Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…In the second phase, the weight of the factors affecting OAEs was investigated using deep learning (DL) and support vector machine (SVM) algorithms. …”
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Exploring the use of tutorial recordings for beginner distance learners of Chinese by Pleines Christine, Kan Qian

“…Dialogue between tutor and learner or between peers may mediate the understanding not just of direct participants, but also of listeners, and listening to interactive recordings may facilitate deep learning. In language learning, both direct and indirect interactions have been shown to contribute to language development. …”
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Rapid and Nondestructive Detection of Proline in Serum Using Near-Infrared Spectroscopy and Partial Least Squares by Kejing Zhu, Shengsheng Zhang, Keyu Yue, Yaming Zuo, Yulin Niu, Qing Wu, Wei Pan

“…With the great advantages of deep learning in feature extraction, near-infrared analysis technology has great potential and has been widely used in various fields. …”
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Customer service complaint work order classification based on matrix factorization and attention multi-task learning by Yong SONG, Zhiwei YAN, Yukun QIN, Dongming ZHAO, Xiaozhou YE, Yuanyuan CHAI, Ye OUYANG

“…The automatic classification of complaint work orders is the requirement of the digital and intelligent development of customer service of communication operators.The categories of customer service complaint work orders have multiple levels, each level has multiple labels, and the levels are related, which belongs to a typical hierarchical multi-label text classification (HMTC) problem.Most of the existing solutions are based on classifiers to process all classification labels at the same time, or use multiple classifiers for each level, ignoring the dependence between hierarchies.A matrix factorization and attention-based multi-task learning approach (MF-AMLA) to deal with hierarchical multi-label text classification tasks was proposed.Under the classification data of real complaint work orders in the customer service scenario of communication operators, the maximum Top1 F1 value of MF-AMLA is increased by 21.1% and 5.7% respectively compared with the commonly used machine learning algorithm and deep learning algorithm in this scenario.It has been launched in the customer service system of one mobile operator, the accuracy of model output is more than 97%, and the processing efficiency of customer service agent unit time has been improved by 22.1%.…”
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Identifying factors that contribute to collision avoidance behaviours while walking in a natural environment by Mohammadamin Nikmanesh, Michael E. Cinelli, Daniel S. Marigold

“…Thus, we filmed unscripted pedestrian walking behaviours on a busy urban path. We leveraged deep learning algorithms to identify and extract pedestrian walking trajectories and had unbiased raters characterize situations where two pedestrians approached each other from opposite ends. …”
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Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region by L. Aneesh Euprazia, A. Rajeswari, K. K. Thyagharajan, N. R. Shanker

“…Results. The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. …”
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On the role of knowledge graphs in AI-based scientific discovery by Mathieu d’Aquin

“…Research and the scientific activity are widely seen as an area where the current trends in AI, namely the development of deep learning models (including large language models), are having an increasing impact. …”
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Advanced Breast Cancer Diagnostics With PolyBreastVit: A Combined PolyNet and Vision Transformer Approach by Visalakshi Annepu, Mohamed Abbas, Hanumatha Rao Bitra, Naveen Kumar Vaegae, Kalapraveen Bagadi

“…Here is a presentation of PolyBreastVit, a novel hybrid deep learning (DL) model for the automatic detection and classification of breast cancer in ultrasound images that combines PolyNet with Vision Transformer (ViT). …”
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Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect by Qikui Zhu, Bo Du, Baris Turkbey, Peter Choyke, Pingkun Yan

“…In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. …”
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A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system by R. Shelishiyah, Deepa Beeta Thiyam, M. Jehosheba Margaret, N. M. Masoodhu Banu

“…The current work focuses on the performance of deep learning methods like – Convolutional Neural Networks (CNN) and Bidirectional Long-Short term memory (Bi-LSTM) in classifying a four-class motor execution of Right Hand, Left Hand, Right Arm and Left Arm taken from the CORE dataset. …”
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CoTF-reg reveals cooperative transcription factors in oligodendrocyte gene regulation using single-cell multi-omics by Jerome J. Choi, John Svaren, Daifeng Wang

“…First, we identify co-binding TF pairs in the same oligodendrocyte-specific regulatory regions. Next, we train a deep learning model to predict each TG expression using the co-binding TFs’ expressions. …”
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Impact of Different Mode Decomposition Methods Combined with LSTM Models on Daily Runoff Forecasting by TAN Yongjie, WANG Xianxun, DUAN Mingxu, LIU Yaru, YAO Huaming

“…A combination of modal decomposition and deep learning forecasting methods was introduced to daily runoff forecasting to address the characteristics of unstable and volatile daily runoff series.Firstly,the complete ensemble empirical modal decomposition method was used to decompose the daily runoff time series,so as to obtain the modal components of different frequency components.Secondly,the daily runoff forecasting model was constructed for different modal components based on the long short-term memory neural network (LSTM),and the hyperparameters of the forecasting model were optimized using the grid search parametric optimization algorithm.Finally,the forecasting results of each model were modally reconstructed to obtain daily runoff forecasting results.The daily runoff forecasting of the Yichang hydrological station was taken as an example.Compared with the single LSTM,the RMSE,MAE,and MAPE of the proposed combination model were reduced by 65.02%,58.35%,and 2.88%,respectively.The decomposition effect of the complete ensemble empirical mode decomposition was better than that of the traditional modal decomposition method,which provided a new method and reference for nonlinear and non-stable daily runoff forecasting in a short time scale.…”
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SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks by M. Suriya, M. G. Sumithra

“…A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. …”
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Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning by Zheng-Yong Zhang, Jian-Sheng Su, Huan-Ming Xiong

“…Finally, with the rise of deep learning algorithms and fusion strategies, some challenges in this field are proposed.…”
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Double adversarial attack against license plate recognition system by Xianyi CHEN, Jun GU, Kai YAN, Dong JIANG, Linfeng XU, Zhangjie FU

“…Recent studies have revealed that deep neural networks (DNN) used in artificial intelligence systems are highly vulnerable to adversarial sample-based attacks.To address this issue, a dual adversarial attack method was proposed for license plate recognition (LPR) systems in a DNN-based scenario.It was demonstrated that an adversarial patch added to the pattern location of the license plate can render the target detection subsystem of the LPR system unable to detect the license plate class.Additionally, the natural rust and stains were simulated by adding irregular single-connected area random points to the license plate image, which results in the misrecognition of the license plate number.or the license plate research, different shapes of adversarial patches and different colors of adversarial patches are designed as a way to generate adversarial license plates and migrate them to the physical world.Experimental results show that the designed adversarial samples are undetectable by the human eye and can deceive the license plate recognition system, such as EasyPR.The success rate of the attack in the physical world can reach 99%.The study sheds light on the vulnerability of deep learning and the adversarial attack of LPR, and offers a positive contribution toward improving the robustness of license plate recognition models.…”
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An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method by Xiaoxun Zhu, Jianhong Zhao, Dongnan Hou, Zhonghe Han

“…By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.…”
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Improving the Machine Learning Stock Trading System: An N-Period Volatility Labeling and Instance Selection Technique by Young Hun Song, Myeongseok Park, Jaeyun Kim

“…However, investors cannot manually analyze all market patterns and establish rules, necessitating the development of supervised learning trading systems that can discover market patterns using machine or deep learning techniques. Many studies on supervised learning trading systems rely on up–down labeling based on price differences, which overlooks the issues of nonstationarity, complexity, and noise in stock data. …”
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Capsule neural network and adapted golden search optimizer based forest fire and smoke detection by Luling Liu, Li Chen, Mehdi Asadi

“…By using advanced deep learning and optimization strategies, the method effectively identifies complex patterns linked to wildfires. …”
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Research and DSP Implementation of Speech Enhancement Technology Based on Dynamic Mixed Features and Adaptive Mask by Jie Yang, Yachun Tang

“…A deep learning speech enhancement algorithm based on dynamic hybrid feature and adaptive mask and DSP implementation is proposed in this paper, which solves the problem of feature loss and improves the performance of speech enhancement. …”
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Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision by Chen Xinyi, Luo Binbin, Xu Ziyue

“…Future research will prioritize developing highly effective deep learning models and optimizing cognitive computing visual systems to ameliorate the efficiency and ensure the safety of autonomous driving. …”
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