arXiv subjects

Mostafa Rahimi Azghadi

Publications and source records attributed to Mostafa Rahimi Azghadi.

At least 37 records · Page 2Linked to original sources

arXiv2025

Analyzing mixed construction and demolition waste in material recovery facilities: evolution, challenges, and applications of computer vision and deep learning

Improving the automatic and timely recognition of construction and demolition waste composition is crucial for enhancing business returns, economic outcomes and sustainability. While deep learning models show promise in recognizing and classifying homogenous materials, the current literature lacks research assessing their performance for mixed, contaminated material in commercial material recycling facility settings. Despite the increasing numbers of deep learning models and datasets generated in this area, the sub-domain of deep learning analysis of construction and demolition waste piles remains underexplored. To address this gap, recent deep learning algorithms and techniques were explored. This review examines the progression in datasets, sensors and the evolution from object detection towards real-time segmentation models. It also synthesizes research from the past five years on deep learning for construction and demolition waste management, highlighting recent advancements while acknowledging limitations that hinder widespread commercial adoption. The analysis underscores the critical requirement for diverse and high-fidelity datasets, advanced sensor technologies, and robust algorithmic frameworks to facilitate the effective integration of deep learning methodologies into construction and demolition waste management systems. This integration is envisioned to contribute significantly towards the advancement of a more sustainable and circular economic model.

cs.CV↗

arXiv2025

NeuroMorse: A Temporally Structured Dataset For Neuromorphic Computing

Neuromorphic engineering aims to advance computing by mimicking the brain's efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic algorithms primarily focus on spatial features, neglecting the temporal dynamics that are inherent to most sequence-based tasks. This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. In this paper, we present NeuroMorse, a temporally structured dataset designed for benchmarking neuromorphic learning systems. NeuroMorse converts the top 50 words in the English language into temporal Morse code spike sequences. Despite using only two input spike channels for Morse dots and dashes, complex information is encoded through temporal patterns in the data. The proposed benchmark contains feature hierarchy at multiple temporal scales that test the capacity of neuromorphic algorithms to decompose input patterns into spatial and temporal hierarchies. We demonstrate that our training set is challenging to categorise using a linear classifier and that identifying keywords in the test set is difficult using conventional methods. The NeuroMorse dataset is available at Zenodo, with our accompanying code on GitHub at https://github.com/Ben-E-Walters/NeuroMorse.

cs.NE↗

arXiv2025

How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach

Tracking fish movements and sizes of fish is crucial to understanding their ecology and behaviour. Knowing where fish migrate, how they interact with their environment, and how their size affects their behaviour can help ecologists develop more effective conservation and management strategies to protect fish populations and their habitats. Deep learning is a promising tool to analyze fish ecology from underwater videos. However, training deep neural networks (DNNs) for fish tracking and segmentation requires high-quality labels, which are expensive to obtain. We propose an alternative unsupervised approach that relies on spatial and temporal variations in video data to generate noisy pseudo-ground-truth labels. We train a multitask DNN using these pseudo-labels. Our framework consists of three stages: (1) an optical flow model generates the pseudo labels using spatial and temporal consistency between frames, (2) a self-supervised model refines the pseudo-labels incrementally, and (3) a segmentation network uses the refined labels for training. Consequently, we perform extensive experiments to validate our method on three public underwater video datasets and demonstrate its effectiveness for video annotation and segmentation. We also evaluate its robustness to different imaging conditions and discuss its limitations.

cs.CV↗

arXiv2025

Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach

Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manual annotation impractical for many applications. This paper introduces a novel self-supervised learning approach for fish segmentation using Deep Learning. Our model, trained without manual annotation, learns robust and generalizable representations by aligning features across augmented views and enforcing spatial-temporal consistency. We demonstrate its effectiveness on three challenging underwater video datasets: DeepFish, Seagrass, and YouTube-VOS, surpassing existing self-supervised methods and achieving segmentation accuracy comparable to fully-supervised methods without the need for costly annotations. Trained on DeepFish, our model exhibits strong generalization, achieving high segmentation accuracy on the unseen Seagrass and YouTube-VOS datasets. Furthermore, our model is computationally efficient due to its parallel processing and efficient anchor sampling technique, making it suitable for real-time applications and potential deployment on edge devices. We present quantitative results using Jaccard Index and Dice coefficient, as well as qualitative comparisons, showcasing the accuracy, robustness, and efficiency of our approach for advancing underwater video analysis

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arXiv2024

Stabilizing Machine Learning for Reproducible and Explainable Results: A Novel Validation Approach to Subject-Specific Insights

Machine Learning is transforming medical research by improving diagnostic accuracy and personalizing treatments. General ML models trained on large datasets identify broad patterns across populations, but their effectiveness is often limited by the diversity of human biology. This has led to interest in subject-specific models that use individual data for more precise predictions. However, these models are costly and challenging to develop. To address this, we propose a novel validation approach that uses a general ML model to ensure reproducible performance and robust feature importance analysis at both group and subject-specific levels. We tested a single Random Forest (RF) model on nine datasets varying in domain, sample size, and demographics. Different validation techniques were applied to evaluate accuracy and feature importance consistency. To introduce variability, we performed up to 400 trials per subject, randomly seeding the ML algorithm for each trial. This generated 400 feature sets per subject, from which we identified top subject-specific features. A group-specific feature importance set was then derived from all subject-specific results. We compared our approach to conventional validation methods in terms of performance and feature importance consistency. Our repeated trials approach, with random seed variation, consistently identified key features at the subject level and improved group-level feature importance analysis using a single general model. Subject-specific models address biological variability but are resource-intensive. Our novel validation technique provides consistent feature importance and improved accuracy within a general ML model, offering a practical and explainable alternative for clinical research.

cs.LG↗

arXiv2024

The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models

Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and progression challenging. EEG has emerged as a valuable tool for studying schizophrenia, with machine learning increasingly applied for diagnosis. This paper assesses the accuracy of ML models for predicting schizophrenia and examines the impact of stress during EEG recording on model performance. We integrate acute stress prediction into the analysis, showing that overlapping conditions like stress during recording can negatively affect model accuracy. Methods Four XGBoost models were built: one for stress prediction, two to classify schizophrenia (at rest and task), and a model to predict schizophrenia for both conditions. XAI techniques were applied to analyze results. Experiments tested the generalization of schizophrenia models using their datasets' healthy controls and independent health-screened controls. The stress model identified high-stress subjects, who were excluded from further analysis. A novel method was used to adjust EEG frequency band power to remove stress artifacts, improving predictive model performance. Results Our results show that acute stress vary across EEG sessions, affecting model performance and accuracy. Generalization improved once these varying stress levels were considered and compensated for during model training. Our findings highlight the importance of thorough health screening and management of the patient's condition during the process. Stress induced during or by the EEG recording can adversely affect model generalization. This may require further preprocessing of data by treating stress as an additional physiological artifact. Our proposed approach to compensate for stress artifacts in EEG data used for training models showed a significant improvement in predictive performance.

eess.SP↗

arXiv2024

Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiency

Weeds present a significant challenge in agriculture, causing yield loss and requiring expensive control measures. Automatic weed detection using computer vision and deep learning offers a promising solution. However, conventional deep learning methods often require large amounts of labelled training data, which can be costly and time-consuming to acquire. This paper introduces a novel method for semi-supervised weed detection, comprising two main components. Firstly, a multi-scale feature representation technique is employed to capture distinctive weed features across different scales. Secondly, we propose an adaptive pseudo-label assignment strategy, leveraging a small set of labelled images during training. This strategy dynamically assigns confidence scores to pseudo-labels generated from unlabeled data. Additionally, our approach integrates epoch-corresponding and mixed pseudo-labels to further enhance the learning process. Experimental results on the COCO dataset and five prominent weed datasets -- CottonWeedDet12, CropAndWeed, Palmer amaranth, RadishWheat, and RoboWeedMap -- illustrate that our method achieves state-of-the-art performance in weed detection, even with significantly less labelled data compared to existing techniques. This approach holds the potential to alleviate the labelling burden and enhance the feasibility and deployment speed of deep learning for weed detection in real-world agricultural scenarios.

cs.CV↗

arXiv2024

Stress Monitoring Using Low-Cost Electroencephalogram Devices: A Systematic Literature Review

Introduction. Low-cost health monitoring devices are increasingly being used for mental health related studies including stress. While cortisol response magnitude remains the gold standard indicator for stress assessment, a growing number of studies have started to use low-cost EEG devices as primary recorders of biomarker data. Methods. This study reviews published works contributing and/or using EEG devices for detecting stress and their associated machine learning methods. The reviewed works are selected to answer three general research questions and are then synthesized into four categories of stress assessment using EEG, low-cost EEG devices, available datasets for EEG-based stress measurement, and machine learning techniques for EEG-based stress measurement. Results. A number of studies were identified where low-cost EEG devices were utilized to record brain function during phases of stress and relaxation. These studies generally reported a high predictive accuracy rate, verified using a number of different machine learning validation methods and statistical approaches. Of these studies, 60% can be considered low-powered studies based on the small number of test subjects used during experimentation. Conclusion. Low-cost consumer grade wearable devices including EEG and wrist-based monitors are increasingly being used in stress-related studies. Standardization of EEG signal processing and importance of sensor location still requires further study, and research in this area will continue to provide improvements as more studies become available.

cs.HC↗

arXiv2024

Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review

Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. We address these gaps in this review. We discuss that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology.

cs.CV↗

arXiv2023

Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices

Introduction. We investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single, large dataset. Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data. Methods. Sensor biomarker data from six public datasets were utilized in this study. To test model generalization, we developed a gradient boosting model trained on one dataset (SWELL), and tested its predictive power on two datasets previously used in other studies (WESAD, NEURO). Next, we merged four small datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of 99 subjects,. In addition, we utilized random sampling combined with another dataset (EXAM) to build a larger training dataset consisting of 200 synthesized subjects,. Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it on two additional, unseen publicly available stress datasets (WESAD and Toadstool). Results. Our method delivers a robust stress measurement system capable of achieving 85% predictive accuracy on new, unseen validation data, achieving a 25% performance improvement over single models trained on small datasets. Conclusion. Models trained on small, single study protocol datasets do not generalize well for use on new, unseen data and lack statistical power. Ma-chine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection.

cs.LG↗

arXiv2023

WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets

Image classification is a crucial task in modern weed management and crop intervention technologies. However, the limited size, diversity, and balance of existing weed datasets hinder the development of deep learning models for generalizable weed identification. In addition, the expensive labelling requirements of mainstream fully-supervised weed classifiers make them cost- and time-prohibitive to deploy widely, for new weed species, and in site-specific weed management. This paper proposes a novel method for Weed Contrastive Learning through visual Representations (WeedCLR), that uses class-optimized loss with Von Neumann Entropy of deep representation for weed classification in long-tailed datasets. WeedCLR leverages self-supervised learning to learn rich and robust visual features without any labels and applies a class-optimized loss function to address the class imbalance problem in long-tailed datasets. WeedCLR is evaluated on two public weed datasets: CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed species. WeedCLR achieves an average accuracy improvement of 4.3\% on CottonWeedID15 and 5.6\% on DeepWeeds over previous methods. It also demonstrates better generalization ability and robustness to different environmental conditions than existing methods without the need for expensive and time-consuming human annotations. These significant improvements make WeedCLR an effective tool for weed classification in long-tailed datasets and allows for more rapid and widespread deployment of site-specific weed management and crop intervention technologies.

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arXiv2023

Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization

Accurate weight estimation and morphometric analyses are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production animals. However, the collection of phenotypic data through traditional manual approaches at industrial scales and in real-time is time-consuming, labour-intensive, and prone to errors. Digital imaging of individuals and subsequent training of prediction models using Deep Learning (DL) has the potential to rapidly and accurately acquire phenotypic data from aquaculture species. In this study, we applied a novel DL approach to automate weight estimation and morphometric analysis using the black tiger prawn (Penaeus monodon) as a model crustacean. The DL approach comprises two main components: a feature extraction module that efficiently combines low-level and high-level features using the Kronecker product operation; followed by a landmark localization module that then uses these features to predict the coordinates of key morphological points (landmarks) on the prawn body. Once these landmarks were extracted, weight was estimated using a weight regression module based on the extracted landmarks using a fully connected network. For morphometric analyses, we utilized the detected landmarks to derive five important prawn traits. Principal Component Analysis (PCA) was also used to identify landmark-derived distances, which were found to be highly correlated with shape features such as body length, and width. We evaluated our approach on a large dataset of 8164 images of the Black tiger prawn (Penaeus monodon) collected from Australian farms. Our experimental results demonstrate that the novel DL approach outperforms existing DL methods in terms of accuracy, robustness, and efficiency.

cs.CV↗

arXiv2023

Security and Privacy Problems in Voice Assistant Applications: A Survey

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.

cs.CR↗

arXiv2023

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review

Introduction. The stress response has both subjective, psychological and objectively measurable, biological components. Both of them can be expressed differently from person to person, complicating the development of a generic stress measurement model. This is further compounded by the lack of large, labeled datasets that can be utilized to build machine learning models for accurately detecting periods and levels of stress. The aim of this review is to provide an overview of the current state of stress detection and monitoring using wearable devices, and where applicable, machine learning techniques utilized. Methods. This study reviewed published works contributing and/or using datasets designed for detecting stress and their associated machine learning methods, with a systematic review and meta-analysis of those that utilized wearable sensor data as stress biomarkers. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 24 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning, and future research directions. Results. A wide variety of study-specific test and measurement protocols were noted in the literature. A number of public datasets were identified that are labeled for stress detection. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and generalization ability. Conclusion. Generalization of existing machine learning models still require further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available for study.

cs.AI↗

arXiv2022

A lightweight Transformer-based model for fish landmark detection

Transformer-based models, such as the Vision Transformer (ViT), can outperform onvolutional Neural Networks (CNNs) in some vision tasks when there is sufficient training data. However, (CNNs) have a strong and useful inductive bias for vision tasks (i.e. translation equivariance and locality). In this work, we developed a novel model architecture that we call a Mobile fish landmark detection network (MFLD-net). We have made this model using convolution operations based on ViT (i.e. Patch embeddings, Multi-Layer Perceptrons). MFLD-net can achieve competitive or better results in low data regimes while being lightweight and therefore suitable for embedded and mobile devices. Furthermore, we show that MFLD-net can achieve keypoint (landmark) estimation accuracies on-par or even better than some of the state-of-the-art (CNNs) on a fish image dataset. Additionally, unlike ViT, MFLD-net does not need a pre-trained model and can generalise well when trained on a small dataset. We provide quantitative and qualitative results that demonstrate the model's generalisation capabilities. This work will provide a foundation for future efforts in developing mobile, but efficient fish monitoring systems and devices.

cs.CV↗

arXiv2022

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to SOTA CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. To the best of our knowledge, we are the first to parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS DL accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low ADC/DAC resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm$^2$ in a 22nm FDSOI CMOS process.

cs.ET↗

arXiv2022

Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. DL is a cutting-edge AI technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localization, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring.

cs.CV↗

arXiv2022

Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation

Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning. However, there is a lack of a systematic approach and a set of standardized evaluation criteria to facilitate direct comparison of both software and hardware implementations. In this paper, we formalize a set of standardized criteria and a publicly available synthetic dataset entitled Synthetic Simulations Of Extracellular Recordings (SSOER), which was constructed by aggregating existing synthetic datasets with varying Signal-To-Noise Ratios (SNRs). Furthermore, we present a benchmark for future comparison, and use our criteria to evaluate a simulated Resistive Random-Access Memory (RRAM) In-Memory Computing (IMC) system using the Discrete Wavelet Transform (DWT) for feature extraction. Our system consumes approximately (per channel) 10.72mW and occupies an area of 0.66mm$^2$ in a 22nm FDSOI Complementary Metal-Oxide-Semiconductor (CMOS) process.

cs.LG↗