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An Enhanced Distribution System Performance with Optimization Techniques for Location of Electrical Vehicle Charging Stations by Sainadh Singh Kshatri, Venkata Anjani Kumar G, Chilakapati Lenin Babu, Palepu Suresh Babu

“…The results demonstrate the GWO algorithm's superiority in minimizing power losses and enhancing voltage profiles across the distribution network. Furthermore, a probabilistic assessment is conducted to evaluate the robustness of the optimal EVCS placement under uncertain EV charging patterns. …”
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Abnormal arm swing movements in Parkinson’s disease: onset, progression and response to L-Dopa by M. Patera, A. Zampogna, L. Pietrosanti, F. Asci, M. Falletti, G. Pinola, E. Bianchini, G. Di Lazzaro, V. Rosati, P. Grillo, F. Giannini, F. Fattapposta, G. Costantini, A. Pisani, G. Saggio, Antonio Suppa

“…Methods Twenty healthy subjects (HS) and 40 PD patients, including 20 early-stage and 20 mid-advanced subjects, underwent a 6-m Timed Up and Go test while monitored through a network of wearable inertial sensors. Arm swing movements were objectively evaluated in both hemibodies and different upper limb joints (shoulder and elbow), using specific time-domain (range of motion and velocity) and frequency-domain measures (harmonics and total harmonic distortion). …”
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High-performance glass classification using advanced machine learning and deep learning algorithms with a comprehensive feature analysis by Mohammed Bouziane, Abdelghani Bouziane, Samia Larguech, Khatir Naima, Mohammad Salman Haque, Younes Menni

“…Advanced learning algorithms like Random Forest (RF), XGBoost, Support Vector Machines, and Bidirectional Long Short-Term Memory (BiLSTM) networks are applied for classification. Findings prove RF and XGBoost to provide the highest classification accuracy, and BiLSTM to be the best in recognizing complex data patterns. …”
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