Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…The machining performance indicators of the first set are optimized using graphical method of contour plots. Artificial neural networks technique, which is well known for its versatility to model linear as well as non-linear data, is used to express the surface roughness as a function of tool geometrical variables. …”
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SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence by G. Sathish Kumar, E. Suganya, S. Sountharrajan, Balamurugan Balusamy, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan

“…The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets. …”
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Hybrid Analysis of Biochar Production from Pyrolysis of Agriculture Waste Using Statistical and Artificial Intelligent-Based Modeling Techniques by Hani Hussain Sait, Ramesh Kanthasamy, Bamidele Victor Ayodele

“…This study used response surface methodology (RSM) and artificial neural networks (ANNs) to optimize and predict the production of biochar from the pyrolysis of palm kernel shells. …”
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Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka by Helani Perera, Miyuru B. Gunathilake, Ravindu Panditharathne, Najib Al-mahbashi, Upaka Rathnayake

“…Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. …”
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Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques by Gunho Jung, Sun-Yong Choi

“…Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. …”
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Components and predictability of pollutants emission intensity by Z. Farajzadeh, M.A. Nematollahi

“…For this purpose, two well-known artificial neural networks, multilayer perceptron, and wavelet-based neural network were applied to forecast the emission intensity of the selected pollutants and their components.FINDINGS: The emission intensity of nitrogen oxides and sulphur dioxide illustrated a decreasing trend. …”
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Deteksi Cepat Kadar Alkohol Pada Minuman Kopi dengan Metode Dielektrik dan Jaringan Syaraf Tiruan by Sucipto Sucipto, Yuyun Rohmawati, Dyah Ayu Widyaningrum, Danang Triagus Setiyawan

“…The purpose of this study was to estimate the alcohol content and pH of coffee drinks based on the bioelectric of material and Artificial Neural Networks (ANN). The back propagation algorithm was used to connect the input of bioelectric properties and output of prediction of alcohol content and pH in liqueur coffee. …”
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Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods by Ahmad Almadhor, K. Mallikarjuna, R. Rahul, G. Chandra Shekara, Rishu Bhatia, Wesam Shishah, V. Mohanavel, S. Suresh Kumar, Sojan Palukaran Thimothy

“…The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. …”
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Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures by Hany A. Dahish, Ahmed D. Almutairi

“…The performance of the created models was compared to experimental data and earlier developed models: fuzzy logic models, artificial neural networks, genetic algorithms, and water cycle algorithms, using several evaluation metrics. …”
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Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. …”
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Multi-thermal recovery layout for a sustainable power and cooling production by biomass-based multi-generation system: Techno-economic-environmental analysis and ANN-GA optimizatio... by Ma Zihao, Naeim Farouk, Pradeep Kumar Singh, Azher M. Abed, Sarminah Samad, Samah G. Babiker, Iskandar Shernazarov, Ahmed Hendy, Ahmad Almadhor, Belgacem Bouallegue, Abdul Rahman Afzal

“…A novel approach combining artificial neural networks (ANN) with a non-dominated sorting genetic algorithm II (NSGA-II) has been developed to optimize the system, substantially reducing computational time and costs associated with system performance analysis. …”
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Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian... by Somayyeh Noei Teymoordash, Hoda Zendehdel, Ali Reza Norouzi, Mahdis Kashian

“…Most studies agree that Artificial Neural Networks (ANN) and Machine Learning (ML) models outperform conventional statistics in predicting postoperative outcomes.…”
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Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms by Yucong Gu, Kaiwen Wang, Zhengyu Zhang, Yi Yao, Ziming Xin, Wenjun Cai, Lin Li

“…The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. …”
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LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition by Ugur Akcal, Ugur Akcal, Ugur Akcal, Ivan Georgiev Raikov, Ekaterina Dmitrievna Gribkova, Ekaterina Dmitrievna Gribkova, Anwesa Choudhuri, Anwesa Choudhuri, Seung Hyun Kim, Mattia Gazzola, Rhanor Gillette, Rhanor Gillette, Ivan Soltesz, Girish Chowdhary, Girish Chowdhary, Girish Chowdhary

“…Despite promising demonstrations, many state-of-the-art (SOTA) VPR approaches based on artificial neural networks (ANNs) suffer from computational inefficiency. …”
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A Novel Model Using ML Techniques for Clinical Trial Design and Expedited Patient Onboarding Process by Iyer A, Narayanaswami S

“…Five ML models—XGBoost, Random Forest, Support Vector Classifier (SVC), Logistic Regression, and Decision Tree—were applied to both datasets, alongside Artificial Neural Networks (ANN) for the second dataset. Model performance was evaluated using precision, recall, balanced accuracy, ROC-AUC, and weighted F1-score, with results averaged across k-fold cross-validation.Results: In the first phase, XGBoost and Random Forest emerged as the best-performing models across all five subsets, achieving an average balanced accuracy of 0.71 and an average ROC-AUC of 0.7. …”
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A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections by Mengxiang Wang, Wang-Chien Lee, Na Liu, Qiang Fu, Fujun Wan, Ge Yu

“…Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. …”
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Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis by Zhongli Wang, Zhongli Wang, Gaopei Zhu, Shixue Li, Shixue Li

“…Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. …”
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Analysis of drought and extreme precipitation events in Thailand: trends, climate modeling, and implications for climate change adaptation by José Francisco de Oliveira-Júnior, David Mendes, Helder Dutra Porto, Kelvy Rosalvo Alencar Cardoso, José Augusto Ferreira Neto, Emannuel Bezerra Cavalcante da Silva, Marlúcia de Aquino Pereira, Monica Cristina Damião Mendes, Bernardo Bruno Dias Baracho, Punyawi Jamjareegulgarn

“…The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. …”
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PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model by Zugang Chen, Shaohua Wang, Kai Wu, Guoqing Li, Jing Li, Jian Wang

“…At last, our method was compared with existing Space2BVec and Buffer2BVec in terms of location prediction accuracy and to demonstrate the robustness of the binary vector encoding methods, two brain-inspired artificial neural networks— HTM and BinaryLSTM were used. The result showed that, for HTM, in smaller geospatial space the PlaceField2BVec and Buffer2BVec had about the same accuracy on average but the highest accuracy of PlaceField2BVec is 100 %; when the geospatial space extended, our method had the highest accuracy and the average accuracy of PlaceField2BVec, Space2BVec, and Buffer2BVec is 83.9 %, 25.2 % and 69.7 % after 20 times’ training. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. …”
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