Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning by Spyridon Chavlis, Panayiota Poirazi

“…Abstract Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. …”
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Facial expression recognition based on adaptation of the classifier to videos of the user by E.N. Churaev, A.V. Savchenko

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Survey on vertical handoff in heterogeneous wireless networks by Yu-wei WANG, Min LIU, Bing-yi FANG

“…Vertical handoff between different access technologies is the basic requirement for convergence of different access technologies.It is also the key technology to implement interconnection of heterogeneous networks and support seamless integration of different access networks.As a result,providing efficient solutions for vertical handoff has received tremendous attention from the academia and industry all over the world.With the trend of diversity of access technologies,unification of networks and synthesis of applications,cellular mobile communication,broadband wireless access and fixed access will be connected to a unified IP-based core network.The integration of existing and emerging heterogeneous wireless networks requires the design of intelligent vertical handoff techniques to enable mobile users to switch network access points and experience uninterrupted service continuity.During the vertical handoff procedure,handoff decision is a crucial issue for an efficient mobility.The definition and key concept of vertical handoff are given and its categories are introduced.Subsequently,the authors classify the vertical handoff decision algorithms into three main categories and overview their basic principles.Especially,the features of these three categories of algorithms are analyzed and compared.Finally,future works about vertical handoff are outlined.…”
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Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients. by Yingjie Kuang, Zhixin Cheng, Jun Zhang, Chunxu Yang, Yue Zhang

“…Logistic regression analysis was employed to identify related factors, and machine learning algorithms were used to construct the risk prediction model.…”
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Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach by Noah Oghenefego Ogwara, Krassie Petrova, Mee Loong Yang

“…The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). …”
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The Use of Machine Learning Methods in Road Safety Research in Poland by Anna Borucka, Sebastian Sobczuk

“…The aim of this study was to develop mathematical models based on ML algorithms describing road safety in Poland. First, variables with the strongest impact on safety were extracted. …”
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Development of risk models for early detection and prediction of chronic kidney disease in clinical settings by Pegah Bahrami, Davoud Tanbakuchi, Monavar Afzalaghaee, Majid Ghayour-Mobarhan, Habibollah Esmaily

“…Interestingly, an interaction was found between age and gender where contrasting data was generated, therefore, to avoid this interaction gender-specific algorithms were developed. Four main algorithms and four algorithms using the stratified K-folds cross-validation technique, consisting of gender-specific Random Forest and feedforward Neural Networks were developed using the preprocessed data of 6855 participants. …”
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Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Fatemeh Mohammadinia, Amirjavad Borhani

“…Input data for these algorithms include CO2, H2S, N2, C1, C2, C3, C4, C5, C6, C7+, MWC5+, MWC7+, T, alongside vol/int. …”
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Enhancing palm precision agriculture: An approach based on deep learning and UAVs for efficient palm tree detection by Yosra Hajjaji, Wadii Boulila, Imed Riadh Farah, Anis Koubaa

“…We are currently investigating deep-learning-based object detection algorithms and remote sensing (RS) technologies to enhance industry efficiency. …”
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Assessing the performance of machine learning and analytical hierarchy process (AHP) models for rainwater harvesting potential zone identification in hilly region, Bangladesh by Md. Mahmudul Hasan, Md. Talha, Most. Mitu Akter, Md Tasim Ferdous, Pratik Mojumder, Sujit Kumar Roy, N.M. Refat Nasher

“…This study aims to evaluate the potential zones for rainwater harvesting (RWH) using machine learning (ML) algorithms and geospatial analysis. Specifically, four ML algorithms—random forest (RF), boosted regression trees (BRT), k-nearest neighbors (KNN), and naïve bayes (NB)—alongside the analytical hierarchy process (AHP) were employed to delineate potential RWH zones in the Chattogram hilly districts, including Chattogram, Rangamati, Bandarban, Khagrachari, and Cox’s Bazar. …”
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Obstacle avoidance and formation control of multiple unmanned vehicles in complex environments based on artificial potential field method by Yilin MEI, Likun CUI, Xueyan HU, Guangqi HU, Hao WANG

“…Specifically, the success rate of obstacle avoidance in dynamic environments increased by 35% compared to traditional algorithms and by 10% compared to improved algorithms. …”
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Prediction of Future Terrorist Activities Using Deep Neural Networks by M. Irfan Uddin, Nazir Zada, Furqan Aziz, Yousaf Saeed, Asim Zeb, Syed Atif Ali Shah, Mahmoud Ahmad Al-Khasawneh, Marwan Mahmoud

“…The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. …”
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Forecasting insect abundance using time series embedding and machine learning by Gabriel R. Palma, Rodrigo F. Mello, Wesley A.C. Godoy, Eduardo Engel, Douglas Lau, Charles Markham, Rafael A. Moral

“…We pre-processed the data using our newly proposed approach and more straightforward approaches commonly used to train learning algorithms. We evaluate the performance of the selected algorithms by looking at the Pearson correlation and Root Mean Squared Error obtained using one-step-ahead forecasting. …”
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The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer by Yan Lu, Long Jin, Ning Ding, Mengjuan Li, Shengnan Yin, Yiding Ji

“…Modelintra, modelperi, modelintra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. …”
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Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence by Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza, Francesca Ardito, Anna Maria Stellacci, Afwa Thameur, Sergio Ruggieri, Sabina Tangaro, Marcello Mastrorilli, Nicola Sanitate, Simone Pietro Garofalo

“…Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. …”
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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

“…Tracking algorithms and depth-sensing technologies are integrated to enable 3D spatial awareness during pick-and-place operations. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. …”
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SIGNAL FILTERING FOR SUDDEN SENSOR FAILURES BASED ON RECURRENT LEAST SQUARES METHOD by V. M. Artemiev, A. O. Naumov, L. L. Kokhan

“…It allows to design simple filtering algorithms with failure detection and to realize filter structure reorganization providing filter functionality.…”
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Optimization problems for construction foundations by Rimantas Belevičius, Saulius Valentinavičius

“…Solutions of a number of problems demonstrate the validity of proposed algorithms. …”
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Units in families of totally complex algebraic number fields by L. Ya. Vulakh

“…Multidimensional continued fraction algorithms associated with GLn(ℤk), where ℤk is the ring of integers of an imaginary quadratic field K, are introduced and applied to find systems of fundamental units in families of totally complex algebraic number fields of degrees four, six, and eight.…”
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