A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Real-Time Railway Hazard Detection Using Distributed Acoustic Sensing and Hybrid Ensemble Learning by Yusuf Yürekli, Cevat Özarpa, İsa Avcı

“…In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. …”
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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

“…The algorithms examined include decision trees, K-nearest neighbors, random forests, support vector machines, XGBoost, gradient boosting, and bagging. …”
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Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach by Waqar Ali, Idriss El-Thalji, Knut Erik Teigen Giljarhus, Andreas Delimitis

“…However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. …”
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Dual Passive-Aggressive Stacking k-Nearest Neighbors for Class-Incremental Multi-Label Stream Classification by Hann Hsen Tan, Chu Kiong Loo, Chaw Seng Woo

“…Most learning algorithms in the literature can fulfill only a subset but not all of these desiderata. …”
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Development of Risk Activity Detection System for Forklifts Based on Inertial Sensors by Luciano Radrigan, Sebastian E. Godoy

“…Videos of the operation were also taken as reference. In this paper, we developed convolutional neural networks (CNN) and long-term memory (LSTM) algorithms to infer a risky maneuver from the inertial sensors data and compared it to the outcome of a video-based model trained on data labeled by a risk-prevention engineer. …”
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Machine learning in predicting firm performance: a systematic review by Yaseen Hezam, Hoa Luong, Lilian Anthonysamy

“…It aims to assess the effectiveness of various ML methods and algorithms used in recent research, focusing on the prediction of firm performance across multiple dimensions. …”
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Use of Aposteriori Information in the Implementation of Radar Recognition Systems Using Neural Network Technologies by Dmitrii F. Beskostyi, Sergei G. Borovikov, Yurii V. Yastrebov, Ilya A. Sozontov

“…It is shown that the removal of restrictions associated with the functional autonomy of REE, allows the use of posterior information in the implementation of radar recognition systems. This also allows for an increase in the number of recognition signs used in the algorithms and for the database of portraits to be replenished. …”
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Single target tracking in high-resolution satellite videos: a comprehensive review by Xin Huang, Ding Wang, Qiqi Zhu, Ying Zheng, Qingfeng Guan

“…In addition, this paper also collects the latest open-source datasets and predicts promising future research directions. …”
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Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches by Cvetkovski Goga, Petkovska Lidija

“…In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…According to this analysis, the most used features are temperature, soil type, and vegetation. Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Frame Theory and Fractional Programming for Sparse Recovery-Based mmWave Channel Estimation by Razvan-Andrei Stoica, Hiroki Iimori, Giuseppe Thadeu Freitas de Abreu, Koji Ishibashi

“…The two algorithms differ from each other in that in the first (slightly more accurate), the resulting convex problem is solved via interior point methods, while the second (stand-alone) makes use of the alternating direction method of multipliers (ADMM). …”
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A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing by Haiwen Yuan, Changshi Xiao, Supu Xiu, Wenqiang Zhan, Zhenyi Ye, Fan Zhang, Chunhui Zhou, Yuanqiao Wen, Qiliang Li

“…The corresponding feature detection and pose estimation algorithms are also presented. In the end, typical simulation and field experiments have been carried out to illustrate the proposed method. …”
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Cranioplasty: Materials and Methods Review by A. V. Yarikov, A. P. Fraerman, V. A. Leonov, O. A. Perlmutter, S. E. Tikhomirov, A. V. Yaksargin, P. V. Smirnov

“…Currently, there are no clear established algorithms or timing for cranioplasty. This paper presents data on the history and stages of the development of reconstructive neurosurgery. …”
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FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events by Yannek Nowatzky, Francesco Friedrich Russo, Jan Lisec, Alexander Kister, Knut Reinert, Thilo Muth, Philipp Benner

“…FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. …”
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Tyre pattern image retrieval – current status and challenges by Liu Ying, Liu Qiqi, Fan Jiulun, Wang Fuping, Fu Jianlong, Yuan Qingan, Chiew Tuan Kiang, Ling Nam

“…This paper also surveys the available tyre pattern datasets used in all available literature. …”
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The Transition To Artificial Intelligence -Based Solutions For Improving Energy Efficiency In Urban Environments by Ikram MENAI, Hana SALAH SALAH, Sara KHELIL, Amina AIDAOUI, Fatima Zahra DJOUAD

“…It addresses issues such as data privacy, algorithmic biases and the need for transparent decision-making processes. …”
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Natural Language Processing Techniques for Information Retrieval Enhancing Search Engines with Semantic Understanding by S Subi, B Shanthini, M SilpaRaj, K Shekar, G Keerthana, R Anitha

“…The proposed ideas focus on reducing the size of the model (one of the biggest problems with large models), training it on domain-specific knowledge (the right knowledge is important for the real application) and ways to efficiently deal with unstructured data (this is also a key issue against NLP frameworks). The study highlights the need for hybrid models that combine generalization and specificity, fast algorithms for big data sets, and automated knowledge extraction. …”
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The urban heat Island effect: A review on predictive approaches using artificial intelligence models by Ali Najah Ahmed, Nouar AlDahoul, Nurhanani A. Aziz, Y.F. Huang, Mohsen Sherif, Ahmed El-Shafie

“…This study provides a comprehensive review of research on the UHI effect, analysing and classifying studies that utilize a variety of input–output datasets. It also examines predictive methods used to estimate UHI intensity, categorizing them into conventional machine learning (ML) algorithms, deep learning (DL) models, and hybrid approaches. …”
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Human responses to the DNA prime/chimpanzee adenovirus (ChAd63) boost vaccine identify CSP, AMA1 and TRAP MHC Class I-restricted epitopes. by Harini Ganeshan, Jun Huang, Maria Belmonte, Arnel Belmonte, Sandra Inoue, Rachel Velasco, Santina Maiolatesi, Keith Limbach, Noelle Patterson, Marvin J Sklar, Lorraine Soisson, Judith E Epstein, Kimberly A Edgel, Bjoern Peters, Michael R Hollingdale, Eileen Villasante, Christopher A Duplessis, Martha Sedegah

“…Two protection-associated epitopes were promiscuous and may have also contributed to protection by recognition of different HLA alleles. …”
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