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182 articles for “benchmark”
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Path Lab-AI: An Autonomous Framework for Error-Free Histopathology Slide Interpretation
Abstract: Path Lab-AI represents a fully autonomous platform for the analysis of histopathology slides with circumscribed structures, designed to obtain highly accurate results using diagnostic methods and avoiding the usual limitations of standard microscopy-based pathology. Leveraging recent deep learning and whole slide image (WSI) analysis innovations, our system takes advantage of automated WSI ingestion along with pre-processing steps to account for staining variability, remove artifacts, and localize tissue from background. Such …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 19–30 Read article
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LQR-Based Optimal Control of Inverted Pendulum System with State Estimation and Stability Analysis
Abstract: The inverted pendulum on a cart is a canonical benchmark problem in control systems engineering, capturing the essential challenges of stabilizing an inherently unstable, underactuated, and nonlinear plant. Classical Proportional-Integral-Derivative (PID) controllers, while widely employed in industrial practice, exhibit fundamental performance limitations when applied to such systems, primarily due to their inability to account for multivariable coupling, process noise, and the absence of a systematic optimization framework. This paper presents …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 1, 2026 · pp. 31–43 Read article
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Autonomous Agentic AI for Adaptive Cure Optimization and Defect Prevention in Thermoset Polymer Composite Manufacturing
Abstract: Thermoset polymer composites occupy a central position in modern structural manufacturing, from aircraft fuselages to wind-turbine blades. Despite progress in resin chemistry and fiber architecture, the “cure process” that transforms compliant preforms into load-bearing structures remains difficult to manage. Manufacturers encounter ‘voids’, “interlaminar delaminations”, and “spring-back distortion” when curing complex or thick-section parts. The cause is not ignorance of the relevant physics, but rather that ‘temperature’, ‘chemistry’, ‘rheology’, and ‘mechanics’ …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 301–320 Read article
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A Comprehensive survey of robust image quality metrics for satellite imagery
Abstract: Satellite imagery is essential for applications like environmental monitoring, urban development, precision agriculture, defence surveillance, and disaster response. The reliability of these applications is closely tied to the quality of the captured images, which may be compromised by atmospheric effects, sensor imperfections, compression artifacts, and transmission noise. As a result, accurate image quality assessment (IQA) is essential to ensure trustworthy analysis and informed decision-making in satellite-based systems. The distinctive properties …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 1, 2026 · pp. 7–20 Read article
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Integrative Structural-Functional Genomics of Fc and Fab: Precision Models for Monoclonal Antibody Stability and Anti-Aggregation Engineering
Abstract: Monoclonal antibodies (mAbs) represent the cornerstone of biotherapeutics, yet aggregation propensity compromises up to 50% of candidates during development, driven by Fab hypervariability and Fc vulnerabilities.(1,2) This review integrates functional genomics from OAS (4B+ sequences)(5) and structural databases (SAbDab: 10K+ structures)(6) with machine learning models achieving R=0.97 for SAP prediction.(11) We dissect biophysical mechanisms, benchmark predictive tools (DeepSP, ESM2), and engineering strategies (YTE, FW mutations) that enhance Tm by 5-10°C …
Published in International Journal of Molecular Biotechnological Research · Vol. 4, Issue 1, 2026 · pp. 1–6 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article
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Thin Film Technology in Sensor Manufacturing – A Technical Discussion
Abstract: Thin‑film technology has become the cornerstone of modern sensor manufacturing, enabling the convergence of miniaturisation, multifunctionality, and cost‑effective mass production. This paper surveys the latest advances in deposition techniques—ranging from magnetron sputtering and chemical vapour deposition to atomic‑layer deposition (ALD) and ink‑jet‑printed sol‑gel processes—and examines how their unique material‑control capabilities translate into performance gains across the sensor spectrum (chemical, physical, and bio‑sensing). By integrating nanoscale thickness control (≤ 10 nm) …
Published in Journal of Thin Films, Coating Science Technology & Application · Vol. 13, Issue 1, 2026 · pp. 47–57 Read article
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Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and graphics processing unit (GPU)-based architectures without centralizing sensitive data. This work proposes a parallel adaptive federated learning (AFL) framework that integrates differential privacy and secure aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 09–16 Read article
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DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives
Abstract: This study investigates the optical properties of rubrene derivatives to develop an accurate predictive model for absorption maxima using computational chemistry and chemoinformatic techniques. We benchmarked various quantum chemical methods, identifying that the M06-2X/aug-cc-pVDZ method in dichloromethane (DCM) provided the strongest correlation with experimental data. Key molecular descriptors such as band gap, ionization potential, and electrophilicity index were calculated and analyzed using principal component analysis (PCA) to identify significant factors …
Published in International Journal of Cheminformatics · Vol. 4, Issue 1, 2026 · pp. 41–56 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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Enhanced Sustainable Concrete Mix Design Using LLMs and Advanced Machine Learning Techniques
Abstract: Large Language Models (LLMs) are emerging as transformative tools in materials science, offering human-like reasoning, zero-shot problem solving, and the ability to integrate fuzzy laboratory knowledge with structured data. This study extends and reinterprets the original systematic benchmark for using LLMs in sustainable concrete design, particularly for Alkali-Activated Concrete (AAC). We introduce an enhanced, multi-model framework combining LLM-based inverse design, Random Forest regression, Gaussian Process Regression (GPR), and a lightweight …
Published in Recent Trends in Civil Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 26–33 Read article
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Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design
Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article
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Structural Performance Evaluation of Circular Perforated Plates under Mechanical Loading
Abstract: Circular perforated plates are structurally critical components in aerospace, automotive, and marine sectors, where perforation-induced stress concentrations govern failure under mechanical loading. This study conducts a systematic finite element analysis of stress distribution, deformation, and interlaminar behavior in aluminum and glass-epoxy laminates ([−45/45/90/0]S and [−45/45/90/0]AS) under uniform transverse pressure with clamped-free boundary conditions. Four perforation configurations—no hole, central hole, single-series, and 25-hole grid—were evaluated using ANSYS Shell-181, verified against Kirchhoff–Love, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 1–20 Read article
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Optimizing Multi-Cloud Infrastructure: Advanced Bash-Based Automation for Automated Security Patching and Health Monitoring in Hybrid Linux Environments
Abstract: The proliferation of multi-cloud and hybrid Linux environments has introduced significant operational complexity, particularly in maintaining security compliance and system reliability across diverse infrastructure silos. Traditional patch management approaches, relying on manual interventions or disparate vendor-specific tools, suffer from latency, configuration drift, and limited visibility. This article presents a novel, lightweight automation framework constructed entirely in advanced Bash scripting to address automated security patching and real-time health monitoring across hybrid …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 2, 2026 Read article
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Recent Developments in Polymer Concrete: Considerations for Materials, Performance, and Sustainability
Abstract: In modern infrastructure Growing need of durability and environmental has driven interest in polymer concrete (PC) as an alternative to conventional cement-based materials. This review critically evaluates recent developments in polymer concrete with emphasis on material innovations, performance characteristics, and sustainability aspects. Demonstration of Advanced polymer binders (e.g. epoxy, polyester and vinyl ester resins) shown significant improvements in mechanical strength, adhesion and chemical resistance. Investigation indicates that use of nanomaterials …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 910–920 Read article
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 10–22 Read article
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An Empirical Analysis of Bluetooth Low Energy Reliability Challenges for Offline Messaging Applications
Abstract: In today's hyper-connected world, modern communication relies heavily on centralised internet infrastructure, making robust offline messaging solutions increasingly essential. A crucial vulnerability is revealed by network failures, natural disasters, and distant region deployments: communication breaks down when internet connectivity does. Due to its low power consumption and almost ubiquitous availability in contemporary smartphones, Bluetooth Low Energy (BLE) has become a promising candidate for offline, device-to-device communications. This study presents an …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 2, 2026 Read article