Encoding and Decoding of Secret Messages Using Matrices by James, Niwagaba

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Interventions to improve referrals from primary care to outpatient specialist services for chronic conditions: a systematic review and framework synthesis update by Layla Bolton Saghdaoui, Smaragda Lampridou, Sara Tavares, Rachael Lear, Alun Huw Davies, Mary Wells, Sarah Onida

“…When used alone, referral algorithms and staff education were less effective at improving referral rates or appropriateness. …”
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A novel transformer-based approach for cardiovascular disease detection by Nimra Noor, Muhammad Bilal, Saadullah Farooq Abbasi, Omid Pournik, Theodoros N. Arvanitis

“…CVDs refer to disorders of the heart and blood vessels such as arrhythmia, atrial fibrillation, congestive heart failure, and normal sinus rhythm. Early prediction of these diseases can significantly reduce the number of annual deaths. …”
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Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data by Gengbo Liu, Logan Brooks, John Canty, Dan Lu, Jin Y. Jin, James Lu

“…In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. …”
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Development of New Radar and Pyroelectric Sensors for Road Safety Increase in Cloud-Based Multi-Agent Control Application by Leslie Robert Adrian, Ansis Avotins, Donato Repole, Olegs Tetervenoks

“…The proposed sensor solutions can detect the road user (vehicle or pedestrian) and determine its movement direction and approximate speed that can be used for dynamic lighting control algorithms, traffic intensity prediction, and increased safety for both driver and pedestrian traffic. …”
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Detection and Tracking of Environmental Sensing System for Construction Machinery Autonomous Operation Application by Junyi Chen, Qipeng Cai, Xinhai Hu, Qihuai Chen, Tianliang Lin, Haoling Ren

“…Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. …”
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LoRa Technology Enhanced with a Custom-Designed High-Gain Yagi-Uda Antenna for Data Transmission from Misti Volcano Monitoring to Arequipa City by Flor de Milagro Yesit Arana Medina, Jorge Rendulich

“…In response, this project integrates Internet of Things (IoT) technology, the LoRa (Long Range) network, and an optimized seven-element Yagi-Uda antenna, developed using advanced optimization algorithms to enhance transmission efficiency. The primary objective is to facilitate the reliable collection and transmission of critical sensor data for subsequent analysis by volcanological experts, thereby supporting improved prediction and mitigation of potential volcanic hazards. …”
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House Value Estimation using Different Regression Machine Learning Techniques by Tarek Ghamrawi, Müesser Nat

“…Future research will explore the use of a broader range of regression models with fewer features to assess their performance and robustness in house price prediction.…”
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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

“…However, existing methods for determining optimal DTRs face limitations, often due to reliance on linear models unsuitable for complex disease analysis and a focus on outcome prediction over treatment effect estimation. To overcome these challenges, decision tree-based reinforcement learning approaches have been proposed. …”
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Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization by Luyu Tian, Chaoyu Dong, Rui Wang, Yunfei Mu, Hongjie Jia

“…After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images.…”
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Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports by Ümit Gökkuş, Mehmet Sinan Yıldırım, Metin Mutlu Aydin

“…Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. …”
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Application of Neural Networks to Analyse the Spatial Distribution of Bicycle Traffic Before, During and After the Closure of the Mill Road Bridge in Cambridgeshire, United Kingdom by Shohel Amin

“…The application of wireless sensors and machine learning algorithms can enhance the analysis and prediction ability of traffic distribution and characteristics in the proximity of road closures. …”
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Enhanced Multi-Criteria DNA Sequence Analysis Using Neutrosophic Logic and Deep Learning: An Integrated Approach for Comparison and Classification by Romany M. Farag, Mahmoud Y. Shams, Dalia A. Aldawody, Hazem M. El-Bakry, Huda E. Khalid, Ahmed K. Essa, A. A. Salama

“…Traditionally, traditional methods such as multiple sequence alignment (MSA) algorithms or the NeedlemanWinch algorithm become extremely cumbersome when faced with large or unclear data. …”
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Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors by Ehsan Masoudian, Ali Mirzaei, Hossein Bagheri

“…Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. …”
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MeDML: Med-Dynamic Meta Learning - A multi-layered representation to identify provider fraud in healthcare by Nitish Kumar, Deepak Chaurasiya, Alok Singh, Siddhartha Asthana, Kushagra Agarwal, Ankur Arora

“…We test the dynamically generated meta embedding using various downstream models and show that it outperforms all baseline algorithms for provider fraud prediction task.…”
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Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area by Aicha Moumni, Abderrahman Lahrouni

“…The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. …”
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Evaluation method of distribution network operation status based on local fuzzy measure in boundary region by Bing Yu, Peng Xie, Zhonglin Ding, Letian Li, Changan Chen, Chunfeng Jing

“…The research results found that the five evaluation indicators of the proposed algorithm were 112, 0, 2, 26, and 5, respectively, all of which were superior to the comparison algorithms. …”
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Role of Bioinformatics in Agriculture by M. N. V. Prasad Gajula, Anuj Kumar, E. A. Siddiq, A. K. Polumetla

“… Bioinformatics is a practical discipline of science that employs a wide range of computational techniques including sequence analysis, data mining, gene finding, phylogenetic tree construction, prediction of protein structure and function, and interaction networks are only a few to mention. …”
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Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…Because of the privacy concerns about the transaction data, it is essential not to leak it when training prediction models for credit card fraud analysis. Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. …”
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