Farhad Imani
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View article: Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy Open
Decentralized Federated Learning (DFL) enables collaborative model training without a central server, but it remains vulnerable to privacy leakage because shared model updates can expose sensitive information through inversion, reconstruct…
View article: Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning
Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning Open
Precision process planning in Computer Numerical Control (CNC) machining demands rapid, context-aware decisions on tool selection, feed-speed pairs, and multi-axis routing, placing immense cognitive and procedural burdens on engineers from…
View article: Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models Open
Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomalies. This study presents a novel multimodal Retrieval-Augmented Generation-based framework …
View article: Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Open
In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptabilit…
View article: Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing Open
Federated Learning (FL) has become a key method for preserving data privacy in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally while transmitting only model updates. Despite this design, FL remains …
View article: Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces
Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces Open
Workplace accidents due to personal protective equipment (PPE) non-compliance raise serious safety concerns and lead to legal liabilities, financial penalties, and reputational damage. While object detection models have shown the capabilit…
View article: Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring
Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring Open
Machine Learning (ML) models integrated with in-situ sensing offer transformative solutions for defect detection in Additive Manufacturing (AM), but this integration brings critical challenges in safeguarding sensitive data, such as part d…
View article: Performance Assessment of Chemical Kinetics Neural Ordinary Differential Equations in Pairwise Mixing Stirred Reactor
Performance Assessment of Chemical Kinetics Neural Ordinary Differential Equations in Pairwise Mixing Stirred Reactor Open
The present study aims to assess the potential of the neural ordinary differential equations (NODE) network for reliable and computationally efficient implementation of chemistry in combustion simulations. Investigations are performed usin…
View article: A Data-Driven Framework for Computationally Efficient Integration of Chemical Kinetics Using Neural Ordinary Differential Equations
A Data-Driven Framework for Computationally Efficient Integration of Chemical Kinetics Using Neural Ordinary Differential Equations Open
A data-driven methodology is introduced for computationally efficient integration of systems of stiff rate equations in chemical kinetics using neural ordinary differential equations (NODE). A systematic algorithm is developed for training…
View article: Neural computation for robust and holographic face detection
Neural computation for robust and holographic face detection Open
Face detection is an essential component of many tasks in computer vision with several applications. However, existing deep learning solutions are significantly slow and inefficient to enable face detection on embedded platforms. In this p…
View article: Memory-inspired spiking hyperdimensional network for robust online learning
Memory-inspired spiking hyperdimensional network for robust online learning Open
Recently, brain-inspired computing models have shown great potential to outperform today’s deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (H…
View article: GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning
GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning Open
Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and defi…
View article: Scalable edge-based hyperdimensional learning system with brain-like neural adaptation
Scalable edge-based hyperdimensional learning system with brain-like neural adaptation Open
In the Internet of Things (IoT) domain, many applications are running machine learning algorithms to assimilate the data collected in the swarm of devices. Sending all data to the powerful computing environment, e.g., cloud, poses signific…
View article: Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework
Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework Open
Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (H…
View article: Using reinforcement learning for tuning genetic algorithms
Using reinforcement learning for tuning genetic algorithms Open
Genetic algorithms (GAs) are a subclass of evolutionary algorithms often used to solve difficult combinatorial or non-linear problems. However, most GAs have to be configured for a particular problem type, and even then, the performance de…
View article: Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound
Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound Open
Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to dire…
View article: Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization
Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization Open
Understanding the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.
View article: Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing
Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing Open
Metal-based powder-bed-fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conven…