Relevant bibliographies by topics / Deep Learning Approaches and Real-Time Applications

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep Learning Approaches and Real-Time Applications'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Deep Learning Approaches and Real-Time Applications"

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Researcher. "RECENT ADVANCES IN HUMAN POSE ESTIMATION: DEEP LEARNING APPROACHES AND REAL-TIME APPLICATIONS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 454–63. https://doi.org/10.5281/zenodo.14178390.

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This comprehensive article explores recent advances in human pose estimation (HPE), a critical computer vision task with wide-ranging applications. The article traces the evolution from traditional methods to cutting-edge deep learning approaches, highlighting the transformative impact of convolutional neural networks and transformer-based architectures. It examines state-of-the-art models such as RTMPose, HRNet, and DEKR, detailing their innovative features and performance improvements. The review discusses significant progress in multi-person pose estimation, real-time processing, and perfor
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Tsangaratos, Paraskevas, Ioanna Ilia, Nikolaos Spanoudakis, Georgios Karageorgiou, and Maria Perraki. "Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications." Applied Sciences 15, no. 4 (2025): 1871. https://doi.org/10.3390/app15041871.

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The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three CNN architectures—VGG-16, Xception, and MobileNet V2—designed to identify multiple minerals within a single frame and output probabilities for various mineral types, including Pyrite, Aragonite, Quartz, Obsidian, Gypsum, Azurite, and Hematite. Among these, MobileNet V2 demonstrated exceptional performance, achieving the highest accuracy (98.98%) and
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Unagar, Ajaykumar, Yuan Tian, Manuel Arias Chao, and Olga Fink. "Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning." Energies 14, no. 5 (2021): 1361. http://dx.doi.org/10.3390/en14051361.

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Lithium-ion (Li-I) batteries have recently become pervasive and are used in many physical assets. For the effective management of the batteries, reliable predictions of the end-of-discharge (EOD) and end-of-life (EOL) are essential. Many detailed electrochemical models have been developed for the batteries. Their parameters are calibrated before they are taken into operation and are typically not re-calibrated during operation. However, the degradation of batteries increases the reality gap between the computational models and the physical systems and leads to inaccurate predictions of EOD/EOL
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Naif Alsharabi. "Real-Time Object Detection Overview: Advancements, Challenges, and Applications." مجلة جامعة عمران 3, no. 6 (2023): 12. http://dx.doi.org/10.59145/jaust.v3i6.73.

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Real-time object detection is a crucial aspect of computer vision with applications spanning autonomous vehicles, surveillance, robotics, and augmented reality. This study examines real-time object detection techniques, highlighting their significance in artificial intelligence. The primary goal is swift and accurate object identification in images or video streams. Traditional methods like sliding windows and region-based approaches had limitations in computational efficiency. Deep learning, particularly Convolutional Neural Networks (CNNs), revolutionized object detection. Models like SSD, Y
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Rosenbaum, Tomer, Emil Winebrand, Omer Cohen, and Israel Cohen. "Deep-Learning Framework for Efficient Real-Time Speech Enhancement and Dereverberation." Sensors 25, no. 3 (2025): 630. https://doi.org/10.3390/s25030630.

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Deep learning has revolutionized speech enhancement, enabling impressive high-quality noise reduction and dereverberation. However, state-of-the-art methods often demand substantial computational resources, hindering their deployment on edge devices and in real-time applications. Computationally efficient approaches like deep filtering and Deep Filter Net offer an attractive alternative by predicting linear filters instead of directly estimating the clean speech. While Deep Filter Net excels in noise reduction, its dereverberation performance remains limited. In this paper, we present a genera
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Li, Hao, Xin Yi, Zhaopeng Zhang, and Yuan Chen. "Magnetic-Controlled Microrobot: Real-Time Detection and Tracking through Deep Learning Approaches." Micromachines 15, no. 6 (2024): 756. http://dx.doi.org/10.3390/mi15060756.

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As one of the most significant research topics in robotics, microrobots hold great promise in biomedicine for applications such as targeted diagnosis, targeted drug delivery, and minimally invasive treatment. This paper proposes an enhanced YOLOv5 (You Only Look Once version 5) microrobot detection and tracking system (MDTS), incorporating a visual tracking algorithm to elevate the precision of small-target detection and tracking. The improved YOLOv5 network structure is used to take magnetic bodies with sizes of 3 mm and 1 mm and a magnetic microrobot with a length of 2 mm as the pretraining
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Saswata Dey, Writuraj Sarma, and Sundar Tiwari. "Deep learning applications for real-time cybersecurity threat analysis in distributed cloud systems." World Journal of Advanced Research and Reviews 17, no. 3 (2023): 1044–58. https://doi.org/10.30574/wjarr.2023.17.3.0288.

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The newest shift in operations known as distributed cloud systems have greatly advanced the structure of digital environments by providing the ability to scale, be versatile, and cost effective. However, this evolution has significantly raised the cybersecurity danger levels where new kinds of threats like zero-day, DDoS and insider threats are more acute. Known security architectures for managing large-scale systems are frequently ill-suited to rapidly evolving, high-throughput data generated in such contexts. Comprehensive cyber threat detection and analysis in real time through enhanced pat
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Ramakrishna, N. "Fruit Freshness Evaluation using a Real-Time Industrial Framework for Deep Learning Ensemble Approaches." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 760–65. http://dx.doi.org/10.22214/ijraset.2023.54651.

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Abstract: Consumers give a high value on fruits' freshness, and manual visual grading presents challenges due to labor effort and inconsistent results. This research suggests an effective machine vision system for automating a visual assessment of fruit freshness and attractiveness based on cutting-edge deep learning algorithms and ensemble methodologies. The suggested architecture enables the non-destructive and economical detection of fruit defects by utilizing convolutional neural networks (CNNs). To attain high classification accuracy, which acts as the performance metric, the system utili
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Chai, Fangming, and Kyoung-Don Kang. "Adaptive Deep Learning for Soft Real-Time Image Classification." Technologies 9, no. 1 (2021): 20. http://dx.doi.org/10.3390/technologies9010020.

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CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic trade-offs between the inference accuracy and time for image data analysis in CNNs is challenging too, since we observe that more complex CNNs that take longer to run even lead to lower accuracy in many cases by evaluating hundreds of CNN models in terms of time and a
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Peng, Siqi. "Deep learning-based real-time ray tracing technology in games." Applied and Computational Engineering 101, no. 1 (2024): 124–31. http://dx.doi.org/10.54254/2755-2721/101/20240992.

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Abstract. In recent years, deep learning-based techniques have revolutionized real-time ray tracing for gaming, significantly enhancing visual fidelity and rendering performance. This paper reviews various state-of-the-art methods, including the use of Generative Adversarial Networks (GANs) for realistic shading, the use of neural temporal adaptive sampling, the use of subpixel sampling reconstruction, and the use of neural scene representation. Key findings highlight improvements in perceived realism, temporal stability, image fidelity, and computational efficiency. Techniques such as neural
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Dissertations / Theses on the topic "Deep Learning Approaches and Real-Time Applications"

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Neau, Maëlic. "Real-Time And Efficient Scene Graph Generation for Real-World Applications : an End-To-End Investigation." Electronic Thesis or Diss., École nationale d'ingénieurs de Brest, 2025. https://cloud.enib.fr/apps/files/files/219208?dir=/ESPACE%20COMMUN%20ENIB/RECHERCHE/Manuscrits.

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Le projet propose de répondre aux problématiques d'aide à la personne dans un environnement domestique. Plus précisément, l'idée est de développer un système capable d'identifier les besoins implicites non-verbaux d'une personne lors de la réalisation d'une tâche et d'aider la personne si nécessaire. La détection des besoins ainsi que l'aide seront apportées par un robot assistant humanoïde tel que le robot de Softbanks Robotics "Pepper" par le biais de données multimodales<br>This proposal tackle the paradigm of human assistance by a companion robot in a home environment. The idea is to build
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Cai, Bill Yang. "Applications of deep learning and computer vision in large scale quantification of tree canopy cover and real-time estimation of street parking." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122317.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2018<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 73-77).<br>A modern city generates a large volume of digital information, especially in the form of unstructured image and video data. Recent advancements in deep learning techniques have enabled effective learning and estimation of high-level attributes and meaningful features from large digital datasets of images and videos. In my thesis, I explore the potential of applying deep learning to image and
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Speranza, Nicholas A. "Adaptive Two-Stage Edge-Centric Architecture for Deeply-Learned Embedded Real-Time Target Classification in Aerospace Sense-and-Avoidance Applications." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1621886997260122.

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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a pr
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Nekrasov, Vladimir. "Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches." Thesis, 2020. http://hdl.handle.net/2440/129333.

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Computer vision-based and deep learning-driven applications and devices are now a part of our everyday life: from modern smartphones with an ever increasing number of cameras and other sensors to autonomous vehicles such as driverless cars and self-piloting drones. Even though a large portion of the algorithms behind those systems has been known for ages, the computational power together with the abundance of labelled data were lacking until recently. Now, following the Occam’s razor principle, we should start re-thinking those algorithms and strive towards their further simplification, both t
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Kibbanahalli, Shivalingappa Marulasidda Swamy. "Real-time human action and gesture recognition using skeleton joints information towards medical applications." Thesis, 2020. http://hdl.handle.net/1866/24320.

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Des efforts importants ont été faits pour améliorer la précision de la détection des actions humaines à l’aide des articulations du squelette. Déterminer les actions dans un environnement bruyant reste une tâche difficile, car les coordonnées cartésiennes des articulations du squelette fournies par la caméra de détection à profondeur dépendent de la position de la caméra et de la position du squelette. Dans certaines applications d’interaction homme-machine, la position du squelette et la position de la caméra ne cessent de changer. La méthode proposée recommande d’utiliser des valeurs de posi
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Books on the topic "Deep Learning Approaches and Real-Time Applications"

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Srivastava, Smriti, Manju Khari, Ruben Gonzalez Crespo, Gopal Chaudhary, and Parul Arora, eds. Concepts and Real-Time Applications of Deep Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76167-7.

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Mahrishi, Mehul, Kamal Kant Hiran, Gaurav Meena, and Paawan Sharma. Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020.

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Mahrishi, Mehul, Kamal Kant Hiran, Gaurav Meena, and Paawan Sharma. Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020.

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Mahrishi, Mehul, Kamal Kant Hiran, Gaurav Meena, and Paawan Sharma. Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020.

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Mahrishi, Mehul, Kamal Kant Hiran, Gaurav Meena, and Paawan Sharma. Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020.

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Mahrishi, Mehul, Kamal Kant Hiran, Gaurav Meena, and Paawan Sharma. Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020.

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Chaudhary, Gopal, Manju Khari, Smriti Srivastava, Ruben Gonzalez Crespo, and Parul Arora. Concepts and Real-Time Applications of Deep Learning. Springer International Publishing AG, 2022.

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Chaudhary, Gopal, Manju Khari, Smriti Srivastava, Ruben Gonzalez Crespo, and Parul Arora. Concepts and Real-Time Applications of Deep Learning. Springer International Publishing AG, 2021.

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Eldar, Yonina C., Andrea Goldsmith, Deniz Gündüz, and H. Vincent Poor, eds. Machine Learning and Wireless Communications. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108966559.

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How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle
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Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes
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Book chapters on the topic "Deep Learning Approaches and Real-Time Applications"

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Sangiorgio, Matteo. "Deep Learning in Multi-step Forecasting of Chaotic Dynamics." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_1.

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AbstractThe prediction of chaotic dynamical systems’ future evolution is widely debated and represents a hot topic in the context of nonlinear time series analysis. Recent advances in the field proved that machine learning techniques, and in particular artificial neural networks, are well suited to deal with this problem. The current state-of-the-art primarily focuses on noise-free time series, an ideal situation that never occurs in real-world applications. This chapter provides a comprehensive analysis that aims at bridging the gap between the deterministic dynamics generated by archetypal c
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Wittenberg, Thomas, Thomas Lang, Thomas Eixelberger, and Roland Grube. "Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_8.

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AbstractFor the development, training, and validation of machine learning (ML) and deep learning (DL) based methods, such as, e.g., image analysis, prediction of critical events, extraction or reconstruction of information from disrupted data streams, searching similarities in data collections, or planning of procedures, a lot of data is needed. Additionally to this data (images, bio-signals, vital-signs, text records, machine states, trajectories, antenna data, ...) adequate supplementary information about the meaning encoded in the data is required. Only with this additional information – th
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Quoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park, and Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.61.

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the ef
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Quoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park, and Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.61.

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the ef
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Rostovski, Jakob, Mohammad Hasan Ahmadilivani, Andrei Krivošei, Alar Kuusik, and Muhammad Mahtab Alam. "Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_17.

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AbstractAnomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device m
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Kamal, Sara A., Samr A. Alhawsaw, Faiza Turkestani, et al. "Efficient Text Extraction from Product Images Using Deep Learning and Parallel Computing." In Proceedings in Technology Transfer. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8588-9_6.

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Abstract The domain of deep learning, particularly in the context of text detection and recognition, has witnessed remarkable progress over the years. Text detection and recognition entail identifying and extracting textual information from images, an essential component in various real-world applications. The ability to extract text robustly and efficiently from scenes is essential for interpreting traffic signs or content-based image retrieval. This domain has been greatly influenced by the advent of Conventional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have demonst
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Karpagavalli, C., and Dr M. Kaliappan. "MACHINE LEARNING AND DEEP LEARNING REAL TIME APPLICATIONS." In Futuristic Trends in Artificial Intelligence Volume 3 Book 10. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bgai10p2ch2.

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Machine learning is a current hot topic in the modern computer world and is a branch of artificial intelligence. Many researchers have contributed their work in this field to enhancing the accuracy and intelligence of machine learning approaches. Learning is a process to create a new concept, which is used in machines too. In addition, another deep learning notion begins to play a significant role. A branch of machine learning called deep learning (DL) uses neural networks and is utilized for many real-time applications owing to its automatic learning strategy. This chapter presents an overvie
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Mohbey, Krishna Kumar. "Employee's Attrition Prediction Using Machine Learning Approaches." In Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch005.

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In any industry, attrition is a big problem, whether it is about employee attrition of an organization or customer attrition of an e-commerce site. If we can accurately predict which customer or employee will leave their current company or organization, then it will save much time, effort, and cost of the employer and help them to hire or acquire substitutes in advance, and it would not create a problem in the ongoing progress of an organization. In this chapter, a comparative analysis between various machine learning approaches such as Naïve Bayes, SVM, decision tree, random forest, and logis
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Yadav, Anju, Venkatesh Gauri Shankar, and Vivek Kumar Verma. "Emotion Recognition With Facial Expression Using Machine Learning for Social Network and Healthcare." In Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch012.

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In this chapter, machine learning application on facial expression recognition (FER) is studied for seven emotional states (disgust, joy, surprise, anger, sadness, contempt, and fear) based on FER describing coefficient. FER has many practical importance in various area like social network, robotics, healthcare, etc. Further, a literature review of existing machine learning approaches for FER is discussed, and a novel approach for FER is given for static and dynamic images. Then the results are compared with the other existing approaches. The chapter also covers additional related issues of ap
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Kumar, E. Sudheer, C. Shoba Bindu, and Sirivella Madhu. "Deep Convolutional Neural Network-Based Analysis for Breast Cancer Histology Images." In Machine Learning and Deep Learning in Real-Time Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch008.

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Breast cancer is one of the main causes of cancer death worldwide, and early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious. The relevance and potential of automatic classification algorithms using Hematoxylin-Eosin stained histopathological images have already been demonstrated, but the reported results are still sub-optimal for clinical use. Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. Based on the predominant cancer type, the goal is to classify images in
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Conference papers on the topic "Deep Learning Approaches and Real-Time Applications"

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Thakur, Kunal, Ashu Taneja, Md Ankushavali, and Saif Obbayeda. "Robust Deep Learning Model for Real-Time Vehicle Detection in IoV Applications." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10984899.

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Murthy, V. S. N., Pabbati Swathi, Jhansi Lakshmi Kotta, Avinash Amaranayani, M. Anusha, and V. S. Divya Sundar. "Towards Real-Time Lung Cancer Diagnosis: A Deep Learning Approach with Optimized CNN for CT Scan Analysis." In 2025 6th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2025. https://doi.org/10.1109/icirca65293.2025.11089566.

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Rathi, Snehal, Gauri Ghule, Sahil Arjapure, Sudarshan Bhagat, Ketaki Bharati, and Vaishnav Gonare. "EmoTract: A Comprehensive Approach for Detecting Learner's Emotions in Real-Time in Virtual Environments using Deep Learning." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009820.

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In, Joo, Kim Sung Hoon, Kim Gi Nam, Kwan Hee Yoo, and F. M. Fahmid Hossain. "A Comprehensive Deep Learning Approach for Real-Time Detection and Classification of Large and Small Defects in Industrial Applications." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00071.

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Sinha, Tanaya, Mahmoud Hayajnh, and J. V. R. Prasad. "Development of Rotor Control Equivalent Gust Input (RCEGI) Models." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-292.

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This study investigates the application of neural network architectures to predict control inputs required to replicate rotorcraft responses under vertical gust disturbances. Two modeling approaches are developed: the Control Equivalent Gust Input (CEGI) model, using body-axis inputs and the Rotor Control Equivalent Gust Input (RCEGI) model using rotor-specific inputs. Initial models employed single-input single-output (SISO) LSTM networks, which demonstrated limitations in capturing transient behavior and exhibited delay in predicted control inputs. By incorporating multiple vehicle response
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Dankan Gowda, V., Praveen Damacharla, Vinod Kumar Maddineni, and Venkata Akhil Kumar Gummadi. "Deep Learning Approaches for Real-Time IoT Data Processing and Analysis." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722226.

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Boulila, Wadii, Ayyub Alzahem, Aseel Almoudi, Muhanad Afifi, Ibrahim Alturki, and Maha Driss. "A Deep Learning-based Approach for Real-time Facemask Detection." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00238.

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Zhan, Tian, and Austin Amakye Ansah. "Enhancing Amateur Photography: A Deep Learning Mobile Application for Real-Time Aesthetic feedback." In 12th International Conference on Computational Science and Engineering. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141602.

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Capturing aesthetically pleasing photographs can be challenging for amateur photographers due to the complexity of factors such as lighting, composition, and contrast. To address this issue, we propose a mobile application powered by deep learning models and regression analysis. This application analyzes real-time image frames using a pre-trained MobileNet backbone and a custom classification layer [8]. By leveraging the Aesthetics and Attributes database, the app calculates an aesthetic score for each photograph, providing instant feedback to users. Challenges encountered during development,
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Liu, Tianlong, Yu Qi, Liang Shi, and Jianan Yan. "Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/656.

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Web attacks such as Cross-Site Scripting and SQL Injection are serious Web threats that lead to catastrophic data leaking and loss. Because attack payloads are often short segments hidden in URL requests/posts that can be very long, classical machine learning approaches have difficulties in learning useful patterns from them. In this study, we propose a novel Locate-Then-Detect (LTD) system that can precisely detect Web threats in real-time by using attention-based deep neural networks. Firstly, an efficient Payload Locating Network (PLN) is employed to propose most suspicious regions from lar
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Thakrar, Om, Prateek Ranka, Vidhita Pai, Stevina Correia, and Ruhina Karani. "Enhancing Driver Safety through Real-Time Feedback on Driving Behavior: A Deep Learning Approach." In 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA). IEEE, 2023. http://dx.doi.org/10.1109/icacta58201.2023.10392647.

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Reports on the topic "Deep Learning Approaches and Real-Time Applications"

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Panta, Manisha, Padam Thapa, Md Hoque, et al. Application of deep learning for segmenting seepages in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49453.

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Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To th
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Dugan, Peter J., Christopher W. Clark, Yann A. LeCun, and Sofie M. Van Parijs. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada573473.

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Dugan, Peter J., Christopher W. Clark, Yann A. LeCun, and Sofie M. Van Parijs. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada617980.

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened att
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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the ot
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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