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Journal articles 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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1

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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Razman, Nur Fatin Shazwani Nor, Haslinah Mohd Nasir, Suraya Zainuddin, Noor Mohd Ariff Brahin, Idnin Pasya Ibrahim, and Mohd Syafiq Mispan. "Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 6929. http://dx.doi.org/10.11591/ijece.v14i6.pp6929-6943.

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Respiration-related disease refers to a wide range of conditions, including influenza, pneumonia, asthma, sudden infant death syndrome (SIDS) and the latest outbreak, coronavirus disease 2019 (COVID-19), and many other respiration issues. However, real-time monitoring for the detection of respiratory disorders is currently lacking and needs to be improved. Real-time respiratory measures are necessary since unsupervised treatment of respiratory problems is the main contributor to the rising death rate. Thus, this paper reviewed the classification of the respiratory signal using two different ap

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Indumathi R. "Deep Learning Approaches for Marine Oil Spill Detection and Monitoring." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 673–80. https://doi.org/10.52783/jisem.v10i26s.4274.

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Oil spill catastrophes critically affect marine environments and coastal economies, resulting in long-term environmental and economic damage. Early identification is essential to reduce damage, but conventional approaches based on Convolutional Neural Networks (CNNs) are encumbered with low accuracy and scalability when working with large datasets for real-time observation. These concerns necessitate improved solutions to enhance efficiency and trustworthiness in oil spill detection. For these problems, the YOLO v8 model has been suggested for oil spill detection. YOLO is a cutting-edge real-t

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Dogra, Shallu, Ishav Mehra, Lavish Pathak, and Nishant Nishant. "Handwritten Digit Recognition by Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem37840.

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This paper evaluates recent advances in handwritten digit recognition models, focusing on strategies developed and deployed in practical applications. The project utilizes both traditional and deep learning approaches, employing architectures such as Convolutional Neural Networks (CNNs). This paper explores the comparative performance of various models, discusses their deployment in real-world scenarios, and highlights future prospects for enhancing handwritten digit recognition technology. Keywords: Handwritten Digit Recognition, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CN

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Jhaveri, Rutvij H., A. Revathi, Kadiyala Ramana, Roshani Raut, and Rajesh Kumar Dhanaraj. "A Review on Machine Learning Strategies for Real-World Engineering Applications." Mobile Information Systems 2022 (August 28, 2022): 1–26. http://dx.doi.org/10.1155/2022/1833507.

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Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing

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PK, Nisha, Hariharan CK, Hima Harikumar, Sandra M.P, and Siva S. "An Overview of Deep Learning Approaches for Bird Sound Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39939.

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The Avian Vocal Recognizer (AVR) is a developing field that utilizes deep learning techniques for bird species recognition from vocalizations. This review highlights recent progress in audio classification using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for feature extraction and temporal pattern recognition. Techniques like Mel Frequency Cepstral Coefficients (MFCCs) further boost the performance of the system along with transfer learning. Hyperparameter tuning has also been found to be promising for enhancing model results, though it is yet to be explored. Dat

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Chandrasekaran, Radhika, and Senthil Kumar Paramasivan. "A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (2022): 92–105. http://dx.doi.org/10.17762/ijritcc.v10i12.5890.

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Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural

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Isha, Anjali, Karuna Sharma, Kirti, and Vibha Pratap. "Classifying Toxic Comments with Machine Learning and Deep Learning Approaches." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1073–82. https://doi.org/10.32628/ijsrst251222664.

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Stepped-up development of online communication has made it essential to identify and remove toxic content to maintain an online environment free from danger. The detection and evaluation of dangerous content found in textual data requires this process. This research explores various ML and DL models for toxic comment classification, and shows comparison of them, which efficiently detects the harmful content such as threats, hate speech, cyberbullying, and offensive language. The study compares various Natural Language Processing (NLP) techniques, procedures starting with tokenization followed

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Zhang, Boyu, and Hongliang Yuan. "High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7006–14. http://dx.doi.org/10.1609/aaai.v38i7.28527.

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Generating high-quality, realistic rendering images for real-time applications generally requires tracing a few samples-per-pixel (spp) and using deep learning-based approaches to denoise the resulting low-spp images. Existing denoising methods necessitate a substantial time expenditure when rendering at high resolutions due to the physically-based sampling and network inference time burdens. In this paper, we propose a novel Monte Carlo sampling strategy to accelerate the sampling process and a corresponding denoiser, subpixel sampling reconstruction (SSR), to obtain high-quality images. Exte

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Yass, Waseem Ghafori, and Mohammad Faris. "A Comprehensive Review of Deep Learning and Machine Learning Techniques for Real-Time Car Detection and Wrong-Way Vehicle Tracking." Babylonian Journal of Machine Learning 2023 (November 25, 2023): 78–90. https://doi.org/10.58496/bjml/2023/013.

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This research focuses on the advancements in car detection techniques, particularly targeting wrong-way driving vehicles, using deep learning and machine learning methodologies. In recent years, numerous techniques have been proposed to address vehicle detection in real-time scenarios, leveraging algorithms such as YOLO (You Only Look Once) and centroid tracking to detect vehicles in various traffic situations. Additionally, methods involving UAV imagery, infrared imaging, and frame differencing approaches have enhanced the capabilities of real-time vehicle detection systems. Despite achieving

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Liu, Yang, Yachao Yuan, Cristhian Balta, and Jing Liu. "A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification." Materials 13, no. 20 (2020): 4629. http://dx.doi.org/10.3390/ma13204629.

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Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image

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V., Jayabharathi, and S. Sukumaran Dr. "Deep Learning Approaches in Cyber Security-A Comprehensive Survey." International Journal of Innovative Science and Research Technology 7, no. 9 (2022): 770–77. https://doi.org/10.5281/zenodo.7141277.

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Recent years have seen the successful application of deep learning techniques, an enhanced model of conventional machine learning, in a variety of fields, including banking, entertainment, coordinating, health care, and cyber security. The study concentrated on a thorough examination of deep learning techniques in cyber security. Adversarial attacks have emerged as a more significant security threat to many deep learning applications than machine learning in the real world as deep learning techniques have become the core components for many security-critical applications such as identity recog

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Ayo, Isaac Odun, Williams Toro Abasi, Marion Adebiyi, and Oladapo Alagbe. "An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2442–53. http://dx.doi.org/10.11591/eei.v10i5.3168.

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Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site s

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Hussain, Sharjeel. "Object Detection using Deep-Learning Techniques: A Comparative Study." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 22, no. 2 (2024): 30–45. https://doi.org/10.52584/qrj.2202.04.

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These days, object detection is one of the important research problems in computer vision, used in applications such as real-time surveillance, security, self-driven vehicles, robotics, human-computer interaction, and image retrieval. Where accurate classification and localization of objects are performed for these applications. This can be achieved through deep learning-based detection techniques, one of the most widely used contemporary approaches, since they have high success rates. This paper presents a comprehensive review of recent advancements in deep learning-based object detection, fo

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Juyal, Amit. "A Deep Learning-Based Approach for Real-Time Object Detection and Recognition." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1304–14. http://dx.doi.org/10.17762/msea.v70i2.2322.

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Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, makin

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Barry, Jessie. "Applications of Deep Learning in Ornithology." Biodiversity Information Science and Standards 2 (June 6, 2018): e27251. https://doi.org/10.3897/biss.2.27251.

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Earth's ecosystems are threatened by anthropogenic change, yet relatively little is known about biodiversity across broad spatial (i.e. continent) and temporal (i.e. year-round) scales. There is a significant gap at these scales in our understanding of species distribution and abundance, which is the precursor to conservation (Hochachka et al. 2012). The cost and availability of experts to collect data does not scale to broad spatial or temporal surveys. With recent advances in artificial intelligence (AI) it is becoming possible to automate some of this data collection and analysis (Joppa 201

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Ji, Zhiwei, Jiaheng Gong, and Jiarui Feng. "A Novel Deep Learning Approach for Anomaly Detection of Time Series Data." Scientific Programming 2021 (July 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/6636270.

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Anomalies in time series, also called “discord,” are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time se

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R., Sanjana, Umesh chandra J., Nikesh M., and Bharathi M. "Bite-Sized Innovations: An In-Depth Review of Deep Learning Approaches to Food Recognition." Recent Trends in Information Technology and its Application 8, no. 1 (2024): 31–43. https://doi.org/10.5281/zenodo.13932942.

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<em>This research centers on creating a cutting-edge application designed to automatically detect and localize food objects in real-time settings. Whether used as a standalone tool or integrated into a connected application framework, this solution aims to offer flexibility and user-friendliness. To ensure accurate food detection, we've trained a variety of advanced algorithms, including Single Shot Detection (SSD), Faster R-CNN, YOLO, EfficientDet, RetinaNet, and custom architectures. Our training utilized a rich dataset gathered from various online sources, providing a diverse array of food

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Teixeira, Bernardo, and Hugo Silva. "Deep Learning Point Cloud Odometry." U.Porto Journal of Engineering 7, no. 3 (2021): 70–79. http://dx.doi.org/10.24840/2183-6493_007.003_0006.

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Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task.In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-

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Taehee, Kim, Ro Cheolwoo, and Suh Kiho. "Experiments on city train vibration anomaly detection using deep learning approaches." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 329–37. https://doi.org/10.11591/ijeecs.v20.i1.pp329-337.

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Anomaly detection is widely in demand in the field where automated detection of anomalous conditions in many observation tasks. While conventional data science approaches have shown interesting results, deep learning approaches to anomaly detection problems reveal new perspectives of possibilities especially where massive amount of data need to be handled. We develop anomaly detection applications on city train vibration data using deep learning approaches. We carried out preliminary research on anomaly detection in general and applied our real world data to existing solutions. In this paper,

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Tang, Liyuan. "Comparison and analysis of deep learning models for vehicle re-identification." Applied and Computational Engineering 20, no. 1 (2023): 138–44. http://dx.doi.org/10.54254/2755-2721/20/20231086.

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The rapid urbanization and increasing number of vehicles on the roads demand efficient and accurate vehicle re-identification (Re-ID) techniques for intelligent transportation systems, traffic monitoring, and surveillance applications. This paper offers a detailed analysis of deep learning approaches to vehicle Re-ID, covering feature learning, attention mechanism, unsupervised learning, self-supervised learning, and specialized loss function. The efficiency of these methods is assessed using VeRi-776 and VehicleID datasets. Key metrics, such as mean Average Precision (mAP) and Rank-n accuracy

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Mrs. R. Jayalakshmi, Dr. R.G. Suresh Kumar, and Thanushree T. "A Survey on Credit Card Fraud Detection using Deep Learning Model." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1325–34. https://doi.org/10.47392/irjaem.2025.0216.

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The research evaluates all recent applications of machine learning (ML) and deep learning (DL) for detecting credit card fraud. The study details multiple approaches to develop fraud detection systems by exploring both data quality enhancement methods along with feature selection approaches and modeling strategies. The implementation of advanced deep learning approaches LSTM together with CNNs leads to high real-time detection of fraud because they excel at detecting sophisticated temporal sequences. XGBoost ensemble methods used with AdaBoost and SMOTE methods make great strides in improving

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Jagtap, Samiksha, Amey Kadam, and Om Kadam. "Inventrove with Real-Time Face Recognition Using AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43628.

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Efficient workforce management is crucial in in- dustrial environments where precise monitoring of employee attendance directly impacts productivity and operational ef- ficiency. This paper presents the design and implementation of an advanced attendance management system tailored for industrial applications, which integrates biometric authentication and facial recognition technologies. The system leverages real- time data processing capabilities to ensure accurate and auto- mated tracking of employee attendance, eliminating traditional manual errors and enhancing security. The proposed soluti

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AlDahoul, Nouar, Aznul Qalid Md Sabri, and Ali Mohammed Mansoor. "Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models." Computational Intelligence and Neuroscience 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/1639561.

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Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature l

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Suparman, Ade, Ekka Pujo Ariesanto Akhmad, and Benny Martha Dinata. "Leveraging Artificial Intelligence for Enhancing Cybersecurity: A Deep Learning Approach to Real-Time Threat Detection." Journal of Academic Science 1, no. 7 (2024): 835–42. https://doi.org/10.59613/0yv79c49.

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This paper explores the transformative potential of Artificial Intelligence (AI), specifically deep learning, in strengthening cybersecurity through real-time threat detection. Given the rapid evolution of cyber threats, traditional detection methods often fall short, necessitating innovative approaches that can adapt and respond swiftly. This study employs a qualitative approach with a literature review and library research methodology to analyze current AI applications in cybersecurity. The research investigates the implementation of deep learning algorithms for identifying patterns and anom

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Villegas, William Eduardo, Santiago Sánchez-Viteri, and Sergio Luján-Mora. "Real-Time Recognition and Tracking in Urban Spaces Through Deep Learning: A Case Study." IEEE Access 12 (July 10, 2024): 95599–612. https://doi.org/10.1109/ACCESS.2024.3426295.

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Real-time object detection in urban environments is critical for security, transportation, and surveillance applications. This work presents an approach based on the You Only Look Once model for real-time object detection in urban scenarios. The methodology employed includes the collection and annotation of a diverse dataset, as well as the implementation of an intuitive user interface for real-time monitoring. A detailed method is designed in stages, including semi-supervised annotation techniques and data collection strategies in various urban and lighting conditions. The model was evaluated

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Bhavani, Dokku Durga, Tandra Nagarjuna, Pradeep H, Preethi K H, Ravi R, and Senthil Kumar S. "Machine Learning for Predictive Maintenance Applications in Industrial Equipment and Manufacturing Processes." ITM Web of Conferences 76 (2025): 01008. https://doi.org/10.1051/itmconf/20257601008.

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Utilization of predictive maintenance, backed by machine learning, has made a difference in monitoring industrial equipment and manufacturing, cutting down on downtime, improving operational efficiency, and ensuring safety. However, current systems suffer limitations, including lack of real-time deployment, low scalability, significant computation footprints, security vulnerabilities and low interpretability. We present a novel, scalable explainable AI based predictive maintenance framework integrating lightweight deep learning models, federated learning, blockchain secure storage and adaptive

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Ji, Ying, Jianhui Wang, Jiacan Xu, Xiaoke Fang, and Huaguang Zhang. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning." Energies 12, no. 12 (2019): 2291. http://dx.doi.org/10.3390/en12122291.

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Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this pro

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Mahendru, Mansi, Sanjay Kumar Dubey, and Divya Gaur. "Deep Convolutional Sequence Approach Towards Real-Time Intelligent Optical Scanning." International Journal of Computer Vision and Image Processing 11, no. 4 (2021): 63–76. http://dx.doi.org/10.4018/ijcvip.2021100105.

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Visual text recognition is the most dynamic computer vision application due to its rising demand in several applications like crime scene detection, assisting blind people, digitizing, book scanning, etc. However, numerous research works were executed on static visuals having organized text and on captured video frames in the past. The key objective of this study is to develop the real-time intelligent optical scanner that will extract every sequence of text from high-speed video, noisy visual input, and offline handwritten script. The scientific work has been carried out with the combination

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Srilekha, N., V. Tejaswini, M. Sneha, Abdul Aas Shaik, Sohail Zahid, and Zaheer Shaik. "Deep Learning for Pest Detection and Extraction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42777.

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Pest infestations pose a significant challenge to agriculture, resulting in substantial crop damage and economic losses. Traditional pest detection systems primarily rely on Convolutional Neural Networks (CNNs) for image classification. While CNNs are effective at categorizing images and identifying pests, they face limitations in handling scenarios involving multiple pests, varying orientations, and complex backgrounds. Additionally, CNNs lack the ability to localize pests within images, providing only image-level classifications rather than detailed spatial information. To address these limi

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Vilar, Cristian, Silvia Krug, and Benny Thörnberg. "Processing chain for 3D histogram of gradients based real-time object recognition." International Journal of Advanced Robotic Systems 18, no. 1 (2021): 172988142097836. http://dx.doi.org/10.1177/1729881420978363.

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3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram o

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Di Pilato, Antonio, Nicolò Taggio, Alexis Pompili, et al. "Deep Learning Approaches to Earth Observation Change Detection." Remote Sensing 13, no. 20 (2021): 4083. http://dx.doi.org/10.3390/rs13204083.

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The interest in change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task; therefore, a standard approach with manual detection of the elements of interest by experts in the domain of Earth Observati

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Hofmann, Julian, and Holger Schüttrumpf. "floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time." Water 13, no. 16 (2021): 2255. http://dx.doi.org/10.3390/w13162255.

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Using machine learning for pluvial flood prediction tasks has gained growing attention in the past years. In particular, data-driven models using artificial neuronal networks show promising results, shortening the computation times of physically based simulations. However, recent approaches have used mainly conventional fully connected neural networks which were (a) restricted to spatially uniform precipitation events and (b) limited to a small amount of input data. In this work, a deep convolutional generative adversarial network has been developed to predict pluvial flooding caused by nonlin

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R R, Shashank. "Security Issues and Defensive Approaches in Deep Learning Frameworks." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1894–906. http://dx.doi.org/10.22214/ijraset.2023.57752.

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Abstract: Deep learning frameworks are instrumental in advancing artificial intelligence, showcasing vast potential across diverse applications. Despite their transformative impact, security concerns pose significant risks, impeding widespread adoption. Malicious internal or external attacks on these frameworks can have far-reaching consequences for society. Our research delves into the intricacies of deep learning algorithms, conducting a thorough analysis of vulnerabilities and potential attacks. To address these challenges, we propose a comprehensive classification system for security issue

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J, Balachandar. "A Real-Time Sign Language Learning System Using LSTM and Mediapipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2177–87. https://doi.org/10.22214/ijraset.2025.67736.

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Sign language is a vital communication method for individuals with hearing and speech impairments, yet real-time recognition remains challenging due to gesture variability, occlusions, and motion tracking issues. This study presents a deep learning-based sign language recognition system integrating Mediapipe’s Holistic model for landmark extraction and an LSTM network for gesture classification. The system accurately converts hand movements into text in real time, overcoming the limitations of CNN-based approaches. Advanced preprocessing techniques, including landmark normalization and data au

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Carrera, Francesco, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo, and Giuseppe Pirlo. "Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection." Applied Sciences 12, no. 3 (2022): 1759. http://dx.doi.org/10.3390/app12031759.

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The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart enterprise as for any system largely dependent on technology. To predict this kind of attack, one solution can be to use unsupervised machine learning approaches, as they guarantee the detection of anomalies regardless of their prior knowledge. It is also essential to identify the anomalous and unknown behaviors that occur within a network i

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Pandey, Ritik, Yadnesh Chikhale, Ritik Verma, and Deepali Patil. "Deep Learning based Human Action Recognition." ITM Web of Conferences 40 (2021): 03014. http://dx.doi.org/10.1051/itmconf/20214003014.

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Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this pa

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Mohamed, Aws Saood, Nidaa Flaih Hassan, and Abeer Salim Jamil. "Real-Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges." Cybernetics and Information Technologies 24, no. 3 (2024): 163–81. http://dx.doi.org/10.2478/cait-2024-0031.

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Abstract Real-time Hand Gesture Recognition (HGR) has emerged as a vital technology in human-computer interaction, offering intuitive and natural ways for users to interact with computer-vision systems. This comprehensive review explores the advancements, challenges, and future directions in real-time HGR. Various HGR-related technologies have also been investigated, including sensors and vision technologies, which are utilized as a preliminary step in acquiring data in HGR systems. This paper discusses different recognition approaches, from traditional handcrafted feature methods to state-of-

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Barwary, M. J., and A. M. Abdulazeez. "Impact of Deep Learning on Transfer Learning : A Review." International Journal of Science and Business 5, no. 3 (2021): 204–16. https://doi.org/10.5281/zenodo.4559668.

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Transfer learning and deep learning approaches have been utilised in several real-world applications and hierarchical systems for pattern recognition and classification tasks. However, in few of the real-world machine learning situations, this presumption does not sustain since there are instances where training data is costly or tough to gather and there is continually a necessity to produce high-performance learners competent with more easily attained data from diverse fields. The objective of this review is to determine more abstract qualities at the greater levels of the representation, by

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Mishra, Yashraj. "A Comprehensive Review on Human Activity and Fitness Tracker using Different Approaches." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1642–49. https://doi.org/10.22214/ijraset.2025.70544.

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The increasing demand for accurate and real-time human activity and fitness tracking has led to the development of diverse computer vision and deep learning models. Among these, OpenPose has emerged as a powerful tool for multi-person 2D pose estimation, enabling precise body posture tracking from RGB images. This review paper presents a comprehensive analysis of state-of-the-art approaches for human activity recognition and posture detection, with a primary focus on comparing OpenPose with other convolutional neural network (CNN)-based architectures currently used in academic research and com

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Dr.S.Thilagavathi, Dr S. Thilagavathi, and Rithick R. Rahul Rithick R Rahul. "Machine Learning and Deep Learning for Cloud Computing Security." International Journal of Advances in Engineering and Management 7, no. 6 (2025): 56–60. https://doi.org/10.35629/5252-07065660.

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The escalating complexity of cyber threats in cloud computing environments necessitates innovative approaches for robust security measures. This paper explores the integration of machine learning algorithms as a proactive strategy to fortify cloud computing security. The abstract delves into the diverse applications of machine learning, including anomaly detection, threat identification, and behavioural analysis, within the context of cloud security. The paper evaluates the efficacy of supervised and unsupervised learning models, highlighting their adaptability to dynamic threat landscapes. Ad

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