Secret sharing in a special linear group by V. I. Yanchevskiĭ, I. A. Havarushka, G. V. Matveev

“…A rigorous mathematical justification is given for the correctness of the algorithms for generating partial secrets and restoring the main secret in the special linear group over the ring of integers. …”
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A System of Remote Patients’ Monitoring and Alerting Using the Machine Learning Technique by M. Dhinakaran, Khongdet Phasinam, Joel Alanya-Beltran, Kingshuk Srivastava, D. Vijendra Babu, Sitesh Kumar Singh

“…The proposed methodology makes use of different machine learning algorithms along with cloud computing for continuous data storage. …”
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Shape Restoration by Active Self-Assembly by D. Arbuckle, A. A. G. Requicha

“…Simulations presented here show that swarms of such robots organize themselves to achieve shape restoration by using distributed algorithms. This is one more example of an interesting geometric problem that can be solved by the Active Self-Assembly paradigm introduced in previous papers by the authors.…”
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QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP AND MOLECULAR DOCKING STUDIES OF HYDROXAMIC ACID DERIVATIVES AS NOVEL CLASS INHIBITORS AGAINST HELICOBACTER PYLORI UREASE by Ibrahim Tijjani Ibrahim, Adamu Uzairu, Balarabe Sagagi

“…Relevant molecular descriptors were selected by Genetic Function Algorithms (GFA). The best model obtained was given a distinct validated, good and robust statistical parameters which include; square correlation coefficient R2 value of (0.9989), adjusted determination coefficient, R2adj value of (0.9984), Leave one out cross validation determination coefficient Q2 value of (0.9948) and external validation as predicted determination coefficient R2 value of(0.8409). …”
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Recent Research Progress on Ground-to-Air Vision-Based Anti-UAV Detection and Tracking Methodologies: A Review by Arowa Yasmeen, Ovidiu Daescu

“…Our study examines recent UAV detection and tracking algorithms, outlining their operational principles, advantages, and disadvantages. …”
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A Model-Driven Realization of AUV Controllers Based on the MDA/MBSE Approach by Ngo Van Hien, Van-Thuan Truong, Ngoc-Tam Bui

“…This paper introduces a model-driven control realization, which is based on the systems engineering concepts of the model-driven architecture (MDA)/model-based systems engineering (MBSE) approach combined with the real-time UML/SysML, extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, in order to conveniently deploy controllers of autonomous underwater vehicles (AUVs). …”
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Motor control method using single-sensor phase current reconstruction by Yin Lu, Yuntian Huang, Hao Guo

“…Simultaneously, optimization algorithms like neural networks are employed to learn from historical data to predict and estimate the current values of the three phases. …”
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Deciphering the Immune Subtypes and Signature Genes: A Novel Approach Towards Diagnosing and Prognosticating Severe Asthma Through Interpretable Machine Learning by Yue Hu, Yating Lin, Bo Peng, Chunyan Xiang, Wei Tang

“…We employ single-sample gene set enrichment analysis (ssGSEA) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms to identify differentially expressed immune cells and utilize machine learning techniques, including Extreme Gradient Boosting (XGBoost) and random forest, to predict severe asthma outcomes and identify key genes associated with immune cells. …”
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Predicting Changes of the Cultivation Areas for Astamaran and Berhi Cultivars in Iran in the 21st Century by Maryam Afzali, javad khoshhal dastjerdi, Aziz Torahi

“…Global warming in the last century has led planners to design pre-awareness programs and algorithms due to future climatic conditions in order to choose long-lived durable plants that can survive in future environmental conditions and have good economic yield. …”
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Minimizing Delay and Power Consumption at the Edge by Erol Gelenbe

“…Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. …”
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Flow and Transport in Tight and Shale Formations: A Review by Amgad Salama, Mohamed F. El Amin, Kundan Kumar, Shuyu Sun

“…Each scale has its own tools and limitations that may not, probably, be suitable at other scales. Multiscale algorithms that effectively couple simulations among various scales of porous media are therefore important. …”
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Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning researchMendeley Data by Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

“…Moreover, the dataset serves as a valuable resource for training and testing machine learning algorithms tailored to agricultural settings, facilitating advancements in automated agricultural technologies. …”
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Evaluation of novel NaOH/activated carbon/zeolite biocomposite as an efficient adsorbent for oilfield produced water treatment by Eghe Amenze Oyedoh, Onyedikachi Praise Igbokwe

“…The adsorption process was optimized using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) integrated with genetic algorithms. The results indicated a hydrocarbon removal efficiency of 99.86 % with RSM and 99.99 % with ANN under optimal conditions. …”
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Three-Dimensional Location and Mapping Analysis in Mobile Robotics Based on Visual SLAM Methods by Gustavo A. Acosta-Amaya, Juan M. Cadavid-Jimenez, Jovani A. Jimenez-Builes

“…Two of the most representative algorithms for solving vSLAM were considered (RTAB-Map and ORB-SLAM2). …”
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A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran by Arezo Mohtaram, Hossein Shafizadeh-Moghadam, Hamed Ketabchi

“…This was accomplished using a robust and flexible multi-scale approach, leveraging machine learning algorithms, specifically random forest and LightGBM. …”
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Spaced Seed Data Structures for De Novo Assembly by Inanç Birol, Justin Chu, Hamid Mohamadi, Shaun D. Jackman, Karthika Raghavan, Benjamin P. Vandervalk, Anthony Raymond, René L. Warren

“…Many scalable assembly algorithms use the de Bruijn graph (DBG) paradigm to reconstruct genomes, where a table of subsequences of a certain length is derived from the reads, and their overlaps are analyzed to assemble sequences. …”
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Fractional Partial Differential Equation: Fractional Total Variation and Fractional Steepest Descent Approach-Based Multiscale Denoising Model for Texture Image by Yi-Fei Pu, Ji-Liu Zhou, Patrick Siarry, Ni Zhang, Yi-Guang Liu

“…The experimental results prove that the abilities of the proposed denoising model to preserve the high-frequency edge and complex texture information are obviously superior to those of traditional integral based algorithms, especially for texture detail rich images.…”
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Detection of GRBs and OTs by All-Sky Optical and SID Monitors by R. Hudec, M. Spurny, M. Krizek, P. Pata, R. Slosiar, M. Rerabek, M. Klima

“…The processing and measuring of image data is complicated, and sophisticated deconvolution algorithms are used for image restoration. The second method is the GRB detection based on their ionospheric response.…”
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Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models by Guojiang Xiong, Jing Zhang, Dongyuan Shi, Xufeng Yuan

“…The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. …”
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Dynamic and Quantitative Method of Analyzing Service Consistency Evolution Based on Extended Hierarchical Finite State Automata by Linjun Fan, Jun Tang, Yunxiang Ling, Benxian Li

“…And also the service consistency evolution algorithms (SCEAs) based on EHS-FSA are developed to quantitatively assess these impact factors. …”
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