Enhancing Operational Efficacy of Smart Parking Facilities through Intelligent License Plate Recognition by Nikta Shamsmohammadi, Sina Shaffiee Haghshenas, Giuseppe Guido, Vittorio Astarita

“…To achieve this objective, two methodologies, namely Inpainting and Super Resolution, were employed in conjunction with the UNet and SRCNN algorithms. The proposed approach was evaluated on a comprehensive dataset comprising 4171 instances of Persian license plates. …”
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Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches by Pei Gao, Ahmad Kamal Nasution, Naoaki Ono, Shigehiko Kanaya, Md. Altaf-Ul-Amin

“…The initial approach utilizes a supervised network analysis, which involves the application of distance measurement algorithms to scrutinize the interconnectivity and relational patterns within the network derived from Ayurvedic formulae. …”
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Machine learning classification of consumption habits of creatine supplements in gym goers by Patrícia C. Magalhães, Samuel Encarnação, Andre C. Schneider, Pedro Forte, José Teixeira, Antonio Miguel Monteiro, Tiago M. Barbosa, Ana M. Pereira

“…Five machine learning algorithms were implemented to classify creatine consumption in gym goers: Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, Xgboost Classifier. …”
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A robust Parkinson’s disease detection model based on time-varying synaptic efficacy function in spiking neural network by Priya Das, Sarita Nanda, Ganapati Panda, Sujata Dash, Amel Ksibi, Shrooq Alsenan, Wided Bouchelligua, Saurav Mallik

“…Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. …”
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The Petawawa Research Forest: Establishment of a remote sensing supersite by Joanne C. White, Hao Chen, Murray E. Woods, Brian Low, Sasha Nasonova

“…Benchmarking sites, with the requisite refer-ence and baseline data for evaluating the capacities of new technologies, algorithms, and approaches, can be extremely valu-able for sparking innovation, as well as for enabling transparent and scientifically sound assessments of technologies, newdata streams, and associated information outcomes. …”
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Postharvest Shading Modulates Saccharide Metabolic Flux and Enhances Soluble Sugar Accumulation in Tobacco Leaves During Curing: A Targeted Glycomics Perspective by Kesu Wei, Yan Wang, Dong Xiang, Lei Yang, Yijun Yang, Heng Wang, Jiyue Wang, Shengjiang Wu, Yonggao Tu, Chenggang Liang

“…D-mannose, D-sorbitol, and D-glucuronic acid were identified as biomarkers for assessing storage-induced metabolic perturbations using random forest algorithms. Collectively, these findings suggest that PS might enhance tobacco quality via carbohydrate metabolism modulation, providing a scientific basis for pre-curing protocol optimization and industrial application.…”
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Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images by Shadman Q. Salih, Hawre Kh. Abdulla, Zanear Sh. Ahmed, Nigar M. Shafiq Surameery, Rasper Dh. Rashid

“…Deep learning and transfer learning algorithms are good techniques to detect disease from different image source types such as X-Ray and CT scan images. …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

“…The empirical evaluations demonstrate that SGLNet consistently outperforms existing state-of-the-art EEG classification algorithms. This work provides a new perspective for exploring high-performance SNNs for future BCIs with rich spatiotemporal dynamics.…”
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The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review by Saran Singh Gill, Hariharan Subbiah Ponniah, Sho Giersztein, Rishi Miriyala Anantharaj, Srikar Reddy Namireddy, Joshua Killilea, DanieleS.C. Ramsay, Ahmed Salih, Ahkash Thavarajasingam, Daniel Scurtu, Dragan Jankovic, Salvatore Russo, Andreas Kramer, Santhosh G. Thavarajasingam

“…Method: ology: The primary aim was to evaluate the performance of AI algorithms in diagnosing and prognosticating tSCI. Subsequent systematic searching of seven databases identified studies evaluating AI models. …”
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Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning by Chunxiu Zhao, Bingbing Xiang, Jie Zhang, Pingliang Yang, Qiaoli Liu, Shun Wang

“…Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. …”
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Hybrid OFDM-Digital Filter Multiple Access PONs Utilizing Spectrally Overlapped Digital Orthogonal Filtering by Abdulai Sankoh, Wei Jin, Zhuqiang Zhong, Jiaxiang He, Yanhua Hong, Roger Philip Giddings, Iestyn Pierce, Maurice O'Sullivan, Jeffrey Lee, Tim Durrant, Jianming Tang

“…This paper reports, for the first time, a new hybrid OFDM-DFMA PON based on intensity modulation and direct detection (IMDD), obtained by modifying digital signal processing (DSP) algorithms embedded in both the OLT and ONUs. The proposed PON allows two spectrally overlapped sub-bands to occupy each individual sub-wavelength spectral region to independently transmit upstream ONU information. …”
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Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea by Hengguang Liu, Shaohong Xia, Chaoyan Fan, Changrong Zhang

“…Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. …”
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Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning by Nalini Manogaran, Mercy Theresa Michael Raphael, Rajalakshmi Raja, Aarav Kannan Jayakumar, Malarvizhi Nandagopal, Balamurugan Balusamy, George Ghinea

“…The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. …”
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Lithological mapping and spectroscopic studies of carbonatite and clinopyroxenite from Hogenakkal carbonatite complex, India by Saraah Imran, Sourav Bhattacharjee, Ajanta Goswami, Aniket Chakrabarty

“…Petrography, Raman spectroscopy of minerals, and spectroradiometric measurements of rock samples support the interpretations derived from Principal Component Analysis (PCA), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), Decision Tree, and Random Forest algorithms, thereby aiding in the identification of lithological variations and potential clinopyroxenite occurrences. …”
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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Traditional approaches for cancer detection rely on handcrafted features and conventional machine learning algorithms, which struggle with high-dimensional spectral data, noise interference, and domain adaptation challenges. …”
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Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI... by Nuray Vakitbilir, Rahul Raj, Donald E. G. Griesdale, Mypinder Sekhon, Francis Bernard, Clare Gallagher, Eric P. Thelin, Logan Froese, Kevin Y. Stein, Andreas H. Kramer, Marcel J. H. Aries, Frederick A. Zeiler

“…These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. …”
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A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study by Jinsong Du, Jinsong Du, Jinsong Du, Xinru Tao, Le Zhu, Wenhao Qi, Xiaoqiang Min, Xiaoqiang Min, Hongyan Deng, Shujie Wei, Xiaoyan Zhang, Xiao Chang

“…This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and older adults, rooted in machine learning and visualization technologies.MethodsUsing cohort data from the China Health and Retirement Longitudinal Study (CHARLS), involving 8,839 middle-aged and older adult participants, the study developed predictive models based on eight machine learning algorithms, primarily including LightGBM, XGBoost, and AdaBoost. …”
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Efficient Photothermoelectric Conversion of CSS@BP/Bi2Te3 Array for Innovative Aircraft Attitude Recognition by Liangshutong Zhang, Yupu Zhang, Xinyu Li, Donghao Han, Wei Zhai, Jianyuan Wang

“…Excitably, the ARB demonstrates over 86.47% accuracy without complex algorithms, showing excellent stability and robustness. …”
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Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies by Sourav Kumar Patra, Nicholas Randolph, Brian Kuhlman, Henry Dieckhaus, Laurie Betts, Jordan Douglas, Peter R. Wills, Charles W. Carter Jr.

“…Here, we used the deep learning algorithms ProteinMPNN and AlphaFold2 to redesign a set of optimized LeuAC urzymes derived from leucyl-tRNA synthetase. …”
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Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta, Giuseppe Loseto

“…The framework prioritizes the customizability of AI algorithms and real-time information sharing between stakeholders. …”
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