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194 articles for “noise”
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Gravitational Wave Astronomy (2015–2026): From Historic Discoveries to Quantum Detection Frontiers
Abstract: Since the landmark detection of GW150914 by the LIGO Scientific Collaboration in 2015, gravitational wave (GW) astronomy has transitioned from a theoretical frontier to a robust observational science. This breakthrough confirmed a major prediction of General Relativity and inaugurated an entirely new method for observing the universe. Over the past decade, continuous upgrades to ground-based interferometers such as LIGO, Virgo Collaboration, and KAGRA have dramatically improved detector sensitivity, enabling the …
Published in International Journal of Universe · Vol. 2, Issue 1, 2026 · pp. 1–5 Read article
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Quantum-Fuzzy Tensor Operators and Uncertainty-Band Bifurcation for Symmetry-Preserving State Discrimination
Abstract: A tensor-operator framework is developed for fuzzy conjunction, fuzzy disjunction, and symmetry-preserving state discrimination in multi-qubit quantum systems. In this formulation, fuzzy membership and non-membership degrees are represented through expectations of effect operators acting on density matrices, providing a natural bridge between fuzzy logic and quantum measurement theory. Conjunction and disjunction operations are extended to the quantum domain via tensorised channels, constructed using projective measurements and unitary transformations, enabling logical …
Published in Emerging Trends in Symmetry · Vol. 2, Issue 1, 2026 · pp. 08–15 Read article
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Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Olfactory Intelligence in Bio-Hybrid UAVs: Integrating Living Lepidoptera Sensors for High-Precision Environmental Monitoring
Abstract: Autonomous aerial systems still face major challenges when attempting to locate airborne volatile organic compounds because many conventional gas sensors react slowly and cannot reliably follow turbulent chemical plumes. To address this limitation, a bio-hybrid sensing approach was explored using the antenna of the silkworm moth, Bombyx mori, as a natural chemical detector. The antenna was connected to an Electroantennogram (EAG) system that converts biological nerve signals into digital signals …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 4, Issue 2, 2026 Read article
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Solar Panel Defect Detection Using Geospatially-Aware Deep Learning framework
Abstract: Large-scale photovoltaic (PV) systems demand reliable inspection techniques to maintain efficiency, as manual methods remain labor-intensive and inconsistent. This study introduces a geospatially informed deep learning framework for defect detection and localization in PV panels from drone and satellite imagery. The framework incorporates an adaptive tiling mechanism that adjusts tile boundaries according to object size, reducing information loss and enhancing detection performance. In addition, coordinate transformation between image pixels and …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Tailoring the Compressive Behavior of Tetra-Chiral Auxetic Structures through FDM Process Parameters
Abstract: This research evaluates how fused deposition modeling (FDM) fabrication process parameters affect the compressive behavior of tetra-chiral auxetic structures created from Polylactic Acid (PLA). Auxetic materials have a number of useful properties, including reversible deformation and high-energy absorbing capabilities, which are beneficial to creating ultra-lightweight structural, protective, and shock-resistance designs. Among the available auxetic topologies, the tetra-chiral configuration is particularly attractive for engineering use, because its rotation-dominated node–ligament deformation gives …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Passive Digital Phenotyping for Longitudinal Burnout and Occupational Mental Health Surveillance: A Transformer-Based Explainable Deep Learning Approach Using Smartphone Behavioral Streams
Abstract: Occupational burnout constitutes a pervasive yet chronically under-surveilled public health threat, its insidious temporal evolution rendering episodic self-report instruments structurally inadequate for early detection. This paper introduces BurnoutSense, a passive digital phenotyping framework that continuously harvests eight heterogeneous smartphone behavioral data streams encompassing application usage ecology, communication metadata, geospatial mobility, screen interaction dynamics, inferred sleep rhythmicity, keystroke kinematics, ambient noise exposure, and battery/charging cadence to construct individualized multivariate behavioral signatures …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 2, 2026 · pp. 44–53 Read article
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Record Linkage in Knowledge Discovery Process Using Angle Based Machine Learning
Abstract: Record linkage is a critical data cleansing step in the knowledge discovery process, aimed at identifying and resolving inconsistencies across datasets. This study proposes an enhanced record linkage framework tailored for uncertain and large-scale data using a combination of distance measurement, probabilistic modeling, and semantic reasoning. A novel angle-based distance measurement technique is introduced to optimize matching between candidate records. To further boost match accuracy, a Finite Mixture Model (FMM) …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1157–1170 Read article
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A Smart Framework that Combines Data Mining and Optimization for Different Applications
Abstract: Blending predictive data mining with metaheuristic optimization has become essential for tackling tough, real-world problems across all kinds of fields. Most existing methods stick to fixed algorithms, each focused on a tiny slice of the puzzle, barely budging when new variables or unpredictability show up—especially with messy, human-generated data. So, here’s the idea: a Unified Metaheuristic and Predictive Data Mining (UMPDM) framework that finally connects adaptive search methods with powerful …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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Design And Fabrication of Contactless Magnetic Gears
Abstract: Modern engineering prioritizes speed, efficiency, and reliability, spurring innovations like contactless magnetic gear systems that transmit torque through interacting magnetic fields from permanent magnets, eliminating physical contact to drastically cut friction, backlash, wear, lubrication needs, and heat—operating silently with minimal vibration and peak efficiencies up to 99% under ideal conditions, outperforming traditional gears in low-speed, high-torque direct-drive applications such as motors. The design centers on two rotors: an outer high-speed …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Thermal Performance Analysis and Optimization of Pin-Fin Heat Sink Using CFD, Taguchi Method, and Machine Learning
Abstract: Efficient thermal management is essential for improving the performance and reliability of modern engineering systems and electronic devices. This study presents the design, simulation, and optimization of a pin-fin heat sink using SolidWorks for three-dimensional modeling and ANSYS for thermal and computational fluid dynamics (CFD) analysis. Four different pin-fin geometries, namely square, pentagon, octagon, and circular fins, are considered to evaluate their thermal performance under varying operating conditions. Aluminum is …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article