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Journal articles on the topic 'AI-based compression'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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1

Sarinova, Assiya, and Alexander Zamyatin. "Methodology for Developing Algorithms for Compressing Hyperspectral Aerospace Images used on Board Spacecraft." E3S Web of Conferences 223 (2020): 02007. http://dx.doi.org/10.1051/e3sconf/202022302007.

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The paper describes a method for constructing and developing algorithms for compressing hyperspectral aerospace images (AI) of hardware implementation for subsequent use in remote sensing Systems (RSS). The developed compression methods based on differential and discrete transformations are proposed as compression algorithms necessary for reducing the amount of transmitted information. The paper considers a method for developing compression algorithms, which is used to develop an adaptive algorithm for compressing hyperspectral AI using programmable devices. Studies have shown that the propose

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Sarinova, Assiya. "Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations." E3S Web of Conferences 333 (2021): 01011. http://dx.doi.org/10.1051/e3sconf/202133301011.

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The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery, taking into account which an adaptive algorithm for co

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Kim, Myung-Jun, and Yung-Lyul Lee. "Object Detection-Based Video Compression." Applied Sciences 12, no. 9 (2022): 4525. http://dx.doi.org/10.3390/app12094525.

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Video compression is designed to provide good subjective image quality, even at a high-compression ratio. In addition, video quality metrics have been used to show the results can maintain a high Peak Signal-to-Noise Ratio (PSNR), even at high compression. However, there are many difficulties in object recognition on the decoder side due to the low image quality caused by high compression. Accordingly, providing good image quality for the detected objects is necessary for the given total bitrate for utilizing object detection in a video decoder. In this paper, object detection-based video comp

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Sarinova, Assiya, Pavel Dunayev, Aigul Bekbayeva, Ali Mekhtiyev, and Yermek Sarsikeyev. "Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations." Eastern-European Journal of Enterprise Technologies 1, no. 2(115) (2022): 22–30. http://dx.doi.org/10.15587/1729-4061.2022.251404.

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The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into acco

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Pinheiro, Antonio. "JPEG column: 89th JPEG meeting." ACM SIGMultimedia Records 12, no. 4 (2020): 1. http://dx.doi.org/10.1145/3548580.3548583.

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JPEG initiates standardisation of image compression based on AI. The 89th JPEG meeting was held online from 5 to 9 October 2020. During this meeting, multiple JPEG standardisation activities and explorations were discussed and progressed. Notably, the call for evidence on learning-based image coding was successfully completed and evidence was found that this technology promises several new functionalities while offering at the same time superior compression efficiency, beyond the state of the art. A new work item, JPEG AI, that will use learning-based image coding as core technology has been p

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Assiya, Sarinova, Dunayev Pavel, Bekbayeva Aigul, Mekhtiyev Ali, and Sarsikeyev Yermek. "Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations." Eastern-European Journal of Enterprise Technologies 1, no. 2(115) (2022): 22–30. https://doi.org/10.15587/1729-4061.2022.251404.

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The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into

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Nagarsenker, Anish, Prasad Khandekar, and Minal Deshmukh. "JPEG2000-Based Semantic Image Compression using CNN." International journal of electrical and computer engineering systems 14, no. 5 (2023): 527–34. http://dx.doi.org/10.32985/ijeces.14.5.4.

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Some of the computer vision applications such as understanding, recognition as well as image processing are some areas where AI techniques like convolutional neural network (CNN) have attained great success. AI techniques are not very frequently used in applications like image compression which are a part of low-level vision applications. Intensifying the visual quality of the lossy video/image compression has been a huge obstacle for a very long time. Image processing tasks and image recognition can be addressed with the application of deep learning CNNs as a result of the availability of lar

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Researcher. "DATA REDUCTION TECHNOLOGIES FOR AI WORKLOADS: ADVANCING COMPRESSION AND DEDUPLICATION TECHNIQUES FOR LARGE-SCALE AI/ML DATASETS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 960–67. https://doi.org/10.5281/zenodo.13970869.

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This comprehensive article explores the cutting-edge advancements in data reduction technologies specifically tailored for large-scale AI and machine learning workloads. As the volume and complexity of AI datasets continue to grow exponentially, traditional compression and deduplication techniques face significant challenges in efficiently managing unstructured, high-dimensional data. We examine the unique characteristics of AI/ML datasets and analyze the limitations of conventional data reduction methods when applied to these workloads. The article presents an in-depth discussion of emer

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Jeong, Woosik, Chang-Heon Baek, Dong-Yeong Lee, et al. "The Classification of Metastatic Spine Cancer and Spinal Compression Fractures by Using CNN and SVM Techniques." Bioengineering 11, no. 12 (2024): 1264. https://doi.org/10.3390/bioengineering11121264.

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Metastatic spine cancer can cause pain and neurological issues, making it challenging to distinguish from spinal compression fractures using magnetic resonance imaging (MRI). To improve diagnostic accuracy, this study developed artificial intelligence (AI) models to differentiate between metastatic spine cancer and spinal compression fractures in MRI images. MRI data from Gyeongsang National University Hospital, collected from January 2019 to April 2022, were processed using Otsu’s binarization and Canny edge detection algorithms. Using these preprocessed datasets, convolutional neural network

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Timmerer, Christian. "MPEG Column: 146th MPEG Meeting in Rennes, France." ACM SIGMultimedia Records 16, no. 2 (2024): 1. https://doi.org/10.1145/3712580.3712584.

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The 146th MPEG meeting was held in Rennes, France from 22-26 April 2024, and the official press release can be found here. It comprises the following highlights: •AI-based Point Cloud Coding: Call for proposals focusing on AI-driven point cloud encoding for applications such as immersive experiences and autonomous driving. •Object Wave Compression: Call for interest in object wave compression for enhancing computer holography transmission. •Open Font Format: Committee Draft of the fifth edition, overcoming previous limitations like the 64K glyph encoding constraint. •Scene Description: Ratifie

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Neeta Kadukar. "Machine Learning-Enhanced Data Transmission for Autonomous Driving and IoT." Journal of Information Systems Engineering and Management 10, no. 22s (2025): 742–53. https://doi.org/10.52783/jisem.v10i22s.3617.

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Managing the exponential growth of data, particularly video and sensor data, is a significant challenge in cloud and IoT environments. This research introduces a novel AI-driven data offloading technique aimed at minimizing latency and optimizing resource utilization in cloud computing systems. By leveraging advanced machine learning models including frame overlap detection, recurrent neural networks, and transformers the proposed approach delivers substantial performance improvements. Experimental results indicate a 28.26% reduction in average latency, a 22.96% decrease in cloud resource util

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Balderas, Luis, Miguel Lastra, and José M. Benítez. "A Green AI Methodology Based on Persistent Homology for Compressing BERT." Applied Sciences 15, no. 1 (2025): 390. https://doi.org/10.3390/app15010390.

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Large Language Models (LLMs) like BERT have gained significant prominence due to their remarkable performance in various natural language processing tasks. However, they come with substantial computational and memory costs. Additionally, they are essentially black-box models, being challenging to explain and interpret. In this article, Persistent BERT Compression and Explainability (PBCE) is proposed, a Green AI methodology to prune BERT models using persistent homology, aiming to measure the importance of each neuron by studying the topological characteristics of their outputs. As a result, P

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Costa, Constantinos, Panos Chrysanthis, Herodotos Herodotou, Marios Costa, Efstathios Stavrakis, and Nicolas Nicolaou. "A Multiple Compression Approach using Attribute-based Signatures." Open Research Europe 5 (February 10, 2025): 49. https://doi.org/10.12688/openreseurope.19247.1.

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Background With the increasing volume of data collected for advanced analytical and AI applications, data storage remains a significant challenge. Despite advancements in storage technologies, the cost of maintaining vast datasets continues to grow. Compression techniques have been widely used to address this issue, but existing systems primarily rely on a single, typically lossless method, which limits adaptability to varying data characteristics. Methods This paper introduces COMPASS, a multiple compression approach that applies different compression techniques to different subsets of data w

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Petraikin, A. V., Zh E. Belaya, A. N. Kiseleva, et al. "Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks." Problems of Endocrinology 66, no. 5 (2020): 48–60. http://dx.doi.org/10.14341/probl12605.

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BACKGROUND: Pathological low-energy (LE) vertebral compression fractures (VFs) are common complications of osteoporosis and predictors of subsequent LE fractures. In 84% of cases, VFs are not reported on chest CT (CCT), which calls for the development of an artificial intelligence-based (AI) assistant that would help radiology specialists to improve the diagnosis of osteoporosis complications and prevent new LE fractures.AIMS: To develop an AI model for automated diagnosis of compression fractures of the thoracic spine based on chest CT images.MATERIALS AND METHODS: Between September 2019 and

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Louati, Hassen, Ali Louati, Elham Kariri, and Abdulla Almekhlafi. "AI-Based Anomaly Detection and Optimization Framework for Blockchain Smart Contracts." Administrative Sciences 15, no. 5 (2025): 163. https://doi.org/10.3390/admsci15050163.

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Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, and difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Networ

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Huang, Chen-Hsiu, and Ja-Ling Wu. "Unveiling the Future of Human and Machine Coding: A Survey of End-to-End Learned Image Compression." Entropy 26, no. 5 (2024): 357. http://dx.doi.org/10.3390/e26050357.

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End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and indu

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Kumbhare, Pratiksha R., and U. M. Gokhale. "Design and Implementation of 2D-DCT by Using Arai Algorithm for Image Compression." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 3 (2015): 08–14. http://dx.doi.org/10.53555/nneee.v2i3.212.

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Discrete Cosine Transform (DCT) exploits cosine functions; it transforms a signal from spatial representation into frequency domain. It is one of the most widely used techniques for the compression of the image. The main goal of image compression using DCT is the reduction or elimination of redundancy in data representation in order to achieve reduction in storage and communication cost. In this work, we proposed the low complexity architecture for the computation of an algebraic integer (AI) based 8-point DCT. The proposed approach is fast and provides low complexity.

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Jane, Robert, Tae Young Kim, Samantha Rose, Emily Glass, Emilee Mossman, and Corey James. "Developing AI/ML Based Predictive Capabilities for a Compression Ignition Engine Using Pseudo Dynamometer Data." Energies 15, no. 21 (2022): 8035. http://dx.doi.org/10.3390/en15218035.

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Energy and power demands for military operations continue to rise as autonomous air, land, and sea platforms are developed and deployed with increasingly energetic weapon systems. The primary limiting capability hindering full integration of such systems is the need to effectively and efficiently manage, generate, and transmit energy across the battlefield. Energy efficiency is primarily dictated by the number of dissimilar energy conversion processes in the system. After combustion, a Compression Ignition (CI) engine must periodically continue to inject fuel to produce mechanical energy, simu

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Ma, Xiaoqian. "Analysis on the Application of Multimedia-Assisted Music Teaching Based on AI Technology." Advances in Multimedia 2021 (December 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5728595.

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In order to improve the effect of modern music teaching, this paper combines AI technology to construct a multimedia-assisted music teaching system, combines music teaching data processing requirements to improve the algorithm, proposes appropriate music data filtering algorithms, and performs appropriate data compression processing. Moreover, the functional structure analysis of the intelligent music teaching system is carried out with the support of the improved algorithm, and the three-tier framework technology that is currently more widely used is used in the music multimedia teaching syst

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Bai, Ye, Fei Bo, Wencan Ma, Hongwei Xu, and Dawei Liu. "Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/5755671.

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In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chron

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Lin, Ji, Jiaming Tang, Haotian Tang, Shang Yang, Guangxuan Xiao, and Song Han. "AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration." GetMobile: Mobile Computing and Communications 28, no. 4 (2025): 12–17. https://doi.org/10.1145/3714983.3714987.

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Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce cloud computing costs and protect users' privacy. However, the astronomical model size and the limited hardware resources pose significant deployment challenges. To solve these issues, we propose Activation-aware Weight Quantization (AWQ) and TinyChat, an algorithm-system full-stack solution for efficient on-device LLM deployment. AWQ is a novel quantization method that identifies and protects salient weights based on activati

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Ly, Hai-Bang, Lu Minh Le, Huan Thanh Duong, et al. "Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections." Applied Sciences 9, no. 11 (2019): 2258. http://dx.doi.org/10.3390/app9112258.

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The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling load of structural members under compression, taking into account the influence of initial geometric imperfections. With this aim, the existing results of compression tests on steel columns were collected and used as a dataset. Eleven input parameters, representing the slenderne

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Saffarini, Mohammed H., and George Z. Voyiadjis. "Atomistic-Continuum Constitutive Modeling Connection for Gold Foams under Compression at High Strain Rates: The Dislocation Density Effect." Metals 13, no. 4 (2023): 652. http://dx.doi.org/10.3390/met13040652.

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Constitutive description of the plastic flow in metallic foams has been rarely explored in the literature. Even though the material is of great interest to researchers, its plasticity remains a topic that has a much room for exploration. With the help of the rich literature that explored the material deformation mechanism, it is possible to introduce a connection between the results of the atomistic simulations and the well-established continuum constitutive models that were developed for various loading scenarios. In this work, we perform large-scale atomistic simulations of metallic gold foa

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Zhang, Weiguo, and Chenggang Zhao. "Exposing Face-Swap Images Based on Deep Learning and ELA Detection." Proceedings 46, no. 1 (2019): 29. http://dx.doi.org/10.3390/ecea-5-06684.

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New developments in artificial intelligence (AI) have significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake are so realistic that it is difficult to distinguish their authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial images, a novel model has been developed based on deep learning and error level analysis (ELA) detection, which is related to entropy and information theory, such as cross-entropy loss function in the final So

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Sai Kalyan Reddy Pentaparthi. "Optimizing generative AI models for edge deployment: Techniques and best practices." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1485–92. https://doi.org/10.30574/wjarr.2025.26.1.1161.

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Generative AI models represent a significant advancement in content creation capabilities but face substantial challenges when deployed at the network edge due to inherent resource constraints. This article examines comprehensive optimization strategies for enabling generative AI functionality on edge devices without requiring cloud connectivity. The exponential growth in model size has created a widening gap between computational requirements and the limited resources available in edge environments. Through systematic model compression, architectural redesign, and hardware-software co-optimiz

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Oday, Oday, Ali Abdullah Ali, Oday Ali Hassen, Saad M. Darwish, and Nur Azman Abu. "Fusion Model of Quantum Wavelet Transform and Neural Network for Video Coding on the Internet of Things Environment." Fusion: Practice and Applications 17, no. 2 (2025): 249–63. http://dx.doi.org/10.54216/fpa.170219.

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Solving the video compression problem requires a multi-faceted approach, balancing quality, efficiency, and computational demands. By leveraging advancements in technology and adapting to the evolving needs of video applications, it is possible to develop compression methods that meet the challenges of the present and future digital landscape. To address these objectives, machine learning and AI approaches can be utilized to predict and remove redundancies more effectively, optimizing compression algorithms dynamically based on content. Still, state-of-the art neural network-based video compre

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Vishwakarma, Mr Ashish. "Implementing Real-Time Anomaly Detection in Mobile Security Logs Using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 112–17. https://doi.org/10.22214/ijraset.2025.68272.

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With the increasing reliance on mobile applications, security threats such as unauthorized access, malware attacks, and network intrusions have become more sophisticated. Traditional rule-based security mechanisms often fail to detect novel and evolving threats, necessitating the integration of Artificial Intelligence (AI) for real-time anomaly detection. This study proposes an AI-driven framework for identifying anomalies in mobile security logs using machine learning (ML) and deep learning (DL) models. The research employs Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) net

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Chander, Dr T. Prem. "Optimizing Memory Efficiency in Large Language Models: Adaptive Compression Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1474–79. https://doi.org/10.22214/ijraset.2025.66591.

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Large Language Models (LLMs) have revolutionized artificial intelligence by achieving unprecedented results across various natural language processing (NLP) tasks. However, their massive memory requirements pose significant challenges for deployment in resource-constrained environments, such as mobile devices and edge computing. This paper introduces an adaptive compression framework to optimize the memory efficiency of LLMs while maintaining their performance. The proposed framework integrates multiple techniques, including quantization, pruning, and knowledge distillation, dynamically adjust

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Sarkar, Dr Bikramjit. "AI-DRIVEN ADAPTIVE WEB CONTENT DELIVERY SYSTEM FOR PERSONALIZED E-COMMERCE EXPERIENCE." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03289.

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ABSTRACT: Our AI-driven adaptive e-commerce platform revolutionizes online shopping by delivering personalized and optimized content in real time. Traditional e-commerce websites often struggle with performance issues due to varying user contexts, such as device type, network speed, and location. Additionally, static content delivery fails to adapt to user preferences, leading to inefficiencies in engagement and conversions. Our solution integrates an intelligent web content delivery system using PHP, MySQL, HTML, CSS, JavaScript, and machine learning. By analysing user behaviour, network cond

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Jain, Dr Prerna, Prof Nidhi Pateria, Prof Gulafsha Anjum, Ashwini Tiwari, and Ayush Tiwari. "Edge AI and On-Device Machine Learning For Real Time Processing." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 05 (2023): 8137–46. http://dx.doi.org/10.15680/ijircce.2024.1205364.

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Edge Artificial Intelligence (Edge AI) and on-device machine learning represent significant advancements in computing paradigms, enabling real-time data processing directly at the edge of the network. This approach minimizes the need for centralized cloud-based processing, thereby reducing latency, enhancing privacy, and improving operational efficiency. This paper provides a comprehensive review of Edge AI and on-device machine learning, focusing on their applications, challenges, and future directions. Key applications include smart home devices, healthcare monitoring, autonomous vehicles, a

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Kamalesh, Jain, and Bendre Sidhant. "Enhancing Multi-Tenant Architectures with AI-Driven Natural Language Processing: Challenges and Solutions." Sarcouncil Journal of Engineering and Computer Sciences 3, no. 6 (2024): 9–16. https://doi.org/10.5281/zenodo.14451710.

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Multi-tenant architectures have become essential in cloud computing, allowing multiple clients to share a single software instance, and thus optimizing costs and resource utilization. However, challenges in data privacy, customization, and scalability limit the effectiveness of traditional multi-tenant systems. This study investigates the potential of AI-driven Natural Language Processing (NLP) to address these limitations by enhancing tenant-specific customizations, improving query handling, and ensuring real-time processing. Using transformer-based models such as BERT and GPT-3, the study im

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Chen, Bo, Hongyu Zhang, Runxi Yang, Xiao Fang, and Yi Ding. "Research on Dynamic Target Detection and Adaptive High-Rate Compression Technology for Power Smart Site Monitoring Video Based on Artificial Intelligence Algorithm." Journal of Combinatorial Mathematics and Combinatorial Computing 127a (April 15, 2025): 903–22. https://doi.org/10.61091/jcmcc127a-053.

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With the deepening of the exploration of informationization in the construction industry, the smart construction site comes into being under the support of technological development and policy. The article combines artificial intelligence technology with electric power smart site, and deeply researches the application of artificial intelligence technology in electric power smart site. For the security monitoring in the smart construction site, a SSD7-FFAM lightweight target detection method is proposed based on the SSD7 algorithm, using feature fusion and attention mechanism methods. Then, bas

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Vaiyapuri, Thavavel, and Huda Aldosari. "SUQ-3: A Three Stage Coarse-to-Fine Compression Framework for Sustainable Edge AI in Smart Farming." Sustainability 17, no. 12 (2025): 5230. https://doi.org/10.3390/su17125230.

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Artificial intelligence of things (AIoT) has become a pivotal enabler of precision agriculture by supporting real-time, data-driven decision-making at the edge. Deep learning (DL) models are central to this paradigm, offering powerful capabilities for analyzing environmental and climatic data in a range of agricultural applications. However, deploying these models on edge devices remains challenging due to constraints in memory, computation, and energy. Existing model compression techniques predominantly target large-scale 2D architectures, with limited attention to one-dimensional (1D) models

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Oluwafemi Oloruntoba. "Green cloud computing: AI for sustainable database management." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 3242–57. https://doi.org/10.30574/wjarr.2024.23.3.2611.

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The exponential growth of digital data has intensified the demand for cloud computing resources, leading to increased energy consumption and environmental concerns. Traditional cloud data centers operate at high energy levels, contributing significantly to carbon emissions and escalating operational costs. Green Cloud Computing (GCC) has emerged as a sustainable solution that integrates energy-efficient technologies, renewable energy sources, and artificial intelligence (AI) to optimize cloud infrastructure. By leveraging AI-driven algorithms, sustainable database management in GCC enhances re

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Kamal Shah,. "Dynamic Algorithm Selection for Replication using RSYNC." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 110–22. https://doi.org/10.52783/jisem.v10i19s.2990.

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Introduction: In the era of big data and distributed systems, efficient data replication is essential for maintaining data availability, consistency, and performance. Rsync, a widely used file synchronization tool, utilizes compression algorithms to optimize data transfer. However, its performance is highly dependent on the choice of compression algorithm, file type, and network conditions. This research introduces a novel approach to dynamic algorithm selection for Rsync, leveraging machine learning to automatically determine the optimal compression algorithm (gzip, zstd, or lz4) based on fil

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Artyukova, Z. R., N. D. Kudryavtsev, A. V. Petraikin, et al. "Using an artificial intelligence algorithm to assess the bone mineral density of the vertebral bodies based on computed tomography data." Medical Visualization 27, no. 2 (2023): 125–37. http://dx.doi.org/10.24835/1607-0763-1257.

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Goal: To develop a method for automated assessment of the volumetric bone mineral density (BMD) of the vertebral bodies using an artificial intelligence (AI) algorithm and a phantom modeling method.Materials and Methods: Evaluation of the effectiveness of the AI algorithm designed to assess BMD of the vertebral bodies based on chest CT data. The test data set contains 100 patients aged over 50 y.o.; the ratio between the subjects with/without compression fractures (Сfr) is 48/52. The X-ray density (XRD) of vertebral bodies at T11-L3 was measured by experts and the AI algorithm for 83 patients

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Ashfaq, Mohammed, Mudassir Iqbal, Mohsin Ali Khan, et al. "GEP tree-based computational AI approach to evaluate unconfined compression strength characteristics of Fly ash treated alkali contaminated soils." Case Studies in Construction Materials 17 (December 2022): e01446. http://dx.doi.org/10.1016/j.cscm.2022.e01446.

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Alam, Shahanur, Chris Yakopcic, Qing Wu, Mark Barnell, Simon Khan, and Tarek M. Taha. "Survey of Deep Learning Accelerators for Edge and Emerging Computing." Electronics 13, no. 15 (2024): 2988. http://dx.doi.org/10.3390/electronics13152988.

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The unprecedented progress in artificial intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and processing in-memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and applica

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Armand, Atiampo Kodjo, Gokou Hervé Fabrice Diédié, and N’Takpé Tchimou Euloge. "Super-tokens Auto-encoders for image compression and reconstruction in IoT applications." International Journal of Advances in Scientific Research and Engineering 10, no. 01 (2024): 29–46. http://dx.doi.org/10.31695/ijasre.2024.1.4.

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New telecommunications networks are enabling powerful AI applications for smart cities and transport. These applications require real-time processing of large amounts of media data. Sending data to the cloud for processing is very difficult due to latency and energy constraints. Lossy compression can help, but traditional codecs may not provide enough quality or be efficient enough for resource-constrained devices. This paper proposes a new image compression and processing approach based on variational auto-encoders (VAEs). This VAE-based method aims to efficiently compress images while still

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Pinheiro, Antonio. "JPEG Column: 94th JPEG Meeting." ACM SIGMultimedia Records 14, no. 1 (2022): 1. http://dx.doi.org/10.1145/3630646.3630650.

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The 94 th JPEG meeting was held online from 17 to 21 January 2022. A major milestone has been reached at this meeting with the release of the final call for proposals under the JPEG AI project. This standard aims at the joint standardization of the first image coding standard based on machine learning by the IEC, ISO and ITU, offering a single stream, compact compressed domain representation, targeting both human visualization with significant compression efficiency improvement over image coding standards in common use at equivalent subjective quality and effective performance for image proces

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Sudars, Kaspars, Ivars Namatēvs, and Kaspars Ozols. "Improving Performance of the PRYSTINE Traffic Sign Classification by Using a Perturbation-Based Explainability Approach." Journal of Imaging 8, no. 2 (2022): 30. http://dx.doi.org/10.3390/jimaging8020030.

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Model understanding is critical in many domains, particularly those involved in high-stakes decisions, e.g., medicine, criminal justice, and autonomous driving. Explainable AI (XAI) methods are essential for working with black-box models such as convolutional neural networks. This paper evaluates the traffic sign classifier of the Deep Neural Network (DNN) from the Programmable Systems for Intelligence in Automobiles (PRYSTINE) project for explainability. The results of explanations were further used for the CNN PRYSTINE classifier vague kernels’ compression. Then, the precision of the classif

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Subbaiah, S., R. Agusthiyar, M. Kavitha, and V. P. Muthukumar. "ARTIFICIAL INTELLIGENCE FOR OPTIMIZED WELL CONTROL AND MANAGEMENT IN SUBSURFACE MODELS WITH UNPREDICTABLE GEOLOGY." Archives for Technical Sciences 31, no. 2 (2024): 140–47. http://dx.doi.org/10.70102/afts.2024.1631.140.

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A comprehensive control policy structure utilizing Artificial Intelligence (AI) is presented for closed-loop management in subsurface models. Conventional Closed-Loop Optimisation (CLO) approaches entail the iterative implementation of information assimilation, past synchronization details, and effective optimizing procedures. Information assimilation is more difficult when there is uncertainty in the geological approach and the specific model conclusions. Closed-Loop Reservoir Monitoring (CLRM) offers a control strategy that promptly correlates flow information obtained from wells, as typical

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Katta, Srikanth Reddy. "Predictive AI Models for Manufacturing Failure Detection in Multi-Site Pharmaceutical Facilities." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–7. https://doi.org/10.55041/ijsrem40032.

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The role of Predictive Artificial Intelligence (AI) models is continuously emerging as critical in the pharmaceutical manufacturing industry particularly in failure detection in multiple sites. These models use artificial intelligence, evolving ML techniques, and large datasets to forecast, monitor and resolve rigorous system failures. This paper aims to analyze how research has embraced the development of effective predictive AI frameworks that are relevant to multi-site pharmaceutical facilities, given the many limitations and challenges associated with such settings. A deeper elucidation of

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Varela-Rey, Iria, Enrique Bandín-Vilar, Francisco José Toja-Camba, et al. "Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review." Antibiotics 13, no. 12 (2024): 1203. https://doi.org/10.3390/antibiotics13121203.

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Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to relate enormous amounts of data and to the more efficient development of new predictive dose models. The development of pharmacokinetic models based on these techniques simplifies the process, reduces time, and allows more factors to be considered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of ant

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Pinheiro, Antonio. "JPEG Column: 93rd JPEG Meeting." ACM SIGMultimedia Records 13, no. 4 (2021): 1. http://dx.doi.org/10.1145/3578508.3578512.

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The 93rd JPEG meeting was held online from 18 to 22 October 2021. The JPEG Committee continued its work on the development of new standardised solutions for the representation of visual information. Notably, the JPEG Committee has decided to release a new call for proposals on point cloud coding based on machine learning technologies that targets both compression efficiency and effective performance for 3D processing as well as machine and computer vision tasks. This activity will be conducted in parallel with JPEG AI standardization. Furthermore, it was also decided to pursue the development

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Teimuraz Goderdzishvili, Teimuraz Goderdzishvili. "Artificial Intelligence and Creative Thinking, the Future of Idea Generation." Economics 105, no. 3-4 (2023): 63–73. http://dx.doi.org/10.36962/ecs105/3-4/2023-63.

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Artificial intelligence has been the most successful modern technology created in recent years. Its development began in the 1960s and was based on simulating the human brain. The scientific community's interest has focused on developing machine-learning neural networks and their potential applications in music, art, architecture, and other fields. AI is increasingly capable of generating creative ideas and assisting humans in decision-making processes. With advanced algorithms, machine learning, and natural language processing, AI has the potential to revolutionize creative thinking. In this

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Pérez, Borja, Mario Resino, Teresa Seco, Fernando García, and Abdulla Al-Kaff. "Innovative Approaches to Traffic Anomaly Detection and Classification Using AI." Applied Sciences 15, no. 10 (2025): 5520. https://doi.org/10.3390/app15105520.

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Video anomaly detection plays a crucial role in intelligent transportation systems by enhancing urban mobility and safety. This review provides a comprehensive analysis of recent advancements in artificial intelligence methods applied to traffic anomaly detection, including convolutional and recurrent neural networks (CNNs and RNNs), autoencoders, Transformers, generative adversarial networks (GANs), and multimodal large language models (MLLMs). We compare their performance across real-world applications, highlighting patterns such as the superiority of Transformer-based models in temporal con

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Subramanian Sendil Kumar, Sneha Singireddy, Botlagunta Preethish Nanan, Mahesh Recharla, Anil Lokesh Gadi, and Srinivasarao Paleti. "Optimizing Edge Computing for Big Data Processing in Smart Cities." Metallurgical and Materials Engineering 31, no. 3 (2025): 31–39. https://doi.org/10.63278/1317.

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The surge of big data and IoT in smart cities requires effective computational models to process massive amounts of real-time data. Edge computing emerges as an innovative solution by minimizing latency, improving security, and maximizing energy efficiency. This paper investigates the convergence of AI-based edge computing for big data processing through a study of four sophisticated algorithms: Federated Learning, TinyML, Edge-Optimized CNNs, and Adaptive Data Compression. Experimental analysis proved a decrease of 37% in latency, 42% increase in computational performance, and 29% decrease in

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Shkalenko, Anna, and Ekaterina Fadeeva. "Impact of Artificial Intelligence on Creative Industries: Trends and Prospects." Vestnik Volgogradskogo gosudarstvennogo universiteta. Ekonomika, no. 3 (October 2022): 44–59. http://dx.doi.org/10.15688/ek.jvolsu.2022.3.4.

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This study is based on the elements of the innovative methodology of post-institutional analysis including interdisciplinary synthesis, the core of which is the evolutionary-genetic concept of production factors and the model of the “development core” of economic systems, which involves overcoming the mono-aspect, dichotomy and dogmatism of many concepts of orthodox neo-institutionalism. The main idea of this study is to apply an interdisciplinary approach to study the impact of artificial intelligence on creative industries. The assessment of the current problems under study and the conceptua

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PANTOLI, LEONARDO, VINCENZO STORNELLI, and GIORGIO LEUZZI. "TUNABLE ACTIVE FILTERS FOR RF AND MICROWAVE APPLICATIONS." Journal of Circuits, Systems and Computers 23, no. 06 (2014): 1450088. http://dx.doi.org/10.1142/s0218126614500881.

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In this paper, we present a low-voltage tunable active filter for microwave applications. The proposed filter is based on a single-transistor active inductor (AI), that allows the reduction of circuit area and power consumption. The three active-cell bandpass filter has a 1950 MHz center frequency with a -1 dB flat bandwidth of 10 MHz (Q ≈ 200), a shape factor (30–3 dB) of 2.5, and can be tuned in the range 1800–2050 MHz, with constant insertion loss. A dynamic range of about 75 dB is obtained, with a P1dB compression point of -5 dBm. The prototype board, fabricated on a TLX-8 substrate, has a

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