14,015 results on '"Hasan, Md"'
Search Results
2. Computer-Aided Fall Recognition Using a Three-Stream Spatial-Temporal GCN Model with Adaptive Feature Aggregation
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Shin, Jungpil, Miah, Abu Saleh Musa, Egawa1, Rei, Hirooka, Koki, Hasan, Md. Al Mehedi, Tomioka, Yoichi, and Hwang, Yong Seok
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent suicide attempts resulting from medication overdose, underscores the critical importance of accurate and efficient fall detection methods. In this scenario, a computer-aided fall detection system is inevitable to save elderly people's lives worldwide. Many researchers have been working to develop fall detection systems. However, the existing fall detection systems often struggle with issues such as unsatisfactory performance accuracy, limited robustness, high computational complexity, and sensitivity to environmental factors due to a lack of effective features. In response to these challenges, this paper proposes a novel three-stream spatial-temporal feature-based fall detection system. Our system incorporates joint skeleton-based spatial and temporal Graph Convolutional Network (GCN) features, joint motion-based spatial and temporal GCN features, and residual connections-based features. Each stream employs adaptive graph-based feature aggregation and consecutive separable convolutional neural networks (Sep-TCN), significantly reducing computational complexity and model parameters compared to prior systems. Experimental results across multiple datasets demonstrate the superior effectiveness and efficiency of our proposed system, with accuracies of 99.51\%, 99.15\%, 99.79\% and 99.85 \% achieved on the ImViA, UR-Fall, Fall-UP and FU-Kinect datasets, respectively. The remarkable performance of our system highlights its superiority, efficiency, and generalizability in real-world fall detection scenarios, offering significant advancements in healthcare and societal well-being.
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- 2024
3. Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter
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Haque, Farhanul, Al-Hasan, Md., Mou, Sumaiya Tabssum, Miah, Abu Saleh Musa, Shin, Jungpil, and Rahim, Md Abdur
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The Bengali language is the 5th most spoken native and 7th most spoken language in the world, and Bengali handwritten character recognition has attracted researchers for decades. However, other languages such as English, Arabic, Turkey, and Chinese character recognition have contributed significantly to developing handwriting recognition systems. Still, little research has been done on Bengali character recognition because of the similarity of the character, curvature and other complexities. However, many researchers have used traditional machine learning and deep learning models to conduct Bengali hand-written recognition. The study employed a convolutional neural network (CNN) with ensemble transfer learning and a multichannel attention network. We generated the feature from the two branches of the CNN, including Inception Net and ResNet and then produced an ensemble feature fusion by concatenating them. After that, we applied the attention module to produce the contextual information from the ensemble features. Finally, we applied a classification module to refine the features and classification. We evaluated the proposed model using the CAMTERdb 3.1.2 data set and achieved 92\% accuracy for the raw dataset and 98.00\% for the preprocessed dataset. We believe that our contribution to the Bengali handwritten character recognition domain will be considered a great development.
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- 2024
4. Cervical Cancer Detection Using Multi-Branch Deep Learning Model
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Baba, Tatsuhiro, Miah, Abu Saleh Musa, Shin, Jungpil, and Hasan, Md. Al Mehedi
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Cervical cancer is a crucial global health concern for women, and the persistent infection of High-risk HPV mainly triggers this remains a global health challenge, with young women diagnosis rates soaring from 10\% to 40\% over three decades. While Pap smear screening is a prevalent diagnostic method, visual image analysis can be lengthy and often leads to mistakes. Early detection of the disease can contribute significantly to improving patient outcomes. In recent decades, many researchers have employed machine learning techniques that achieved promise in cervical cancer detection processes based on medical images. In recent years, many researchers have employed various deep-learning techniques to achieve high-performance accuracy in detecting cervical cancer but are still facing various challenges. This research proposes an innovative and novel approach to automate cervical cancer image classification using Multi-Head Self-Attention (MHSA) and convolutional neural networks (CNNs). The proposed method leverages the strengths of both MHSA mechanisms and CNN to effectively capture both local and global features within cervical images in two streams. MHSA facilitates the model's ability to focus on relevant regions of interest, while CNN extracts hierarchical features that contribute to accurate classification. Finally, we combined the two stream features and fed them into the classification module to refine the feature and the classification. To evaluate the performance of the proposed approach, we used the SIPaKMeD dataset, which classifies cervical cells into five categories. Our model achieved a remarkable accuracy of 98.522\%. This performance has high recognition accuracy of medical image classification and holds promise for its applicability in other medical image recognition tasks.
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- 2024
5. Personalized graph feature-based multi-omics data integration for cancer subtype identification
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Islam, Saiful and Hasan, Md. Nahid
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Quantitative Biology - Quantitative Methods ,Computer Science - Social and Information Networks ,Quantitative Biology - Genomics ,92B05, 62H30, 62P10, 68T09 - Abstract
Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment challenging. In recent years, high-throughput omic technologies have facilitated the discovery of mechanisms underlying various cancer subtypes by providing diverse omics data, such as gene expression, DNA methylation, and miRNA expression. However, the complexity and heterogeneity of multi-omics data present significant challenges for their integration in exploring cancer subtypes. Various methods have been proposed to address these challenges. In this paper, we propose a novel and straightforward approach for identifying cancer subtypes by integrating patient-specific subnetworks features from different omics data. We construct patient-specific induced subnetwork using a random walk with restart algorithm from patient similarity networks (PSNs) and compute nine structural properties that capture essential network topology. These features are integrated across the three omic datasets to form comprehensive patient profiles. K-means clustering is then applied for cancer subtype identification. We evaluate our approach on five cancer datasets, including breast invasive carcinoma, colon adenocarcinoma, glioblastoma multiforme, kidney renal clear cell carcinoma, and lung squamous cell carcinoma, for three different omic data types. The evaluation shows that our method produces promising and effective results, demonstrating competitive or superior performance compared to existing methods and underscoring its potential for advancing personalized cancer diagnosis and treatment., Comment: 12, 3
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- 2024
6. A Structural Feature-Based Approach for Comprehensive Graph Classification
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Islam, Saiful, Hasan, Md. Nahid, and Khanra, Pitambar
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Computer Science - Machine Learning ,Computer Science - Social and Information Networks ,68R10, 05C85, 68R10 - Abstract
The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical applications., Comment: 25 pages, 6 Figures
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- 2024
7. Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
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Hasan, Md. Arid, Tarannum, Prerona, Dey, Krishno, Razzak, Imran, and Naseem, Usman
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Computer Science - Computation and Language ,F.2.2 ,I.2.7 - Abstract
Large language models (LLMs) have garnered significant interest in natural language processing (NLP), particularly their remarkable performance in various downstream tasks in resource-rich languages. Recent studies have highlighted the limitations of LLMs in low-resource languages, primarily focusing on binary classification tasks and giving minimal attention to South Asian languages. These limitations are primarily attributed to constraints such as dataset scarcity, computational costs, and research gaps specific to low-resource languages. To address this gap, we present datasets for sentiment and hate speech tasks by translating from English to Bangla, Hindi, and Urdu, facilitating research in low-resource language processing. Further, we comprehensively examine zero-shot learning using multiple LLMs in English and widely spoken South Asian languages. Our findings indicate that GPT-4 consistently outperforms Llama 2 and Gemini, with English consistently demonstrating superior performance across diverse tasks compared to low-resource languages. Furthermore, our analysis reveals that natural language inference (NLI) exhibits the highest performance among the evaluated tasks, with GPT-4 demonstrating superior capabilities.
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- 2024
8. Robust CNN Multi-Nested-LSTM Framework with Compound Loss for Patch-based Multi-Push Ultrasound Shear Wave Imaging and Segmentation
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Alam, Md. Jahin, Habib, Ahsan, and Hasan, Md. Kamrul
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Electrical Engineering and Systems Science - Image and Video Processing ,Quantitative Biology - Quantitative Methods - Abstract
Ultrasound Shear Wave Elastography (SWE) is a noteworthy tool for in-vivo noninvasive tissue pathology assessment. State-of-the-art techniques can generate reasonable estimates of tissue elasticity, but high-quality and noise-resiliency in SWE reconstruction have yet to demonstrate advancements. In this work, we propose a two-stage DL pipeline producing reliable reconstructions and denoise said reconstructions to obtain lower noise prevailing elasticity mappings. The reconstruction network consists of a Resnet3D Encoder to extract temporal context from the sequential multi-push data. The encoded features are sent to multiple Nested CNN LSTMs which process them in a temporal attention-guided windowing basis and map the 3D features to 2D using FFT-attention, which are then decoded into an elasticity map as primary reconstruction. The 2D maps from each multi-push region are merged and cleaned using a dual-decoder denoiser network, which independently denoises foreground and background before fusion. The post-denoiser generates a higher-quality reconstruction and an inclusion-segmentation mask. A multi-objective loss is designed to accommodate the denoising, fusing, and segmentation processes. The method is validated on sequential multi-push SWE motion data with multiple overlapping regions. A patch-based training procedure is introduced with network modifications to handle data scarcity. Evaluations produce 32.66dB PSNR, 43.19dB CNR in noisy simulation, and 22.44dB PSNR, 36.88dB CNR in experimental data, across all test samples. Moreover, IoUs (0.909 and 0.781) were quite satisfactory in the datasets. After comparing with other reported deep-learning approaches, our method proves quantitatively and qualitatively superior in dealing with noise influences in SWE data. From a performance point of view, our deep-learning pipeline has the potential to become utilitarian in the clinical domain.
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- 2024
9. A Constrained Optimization approach for Ultrasound Shear Wave Speed Estimation with Time-Lateral Plane Cleaning
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Alam, Md. Jahin and Hasan, Md. Kamrul
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Electrical Engineering and Systems Science - Signal Processing - Abstract
Ultrasound shear wave elastography (SWE) is a noninvasive way to measure stiffness of soft tissue for medical diagnosis. In SWE imaging, an acoustic radiation force induces tissue displacement, which creates shear waves (SWs) that travel laterally through the medium. Finding the lateral arrival times of SWs at different tissue locations helps figure out the shear wave speed (SWS), which is directly linked to the stiffness of the medium. Traditional SWS estimation techniques, however, are not noise resilient enough handling noise and reflection artifacts filled data. This paper proposes new techniques to estimate SWS in both time and frequency domains. These new methods optimize a loss function that is based on the lateral signal shift parameter between known locations and is constrained by neighborhood displacement group shift determined from the time-lateral plane-denoised SW propagation data. The proposed constrained optimization is formed by coupling losses of local particles with a Gaussian kernel giving an optimum arrival time for the center particle by enforcing stiffness homogeneity in a small neighborhood to enable inherent noise resilience. The denoising scheme involves isolating the transitioning SW profile in each time-lateral plane, creating a parameterized mask. Moreover, lateral interpolation is performed to enhance reconstruction resolution and obtain increased displacement groups to enhance the reliability of the optimization. The proposed noise robust scheme is tested on a simulation and three experimental datasets. The performance of the method is compared with 3 ToF and 2 frequency-domain methods. The evaluations show visually and quantitatively superior and noise-robust reconstructions compared to state-of-the-art methods. Due to its high contrast and minimal error, the proposed technique can find its application in tissue health inspection and cancer diagnosis.
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- 2024
10. NativQA: Multilingual Culturally-Aligned Natural Query for LLMs
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Hasan, Md. Arid, Hasanain, Maram, Ahmad, Fatema, Laskar, Sahinur Rahman, Upadhyay, Sunaya, Sukhadia, Vrunda N, Kutlu, Mucahid, Chowdhury, Shammur Absar, and Alam, Firoj
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,68T50 ,F.2.2 ,I.2.7 - Abstract
Natural Question Answering (QA) datasets play a crucial role in developing and evaluating the capabilities of large language models (LLMs), ensuring their effective usage in real-world applications. Despite the numerous QA datasets that have been developed, there is a notable lack of region-specific datasets generated by native users in their own languages. This gap hinders the effective benchmarking of LLMs for regional and cultural specificities. In this study, we propose a scalable framework, NativQA, to seamlessly construct culturally and regionally aligned QA datasets in native languages, for LLM evaluation and tuning. Moreover, to demonstrate the efficacy of the proposed framework, we designed a multilingual natural QA dataset, MultiNativQA, consisting of ~72K QA pairs in seven languages, ranging from high to extremely low resource, based on queries from native speakers covering 18 topics. We benchmark the MultiNativQA dataset with open- and closed-source LLMs. We made both the framework NativQA and MultiNativQA dataset publicly available for the community. (https://nativqa.gitlab.io), Comment: LLMs, Native, Multilingual, Language Diversity, Contextual Understanding, Minority Languages, Culturally Informed, Foundation Models, Large Language Models
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- 2024
11. MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disorder
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Hasan, Md Rakibul, Liu, Xuehan, Gedeon, Tom, and Hossain, Md Zakir
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset $-$ both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as \emph{precuneus} and anterior \emph{cingulate/ventromedial}. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at TBA., Comment: Md Rakibul Hasan and Xuehan Liu contributed equally to this work
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- 2024
12. ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content
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Hasanain, Maram, Hasan, Md. Arid, Ahmed, Fatema, Suwaileh, Reem, Biswas, Md. Rafiul, Zaghouani, Wajdi, and Alam, Firoj
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,68T50 ,F.2.2 ,I.2.7 - Abstract
We present an overview of the second edition of the ArAIEval shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively. Finally, 11 teams submitted system description papers. Across both tasks, we observed that fine-tuning transformer models such as AraBERT was at the core of the majority of the participating systems. We provide a description of the task setup, including a description of the dataset construction and the evaluation setup. We further provide a brief overview of the participating systems. All datasets and evaluation scripts are released to the research community (https://araieval.gitlab.io/). We hope this will enable further research on these important tasks in Arabic., Comment: propaganda, span detection, disinformation, misinformation, fake news, LLMs, GPT-4, multimodality, multimodal LLMs
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- 2024
13. Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
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Yang, Chih-Hsuan, Feuer, Benjamin, Jubery, Zaki, Deng, Zi K., Nakkab, Andre, Hasan, Md Zahid, Chiranjeevi, Shivani, Marshall, Kelly, Baishnab, Nirmal, Singh, Asheesh K, Singh, Arti, Sarkar, Soumik, Merchant, Nirav, Hegde, Chinmay, and Ganapathysubramanian, Baskar
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We introduce Arboretum, the largest publicly accessible dataset designed to advance AI for biodiversity applications. This dataset, curated from the iNaturalist community science platform and vetted by domain experts to ensure accuracy, includes 134.6 million images, surpassing existing datasets in scale by an order of magnitude. The dataset encompasses image-language paired data for a diverse set of species from birds (Aves), spiders/ticks/mites (Arachnida), insects (Insecta), plants (Plantae), fungus/mushrooms (Fungi), snails (Mollusca), and snakes/lizards (Reptilia), making it a valuable resource for multimodal vision-language AI models for biodiversity assessment and agriculture research. Each image is annotated with scientific names, taxonomic details, and common names, enhancing the robustness of AI model training. We showcase the value of Arboretum by releasing a suite of CLIP models trained using a subset of 40 million captioned images. We introduce several new benchmarks for rigorous assessment, report accuracy for zero-shot learning, and evaluations across life stages, rare species, confounding species, and various levels of the taxonomic hierarchy. We anticipate that Arboretum will spur the development of AI models that can enable a variety of digital tools ranging from pest control strategies, crop monitoring, and worldwide biodiversity assessment and environmental conservation. These advancements are critical for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. Arboretum is publicly available, easily accessible, and ready for immediate use. Please see the \href{https://baskargroup.github.io/Arboretum/}{project website} for links to our data, models, and code., Comment: Preprint under review
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- 2024
14. AniFaceDiff: High-Fidelity Face Reenactment via Facial Parametric Conditioned Diffusion Models
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Chen, Ken, Seneviratne, Sachith, Wang, Wei, Hu, Dongting, Saha, Sanjay, Hasan, Md. Tarek, Rasnayaka, Sanka, Malepathirana, Tamasha, Gong, Mingming, and Halgamuge, Saman
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Face reenactment refers to the process of transferring the pose and facial expressions from a reference (driving) video onto a static facial (source) image while maintaining the original identity of the source image. Previous research in this domain has made significant progress by training controllable deep generative models to generate faces based on specific identity, pose and expression conditions. However, the mechanisms used in these methods to control pose and expression often inadvertently introduce identity information from the driving video, while also causing a loss of expression-related details. This paper proposes a new method based on Stable Diffusion, called AniFaceDiff, incorporating a new conditioning module for high-fidelity face reenactment. First, we propose an enhanced 2D facial snapshot conditioning approach by facial shape alignment to prevent the inclusion of identity information from the driving video. Then, we introduce an expression adapter conditioning mechanism to address the potential loss of expression-related information. Our approach effectively preserves pose and expression fidelity from the driving video while retaining the identity and fine details of the source image. Through experiments on the VoxCeleb dataset, we demonstrate that our method achieves state-of-the-art results in face reenactment, showcasing superior image quality, identity preservation, and expression accuracy, especially for cross-identity scenarios. Considering the ethical concerns surrounding potential misuse, we analyze the implications of our method, evaluate current state-of-the-art deepfake detectors, and identify their shortcomings to guide future research.
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- 2024
15. Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification
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Hasan, Md. Mahmudul, Shaqib, SM, Akter, Ms. Sharmin, Alam, Rabiul, Haque, Afraz Ul, and khushbu, Shahrun akter
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The purpose of the Insect Detection System for Crop and Plant Health is to keep an eye out for and identify insect infestations in farming areas. By utilizing cutting-edge technology like computer vision and machine learning, the system seeks to identify hazardous insects early and accurately. This would enable prompt response to save crops and maintain optimal plant health. The Method of this study includes Data Acquisition, Preprocessing, Data splitting, Model Implementation and Model evaluation. Different models like MobileNetV2, ResNet152V2, Xecption, Custom CNN was used in this study. In order to categorize insect photos, a Convolutional Neural Network (CNN) based on the ResNet152V2 architecture is constructed and evaluated in this work. Achieving 99% training accuracy and 97% testing accuracy, ResNet152V2 demonstrates superior performance among four implemented models. The results highlight its potential for real-world applications in insect classification and entomology studies, emphasizing efficiency and accuracy. To ensure food security and sustain agricultural output globally, finding insects is crucial. Cutting-edge technology, such as ResNet152V2 models, greatly influence automating and improving the accuracy of insect identification. Efficient insect detection not only minimizes crop losses but also enhances agricultural productivity, contributing to sustainable food production. This underscores the pivotal role of technology in addressing challenges related to global food security.
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- 2024
16. ArMeme: Propagandistic Content in Arabic Memes
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Alam, Firoj, Hasnat, Abul, Ahmed, Fatema, Hasan, Md Arid, and Hasanain, Maram
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,68T50 ,I.2.7 - Abstract
With the rise of digital communication, memes have become a significant medium for cultural and political expression that is often used to mislead audiences. Identification of such misleading and persuasive multimodal content has become more important among various stakeholders, including social media platforms, policymakers, and the broader society as they often cause harm to individuals, organizations, and/or society. While there has been effort to develop AI-based automatic systems for resource-rich languages (e.g., English), it is relatively little to none for medium to low resource languages. In this study, we focused on developing an Arabic memes dataset with manual annotations of propagandistic content. We annotated ~6K Arabic memes collected from various social media platforms, which is a first resource for Arabic multimodal research. We provide a comprehensive analysis aiming to develop computational tools for their detection. We will make them publicly available for the community., Comment: disinformation, misinformation, factuality, harmfulness, fake news, propaganda, multimodality, text, images
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- 2024
17. WeedScout: Real-Time Autonomous blackgrass Classification and Mapping using dedicated hardware
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Gazzard, Matthew, Hicks, Helen, Ihianle, Isibor Kennedy, Bird, Jordan J., Hasan, Md Mahmudul, and Machado, Pedro
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Computer Science - Robotics ,Computer Science - Artificial Intelligence - Abstract
Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a cutting-edge solution tailored for real-time detection of blackgrass, for precision weed management practices. Leveraging Artificial Intelligence (AI) algorithms, the system processes live image feeds, infers blackgrass density, and covers two stages of maturation. The research investigates the deployment of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices, offering potential solutions to challenges such as herbicide resistance and environmental impact. Additionally, two datasets and model weights are made available to the research community, facilitating further advancements in weed detection and precision farming technologies.
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- 2024
18. FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time
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Jobayer, Md, Shawon, Md. Mehedi Hasan, Hasan, Md Rakibul, Ghosh, Shreya, Gedeon, Tom, and Hossain, Md Zakir
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Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Machine Learning ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Objective: Heart murmurs are abnormal sounds caused by turbulent blood flow within the heart. Several diagnostic methods are available to detect heart murmurs and their severity, such as cardiac auscultation, echocardiography, phonocardiogram (PCG), etc. However, these methods have limitations, including extensive training and experience among healthcare providers, cost and accessibility of echocardiography, as well as noise interference and PCG data processing. This study aims to develop a novel end-to-end real-time heart murmur detection approach using traditional and depthwise separable convolutional networks. Methods: Continuous wavelet transform (CWT) was applied to extract meaningful features from the PCG data. The proposed network has three parts: the Squeeze net, the Bottleneck, and the Expansion net. The Squeeze net generates a compressed data representation, whereas the Bottleneck layer reduces computational complexity using a depthwise-separable convolutional network. The Expansion net is responsible for up-sampling the compressed data to a higher dimension, capturing tiny details of the representative data. Results: For evaluation, we used four publicly available datasets and achieved state-of-the-art performance in all datasets. Furthermore, we tested our proposed network on two resource-constrained devices: a Raspberry PI and an Android device, stripping it down into a tiny machine learning model (TinyML), achieving a maximum of 99.70%. Conclusion: The proposed model offers a deep learning framework for real-time accurate heart murmur detection within limited resources. Significance: It will significantly result in more accessible and practical medical services and reduced diagnosis time to assist medical professionals. The code is publicly available at TBA., Comment: 8-page main paper and 4-page supplementary material
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- 2024
19. Intrinsic Voltage Offsets in Memcapacitive Bio-Membranes Enable High-Performance Physical Reservoir Computing
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Mohamed, Ahmed S., Dhungel, Anurag, Hasan, Md Sakib, and Najem, Joseph S.
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Computer Science - Emerging Technologies ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical substrates, including atomic switches, photonics, volatile memristors, and, recently, memcapacitors, to achieve efficient high-dimensional mapping. Traditional PRCs often consist of homogeneous device arrays, which rely on input encoding methods and large stochastic device-to-device variations for increased nonlinearity and high-dimensional mapping. These approaches incur high pre-processing costs and restrict real-time deployment. Here, we introduce a novel heterogeneous memcapacitor-based PRC that exploits internal voltage offsets to enable both monotonic and non-monotonic input-state correlations crucial for efficient high-dimensional transformations. We demonstrate our approach's efficacy by predicting a second-order nonlinear dynamical system with an extremely low prediction error (0.00018). Additionally, we predict a chaotic H\'enon map, achieving a low normalized root mean square error (0.080). Unlike previous PRCs, such errors are achieved without input encoding methods, underscoring the power of distinct input-state correlations. Most importantly, we generalize our approach to other neuromorphic devices that lack inherent voltage offsets using externally applied offsets to realize various input-state correlations. Our approach and unprecedented performance are a major milestone towards high-performance full in-materia PRCs., Comment: Supplementary Information is included under the main text
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- 2024
20. Performance Analysis for Deterministic System using Time Sensitive Network
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Hasan, Md Mehedi and Feng, He
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Computer Science - Networking and Internet Architecture ,Review - Abstract
Modern technology necessitates the use of dependable, fast, and inexpensive networks as the backbone for data transmission. Switched Ethernet coupled with the Time Sensitive Networking, Comment: No Comment
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- 2024
21. Artificial Neural Networks to Recognize Speakers Division from Continuous Bengali Speech
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Ali, Hasmot, Hossain, Md. Fahad, Hasan, Md. Mehedi, Abujar, Sheikh, and Noori, Sheak Rashed Haider
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Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning ,Computer Science - Sound - Abstract
Voice based applications are ruling over the era of automation because speech has a lot of factors that determine a speakers information as well as speech. Modern Automatic Speech Recognition (ASR) is a blessing in the field of Human-Computer Interaction (HCI) for efficient communication among humans and devices using Artificial Intelligence technology. Speech is one of the easiest mediums of communication because it has a lot of identical features for different speakers. Nowadays it is possible to determine speakers and their identity using their speech in terms of speaker recognition. In this paper, we presented a method that will provide a speakers geographical identity in a certain region using continuous Bengali speech. We consider eight different divisions of Bangladesh as the geographical region. We applied the Mel Frequency Cepstral Coefficient (MFCC) and Delta features on an Artificial Neural Network to classify speakers division. We performed some preprocessing tasks like noise reduction and 8-10 second segmentation of raw audio before feature extraction. We used our dataset of more than 45 hours of audio data from 633 individual male and female speakers. We recorded the highest accuracy of 85.44%.
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- 2024
22. An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
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Alhanahnah, Mohannad, Hasan, Md Rashedul, and Bagheri, Hamid
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Computer Science - Software Engineering - Abstract
Automatic Program Repair (APR) has garnered significant attention as a practical research domain focused on automatically fixing bugs in programs. While existing APR techniques primarily target imperative programming languages like C and Java, there is a growing need for effective solutions applicable to declarative software specification languages. This paper presents a systematic investigation into the capacity of Large Language Models (LLMs) for repairing declarative specifications in Alloy, a declarative formal language used for software specification. We propose a novel repair pipeline that integrates a dual-agent LLM framework, comprising a Repair Agent and a Prompt Agent. Through extensive empirical evaluation, we compare the effectiveness of LLM-based repair with state-of-the-art Alloy APR techniques on a comprehensive set of benchmarks. Our study reveals that LLMs, particularly GPT-4 variants, outperform existing techniques in terms of repair efficacy, albeit with a marginal increase in runtime and token usage. This research contributes to advancing the field of automatic repair for declarative specifications and highlights the promising potential of LLMs in this domain.
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- 2024
23. Real-time accident detection and physiological signal monitoring to enhance motorbike safety and emergency response
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Siam, S. M. Kayser Mehbub, Sumaiya, Khadiza Islam, Al-Amin, Md Rakib, Turjo, Tamim Hasan, Islam, Ahsanul, Rahim, A. H. M. A., and Hasan, Md Rakibul
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Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Computers and Society ,Computer Science - Human-Computer Interaction - Abstract
Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road, consequently resulting in a rise in the frequency of accidents. Among these accidents, motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. A significant number of these fatalities occur due to delayed or inadequate medical attention. To this end, we propose a novel automatic detection and notification system specifically designed for motorbike accidents. The proposed system comprises two key components: a detection system and a physiological signal monitoring system. The detection system is integrated into the helmet and consists of a microcontroller, accelerometer, GPS, GSM, and Wi-Fi modules. The physio-monitoring system incorporates a sensor for monitoring pulse rate and SpO$_{2}$ saturation. All collected data are presented on an LCD display and wirelessly transmitted to the detection system through the microcontroller of the physiological signal monitoring system. If the accelerometer readings consistently deviate from the specified threshold decided through extensive experimentation, the system identifies the event as an accident and transmits the victim's information -- including the GPS location, pulse rate, and SpO$_{2}$ saturation rate -- to the designated emergency contacts. Preliminary results demonstrate the efficacy of the proposed system in accurately detecting motorbike accidents and promptly alerting emergency contacts. We firmly believe that the proposed system has the potential to significantly mitigate the risks associated with motorbike accidents and save lives.
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- 2024
24. GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning
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Yang, Hsin-Jung, Beck, Joe, Hasan, Md Zahid, Beyazit, Ekin, Chakraborty, Subhadeep, Wongpiromsarn, Tichakorn, and Sarkar, Soumik
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Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Robotics - Abstract
In the rapidly evolving field of autonomous systems, the safety and reliability of the system components are fundamental requirements. These components are often vulnerable to complex and unforeseen environments, making natural edge-case generation essential for enhancing system resilience. This paper presents GENESIS-RL, a novel framework that leverages system-level safety considerations and reinforcement learning techniques to systematically generate naturalistic edge cases. By simulating challenging conditions that mimic the real-world situations, our framework aims to rigorously test entire system's safety and reliability. Although demonstrated within the autonomous driving application, our methodology is adaptable across diverse autonomous systems. Our experimental validation, conducted on high-fidelity simulator underscores the overall effectiveness of this framework.
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- 2024
25. Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
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Kadir, Md Abdul, Alam, Hasan Md Tusfiqur, Maul, Pascale, Profitlich, Hans-Jürgen, Wolf, Moritz, and Sonntag, Daniel
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging and disease diagnosis. However, manually annotating a lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL) based segmentation algorithms have completely transformed this process and made it possible to automate image segmentation. By accurately segmenting medical images, these algorithms can greatly minimize the time and effort necessary for manual annotation. Additionally, by incorporating Active Learning (AL) methods, these segmentation algorithms can perform far more effectively with a smaller amount of ground truth data. We introduce MedDeepCyleAL, an end-to-end framework implementing the complete AL cycle. It provides researchers with the flexibility to choose the type of deep learning model they wish to employ and includes an annotation tool that supports the classification and segmentation of medical images. The user-friendly interface allows for easy alteration of the AL and DL model settings through a configuration file, requiring no prior programming experience. While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project., Comment: DFKI Technical Report
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- 2024
26. A Deep Learning Approach to Diabetes Diagnosis
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Zhang, Zeyu, Ahmed, Khandaker Asif, Hasan, Md Rakibul, Gedeon, Tom, and Hossain, Md Zakir
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Diabetes, resulting from inadequate insulin production or utilization, causes extensive harm to the body. Existing diagnostic methods are often invasive and come with drawbacks, such as cost constraints. Although there are machine learning models like Classwise k Nearest Neighbor (CkNN) and General Regression Neural Network (GRNN), they struggle with imbalanced data and result in under-performance. Leveraging advancements in sensor technology and machine learning, we propose a non-invasive diabetes diagnosis using a Back Propagation Neural Network (BPNN) with batch normalization, incorporating data re-sampling and normalization for class balancing. Our method addresses existing challenges such as limited performance associated with traditional machine learning. Experimental results on three datasets show significant improvements in overall accuracy, sensitivity, and specificity compared to traditional methods. Notably, we achieve accuracies of 89.81% in Pima diabetes dataset, 75.49% in CDC BRFSS2015 dataset, and 95.28% in Mesra Diabetes dataset. This underscores the potential of deep learning models for robust diabetes diagnosis. See project website https://steve-zeyu-zhang.github.io/DiabetesDiagnosis/, Comment: Accepted to ACIIDS 2024
- Published
- 2024
27. Ensemble Language Models for Multilingual Sentiment Analysis
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Hasan, Md Arid
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Computer Science - Computation and Language ,Computer Science - Machine Learning ,I.2.7 - Abstract
The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get little research due to resource limitations. In this study, we explore sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset. Moreover, We investigated four pretrained language models and proposed two ensemble language models. Our findings include monolingual models exhibiting superior performance and ensemble models outperforming the baseline while the majority voting ensemble outperforms the English language., Comment: This is one of my graduate course project reports and currently, I'm not planning to submit to any conferences
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- 2024
28. Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations
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Hasan, Md Kamrul, Aziz, Md Hasanul, Zarif, Md Ishrak Islam, Hasan, Mahmudul, Hashem, MMA, Guha, Shion, Love, Richard R, and Ahamed, Sheikh
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Information technology ,T58.5-58.64 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundThere is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device. ObjectiveIn this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone. MethodsWe explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner. ResultsThe fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot. ConclusionsWe addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone’s camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal’s features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.
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- 2021
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29. Phosphine resistance status in lesser grain borer Rhyzopertha dominica (Fab.) (Coleoptera: Bostrichidae) strains originating from the tropical countries
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Hasan, Md Mahbub, Adler, Cornel, Reichmuth, Christoph, and Phillips, Thomas W.
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phosphine ,resistance ,grain borer ,tropical countries ,Agriculture ,Botany ,QK1-989 - Abstract
Stored product beetles that are resistant to the fumigant phosphine (hydrogen phosphide) have been reported for more than 50 years in many places worldwide. The high levels of phosphine resistance in lesser grain borer Rhyzopertha dominica (F.) have been noted from several countries including Bangladesh. This study was designed to evaluate the status of resistance to phosphine in Bangladeshi R. dominica and to verify the possible comparison among other phosphine resistant strains from tropical countries viz. Burkina Faso and Malaysia. The data reported and summarized here showed varied levels of resistance compared to the laboratory phosphine susceptible strain (RDLAB). Rhyzopertha dominic/i>a strains originating from Bangladesh (RDBGD) and Burkina Faso (RDBKF) exhibited higher levels of resistance to phosphine compared to the Malaysian strains (RDMAL). Analysis of dose–response data indicated that the RDBGD and RDBKF strains were the most resistant to phosphine under different exposure periods. At LC50, these two strains were more than 80-fold more resistant at all exposures compared to the susceptible strain. Results also revealed that RDBGD and RDBKF strains required a relatively high concentration of 334.94 and 240.081 mg L-1 for 99% mortality. The mean survival time (MST) for the phosphine resistant and susceptible also varied significantly. The maximum MST was recorded for RDBGD and RDBKF strains. The present findings further confirmed that the Bangladeshi originated R. dominica strain contained higher resistance to phosphine compared to strains from other countries. This study could be useful in developing management strategies to prevent stored grain from being infested by resistant strains of R. dominica in tropical countries.
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- 2018
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30. Separation of biocrude produced from hydrothermal liquefaction of faecal sludge without any solvent
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Adnan, H M Fairooz, Khalekuzzaman, Md, Fayshal, Md. Atik, and Hasan, Md. Mehedi
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Electrical Engineering and Systems Science - Signal Processing - Abstract
In this study faecal sludge is used as raw biomass due to its abundance, low cost, and easy availability. After HTL operation, product separation is getting challenging. Current developed studies observed the separation of aqueous and biocrude oil products occurs during the HTL process more popularly with the use of an organic solvent which is quite expensive. Focusing on this critical issue, this study aims to separate the biocrude and aqueous phase without using any solvent by gravity separation technique. From FTIR analysis data it showed that centrifuged at 6000 rpm partial separation of biocrude and aqueous phase (AP) was noticed. however, at 9000 rpm, FTIR analysis showed that biocrude samples included aliphatic hydrocarbons, phenols, and esters where no signs of any carbon chain were found at AP which indicated the products are successfully separated. The separated Crude portion had the higher A-Factor (0.68) and lower C-Factor (0.58) value which indicates the oil quality was immature grade of lower kerogen type II (i.e., moderate oil-prone). This low-cost technique can be economically advantageous for commercial-scale biocrude production., Comment: Conference: WasteSafe 2023At: Khulna University of Engineering & Technology, Khulna, Bangladesh Volume: 8
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- 2024
31. A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters
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Hasan, Md Mahmudul and Fattah, Dr. Shaikh Anowarul
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Computer Science - Machine Learning ,Computer Science - Computation and Language - Abstract
Since internet technologies have advanced, one of the primary factors in company development is customer happiness. Online platforms have become prominent places for sharing reviews. Twitter is one of these platforms where customers frequently post their thoughts. Reviews of flights on these platforms have become a concern for the airline business. A positive review can help the company grow, while a negative one can quickly ruin its revenue and reputation. So it's vital for airline businesses to examine the feedback and experiences of their customers and enhance their services to remain competitive. But studying thousands of tweets and analyzing them to find the satisfaction of the customer is quite a difficult task. This tedious process can be made easier by using a machine learning approach to analyze tweets to determine client satisfaction levels. Some work has already been done on this strategy to automate the procedure using machine learning and deep learning techniques. However, they are all purely concerned with assessing the text's sentiment. In addition to the text, the tweet also includes the time, location, username, airline name, and so on. This additional information can be crucial for improving the model's outcome. To provide a machine learning based solution, this work has broadened its perspective to include these qualities. And it has come as no surprise that the additional features beyond text sentiment analysis produce better outcomes in machine learning based models.
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- 2024
32. Deployment of Advanced and Intelligent Logistics Vehicles with Enhanced Tracking and Security Features
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Siddique, Iqtiar Md, Molla, Selim, Hasan, MD Rakib, and Siddique, Anamika Ahmed
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Computer Science - Cryptography and Security ,Electrical Engineering and Systems Science - Systems and Control - Abstract
This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features. In response to the evolving landscape of logistics management, the proposed system integrates cutting edge technologies to enhance efficiency and ensure the security of the entire logistics process. The core component of this implementation is the incorporation of state-of-the art tracking mechanisms, enabling real-time monitoring of vehicle locations and movements. Furthermore, the system addresses the paramount concern of security by introducing advanced security measures. Through the utilization of sophisticated tracking technologies and security protocols, the proposed logistics vehicles aim to safeguard both customer and provider data. The implementation includes the integration of QR code concepts, creating a binary image system that conceals sensitive information and ensures access only to authorized users. In addition to tracking and security, the study delves into the realm of information mining, employing techniques such as classification, clustering, and recommendation to extract meaningful patterns from vast datasets. Collaborative filtering techniques are incorporated to enhance customer experience by recommending services based on user preferences and historical data. This abstract encapsulates the comprehensive approach of deploying modern logistics vehicles, emphasizing their intelligence through advanced tracking, robust security measures, and data-driven insights. The proposed system aims to revolutionize logistics management, providing a seamless and secure experience for both customers and service providers in the dynamic logistics landscape., Comment: 8 pages
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- 2024
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33. BdSLW60: A Word-Level Bangla Sign Language Dataset
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Rubaiyeat, Husne Ara, Mahmud, Hasan, Habib, Ahsan, and Hasan, Md. Kamrul
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Sign language discourse is an essential mode of daily communication for the deaf and hard-of-hearing people. However, research on Bangla Sign Language (BdSL) faces notable limitations, primarily due to the lack of datasets. Recognizing wordlevel signs in BdSL (WL-BdSL) presents a multitude of challenges, including the need for well-annotated datasets, capturing the dynamic nature of sign gestures from facial or hand landmarks, developing suitable machine learning or deep learning-based models with substantial video samples, and so on. In this paper, we address these challenges by creating a comprehensive BdSL word-level dataset named BdSLW60 in an unconstrained and natural setting, allowing positional and temporal variations and allowing sign users to change hand dominance freely. The dataset encompasses 60 Bangla sign words, with a significant scale of 9307 video trials provided by 18 signers under the supervision of a sign language professional. The dataset was rigorously annotated and cross-checked by 60 annotators. We also introduced a unique approach of a relative quantization-based key frame encoding technique for landmark based sign gesture recognition. We report the benchmarking of our BdSLW60 dataset using the Support Vector Machine (SVM) with testing accuracy up to 67.6% and an attention-based bi-LSTM with testing accuracy up to 75.1%. The dataset is available at https://www.kaggle.com/datasets/hasaniut/bdslw60 and the code base is accessible from https://github.com/hasanssl/BdSLW60_Code.
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- 2024
34. Assessing Uncertainty Estimation Methods for 3D Image Segmentation under Distribution Shifts
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Javanbakhat, Masoumeh, Hasan, Md Tasnimul, and Lippert, Cristoph
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Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
In recent years, machine learning has witnessed extensive adoption across various sectors, yet its application in medical image-based disease detection and diagnosis remains challenging due to distribution shifts in real-world data. In practical settings, deployed models encounter samples that differ significantly from the training dataset, especially in the health domain, leading to potential performance issues. This limitation hinders the expressiveness and reliability of deep learning models in health applications. Thus, it becomes crucial to identify methods capable of producing reliable uncertainty estimation in the context of distribution shifts in the health sector. In this paper, we explore the feasibility of using cutting-edge Bayesian and non-Bayesian methods to detect distributionally shifted samples, aiming to achieve reliable and trustworthy diagnostic predictions in segmentation task. Specifically, we compare three distinct uncertainty estimation methods, each designed to capture either unimodal or multimodal aspects in the posterior distribution. Our findings demonstrate that methods capable of addressing multimodal characteristics in the posterior distribution, offer more dependable uncertainty estimates. This research contributes to enhancing the utility of deep learning in healthcare, making diagnostic predictions more robust and trustworthy.
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- 2024
35. Towards Industry 5.0: A Systematic Literature Review on Sustainable and Green Composite Materials Supply Chains
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Hasan, Md Rabiul, Khan, Muztoba Ahmed, and Wuest, Thorsten
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Mathematics - Optimization and Control ,Economics - General Economics - Abstract
Sustainable supply chain management is a key objective of Industry 5.0, leveraging technologies like real-time data analytics, connectivity, and intelligent automation. At the same time, composite materials present benefits such as lightweight structures, crucial for reducing fuel consumption. This study investigates the intersection between sustainable supply chains and composites by analyzing the current status, research gaps, methodologies, and future research opportunities through bibliometric analysis and a systematic review of the state of the art in the composite materials supply chain. A systematic literature review approach is employed to analyze the Scopus and Web of Science (WOS) databases, offering a comprehensive overview of the existing literature. Through bibliometric analysis, the study investigates countries, authors, citations, keywords, subject areas, and article themes within the metadata to provide additional context. An in-depth analysis of thirty selected papers (n=30) sheds light on the key contributions, major challenges, and Key Performance Indicators (KPIs) across various instances of composite material supply chains, resulting in a generalized overview. Furthermore, this research suggests future directions to link the sustainability efforts in composite materials supply chains with current research gaps. The study underscores diverse research themes in the field, highlighting a few influential works and presenting opportunities for advancement in this emerging area. Collectively, these findings offer valuable insights and a robust roadmap for future research in this domain.
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- 2024
36. Predictive Health Analysis in Industry 5.0: A Scientometric and Systematic Review of Motion Capture in Construction
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Rahman, Md Hadisur, Hasan, Md Rabiul, Chowdhury, Nahian Ismail, Syed, Md Asif Bin, and Farah, Mst Ummul
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Computer Science - Computers and Society - Abstract
In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems.
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- 2024
37. Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation
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Sami, Shoaib Meraj, Hasan, Md Mahedi, Nasrabadi, Nasser M., and Rao, Raghuveer
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing ,Statistics - Machine Learning - Abstract
Annotating automatic target recognition (ATR) is a highly challenging task, primarily due to the unavailability of labeled data in the target domain. Hence, it is essential to construct an optimal target domain classifier by utilizing the labeled information of the source domain images. The transductive transfer learning (TTL) method that incorporates a CycleGAN-based unpaired domain translation network has been previously proposed in the literature for effective ATR annotation. Although this method demonstrates great potential for ATR, it severely suffers from lower annotation performance, higher Fr\'echet Inception Distance (FID) score, and the presence of visual artifacts in the synthetic images. To address these issues, we propose a hybrid contrastive learning base unpaired domain translation (H-CUT) network that achieves a significantly lower FID score. It incorporates both attention and entropy to emphasize the domain-specific region, a noisy feature mixup module to generate high variational synthetic negative patches, and a modulated noise contrastive estimation (MoNCE) loss to reweight all negative patches using optimal transport for better performance. Our proposed contrastive learning and cycle-consistency-based TTL (C3TTL) framework consists of two H-CUT networks and two classifiers. It simultaneously optimizes cycle-consistency, MoNCE, and identity losses. In C3TTL, two H-CUT networks have been employed through a bijection mapping to feed the reconstructed source domain images into a pretrained classifier to guide the optimal target domain classifier. Extensive experimental analysis conducted on three ATR datasets demonstrates that the proposed C3TTL method is effective in annotating civilian and military vehicles, as well as ship targets., Comment: This Paper is Accepted in IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. This Arxiv version is an older version than the reviewed version
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- 2024
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38. Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disorder
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Islam, Md. Farhadul, Manab, Meem Arafat, Mondal, Joyanta Jyoti, Zabeen, Sarah, Rahman, Fardin Bin, Hasan, Md. Zahidul, Sadeque, Farig, and Noor, Jannatun
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Autism Spectrum Disorder (ASD) is a complicated neurological condition which is challenging to diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with maintaining attention spans and have less focused vision. The eye-tracking technology has drawn special attention in the context of ASD since anomalies in gaze have long been acknowledged as a defining feature of autism in general. Deep Learning (DL) approaches coupled with eye-tracking sensors are exploiting additional capabilities to advance the diagnostic and its applications. By learning intricate nonlinear input-output relations, DL can accurately recognize the various gaze and eye-tracking patterns and adjust to the data. Convolutions alone are insufficient to capture the important spatial information in gaze patterns or eye tracking. The dynamic kernel-based process known as involutions can improve the efficiency of classifying gaze patterns or eye tracking data. In this paper, we utilise two different image-processing operations to see how these processes learn eye-tracking patterns. Since these patterns are primarily based on spatial information, we use involution with convolution making it a hybrid, which adds location-specific capability to a deep learning model. Our proposed model is implemented in a simple yet effective approach, which makes it easier for applying in real life. We investigate the reasons why our approach works well for classifying eye-tracking patterns. For comparative analysis, we experiment with two separate datasets as well as a combined version of both. The results show that IC with three involution layers outperforms the previous approaches., Comment: 17 pages, 13 figures, Submitted to Engineering Applications of Artificial Intelligence
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- 2024
39. UDEEP: Edge-based Computer Vision for In-Situ Underwater Crayfish and Plastic Detection
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Monari, Dennis, Larkin, Jack, Machado, Pedro, Bird, Jordan J., Ihianle, Isibor Kennedy, Yahaya, Salisu Wada, Tash, Farhad Fassihi, Hasan, Md Mahmudul, and Lotfi, Ahmad
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources and leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90% in certain English counties, making them highly susceptible to extinction. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and discarded plastics in the United Kingdom's river ecosystem's. The UDEEP platform can play a crucial role in environmental monitoring by performing on-the-fly classification of Signal crayfish and plastic debris while leveraging the efficacy of AI, IoT devices and the power of edge computing (i.e., NJN). By providing accurate data on the presence, spread and abundance of these species, the UDEEP platform can contribute to monitoring efforts and aid in mitigating the spread of invasive species.
- Published
- 2023
40. Bengali License Plate Recognition: Unveiling Clarity with CNN and GFP-GAN
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Afrin, Noushin, Hasan, Md Mahamudul, Safin, Mohammed Fazlay Elahi, Amin, Khondakar Rifat, Haque, Md Zahidul, Ahmed, Farzad, and Shawon, Md. Tanvir Rouf
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Automated License Plate Recognition(ALPR) is a system that automatically reads and extracts data from vehicle license plates using image processing and computer vision techniques. The Goal of LPR is to identify and read the license plate number accurately and quickly, even under challenging, conditions such as poor lighting, angled or obscured plates, and different plate fonts and layouts. The proposed method consists of processing the Bengali low-resolution blurred license plates and identifying the plate's characters. The processes include image restoration using GFPGAN, Maximizing contrast, Morphological image processing like dilation, feature extraction and Using Convolutional Neural Networks (CNN), character segmentation and recognition are accomplished. A dataset of 1292 images of Bengali digits and characters was prepared for this project.
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- 2023
41. MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
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Peng, Hongwu, Xie, Xi, Shivdikar, Kaustubh, Hasan, MD Amit, Zhao, Jiahui, Huang, Shaoyi, Khan, Omer, Kaeli, David, and Ding, Caiwen
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Distributed, Parallel, and Cluster Computing ,I.2 ,C.5 - Abstract
In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory access irregularities, leading to underutilized hardware. Existing solutions such as PyG, DGL with cuSPARSE, and GNNAdvisor frameworks partially address these challenges but memory traffic is still significant. We argue that drastic performance improvements can only be achieved by the vertical optimization of algorithm and system innovations, rather than treating the speedup optimization as an "after-thought" (i.e., (i) given a GNN algorithm, designing an accelerator, or (ii) given hardware, mainly optimizing the GNN algorithm). In this paper, we present MaxK-GNN, an advanced high-performance GPU training system integrating algorithm and system innovation. (i) We introduce the MaxK nonlinearity and provide a theoretical analysis of MaxK nonlinearity as a universal approximator, and present the Compressed Balanced Sparse Row (CBSR) format, designed to store the data and index of the feature matrix after nonlinearity; (ii) We design a coalescing enhanced forward computation with row-wise product-based SpGEMM Kernel using CBSR for input feature matrix fetching and strategic placement of a sparse output accumulation buffer in shared memory; (iii) We develop an optimized backward computation with outer product-based and SSpMM Kernel. We conduct extensive evaluations of MaxK-GNN and report the end-to-end system run-time. Experiments show that MaxK-GNN system could approach the theoretical speedup limit according to Amdahl's law. We achieve comparable accuracy to SOTA GNNs, but at a significantly increased speed: 3.22/4.24 times speedup (vs. theoretical limits, 5.52/7.27 times) on Reddit compared to DGL and GNNAdvisor implementations., Comment: ASPLOS 2024 accepted publication
- Published
- 2023
42. Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learning
- Author
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Hasan, Md Mahedi, Sami, Shoaib Meraj, and Nasrabadi, Nasser
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
State-of-the-art face recognition (FR) models often experience a significant performance drop when dealing with facial images in surveillance scenarios where images are in low quality and often corrupted with noise. Leveraging facial characteristics, such as freckles, scars, gender, and ethnicity, becomes highly beneficial in improving FR performance in such scenarios. In this paper, we introduce text-guided face recognition (TGFR) to analyze the impact of integrating facial attributes in the form of natural language descriptions. We hypothesize that adding semantic information into the loop can significantly improve the image understanding capability of an FR algorithm compared to other soft biometrics. However, learning a discriminative joint embedding within the multimodal space poses a considerable challenge due to the semantic gap in the unaligned image-text representations, along with the complexities arising from ambiguous and incoherent textual descriptions of the face. To address these challenges, we introduce a face-caption alignment module (FCAM), which incorporates cross-modal contrastive losses across multiple granularities to maximize the mutual information between local and global features of the face-caption pair. Within FCAM, we refine both facial and textual features for learning aligned and discriminative features. We also design a face-caption fusion module (FCFM) that applies fine-grained interactions and coarse-grained associations among cross-modal features. Through extensive experiments conducted on three face-caption datasets, proposed TGFR demonstrates remarkable improvements, particularly on low-quality images, over existing FR models and outperforms other related methods and benchmarks., Comment: Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
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- 2023
43. Theoretical prediction of the mechanical, electronic, optical and thermodynamic properties of antiperovskites A3BO (A = K, Rb and B = Au, Br) using DFT scheme: new candidate for optoelectronic devices application
- Author
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Uddin, Salah, Das, Akash, Rayhan, M. A., Ahmad, Sohail, Khokan, Rashel Mohammad, Rasheduzzaman, Md., Das, Remon, Ullah, Aasim, Arafat, Yasir, and Hasan, Md. Zahid
- Published
- 2024
- Full Text
- View/download PDF
44. SkinNet-14: a deep learning framework for accurate skin cancer classification using low-resolution dermoscopy images with optimized training time
- Author
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Al Mahmud, Abdullah, Azam, Sami, Khan, Inam Ullah, Montaha, Sidratul, Karim, Asif, Haque, Aminul, Zahid Hasan, Md., Brady, Mark, Biswas, Ritu, and Jonkman, Mirjam
- Published
- 2024
- Full Text
- View/download PDF
45. Evaluation of Thirty Lentil (Lens culinaris Medik.) Genotypes Suitable for Drought Stress Based on Morpho-biochemical Traits and Stress Tolerance Indices
- Author
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Hasan, Md. Mostofa Ali, Mondal, Upama, Azam, Md. Golam, Hossain, Md. Amir, Sarkar, Umakanta, Gaber, Ahmed, and Hossain, Akbar
- Published
- 2024
- Full Text
- View/download PDF
46. Design of a Novel Aerofoil of a Light Aircraft for Robust Aerodynamic Performance at High Reynolds Number Regime Using XFLR5
- Author
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Akter, Farzana, Hoque, Md. Araful, Ekram, Kazi Ahasan, Saha, Badhan, and Hasan, Md. Jahid
- Published
- 2024
- Full Text
- View/download PDF
47. Fast Aerodynamics Prediction of Wedge Tail Airfoils Using Multi-head Perceptron Network
- Author
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Hasan, Md. Moynul, Rahaman, Md. Mashiur, and Zakaria, N. M. Golam
- Published
- 2024
- Full Text
- View/download PDF
48. In Silico Functional and Structural Analysis of Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in Human Paired Box 4 Gene
- Author
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Kamal, Md. Mostafa, Islam, Md. Numan, Rabby, Md. Golam, Zahid, Md. Ashrafuzzaman, and Hasan, Md. Mahmudul
- Published
- 2024
- Full Text
- View/download PDF
49. Mesoscopic CUDA 3D MRT-LBM Simulation of Natural Convection of Power-Law Fluids in a Differentially Heated Cubic Cavity with a Machine Learning Cross-Validation
- Author
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Hasan, Md Farhad, Molla, Md. Mamun, Siddiqa, Sadia, and Khan, Amirul Islam
- Published
- 2024
- Full Text
- View/download PDF
50. Screening of heat stress-tolerant weedy rice and SNP identification of heat-tolerance-related genes
- Author
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Sarker, Mainul Hasan, Hussain, Md Hamed, Neik, Ting Xiang, Hasan, Md Zobaer, Wee, Wei Yee, Tan, Hock Siew, Ko, Swee-suak, and Song, Beng-Kah
- Published
- 2024
- Full Text
- View/download PDF
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