Machine learning and AI for advancing Parkinson's disease diagnosis: exploring promising applications by Shohreh Abdollahi, Ramin Safa

“…The analysis reveals that Artificial Neural Network achieves the highest accuracy of 92.4%, surpassing Logistic Regression and Support Vector Machine. …”
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Adaptive and Resilient Flight Control System for a Small Unmanned Aerial System by Gonzalo Garcia, Shahriar Keshmiri

“…The real-time artificial neural network parameter identification is done using the sliding mode learning concept and a modified version of the self-adaptive Levenberg algorithm. …”
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Forecasting energy production of a PV system connected by using NARX neural network model by Marwa M. Ibrahim, Amr A. Elfeky, Amal El Berry

“…Applying artificial neural network techniques to forecast the electricity production of photovoltaic (PV) power plants is a novel concept. …”
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Federated Learning-Based Load Forecasting for Energy Aggregation Service Providers by HUANG Yichuan, SONG Yuhui, JING Zhaoxia

“…Subsequently，a weighted-average strategy was used to jointly train the artificial neural network，bridging the gap between individual energy entities and environmental features. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…The impact analysis on the factors such as input length, neuron number, artificial neural network (ANN) optimizer, and output scope, are taken. …”
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Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks by A. Bakiya, A. Anitha, T. Sridevi, K. Kamalanand

“…Correspondingly, the developed conventional single layer artificial neural network (neurons=7) with time domain features has shown an accuracy of 83.3%, sensitivity of 100%, and specificity of 71.42%. …”
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EM Wave-Based Hand Gesture Recognition for Astronauts Using 3D Memristive Neural Network by Shilpa Pavithran, Sruthi Pallathuvalappil, Elizabeth George, Javed G S, Alex James

“…Four hand gestures are performed and classified using a three-dimensional memristive Artificial Neural Network (3D-ANN) based on Skywater 130 nm PDK (SKY130) for the ON-body to OFF-body scenario with an accuracy of 80%. …”
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Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence by Mingzhong Li, Guodong Zhang, Jianquan Xue, Yanchao Li, Shukai Tang

“…Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. …”
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Analysis of the Strategic Emission-Based Energy Policies of Developing and Developed Economies with Twin Prediction Model by Yulian Jiang, Wuchang Wei, Ramesh Chandra Das, Tonmoy Chatterjee

“…One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. …”
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A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection by Farah Aqilah Bohani, Azizah Suliman, Mulyana Saripuddin, Sera Syarmila Sameon, Nur Shakirah Md Salleh, Surizal Nazeri

“…In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. …”
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Improving Fuel Consumption Prediction for Marine Diesel Engines Using Hierarchical Neural Networks and Pulsating Exhaust Models by Anibal Aguillon Salazar, Georges Salameh, Pascal Chesse, Nicolas Bulot, Yoann Thevenoux

“…This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. …”
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Robust Prediction of Healthcare Inflation Rate With Statistical and AI Methods in Iran by Mohammad Javad Shaibani, Ali Akbar Fazaeli

“…In the next process, by doubling the forecasting window, it is observed that artificial neural network (ANN) (i.e., Bayesian NARANN) strictly outperformed other models. …”
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Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete by Liyun Cui, Peiyuan Chen, Liang Wang, Jin Li, Hao Ling

“…Another highlight is that its performance was compared with the frequently used artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) by using random dataset and the same testing datasets. …”
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A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption by Osama Sohaib, Walayat Hussain, Muhammad Asif, Muhammad Ahmad, Manuel Mazzara

“…The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. …”
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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…Maka dari itu dapat disimpulkan penerapan menggunakan algoritma Naïve Bayes(NB) berbasis PSO mendapatkan hasil kenerja dengan bobot sebesar 94.40% dan angka AUC sebesar 94.50%, berikutnya Algoritma Support Vectore Machine(SVM) Berbasis PSO dengan hasil kinerja akurasi sebesar 88.20 dan nilai AUC seberar 91.10%, dan Artificial Neural Network(NN) berbasis Particle Swarm Optimizatio(PSO) menghasilkan skor hasil kinerja akurasi dengan bobot 99.20% dan nilai akurasi sebesar 98.50%, maka Artificial Neural Network(NN)  berbasis PSO memiliki keunggulan lebih besar dari pada algoritma naïve bayer berbasis PSO dan Support Vector Machine(SVM) dengan PSO. …”
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Evaluation of sustainable energy use in sugarcane production: A holistic model from planting to harvest and life cycle assessment by Molood Behnia, Mohammad Ghahderijani, Ali Kaab, Marjan Behnia

“…The analysis compares Artificial Neural Network and Adaptive Neuro-Fuzzy Inference Systems models for predicting energy outputs and environmental effects. …”
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Vector semantic computing method study for short sentence by Fu CHEN, Chuang LIN, Chao XUE, Yue-mei XU, Kun MENG, Yi-han NI

“…A vector semantic computing method study for short sentence based on artificial neural network was proposed. And a semantic computational algorithm for social network texts as well as a discovery algorithm for mergencies was provided with reference to the information provided by the social nodes itself and the semantic of the text. …”
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An emotional neural network based approach for wind power prediction by Guoling ZHANG

“…The results demonstrate that, compared with artificial neural network, ENN can improve the accuracy and reliability of the forecast by 3.8% and 46% respectively.…”
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Design and application of electrocardiograph diagnosis system based on multifractal theory by Chun-kai ZHANG, Ao,ZHANGJing-wang YIN, Pan-bo TIAN, Ying ZHOU, Chang-qing QI

“…An automatic segmentation algorithm for ECG data using differential threshold method was designed,which could identify the various ECG cycles of continuous ECG data.And it could obtain the multifractal features of multiple fractal and generalize hurst index feature of ECG data,these features were used to train artificial neural network in order to classify ECG data,the accuracy of the classifier could reach 97%.An ECG diagnosis system was implemented,which can automatically identify ECG sequences that contain multiple ECG cycles,and can automatically ignore the incomplete ECG cycle data,and could annotate every cycle of ECG data.…”
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An Intelligent System for Load Forecasting on a Distribution Network by Saheed Gbadamosi, Ojo Olukayode Adedayo

“…This technique is an integrated system consisting of fuzzy logic systems and Artificial Neural network (ANN). The inputs to the system include days of the week, temperature, time, current and previous hourly load on the distribution network. …”
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