KDDC: a new framework that integrates kmers, dataset filtering, dimension reduction and classification algorithms to achieve immune cell heterogeneity classification by Nan Zhang, Nan Zhang, Shishun Zhao, Runze Wu, Xizi Luo, Ming Yang, Zecheng Chang, Jianting Xu

“…IntroductionIntegrating immune repertoire sequencing data with single cell sequencing data offers profound insights into the diversity of immune cells and their dynamic changes across various disease states.MethodsHere, we propose a novel KDDC framework that integrates kmers, dataset selection, dimensionality reduction and classification algorithms to facilitate the heterogeneous classification of immune cells.Results and DiscussionBy comparing various kmer length combinations across seven different classification algorithms, we found that B cell receptor-based cellsubset classification outperforms T cell receptor-based classification, achievingan average AUC of over 96%. …”
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Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction by Shweta Ashish Koparde, Sonali Kothari, Sharad Adsure, Kapil Netaji Vhatkar, Vinod V. Kimbahune

“…This research • Introduces a novel approach for predicting the host of influenza viruses by leveraging protein sequences. • Extraction of features, including sequence length, Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), aromaticity, secondary structure fraction, and entropy from protein sequence. • Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. …”
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Comparing Geodesic Filtering to State-of-the-Art Algorithms: A Comprehensive Study and CUDA Implementation by Pierre Boulanger, Sadid Bin Hasan

“…Additionally, we present a highly optimized GPU implementation featuring innovative wave-propagation algorithms and memory access optimization techniques that achieve a 200× speedup, making geodesic filtering practical for real-time applications. …”
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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model by Jingjing Sun, Xin Guan, Xiaojun Sun, Xiaojing Cao, Yepei Tan, Jiarong Liao

“…The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model’s prediction accuracy and stability. …”
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Dual-Closed-Loop Control System for Polysilicon Reduction Furnace Power Supply Based on Hysteresis PID and Predictive Control by Shihao Li, Tiejun Zeng, Shan Jian, Guiping Cui, Ziwen Che, Genghong Lin, Zeyu Yan

“…In the power system of a polysilicon reduction furnace, especially during the silicon rod growth process, the issue of insufficient temperature control accuracy arises due to the system’s nonlinear and time-varying characteristics. …”
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Design of weighted based divided-search enhanced Karnik–Mendel algorithms for type reduction of general type-2 fuzzy logic systems by Yang Chen

“…Here the weighted type-reduction algorithms based on the Newton and Cotes quadrature formulas of numerical methods of integration technique are first given, and the searching spaces are divided. …”
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GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model by M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi

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Joint reduction of peak-to-average power ratio and out-of-band power based on subcarrier weighting in OFDM systems by Qing LI, Han-ying HU

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Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs by Yu-feng Qian, Wan-liang Guo

“…Conclusions Deep learning algorithms developed using multimodal medical imaging may help predict recurrent intussusception. …”
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Improving lameness detection in cows: A machine learning algorithm application by Elma Dervić, Caspar Matzhold, Christa Egger-Danner, Franz Steininger, Peter Klimek

“…A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. …”
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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. …”
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AEA-YOLO: Adaptive Enhancement Algorithm for Challenging Environment Object Detection by Abdulrahman Kariri, Khaled Elleithy

“…To address these issues, we provide an Adaptive Enhancement Algorithm YOLO (AEA-YOLO) framework that allows for an enhancement in each image for improved detection capabilities. …”
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Dye-cleaning prediction with a variant of nature-inspired algorithms coupled with extreme gradient boosting by Tiyasha Tiyasha, Chijioke Elijah Onu, Mohamed A. Ismail, Rama Rao Karri, Abdelfattah Amari, Vinay Kumar, Suraj Kumar Bhagat

“…Hyperparameter tuning via differential evolution (DE), genetic algorithm (GA), random search (RS), and grid search (GS) with the XGBoost model was conducted to achieve more accurate results. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. …”
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Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar, Punit Fulzele

“…<b>Results:</b> The performance of three boosting algorithms is studied for stroke prediction, which include Gradient Boosting (GB), AdaBoost (ADB), and XGBoost (XGB) with XGB achieved the best outcome overall with a training accuracy of 96.97% and testing accuracy of 92.13%. …”
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The CORDIC algorithm for high-speed image rotation on FPGA platform by Gao Yujie, Li Wusen, Qi Yunfei, Chen Wenjian

Subjects: “…coordinate rotation digital algorithm…”
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Comparative Study of Sphere Decoding Algorithm and FCS-MPC for PMSMs in Aircraft Application by Joseph O. Akinwumi, Yuan Gao, Xin Yuan, Sergio Vazquez, Harold S. Ruiz

“…In this study, we propose a long prediction horizon finite control set model predictive control (FCS-MPC) framework for PMSMs. …”
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Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition by Faheed A.F. Alrslani, Manal Abdullah Alohali, Mohammed Aljebreen, Hamed Alqahtani, Asma Alshuhail, Menwa Alshammeri, Wafa Sulaiman Almukadi

“…The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimization algorithm (MLOA) to choose optimal feature sets. …”
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A Distribution Network Expansion Project Classification Model Based on Data Augmentation and Dimensionality Reduction Method by Xin ZHOU, Jingxing LIN, Zhiwei XIE, Zheng ZHANG, Ruduo LIANG, Zuhong OU

“…Based on the data of a distribution network expansion project of a power supply bureau, the simulation results show that the classification accuracy of the algorithm used in this paper is better than other algorithms. …”
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Electricity usage prediction using developed human evolutionary optimization algorithm and Xception neural network by Dongxian Yu, Di Wu, Chongyang Liao, Zaihui Cao, Somayeh Pouramini

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