EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS by SABRINA AKTER, BIMAL KUMAR PRAMANIK, MD EKRAMUL HAMID

Subjects: “…EMG, deep learning, CWT, PCA, hand gesture recognition…”
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Comparative Analysis of Deep Learning Algorithm for Cancer Classification using Multi-omics Feature Selection by Nur Sabrina Azmi, Azurah A Samah, Vivekaanan Sirgunan, Zuraini Ali Shah, Hairudin Abdul Majid, Chan Weng Howe, Nies Hui Wen, Nuraina Syaza Azman

“…The recent development of advanced computational algorithms, especially deep learning, had sparked numerous efforts in applying these algorithms in multi-omics studies. …”
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Hierarchical Deep Learning for Bearing Fault Detection in BLDC Motors Using Time-Frequency Analysis by Ahmed K. Ali, Wathiq Rafa Abed

“…We tested the proposed HTFICNN with data from BLDC motors and found that it works better than other deep learning models to detect bearing faults. Moreover, this work contributes to the advancement of fault diagnosis and predictive maintenance for BLDC motors, with potential applications in other fault detection scenarios.…”
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Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning by Pengjiao Li, Qian Meng

“…The purpose of this article is to study emotional interaction in deep learning network education and analyze the status quo of its behavioral decision-making mechanism. …”
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Automatic etiological classification of stroke thrombus digital photographs using a deep learning model by Álvaro Lucero-Garófano, Álvaro Lucero-Garófano, Alicia Aliena-Valero, Isabel Vielba-Gómez, Isabel Vielba-Gómez, Irene Escudero-Martínez, Irene Escudero-Martínez, Lluís Morales-Caba, Lluís Morales-Caba, Fernando Aparici-Robles, Fernando Aparici-Robles, Diana L. Tarruella Hernández, Diana L. Tarruella Hernández, Gerardo Fortea, Gerardo Fortea, José I. Tembl, José I. Tembl, Juan B. Salom, Juan B. Salom, José V. Manjón

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Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning by Ying WANG, Jun REN, Ke SHI, Bin LIN

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Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index by Roopini Sathiasai Kumar, Swapnil Sharma, Arunima Halder, Vipin Gupta

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Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics by Hayam Alamro, Mohammed Maray, Jawhara Aljabri, Saad Alahmari, Monir Abdullah, Jehad Saad Alqurni, Faiz Abdullah Alotaibi, Abdelmoneim Ali Mohamed

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Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets by Hsin-Chang Tsai, Ming-Chun Lee, Chao-Hao Hsu

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Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification by Mohammed Hussain, Thaer Thaher, Mohamed Basel Almourad, Majdi Mafarja

“…Abstract Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. …”
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Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research by Xiang Li, Jun Zhao, Changchang Zeng, Yong Yao, Sen Zhang, Suixian Yang

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DESNet: Real-time human pose estimation for sports applications combining IoT and deep learning by Rongbao Huang, Bo Zhang, Zhixin Yao, Bojun Xie, Jia Guo

“…However, current methods often fall short in balancing high accuracy with low computational resource requirements, especially in resource-constrained environments. Deep learning has shown significant potential in enhancing computer vision tasks, including human pose estimation. …”
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Versatile waste sorting in small batch and flexible manufacturing industries using deep learning techniques by Arso M. Vukicevic, Milos Petrovic, Nebojsa Jurisevic, Marko Djapan, Nikola Knezevic, Aleksandar Novakovic, Kosta Jovanovic

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Deep Learning-Based Computer Vision Is Not Yet the Answer to Taphonomic Equifinality in Bone Surface Modifications by Lloyd Austin Courtenay, Nicolas Vanderesse, Luc Doyon, Antoine Souron

“…Recent years have seen a notable increase in the number of studies proposing the use of deep learning (DL)-based computer vision (CV) algorithms on stereomicroscope images to overcome these issues. …”
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Retracted: Deep Learning-Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video by null Complexity

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PUPIL DIAMETER AND MACHINE LEARNING FOR DEPRESSION DETECTION: A COMPARATIVE STUDY WITH DEEP LEARNING MODELS by Islam MOHAMED, Mohamed EL-WAKAD, Khaled ABBAS, Mohamed ABOAMER, Nader A. Rahman MOHAMED

“…Despite the higher accuracy of deep learning, machine learning is recommended for its faster execution times. …”
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Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather With Deep Learning and Thermal Camera Fusion by Mehmood Nawaz, Sheheryar Khan, Muhammad Daud, Muhammad Asim, Ghazanfar Ali Anwar, Ali Raza Shahid, Ho Pui Aaron HO, Tom Chan, Daniel Pak Kong, Wu Yuan

“…When integrated with RGB and LiDAR sensors, the thermal sensor becomes highly beneficial for detecting and locating objects in adverse weather conditions. The proposed deep learning-assisted multi-sensor fusion technique consists of two parts: (1) visual information fusion and (2) object detection using LiDAR, RGB, and Thermal sensors. …”
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Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach by Pei Yang, Zeao Zhang, Jianan Wei, Lisha Jiang, Liqian Yu, Huawei Cai, Lin Li, Quan Guo, Zhen Zhao

Subjects: “…Deep learning…”
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Automated classification of elongated styloid processes using deep learning models-an artificial intelligence diagnostics by Anuradha Ganesan, N. Gautham Kumar, Prabhu Manickam Natarajan, Jeevitha Gauthaman

Subjects: “…deep learning…”
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Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network by Yaxing QIU, Xidong WANG, Sen BIAN, Lei YUE

“…Load balancing is a huge challenge for LTE multi-frequency and multi-mode network.Hundreds of parameters are involved in load balancing for the complex network structure.Therefore,it is difficult to perform precise and meticulous configuration only relying on human experience.In order to cope with the challenge,a load balancing scheme based on clustering analysis and deep learning was proposed.Firstly,the key indicators were selected to identify the network scenes,and then big data and deep learning technologies were used to mine the relationship between data.Finally,the optimum system parameters for different network scenes were found.It has been proved that machine learning technology can greatly improve the accuracy and the efficiency of parameter configuration.…”
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