Harmonic Classification with Enhancing Music Using Deep Learning Techniques by Wen Tang, Linlin Gu

“…Technique of machine learning such the convolutional neural network (CNN) will systematically extract the chord sequence to achieve the superiority context model. …”
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The Application of the Depth Model of Precise Matching between People and Posts Based on Ability Perception in Human Resource Management by Hui Cao

“…In order to solve the problem that job seekers’ job-seeking ability is difficult to match the job requirements, this paper combines neural network with traditional HRM (human resource management) algorithm based on ability perception and designs a depth model of accurate matching of people and posts in HR field, which can improve the quality of data training of traditional algorithm. …”
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Forecasting Stock Prices with Artificial Intelligence by Zhao Danxuan

“…The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). …”
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Research on Literary Translation Based on the Improved Optimization Model by Hongjian Liu

“…In order to improve the accuracy of the intelligent translation literature of the semantic ontology optimization model, the conversion layer, including the forward neural network layer, residual connection layer, and normalization layer, is added between the encoder and decoder of the semantic ontology optimization model. …”
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Construction and Practice of Multiple Mixed Teaching Mode Based on Big Data Analysis: A Case Study of “International Trade” Course by Xiaoyuan Wu

“…Furthermore, the convolution neural network model is used to improve the consistency between the talent cultivation of international business major and the talent demand of enterprises by interviewing teachers and questionnaire survey of students. …”
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Automatic diagnosis of selected retinal diseases based on OCT B-scan by Tomasz Marciniak, Agnieszka Stankiewicz

“…The prepared software allows the reader to familiarize themselves with the topic of current artificial neural network solutions.…”
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Coordinated Control of Slip Ratio for Wheeled Mobile Robots Climbing Loose Sloped Terrain by Zhengcai Li, Yang Wang

“…To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system.…”
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Adaptive Integral Observer-Based Synchronization for Chaotic Systems with Unknown Parameters and Disturbances by Xiuchun Li, Jianhua Gu, Wei Xu

“…Numerical simulations are performed in the end and the results show that the proposed method is not only suitable to the representative chaotic systems but also applied to some neural network chaotic systems.…”
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ANN-Enhanced Energy Reference Models for Industrial Buildings: Multinational Company Case Study by Younes Ouaomar, Said Benkechcha, Mourad Kaddiri

“…This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. …”
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Optimization of Solar Panel Deployment Using Machine Learning by Shoaib Kamal, P. S. Ramapraba, Avinash Kumar, Bikash Chandra Saha, M. Lakshminarayana, S. Sanal Kumar, Anitha Gopalan, Kuma Gowwomsa Erko

“…The study takes into concern several topologies that includes series parallel topology, parallel topology, bridge link topology, honeycomb topology, and total cross tied. The artificial neural network-based topology reconfiguration strategy allows for optimal working conditions for PV arrays. …”
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Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic by Ofra Tirosh-Becker, Michal Kessler, Oren Becker, Yonatan Belinkov

“…Then, we experiment with both an off-the-shelf morphological tagger and several specially designed neural network taggers. Finally, we perform a real-world evaluation of new texts that were never tagged before in comparison with human expert annotators. …”
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Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder by Muhammad Faqih Dzulqarnain, Abdul Fadlil, Imam Riadi

“…The performance of this enhanced model was compared against a standard convolutional neural network (CNN) without the autoencoder. Experimental results demonstrate that the incorporation of the autoencoder significantly improved the classification accuracy, achieving 99% accuracy on the testing data and loss value of 3.4%. …”
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images by Anne M. Davis, Asahi Tomitaka

“…The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. …”
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Epilepsy: The Quintessential Pathology of Consciousness by Andrea Eugenio Cavanna, Fizzah Ali

“…This article provides a description of the phenomenology of ictal consciousness and reviews the underlying shared neural network, dubbed the 'consciousness system', which overlaps with the 'default mode' network. …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Currently, hybrid convolutional neural network-transformer architectures achieve state-of-the-art performances on benchmark datasets over standalone models. …”
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Generation of Variations on Theme Music Based on Impressions of Story Scenes Considering Human's Feeling of Music and Stories by Kenkichi Ishizuka, Takehisa Onisawa

“…Genetic algorithms (GAs) using modular neural network (MNN) models as fitness functions are applied to music generation in order to reflect user's feeling of music and stories. …”
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Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination by Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie

“…Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement. …”
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Adaptive Finite-Time Fault-Tolerant Control for Half-Vehicle Active Suspension Systems with Output Constraints and Random Actuator Failures by Jie Lan, Tongyu Xu

“…Unknown functions and coefficients are approximated by the neural network (NN). Assisted by the stochastic practical finite-time theory and FTC theory, the proposed controller can ensure systems achieve stability in a finite time. …”
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Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete by Palika Chopra, Rajendra Kumar Sharma, Maneek Kumar, Tanuj Chopra

“…Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.…”
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A tactile sensor for recognition of softness using interlocking structure of carbon nanoparticle- polydimethylsiloxane composite by Sangmin Lee, Jaewon Jang, Wanjun Park

“…This is supported by tests conducted using a one-dimensional convolutional neural network (1D-CNN).…”
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