Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China by Baran Yılmaz, Rachel Samra

“…In this work, long short-term memory has been optimized using Particle swarm optimization, Grasshopper optimization algorithm, and Slime mold algorithm. SMA-LSTM, which has the best performance result compared to other developed models, is presented as the main method for this work. …”
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Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according... by Lulu Liu, Qian Ma, Guangzan Yu, Xuhou Ji, Hua He

“…Moreover, we employed Boruta algorithm to evaluate the predictive potential of the NMa ratio and established the prediction models utilizing machine learning algorithms. …”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Input features are identified using the Partial Autocorrelation Function , and models are optimized using the Optuna algorithm. The final prediction combines the trend, seasonal, and residual components’ predictions. …”
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Automatic Bone Fracture Reduction Technique with Section Registration by Qinhui YUAN, Mengxing LIU, Chu GUO, Yukun AN, Ping ZHOU

“…In order to enhance treatment efficiency and accuracy, an automatic fracture reduction algorithm is proposed. This algorithm utilizes the similarity of fracture cross-sections for registration, thereby reducing the workload of physicians and eliminating the need for a healthy-side bone template. …”
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Big Data Reduction and Optimization in Sensor Monitoring Network by Bin He, Yonggang Li

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A Parallel Attribute Reduction Method Based on Classification by Deguang Li, Zhanyou Cui

“…Finally, the proposed algorithm and the traditional algorithm are analyzed and compared by experiments, and the results show that the proposed method in this paper has more advantages in time efficiency, which proves that the method could improve the processing efficiency of attribute reduction and makes it more suitable for massive data sets.…”
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The application of adaptive symmetry reduction for LTL model checking by I. V. Konnov, V. A. Zakharov

“…Adaptive symmetry reduction is a technique which exploits the similarity of com- ponents in systems of regular structure. …”
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Enhanced Wind Power Forecasting Using a Hybrid Multi-Strategy Coati Optimization Algorithm and Backpropagation Neural Network by Hua Yang, Zhan Shu, Zhonger Li

Subjects: “…wind power prediction…”
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Modern algorithms for statin therapy by V. A. Susekov

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Retracted: Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm by Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha

“…Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12092)]. …”
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Cooperative Sleep and Energy-Sharing Strategy for a Heterogeneous 5G Base Station Microgrid System Integrated with Deep Learning and an Improved MOEA/D Algorithm by Ming Yan, Tuanfa Qin, Wenhao Guo, Yongle Hu

“…Numerical results indicate that our approach achieves significant energy savings while ensuring accurate predictions of BSMG energy demands through a multi-objective evolutionary algorithm based on decomposition.…”
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A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors by Huiling Qin, Mudassar Rehman, Muhammad Farhan Hanif, Muhammad Yousaf Bhatti, Muhammad Kamran Siddiqui, Mohamed Abubakar Fiidow

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Classification of imbalanced travel mode choice dataset with SMOTE and prediction using interpretable machine learning by Mujahid Ali

“…Therefore, the current study used the Synthetic Minority Oversampling Technique (SMOTE) to balance the TMC dataset and used several ML algorithms such as random forest (RF), decision tree (DT), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), and logistic regression for the prediction of TMC. …”
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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms by Priya Mathur, Hammad Shaikh, Farhan Sheth, Dheeraj Kumar, Amit Kumar Gupta

“…The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. …”
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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). …”
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Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions by Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan, Ateeq Ur Rehman, Mamoon M. Saeed, Habib Hamam

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i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus by Sakshi Gautam, Anamika Thakur, Manoj Kumar

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A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes by Hossein Moayedi, Atefeh Ahmadi Dehrashid, Binh Nguyen Le

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Development and Evaluation of a Calibration Methodology for Pressure-Resistive Sensors in a Specialized Diagnostic Laboratory by Roman Zelnik, Roman Ruzarovsky, Tibor Horak, Martin Csekei, David Michal, Jan Sido

Subjects: “…Autonomous calibration algorithms…”
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Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Zolbanin

“…To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. …”
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