Künstliche Intelligenz im Englischunterricht – Grundwissen und Praxisbeispiele by Inez De Florio-Hansen

“…What are the meanings of fundamental concepts such as algorithms, machine learning, and artificial neural networks? What are the best practices for inputting prompts (prompt engineering), and how can prompt quality be improved to achieve desired outcomes? …”
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Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height by Tzu-Chia Chen, Zryan Najat Rashid, Biju Theruvil Sayed, Arif Sari, Ahmed Kateb Jumaah Al-Nussairi, Majid Samiee-Zenoozian, Mehrdad Shokatian-Beiragh

“…This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. …”
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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

“…The proposed method of machine learning and the model of quantum neural networks can be used to create adaptive artificial intelligence systems as part of an online learning module. …”
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Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination by Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang

“…Abstract Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data by Nuraina Syaza Azman, Azurah A Samah, Ji Tong Lin, Hairudin Abdul Majid, Zuraini Ali Shah, Nies Hui Wen, Chan Weng Howe

“…The five smallest feature subsets (FS) are used in classification using SDAE and VAE neural networks to assess the quality of the feature subsets. …”
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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

“…Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. Our results show that incorporating averaging-based feature-engineered climate attributes significantly improves pasture yield predictions, with enhancements of up to 20.28%, 31.81%, and 31.11% across the three evaluation measures, RMSE, MAE, and R2, respectively. …”
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An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification by Cuiping Shi, Mengxiang Ding, Liguo Wang

“…Remote sensing scene images exhibit greater complexity in terms of land features, with large intra class differences and high inter class similarity, posing challenges in effectively extracting discriminative features. Convolutional neural networks are extensively used in RSSC tasks, where convolution focuses more on the high-frequency components of the image. …”
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Machine Learning-Integrated Usability Evaluation and Monitoring of Human Activities for Individuals With Special Needs During Hajj and Umrah by Ghadah Naif Alwakid

“…., random forest and sequential neural networks, both with 93% accuracy. The performance was fairly accurate, with RF scoring 1 for ‘LAYING’ and SNN scoring 0.99 for ‘WALKING_UPSTAIRS’. …”
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Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier by Zainab N. Ali, Iman Askerzade, Saddam Abdulwahab

“…The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers.…”
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Deep Learning for Traffic Scene Understanding: A Review by Parya Dolatyabi, Jacob Regan, Mahdi Khodayar

“…The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). …”
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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. …”
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Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach by Sajadul Hassan Kumhar, Prabhakara Rao Kapula, Harveen Kaur, Radeep R. Krishna, Mudasir M Kirmani, Vijay Anant Athavale, Mohd Wazih Ahmad

“…The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. …”
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DECTNet: A detail enhanced CNN-Transformer network for single-image deraining by Liping Wang, Guangwei Gao

“…Recently, Convolutional Neural Networks (CNN) and Transformers have been widely adopted in image restoration tasks. …”
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Neurophysiological Endophenotypes, CNS Disinhibition, and Risk for Alcohol Dependence and Related Disorders by Bernice Porjesz, Madhavi Rangaswamy

“…We found significant linkage and association between brain oscillations and genes involved with inhibitory neural networks (e.g., GABRA2, CHRM2), including frontal networks that are deficient in individuals with alcohol dependence, impulsivity, and related disinhibitory disorders. …”
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Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…For this purpose, Artificial Neural Networks (ANN), Automatic Linear Model (ALM), Random Forest (RF) Algorithm and Multivariate Adaptive Regression Spline (MARS) Algorithm were used, and the prediction performances of these methods were compared. …”
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The Multistream Self: Biophysical, Mental, Social, and Existential by Vinod D. Deshmukh

“…The experience of self is continuously generated by spontaneous activation of neural networks in the cerebral neocortex by the brainstem-diencephalic arousal system. …”
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Data-Driven Methodology for the Prediction of Fluid Flow in Ultrasonic Production Logging Data Processing by Hongwei Song, Ming Li, Chaoquan Wu, Qingchuan Wang, Shunke Wei, Mingxing Wang, Wenhui Ma

“…The extracted features were used as inputs to multilayer back-propagation neural networks, which water cuts as output. The present method was used to calibrate an ultrasonic Doppler sensor to estimate the flow rates of both phases in oil–water flow in a vertical pipe of diameter 159 mm. …”
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Enhancing Attendance Management Through Face Recognition Technology: A Case Study at Rugarama School of Nursing and Midwifery. by Taremwa, Benjamin

“…This study, titled "Enhancing Attendance Management through Face Recognition Technology: A Case Study at Rugarama School of Nursing and Midwifery," aimed to develop a more accurate and efficient solution using Local Binary Pattern Histogram and Convolutional Neural Networks algorithms to automate attendance tracking.A mixed-method approach was employed, combining system testing with user feedback from administrators, staff, and students. …”
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Vision-based manipulation of transparent plastic bags in industrial setups by F. Adetunji, F. Adetunji, A. Karukayil, A. Karukayil, P. Samant, P. Samant, S. Shabana, S. Shabana, F. Varghese, F. Varghese, U. Upadhyay, U. Upadhyay, R. A. Yadav, R. A. Yadav, A. Partridge, E. Pendleton, R. Plant, Y. R. Petillot, Y. R. Petillot, M. Koskinopoulou, M. Koskinopoulou

“…Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. …”
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