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Journal articles on the topic 'AI throughput'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Wu, Jiajie, Weixing Dai, Claudia T. K. Lo, Lauren W. K. Tsui, and Terence T. W. Wong. "High-throughput, nondestructive, and low-cost histological imaging with deep-learning-assisted UV microscopy." Advanced Imaging 1, no. 2 (2024): 021001. http://dx.doi.org/10.3788/ai.2024.10007.

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Kurowski, Konrad, Sylvia Timme, Melanie Christine Föll, et al. "AI-Assisted High-Throughput Tissue Microarray Workflow." Methods and Protocols 7, no. 6 (2024): 96. http://dx.doi.org/10.3390/mps7060096.

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Immunohistochemical (IHC) studies of formalin-fixed paraffin-embedded (FFPE) samples are a gold standard in oncology for tumor characterization, and the identification of prognostic and predictive markers. However, despite the abundance of archived FFPE samples, their research use is limited due to the labor-intensive nature of IHC on large cohorts. This study aimed to create a high-throughput workflow using modern technologies to facilitate IHC biomarker studies on large patient groups. Semiautomatic constructed tissue microarrays (TMAs) were created for two tumor patient cohorts and IHC stai

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Machek, Ondřej, Tereza Konečná, and Daniela Slamková. "High Throughput 3D Volumes Data Acquisition Using AI." Microscopy and Microanalysis 28, S1 (2022): 3092–93. http://dx.doi.org/10.1017/s1431927622011527.

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Xiao, Qi, Fangfei Zhang, Luang Xu, et al. "High-throughput proteomics and AI for cancer biomarker discovery." Advanced Drug Delivery Reviews 176 (September 2021): 113844. http://dx.doi.org/10.1016/j.addr.2021.113844.

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Salinel, Brandon, Matthew Grudza, Sarah Zeien, et al. "Comparison of segmentation methods to improve throughput in annotating AI-observer for detecting colorectal cancer." Journal of Clinical Oncology 40, no. 4_suppl (2022): 142. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.142.

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142 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its outcome can be improved with better detection of incidental early CRC on routine CT of the abdomen and pelvis (CTAP). AI-second observer (AI) has the potential as shown in our companion abstract. The bottleneck in training AI is the time required for radiologists to segment the CRC. We compared two techniques for accelerating the segmentation process: 1) Sparse annotation (annotating some of the CT slice containing CRC instead of every slice); 2) Allowing AI to perform initial segmentation fol

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Eschemann, Patrick, Astrid Nieße, and Jürgen Sauer. "Prediction of simulated factory layout throughput using artificial intelligence." New Trends in Computer Sciences 2, no. 2 (2024): 101–16. https://doi.org/10.3846/ntcs.2024.22160.

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The use of artificial neural networks for the optimisation of factory layouts is not a common practice, primarily due to the challenge of collecting sufficient layout data to form datasets for artificial intelligence (AI) model training. This paper presents a supervised learning method derived from a PhD thesis that employs neural networks to assess factory layouts. The training data is generated using a random layout algorithm, which is capable of producing numerous layouts. These layouts are then labeled through a discrete event simulation. The combination of layouts and simulation metrics s

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Khaled, Haitham, and Emad Alkhazraji. "AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems." Sensors 24, no. 15 (2024): 4956. http://dx.doi.org/10.3390/s24154956.

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Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput

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Xue, Yexiang, Junwen Bai, Ronan Le Bras, et al. "Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (2017): 4635–42. http://dx.doi.org/10.1609/aaai.v31i2.19087.

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High-throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally related materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. We present Phase-Mapper, a novel solution platform that allows humans to interact with both the data and products of AI algorithms, including the incorporation of human feedback to constrain or initialize solutions. Phase-Mapper is comp

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Angelino, Antimo. "Data & AI for Industrial Application." Advances in Machine Learning & Artificial Intelligence 5, no. 4 (2024): 01–07. https://doi.org/10.33140/amlai.05.04.05.

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The use of Artificial Intelligence in the Industry can lead to recovery of efficiency for industrial processes (such as reduce scrap and rework rate, increase throughput time), and this can carry competitive advantages. Nevertheless, to correctly deploy artificial intelligence projects it is needed to have connectivity and quality data. Both are enabling factors for AI projects, and industries must put in place processes to reach them before to start the AI journey

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Devi Rahmayanti. "Analysis of AI-Driven Modulation for Cognitive Cellular Networks : DNN Approach." International Journal of Mechanical, Electrical and Civil Engineering 1, no. 4 (2024): 86–101. https://doi.org/10.61132/ijmecie.v1i4.138.

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Objective: analyze the modulation scheme that can intelligently select the appropriate modulation model for service conditions to obtain a high Signal to Noise Ratio, as well as throughput efficiency on wireless networks through the DNN approach. Method: this study uses simulations with the Python language, through AI-Driven on BPSK, QPSK, 16-QAM, and 64-QAM modulation, to determine the SNR and Quality of Service (QoS) produced, both through conventional approaches and Deep Neuro Network (DNN). Researh Finding: AI-Driven modulation used for Cognitive Cellular Networks (CCN), through Deep Neuro

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N, Arathy Raj, and Gnana Sheela K. "Review on Energy Efficiency and Throughput Optimization in UAV Networks for AI-Driven Smart Construction." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–8. https://doi.org/10.55041/ijsrem.icites006.

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Unmanned Aerial Vehicles (UAVs) are used as an important element of smart construction and infrastructure management. These systems have been helpful for real time monitoring, site inspection, and safety assessment. The challenge is, however, optimization of energy consumption while maintaining a high data throughput in dynamic and resource constrained environments. This paper reviews several AI-driven approaches, such as deep reinforcement learning, multi-agent frameworks, and resource allocation techniques, which improve the efficiency of UAVs in construction applications. The review focuses

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Park, Jeman, Misun Yu, Jinse Kwon, Junmo Park, Jemin Lee, and Yongin Kwon. "NEST‐C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators." ETRI Journal 46, no. 5 (2024): 851–64. http://dx.doi.org/10.4218/etrij.2024-0139.

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AbstractDeep learning (DL) has significantly advanced artificial intelligence (AI); however, frameworks such as PyTorch, ONNX, and TensorFlow are optimized for general‐purpose GPUs, leading to inefficiencies on specialized accelerators such as neural processing units (NPUs) and processing‐in‐memory (PIM) devices. These accelerators are designed to optimize both throughput and energy efficiency but they require more tailored optimizations. To address these limitations, we propose the NEST compiler (NEST‐C), a novel DL framework that improves the deployment and performance of models across vario

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Kwemoi, Kabiga Chelule. "The Role of Artificial Intelligence in Accelerating Drug Discovery Innovations." RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 5, no. 1 (2025): 9–13. https://doi.org/10.59298/rijses/2025/519130.

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Drug discovery is a complex, costly, and time-intensive process, often taking over a decade and billions of dollars to identify novel therapeutic compounds. Recent advancements in artificial intelligence (AI) have transformed this domain, enabling more efficient, cost-effective, and innovative approaches. This paper examines the application of AI in various stages of drug discovery, from target identification to compound screening and toxicity prediction. Machine learning and deep learning techniques are highlighted as key contributors to enhancing predictive accuracy, optimizing molecular pro

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Khan, Muhammad Hafeez Ullah, Shoudong Wang, Jun Wang, et al. "Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding." International Journal of Molecular Sciences 23, no. 19 (2022): 11156. http://dx.doi.org/10.3390/ijms231911156.

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Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the

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Alghazo, Jaafar, and Ghazanfar Latif. "AI/ML-Based Medical Image Processing and Analysis." Diagnostics 13, no. 24 (2023): 3671. http://dx.doi.org/10.3390/diagnostics13243671.

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The medical field is experiencing remarkable advancements, notably with the latest technologies—artificial intelligence (AI), big data, high-performance computing (HPC), and high-throughput computing (HTC)—that are in place to offer groundbreaking solutions to support medical professionals in the diagnostic process [...]

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Kim, Youngbae, Shreyash Patel, Heekyung Kim, Nandakishor Yadav, and Kyuwon Ken Choi. "Ultra-Low Power and High-Throughput SRAM Design to Enhance AI Computing Ability in Autonomous Vehicles." Electronics 10, no. 3 (2021): 256. http://dx.doi.org/10.3390/electronics10030256.

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Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). Existing state-of-the-art SRAM architectures for AI computing are highly accurate and can provide high throughput. However, these SRAMs have problems that they consume high power and occupy a large area to accommodate complex AI models. A carbon nanotube field-effect transistors (CNFET) device has been reported as a potential candidates for AI devices requiring ultra-low power and

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Pistellato, Mara, Filippo Bergamasco, Gianluca Bigaglia, et al. "Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI." Sensors 23, no. 10 (2023): 4667. http://dx.doi.org/10.3390/s23104667.

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Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable

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Tulasiram, Yadavalli. "Challenges of Injecting ML in A Data Stream and How Vertex AI and Dataflow Pipelines Circumvent These Challenges." European Journal of Advances in Engineering and Technology 10, no. 5 (2023): 124–31. https://doi.org/10.5281/zenodo.15044405.

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Injecting machine learning (ML) models into real-time data streams presents several challenges. These challenges include issues such as latency, scalability, throughput, data quality, preprocessing, model drift, and continuous learning. Latency and scalability become critical when processing large, fast-moving datasets. Throughput is often hindered by the sheer volume of data. Ensuring data quality is essential to prevent poor model performance. Preprocessing, a key part of model input, adds complexity when dealing with dynamic data. Additionally, model drift impacts the reliability of ML mode

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Iyer, Venkatraman, Sungho Lee, Semun Lee, Juitem Joonwoo Kim, Hyunjun Kim, and Youngjae Shin. "Automated Backend Allocation for Multi-Model, On-Device AI Inference." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (2024): 27–28. http://dx.doi.org/10.1145/3673660.3655046.

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On-Device Artificial Intelligence (AI) services such as face recognition, object tracking and voice recognition are rapidly scaling up deployments on embedded, memory-constrained hardware devices. These services typically delegate AI inference models for execution on CPU and GPU computing backends. While GPU delegation is a common practice to achieve high speed computation, the approach suffers from degraded throughput and completion times under multi-model scenarios, i.e., concurrently executing services. This paper introduces a solution to sustain performance in multi-model, on-device AI con

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Kolosov, Dimitrios, Vasilios Kelefouras, Pandelis Kourtessis, and Iosif Mporas. "Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware." Sensors 23, no. 9 (2023): 4550. http://dx.doi.org/10.3390/s23094550.

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Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe’s B

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Salehi, Pouria, Erin K. Chiou, Michelle Mancenido, Ahmadreza Mosallanezhad, Myke C. Cohen, and Aksheshkumar Shah. "Decision Deferral in a Human-AI Joint Face-Matching Task: Effects on Human Performance and Trust." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 638–42. http://dx.doi.org/10.1177/1071181321651157.

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This study investigates how human performance and trust are affected by the decision deferral rates of an AI-enabled decision support system in a high criticality domain such as security screening, where ethical and legal considerations prevent full automation. In such domains, deferring cases to a human agent becomes an essential process component. However, the systemic consequences of the rate of deferrals on human performance are unknown. In this study, a face-matching task with an automated face verification system was designed to investigate the effects of varying deferral rates. Results

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Wobiageri, Ndidi Abidde, Eyidia Nkechinyere, and Iyaminapu Iyoloma Collins. "Optimization of Wireless Mesh Networks for Disaster Response Communication." International Journal of Current Science Research and Review 08, no. 03 (2025): 1312–19. https://doi.org/10.5281/zenodo.15040499.

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Abstract : Wireless Mesh Networks (WMNs) have emerged as a resilient and adaptable solution for disaster response communication, offering self-healing and self-organizing capabilities that ensure uninterrupted connectivity in emergency scenarios. Traditional communication infrastructures often fail due to network congestion, power outages, and physical damage during disasters, necessitating an optimized approach for rapid and reliable data transmission. This study presents an AI-optimized WMN framework aimed at enhancing network performance by improving packet delivery ratio (PDR), reducing en

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Gatta, Viviana, Polina Ilina, Alison Porter, Stuart McElroy, and Päivi Tammela. "Targeting Quorum Sensing: High-Throughput Screening to Identify Novel LsrK Inhibitors." International Journal of Molecular Sciences 20, no. 12 (2019): 3112. http://dx.doi.org/10.3390/ijms20123112.

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Since quorum sensing (QS) is linked to the establishment of bacterial infection, its inactivation represents one of the newest strategies to fight bacterial pathogens. LsrK is a kinase playing a key role in the processing of autoinducer-2 (AI-2), a quorum-sensing mediator in gut enteric bacteria. Inhibition of LsrK might thus impair the quorum-sensing cascade and consequently reduce bacterial pathogenicity. Aiming for the development of a target-based assay for the discovery of LsrK inhibitors, we evaluated different assay set-ups based on ATP detection and optimized an automation-compatible m

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Ha, Jae Baek, Jaewoon Jeong, Jeongyoon Suh, et al. "Artificial Intelligence on Urology Lab." Korean Journal of Urological Oncology 20, no. 3 (2022): 163–76. http://dx.doi.org/10.22465/kjuo.2022.20.3.163.

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The development of lab-on-a-chip technology based on microfluidics has been used from diagnostic test to drug screening in biomedical science. Lab-on-a-chip technology is also being expanded to the concept of an organ-on-a-chip with the development of cell biology and biocompatible material development. In addition, artificial intelligence (AI) has brought dramatic changes over the past few years in science, industry, defense, science and healthcare. AI-generated output is beginning to prove comparable or even superior to that of human experts. Lab-on-a-chip technology in specific microfluidic

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Aliyu, Bala Sidi. "Artificial intelligence (AI): A powerful tool for advancing plant study and research." Nigerian Journal of Botany 37, no. 1 (2024): 61–68. http://dx.doi.org/10.4314/njbot.v37i1.5.

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Artificial Intelligence (AI) has revolutionised various fields and plant science is no exception. In recent years, AI techniques, particularly deep learning, have emerged as powerful tools for advancing plant science research. This paper explores the transformative potential of integrating multi-omics data and AI in plant science research, phenotyping, etc., providing a comprehensive and high-throughput approach to understanding and application to agriculture and plant biology. The advantages, limitations and future prospects of this tool to the study of plants are discussed.

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Nair, Sachin, Jun Gao, Qirong Yao, Michael H. G. Duits, Cees Otto, and Frieder Mugele. "Algorithm-improved high-speed and non-invasive confocal Raman imaging of 2D materials." National Science Review 7, no. 3 (2019): 620–28. http://dx.doi.org/10.1093/nsr/nwz177.

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Abstract Confocal Raman microscopy is important for characterizing 2D materials, but its low throughput significantly hinders its applications. For metastable materials such as graphene oxide (GO), the low throughput is aggravated by the requirement of extremely low laser dose to avoid sample damage. Here we introduce algorithm-improved confocal Raman microscopy (ai-CRM), which increases the Raman scanning rate by one to two orders of magnitude with respect to state-of-the-art works for a variety of 2D materials. Meanwhile, GO can be imaged at a laser dose that is two to three orders of magnit

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Saurabh Suman. ""Innovative Roles of Operating Systems in Driving Emerging Technologies"." Journal of Information Systems Engineering and Management 10, no. 53s (2025): 216–22. https://doi.org/10.52783/jisem.v10i53s.10860.

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Operating systems (OS) have undergone significant transformation from monolithic batch processors to smart, adaptive platforms enabling AI, IoT, edge, and quantum computing. This paper presents a comprehensive review of modern OS evolution and highlights innovations enabling real-time performance, modular design, and scalable deployment across emerging hardware platforms. Comparative analysis across experimental metrics, such as inference latency, response time, and energy efficiency, illustrates the OS’s strategic role in future computing architectures. Objectives: This paper aims to: Examine

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Hartfield, Cheryl. "Nondestructive 3D X-ray Microscopy Speeds Throughput in New Failure Analysis Workflows." EDFA Technical Articles 26, no. 4 (2024): 14–19. http://dx.doi.org/10.31399/asm.edfa.2024-4.p014.

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Abstract This article shows how 3D XRM can be applied to nondestructively detect non-optimized assembly processes that can influence local stresses and overall device reliability. This makes it useful for process development and failure analysis. When used along with AI training models, 3D XRM can achieve analysis of highly integrated packaging structures with reasonable throughput for process validation and error correction guidance.

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Mahender Singh. "AI-Driven Cross-Blockchain Automation for Serverless Quantum Workflows." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 79–91. https://doi.org/10.52783/jisem.v10i32s.5190.

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This research paper investigates the way that AI driven cross chain interoperability for blockchain technology has the transformative potential. It analyzes of performance, cost efficiency and scalability between classical and quantum approaches. Results demonstrate that Hybrid AI Driven is more efficient in terms of throughput, cost reduction, and scalability compared to other approaches used in blockchain, and thus can contribute to transforming blockchain into one of many available application options.

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Xu, Wei, Jiajun Shen, Han Chen, et al. "P‐104: Graph‐Based AI Workflow for OLED Materials Discovery." SID Symposium Digest of Technical Papers 54, no. 1 (2023): 1571–74. http://dx.doi.org/10.1002/sdtp.16893.

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Artificial Intelligence (AI) is becoming an emerging technique in scientific research including novel materials discovery. In this work, we present a novel graph‐based AI workflow for discovering Organic Light‐Emitting Diode (OLED) materials. This workflow contains two graph‐based AI models: a molecular structure generative model and a molecular property predictive model. The target materials here are Red‐Prime (RP) materials, which are widely used to pair with the red light emitters in OLED devices. Based on the desired properties required by our OLED devices, we apply the AI‐based workflow t

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Bai, Junwen, Yexiang Xue, Johan Bjorck, et al. "Phase Mapper: Accelerating Materials Discovery with AI." AI Magazine 39, no. 1 (2018): 15–26. http://dx.doi.org/10.1609/aimag.v39i1.2785.

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From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of th

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Amer-Yahia, Sihem. "Towards AI-powered data-driven education." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3798–806. http://dx.doi.org/10.14778/3554821.3554900.

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Educational platforms are increasingly becoming AI-driven. Besides providing a wide range of course filtering options, personalized recommendations of learning material and teachers are driving today's research. While accuracy plays a major role in evaluating those recommendations, many factors must be considered including learner retention, throughput, upskilling ability, equity of learning opportunities, and satisfaction. This creates a tension between learner-centered and platform-centered approaches. I will describe research at the intersection of data-driven recommendations and education

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Pham-Quoc, Cuong, Tran Hoang Quoc Bao, and Tran Ngoc Thinh. "FPGA/AI-Powered Architecture for Anomaly Network Intrusion Detection Systems." Electronics 12, no. 3 (2023): 668. http://dx.doi.org/10.3390/electronics12030668.

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This paper proposes an architecture to develop machine learning/deep learning models for anomaly network intrusion detection systems on reconfigurable computing platforms. We build two models to validate the framework: Anomaly Detection Autoencoder (ADA) and Artificial Neural Classification (ANC) in the NetFPGA-sume platform. Three published data sets NSL-KDD, UNSW-NB15, and CIC-IDS2017 are used to test the deployed models’ throughput, latency, and accuracy. Experimental results with the NetFPGA-SUME show that the ADA model uses 20.97% LUTs, 15.16% FFs, 19.42% BRAM, and 6.81% DSP while the ANC

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Oluwatosin Oladayo Aramide. "Architecting highly resilient AI Fabrics: A Blueprint for Next-Gen Data Centers." World Journal of Advanced Engineering Technology and Sciences 8, no. 1 (2023): 529–39. https://doi.org/10.30574/wjaets.2023.8.1.0049.

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The fast-growing advancement in AI technologies has resulted in huge loads on the data center architecture resulting in the need to create extremely resistant, and fault-tolerant AI fabrics. This paper looks at AI design principles and technologies necessitated in the construction of fault-tolerant AI infrastructures that can support complex, data-heavy workloads. The major technologies of VXLAN EVPN, RDMA and ultra-low latency interconnect like RoCEv2, NV Link and PCIe Gen5 are paramount to high availability, low latency and high throughput. This article reviews industrial best practice by ob

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Nayak, Ujjawal. "AI-Powered Data Pipelines: Leveraging Machine Learning for ETL Optimization." Journal of Software Engineering and Simulation 11, no. 1 (2025): 53–55. https://doi.org/10.35629/3795-11015355.

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Modern ETL (Extract, Transform, Load) workflows must adapt to growing data volumes, diverse sources, and tight SLAs. Embedding machine learning (ML) into data pipelines enables dynamic optimization of data transformations, anomaly detection, and resource allocation, leading to improved throughput, reliability, and cost efficiency. This article surveys key AI techniques for ETL optimization, presents a reference architecture for AI-powered data pipelines, discusses implementation considerations, and highlights future research directions.

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Enad, Huda Ghazi, and Mazin Abed Mohammed. "A Review on Artificial Intelligence and Quantum Machine Learning for Heart Disease Diagnosis: Current Techniques, Challenges and Issues, Recent Developments, and Future Directions." Fusion: Practice and Applications 11, no. 1 (2023): 08–25. http://dx.doi.org/10.54216/fpa.110101.

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This study presents a comprehensive analysis of the existing techniques and applications of artificial intelligence (AI) to cardiovascular disease diagnosis. The application of AI to the diagnosis of cardiac diseases can enhance diagnostic precision, diagnostic throughput, and patient outcomes. This literature survey analyzes state-of-the-art AI-based methods, rates their efficiency, examines potential future research and development avenues, and finds challenges and limitations, providing a foundational overview of main developments in AI, machine learning, deep learning, and quantum computin

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Machura, Michal, Michal Danilowicz, and Tomasz Kryjak. "Embedded Object Detection with Custom LittleNet, FINN and Vitis AI DCNN Accelerators." Journal of Low Power Electronics and Applications 12, no. 2 (2022): 30. http://dx.doi.org/10.3390/jlpea12020030.

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Object detection is an essential component of many systems used, for example, in advanced driver assistance systems (ADAS) or advanced video surveillance systems (AVSS). Currently, the highest detection accuracy is achieved by solutions using deep convolutional neural networks (DCNN). Unfortunately, these come at the cost of a high computational complexity; hence, the work on the widely understood acceleration of these algorithms is very important and timely. In this work, we compare three different DCNN hardware accelerator implementation methods: coarse-grained (a custom accelerator called L

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Feugang, J. M., K. Pendarvis, M. Crenshaw, S. T. Willard, and P. L. Ryan. "185 HIGH-THROUGHPUT PROTEOMIC ASSESSMENT OF FROZEN - THAWED BOAR SPERMATOZOA." Reproduction, Fertility and Development 23, no. 1 (2011): 194. http://dx.doi.org/10.1071/rdv23n1ab185.

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Cryopreservation is a tool of choice for seedstock constitution of genetically superior males. Its successful application in swine AI industries is limited because of the poor freezability of boar semen. Indeed, a subset of boars exists that can be successfully frozen–thawed for AI, whereas another group appears highly cryosusceptible, and therefore unusable for long-term semen storage. The reasons for such differences are unknown, and the full characterisation of the protein composition of boar spermatozoa will help determine potential cryosensitive proteins. Here, we performed high-throughpu

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Sutovsky, Peter. "56 Biomarker-Based High Throughput Sperm Phenotyping: the Good, the Bad and the Ugly." Journal of Animal Science 101, Supplement_3 (2023): 47–48. http://dx.doi.org/10.1093/jas/skad281.058.

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Abstract Research in our laboratory addresses the unmet need for developing high throughput phenotyping for collecting comprehensive phenotypes of production, reproduction and fitness traits, a major emphasis area of the USDA Blueprint for Animal Genome Research 2022-2027. Our short- and long-term goal within this emphasis area is to identify specific genomic determinants of aberrant sperm quality and integrity that impart unique morphometric and optical properties on functionally defective spermatozoa that can be captured in next generation flow cytometry at a high speed/throughput, and witho

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Maddel, Neeraj, Shantipal Ohol, and Anish Khobragade. "OPTIMIZING LLAMA 3.2 1B USING QUANTIZATION TECHNIQUES USINGBITSANDBYTES FOR EFFICIENT AI DEPLOYMENT." International Journal of Advanced Research 13 (March 31, 2025): 78–88. https://doi.org/10.21474/ijar01/20538.

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Large Language Models (LLMs) have transformed natural language processing, which has achieved state-of-the-art performance on various tasks. However, their high computational and memory requirements lead to significant challenges for deployment, especially on resource-constrained hardware. In this paper, we conduct a controlled experiment to optimize the LLaMA 3.2 1B model using post-training quantization techniques implemented using the Bitsandbytes library. Evaluating multiple precision settings like BF16, FP16, INT8, and INT4 compare their accuracy, throughput, latency, and resource utiliza

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Zhong, Junjie, Jason Riordon, Tony C. Wu, et al. "When robotics met fluidics." Lab on a Chip 20, no. 4 (2020): 709–16. http://dx.doi.org/10.1039/c9lc01042d.

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The marriage of robotics and fluidics provides a route to AI-guided high-throughput synthesis and testing in two modalities: integrated centralized facilities that produce data, and distributed systems that synthesize products and conduct disease surveillance.

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Oluwatosin Oladayo ARAMIDE. "Ultra Ethernet vs. InfiniBand for AI/ML Clusters: A comparative study of performance, cost and ecosystem viability." Open Access Research Journal of Science and Technology 12, no. 2 (2024): 169–79. https://doi.org/10.53022/oarjst.2024.12.2.0149.

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The use of artificial intelligence (AI) and machine learning (ML) workloads is becoming increasingly complex and requires increasingly large volumes of data, which has increased the need and importance of high-performance interconnects in training and inference clusters. Two emerging and competing technologies powerful enough to facilitate low latency and high-bandwidth communication of a distributed AI system have emerged: InfiniBand and the new Ultra Ethernet. The comparison contained in this paper provides a detailed discussion of major dimensions that should matter most to any AI/ML infras

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Lim, Hyung-Jun, Gye Wan Kim, Geon Hyeock Heo, et al. "Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis." Biosensors and Bioelectronics 263 (November 2024): 116629. http://dx.doi.org/10.1016/j.bios.2024.116629.

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Srinivasan, Sathish, Suresh Bysani Venkata Naga, and Krishnaiah Narukulla. "AI-Enhanced Distributed Databases: Optimizing Query Processing and Replication Strategies for High-Throughput Applications." International Journal of Artificial Intelligence, Data Science, and Machine Learning 3 (2022): 70–79. https://doi.org/10.63282/3050-9262.ijaidsml-v3i2p109.

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Naresh, Erukulla, Jain Vishal, and Puthraya Karthik. "Efficient Orchestration of AI Workloads: Data Engineering Solutions for Distributed Cloud Computing." Sarcouncil Journal of Applied Sciences 5, no. 3 (2025): 8–14. https://doi.org/10.5281/zenodo.15053639.

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The rapid expansion of artificial intelligence (AI) applications has increased the demand for efficient workload management in distributed cloud environments. This study explores AI-powered orchestration strategies to optimize workload execution, improve resource utilization, and enhance system scalability. By leveraging machine learning-based predictive analytics, automated scheduling, and dynamic resource allocation, AI-driven orchestration reduces execution time, improves fault tolerance, and enhances network efficiency. Comparative analysis with traditional workload management techniques h

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Obomeghie, Mariam Abdul-Wajid, and Minah-Eeba Winner. "AI-Enhanced Dynamic Beamforming for High-Mobility mm Wave 5G Networks." Journal of Advancement in Communication System 8, no. 2 (2025): 6–21. https://doi.org/10.5281/zenodo.15532581.

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<em>This paper addresses the critical challenges of millimeter wave (mmWave) beamforming in high-mobility 5G scenarios, where conventional approaches fail due to rapid channel variations, complex beam alignment requirements, and severe blockage sensitivity. Traditional beamforming techniques struggle with the sub-millisecond processing windows required for vehicles traveling at highway speeds, resulting in frequent connection losses and throughput degradation. We presented a novel AI-enhanced dynamic beamforming framework that integrates predictive mobility modeling, contextual channel learnin

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Basha, Shaik Asif, Amir Zia, Kirankumar B, Chandra Sekhar S, Sumithra S, and Monisha Jothi R. "Artificial Intelligence in Financial Trading Predictive Models and Risk Management Strategies." ITM Web of Conferences 76 (2025): 01007. https://doi.org/10.1051/itmconf/20257601007.

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Financial industry is a prime target for Artificial Intelligence (AI) driven solutions, opening up avenues of predictive. Nevertheless, hurdles around model transparency, compatibility with legacy financial systems, and the high bar of computational resources persist as major pieces of resistance. Therefore, this research is focused on establishing new AI-based models to tackle this problem in predictive models, risk management strategies in financial trading domain. Through computational efficiency enhancement, explainable AI methodologies application, along with Path-independent adaptation t

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Topol, Eric J. "The revolution in high-throughput proteomics and AI." Science 385, no. 6716 (2024). http://dx.doi.org/10.1126/science.ads5749.

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The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance our understanding of human health. For example, the company SomaLogic has developed the means to measure more than 10,000 proteins and Thermo Fisher’s Olink assays over 5400 proteins from as little as 2 μl. When these rich data are integrated with other layers of information from large patient cohorts, such as the UK Biobank’s genetic, health, and lifestyle information from half a million participants, we get new insights about the underpinnings

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Feng, Shuo, Aoran Cai, Yang Wang, et al. "A robotic AI-Chemist system for multi-modal AI-ready database." National Science Review, December 27, 2023. http://dx.doi.org/10.1093/nsr/nwad332.

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By fusing literature data mining, high-performance simulations, and high-accuracy experiments, robotic AI-Chemist can achieve automated high-throughput production, classification, cleaning, association and fusion of data, and thus develop a multi-modal AI-ready database.

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Jonnakuti, Srikanth. "Redesigning Cloud Infrastructure for AI Throughput Optimization in Real-Time Analytics." Journal of AI-Assisted Scientific Discovery 1, no. 2 (2021). https://doi.org/10.5281/zenodo.15347264.

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The exponential growth of real-time data streams has imposed unprecedented demands on cloud infrastructure, particularly in the context of AI-driven analytics. Traditional cloud architectures, primarily optimized for general-purpose workloads, exhibit performance bottlenecks in memory bandwidth, I/O throughput, and data locality when exposed to high-throughput, low-latency AI inference and training pipelines. This paper presents a comprehensive analysis of cloud infrastructure redesign strategies tailored for AI throughput optimization, emphasizing memory- and I/O-centric architectural reconfi

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