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Journal articles on the topic 'Deep machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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Jain, Migul. "Machine Learning and Deep Learning Approaches for Cybersecurity: A Review." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1706–10. http://dx.doi.org/10.21275/sr231023115126.

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Rebecca, Dr B., Bathul Spandana, and Bingi Swathi. "Facial Emotion Detection using Machine Learning and Deep Learning Algorithms." International Journal of Research Publication and Reviews 6, no. 4 (2025): 14604–8. https://doi.org/10.55248/gengpi.6.0425.1663.

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Shivareddy, Nareddy, and Dr V. Uma Rani. "Enhancing Image Forgery Detection Using Machine Learning And Deep Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 12129–33. https://doi.org/10.55248/gengpi.6.0625.2390.

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P, Jayapal. "Efficient Human-Machine Interface through Deep Learning Fusion." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 680–86. http://dx.doi.org/10.21275/sr24109210845.

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Madhavappa Bachala Sathyanarayana, T. "A Review on Fraud Detection Using Machine Learning and Deep Learning." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 438–43. http://dx.doi.org/10.21275/sr24114141555.

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Fernandes, Carlos Ropelatto. "Machine Learning, Deep Learning e Aplicações." Monumenta - Revista Científica Multidisciplinar 9, no. 9 (2024): 1–2. https://doi.org/10.57077/monumenta.v9i9.261.

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Neste minicurso será apresentado e comentado brevemente sobre alguns conceitos básicos de Aprendizagem de Máquina (Machine Learning) relacionados aos tipos de aprendizagem que elas desenvolvem as quais podem ser: Aprendizagem Supervisionada e Aprendizagem Não Supervisionada. Dentro da Aprendizagem Supervisionada encontramos os seguintes tipos de Redes Neurais: Artificiais, Convolucionais e Recorrentes. Já em Aprendizagem Não Supervisionada temos: os Mapas Auto Organizáveis, Boltz Machines, Autoencoders e Redes Adversárias Generativas. Aprendizagem Supervisionada temos algumas aplicações como c

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J, Jayashree. "Protecting the Internet of Things (IOT) with Machine Learning and Deep Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–9. http://dx.doi.org/10.55041/ijsrem27782.

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Abstract- Deep learning (DL) and Machine learning (ML) as an IoT paradigm have improved problem-solving, and as a result, their application has expanded to many different fields. This has led to the idea that there are two powerful ways to use data—deep learning (DL) and machine learning (ML)—to solve specific problems. Thus, this article's objective is to provide a thorough analysis of "Scanning Machines and Deep Learning Techniques for Internet of Things (IOT) Security and Privacy," which addresses the current state of IoT research as well as its joint endeavor with DL. This technique stops

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Arya, Anil, A. Ashiq, M. S. Aswathy, and P. S. Akhila. "A Comparative Review of Different Techniques for Handwriting to Text Conversion." Advanced Innovations in Computer Programming Languages 7, no. 1 (2024): 1–9. https://doi.org/10.5281/zenodo.13766826.

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<em>Handwriting to text conversion, also known as handwriting recognition, is the process of converting handwritten text into machine-readable text. This article presents a comparative review of the different machine learning techniques for handwriting to text conversion. It highlights the works of many researchers and provides an analysis of the various machine-learning techniques that are used for the handwriting to text conversion<strong>.</strong></em>

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Gaurav, Singh, Kumar Shubham, Vijayan Surya, Perumal Thinagaran, and Sathiyanarayanan Mithileysh. "CYBER BULLYING DETECTION USING MACHINE LEARNING AND DEEP LEARNING." International Journal For Technological Research In Engineering 9, no. 7 (2022): 11–17. https://doi.org/10.5281/zenodo.6392440.

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The use of information and technology to bully a person online is referred to as cyberbullying. Individuals use Information and Communication Technology (ICT) to ridicule, embarrass, taunt, defame, intimidate, and criticise a person without making a direct eye contact. With the rise of social media, bullies have created a “virtual playground” in Facebook, Instagram, WhatsApp, Twitter and YouTube by targeting specific set of individuals or groups. It is necessary to deploy models and mechanisms in place for bullying contents, where the content is automatically detected and resolved,

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Ofordum, Ikenna David, Chizoba Geraldine Okabuonye, Jerry Kaka Okoh, Okechukwu Ugwu, Ndinyelum Onyebuchi Miracle, and Ndifor Kelly Bojor. "Revolutionizing Cancer Treatment in Nigeria Using Machine Learning and Deep Learning Algorithms." International Journal of Research Publication and Reviews 6, no. 2 (2025): 1160–70. https://doi.org/10.55248/gengpi.6.0225.0740.

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Mishra, Chandrahas, and D. L. Gupta. "Deep Machine Learning and Neural Networks: An Overview." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 66. http://dx.doi.org/10.11591/ijai.v6.i2.pp66-73.

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Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine

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Chandrahas, Mishra, and L. Gupta D. "Deep Machine Learning and Neural Networks: An Overview." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 66–73. https://doi.org/10.5281/zenodo.4108266.

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Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement t

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Siddesh, Kumar B., and Naduvinamani Onkarappa. "Machine Learning in Power Electronics: Focusing on Convolutional Neural Networks." International Journal of Computational Engineering and Management (IJCEM), A Peer Reviewed Refereed Multidisciplinary Research Journal 9, no. 1 (2021): 112–17. https://doi.org/10.5281/zenodo.14899610.

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Deep Neural Networks (DNNs) have revolutionized various fields, but their resource-intensive nature poses significant challenges for deployment, especially on edge devices with limited power and area budgets. This dissertation focuses on the development of efficient and low-power Very-Large-Scale Integration (VLSI) architectures for DNN accelerators, addressing the key bottlenecks in DNN hardware implementation. One of the major challenges in DNN hardware is the high computational cost associated with Multiply-Accumulate (MAC) operations and non-linear Activation Functions (AFs). While CORDIC-

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Ahmed, Alhassan Ali, Mohamed Abouzid, and Elżbieta Kaczmarek. "Deep Learning Approaches in Histopathology." Cancers 14, no. 21 (2022): 5264. http://dx.doi.org/10.3390/cancers14215264.

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The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartial

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Tarun, Jaiswal, and Jaiswal Sushma. "Deep Learning Based Pain Treatment." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 193–211. https://doi.org/10.31142/ijtsrd23639.

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The evolving discipline of computational pain investigation provides modern gears to recognize the pain. This discipline uses Computational processing of difficult pain associated records and relies on "intelligent" Machine learning algorithms. By mining information from difficult pain associated records and generating awareness from this, facts will be simplified. Therefore, machine learning has the capability to encouragement the training and dealing of pain greatly. Indeed, the application of machine learning for pain investigation -associated non imaging problems has been mention

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Nivas, K., M. Rajesh Kumar, G. Suresh, T. Ramaswamy, and Yerraboina Sreenivasulu. "Facial Emotion Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 427–33. http://dx.doi.org/10.22214/ijraset.2023.48585.

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Abstract: The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure con

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Soylu, Ufuk, and Michael L. Oelze. "Machine-to-machine transfer function: Transferring deep learning models between ultrasound machines." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A350. http://dx.doi.org/10.1121/10.0019120.

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In our previous work, we proposed a calibration method for deep learning (DL) to mitigate the effects of acquisition-related data mismatches in the context of tissue characterization. We showed that the “setting” transfer function can transfer deep learning models between imaging settings. We now extend the calibration method to transfer deep learning models between ultrasound machines. This can lead to reduced cost of model development and also improved understanding of the issues related to the security of deep learning based algorithms in biomedical ultrasound imaging. We gathered four data

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Ibrahim, Dr Abdul-Wahab Sami, and Dr Baidaa Abdul khaliq Atya. "Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques." Webology 19, no. 1 (2022): 1493–503. http://dx.doi.org/10.14704/web/v19i1/web19100.

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Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image proce

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20

Gupta, Adhyayan. "Machine Learning and Deep Learning: A Comprehensive Overview." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1620–26. https://doi.org/10.22214/ijraset.2025.72470.

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Machine Learning (ML) and Deep Learning (DL) are two core areas of Artificial Intelligence (AI) that have significantly transformed technology and research. Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, te

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Vinnik, Dmitriy Vladimirovich. "DEEP RISKS OF MACHINE LEARNING." Философия науки, no. 2 (2022): 110–23. http://dx.doi.org/10.15372/ps20220208.

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22

Wang, Tianlei, Jiuwen Cao, Xiaoping Lai, and Badong Chen. "Deep Weighted Extreme Learning Machine." Cognitive Computation 10, no. 6 (2018): 890–907. http://dx.doi.org/10.1007/s12559-018-9602-9.

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23

Fischer, Andreas M., Basel Yacoub, Rock H. Savage, et al. "Machine Learning/Deep Neuronal Network." Journal of Thoracic Imaging 35 (May 2020): S21—S27. http://dx.doi.org/10.1097/rti.0000000000000498.

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Cranford, Steve. "Getting DEEP with machine learning." Matter 6, no. 10 (2023): 3113–16. http://dx.doi.org/10.1016/j.matt.2023.07.021.

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Kiran, K. Joshi. "Detection of Android Malware using Machine Learning and Deep Learning Review." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 1 (2022): 134–39. https://doi.org/10.35940/ijrte.A6963.0511122.

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<strong>Abstract: </strong>Android apps are fast evolving throughout the mobile ecosystem, yet Android malware is always appearing. Various researchers have looked at the issue related with detection of Android malware and proposed hypothesis and approaches from various angles. According to existing studies, machine learning and deep learning seems to be an effective and promising method for detecting Android malware. Despite this, machine learning is used to detect Android malware from various angles. By evaluating a broader variety of facets of the issue, the review work complements prior ev

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Poomka, Pumrapee, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews." International Journal of Machine Learning and Computing 11, no. 2 (2021): 103–9. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1021.

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At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfa

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Rajendra Kumar, P., and E. B. K. Manash. "Deep learning: a branch of machine learning." Journal of Physics: Conference Series 1228 (May 2019): 012045. http://dx.doi.org/10.1088/1742-6596/1228/1/012045.

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Khan, U., K. Khan, F. Hassan, A. Siddiqui, and M. Afaq. "Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines." Engineering, Technology & Applied Science Research 9, no. 4 (2019): 4423–27. http://dx.doi.org/10.48084/etasr.2734.

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Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningfu

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Khan, Uzair, Khalid Khan, Fadzil Hasssan, Anam Siddiqui, and Muhammad Afaq. "Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines." Engineering, Technology & Applied Science Research 9, no. 4 (2019): 4423–27. https://doi.org/10.5281/zenodo.3370612.

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Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningfu

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Gadri, Said. "Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 9–17. http://dx.doi.org/10.5373/jardcs/v12sp7/20202076.

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31

Liu, Qingzhong, Zhaoxian Zhou, Sarbagya Ratna Shakya, Prathyusha Uduthalapally, Mengyu Qiao, and Andrew H. Sung. "Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms." International Journal of Machine Learning and Computing 8, no. 2 (2018): 121–26. http://dx.doi.org/10.18178/ijmlc.2018.8.2.674.

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Shankar Kumar, Shiv, and Ayonija Pathre. "A Survey on Human Activity Identification using Machine Learning and Deep Learning Approach." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 33–37. http://dx.doi.org/10.21275/sr221128143302.

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R, Srivaramangai. "Discovering the Use of Machine Learning and Deep Learning for AntiMicrobial Resistance Detection." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 1394–97. http://dx.doi.org/10.21275/sr23322231026.

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Louis Uzoegwu Farah, Chidozie. "Comparative Analysis for Predicting Cardiovascular Diseases Using Machine Learning and Deep Learning Approaches." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 945–64. http://dx.doi.org/10.21275/sr23809044938.

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35

Malik, Soham, and Mr Ajay Kumar Kaushik. "Lumpy Skin Disease Detection in Cattle: A Machine Learning and Deep Learning Approach." International Journal of Research Publication and Reviews 6, no. 4 (2025): 15500–15508. https://doi.org/10.55248/gengpi.6.0425.1690.

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36

YANG, Yifan, Tao ZHANG, and Weiyu LI. "Deep Learning-Based Lecture Posture Evaluation." Wuhan University Journal of Natural Sciences 29, no. 4 (2024): 315–22. http://dx.doi.org/10.1051/wujns/2024294315.

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Computer vision, a scientific discipline enables machines to perceive visual information, aims to supplant human eyes in tasks encompassing object recognition, localization, and tracking. In traditional educational settings, instructors or evaluators evaluate teaching performance based on subjective judgment. However, with the continuous advancements in computer vision technology, it becomes increasingly crucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations. Against this backdrop, this paper proposes a deep learning-based approach fo

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37

Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.

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In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods al

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Vamsi, Avulakunta. "Deep Learning-Based Cancer Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 67–72. https://doi.org/10.22214/ijraset.2025.68148.

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This area under reference is concerned with such a part of artificial intelligence that receives artificial neural networks as machine learning techniques to recognize patterns or predict from very large data sets. The enthusiastic adoption of deep learning in the healthcare sector, and accordingly, the availability of deeply well-characterized datasets for the cancer prognosis have made the field much more electrifying in applying deep learning tools to unravel cancer complex biology. Early findings are promising, but it remains an ever-changing world where information on cancer biology and d

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Singh, Nidhi. "Advancing Plant Disease Detection: Harnessing Deep Learning and Machine Vision." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 1272–77. http://dx.doi.org/10.21275/sr24319153135.

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Jinqiang, Wang, Prabhat Basnet, and Shakil Mahtab. "Review of machine learning and deep learning application in mine microseismic event classification." Mining of Mineral Deposits 15, no. 1 (2021): 19–26. http://dx.doi.org/10.33271/mining15.01.019.

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Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fractu

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Wasiu Eyinade, Onyinye Jacqueline Ezeilo, and Ibidapo Abiodun Ogundeji. "Deep Learning vs. Traditional Machine Learning in Financial Market Predictions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 4 (2024): 379–406. https://doi.org/10.32628/cseit25113479.

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Financial market predictions have long relied on machine learning techniques to analyze historical data, identify patterns, and forecast future trends. Traditional machine learning models such as linear regression, decision trees, and support vector machines (SVM) have been widely used for predictive modeling in stock price forecasting, risk assessment, and algorithmic trading. However, the rise of deep learning has introduced more sophisticated methods capable of capturing complex, non-linear relationships in financial data. Deep learning models, particularly recurrent neural networks (RNNs),

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Mushtaq, Shiza, M. M. Manjurul Islam, and Muhammad Sohaib. "Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review." Energies 14, no. 16 (2021): 5150. http://dx.doi.org/10.3390/en14165150.

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This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely

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Rajendran, Dorothy, Thankappan Sasilatha, Susai Rajendran, et al. "Application of machine learning in corrosion inhibition study." Zastita materijala 63, no. 3 (2022): 280–90. http://dx.doi.org/10.5937/zasmat2203280r.

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Artificial intelligence is a branch of science concerned with teaching machines to think and act like humans. Machine learning is concerned with enabling computers to perform tasks without the need for explicit programming. Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models. Machine Learning (ML) and Deep Learning (DL) research is now finding a home in both industry and academia. Machine L

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Raniah, Ali Mustafa, and Salman Chyad Haitham. "Subject review: Cyber security using machine learning and deep learning techniques." Global Journal of Engineering and Technology Advances 16, no. 2 (2023): 212–19. https://doi.org/10.5281/zenodo.10625682.

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In order to protection computers, programs, networks and data from intrusions and unauthorised access (UA), alteration, or demolition, a set of technologies and procedures is known as cybersecurity. A significant concern is the identification and prevention of a network intrusion. Methods like machine learning & deep learning identify network intrusions through estimating risk utilizing training data. Through the years, a number of machine learning & deep learning techniques have been introduced, and it has been demonstrated that these techniques are more accurate than other network in

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R.D., Dhaniya, and Dr Umamaheswari K.M. "Brain Tumor Analysis Empowered with Machine Learning and Deep Learning: A Comprehensive Review with its Recent Computational Techniques." Webology 19, no. 1 (2022): 764–79. http://dx.doi.org/10.14704/web/v19i1/web19054.

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In driving the medical image research machine-learning and deep-learning algorithm are growing expeditiously. The premature conjecture of disease needs substantial attempts to diagnose the disease. The machine learning algorithm confesses the software application to study from the data and predicts more accurate outcome. The deep learning algorithm drives on extensive dataset imparts on high end machine and clarifies the problem end to end. The primary focus on the survey is to high-spots the machine and deep-learning approaches in medical image analysis that endorses the decision-making pract

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Bublin, Mugdim. "Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches." Sensors 21, no. 22 (2021): 7527. http://dx.doi.org/10.3390/s21227527.

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Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable per

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Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.

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Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.

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Visweswaran, Shyam, Jason B. Colditz, Patrick O’Halloran, et al. "Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study." Journal of Medical Internet Research 22, no. 8 (2020): e17478. http://dx.doi.org/10.2196/17478.

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Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available u

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Kibria, Md Golam, and Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques." International Journal of Machine Learning and Computing 11, no. 4 (2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.

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The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the o

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Wang, Xiang, Zhichao Qin, Xiaoyu Bai, Zengming Hao, Nan Yan, and Jianyong Han. "Research Progress of Machine Learning in Deep Foundation Pit Deformation Prediction." Buildings 15, no. 6 (2025): 852. https://doi.org/10.3390/buildings15060852.

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During deep foundation pit construction, slight improper operations may lead to excessive deformation, resulting in engineering accidents. Therefore, how to accurately predict the deformation of the deep foundation pit is of significant importance. With advancements in artificial intelligence technology, machine learning has been utilized to learn and simulate complex nonlinear relationships among various factors influencing foundation pit deformation. Prediction accuracy is significantly improved, and the dynamic trend of foundation pit deformation is accurately grasped to curb the risk of sa

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