TasnimAhmed

Mathematical optimization drives decisions across domains such as supply chains, energy grids, and financial systems, among others. Linear programming (LP), a tool for optimizing objectives under constraints, requires domain expertise to translate real-world problems into executable models. We explore automating this translation using Large Language Models (LLMs), generating solver-ready code from textual descriptions to reduce reliance on specialized knowledge. We propose OPT2CODE, a Retrieval-Augmented Generation (RAG) framework that utilizes compact LLMs to transform problem descriptions into optimization solver executable code. OPT2CODE utilizes code documentation for document retrieval and incorporates multiple LLM-as-a-Judge components to improve baseline performance. In addition, OPT2CODE is solver flexible and LLM flexible, and it can generate code for a broad range of …

In the fast-paced domain of natural language processing, converting linguistic descriptions into mathematical optimization problems is a complex task, requiring profound comprehension and processing skills from Large Language Models (LLMs). In this study, various LLMs were evaluated, including GPT-3.5, GPT-4, and smaller variants with seven billion parameters: Llama-2, Falcon, Mistral, and Zephyr. This research investigated their performance in both zero-shot and one-shot settings for this task, revealing that GPT-4 outperformed others, particularly in the one-shot scenario. A core contribution of this study is the development of LM4OPT, a progressive fine-tuning framework specifically designed for smaller LLMs. This framework leverages noisy embeddings and specialized datasets to enhance the performance of the models. Regardless of the inherent limitations of smaller models in processing complex and …

Depression is a mental state characterized by recurrent feelings of melancholy, hopelessness, and disinterest in activities, having a significant negative influence on everyday functioning and general well-being. Millions of users express their thoughts and emotions on social media platforms, which can be used as a rich source of data for early detection of depression. In this connection, this work leverages an ensemble of transformer-based architectures for quantifying the severity of depression from social media posts into four categories — non-depressed, mild, moderate, and severe. At first, a diverse range of preprocessing techniques is employed to enhance the quality and relevance of the input. Then, the preprocessed samples are passed through three variants of transformer-based models, namely vanilla BERT, BERTweet, and ALBERT, for generating predictions, which are combined using a weighted soft …

Road infrastructure is essential for transportation safety and efficiency. However, the current methods for assessing road conditions, crucial for effective planning and maintenance, suffer from high costs, time-intensive procedures, infrequent data collection, and limited real-time capabilities. This article presents an efficient lightweight system to analyze road quality from video feeds in real time. The backbone of the system is EdgeFusionViT, a novel vision transformer (ViT)-based architecture that uses an attention-based late fusion mechanism. The proposed architecture outperforms lightweight convolutional neural network (CNN)-based and ViT-based models. Its practicality is demonstrated by its deployment on an edge device, the Nvidia Jetson Orin Nano, enabling real-time road analysis at 12 frames per second. EdgeFusionViT outperforms existing benchmarks, achieving an impressive accuracy of 89.76% on the …

Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as ‘non-depressed’ or ‘depressed’. Moreover, three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing …

Digital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. The development of algorithms demands large datasets with proper annotations. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of …

Gait recognition is becoming one of the promising methods for biometric authentication owing to its self-effacing nature. Contemporary approaches of joint position-based gait recognition generally model gait features using spatio-temporal graphs which are often prone to overfitting. To incorporate long-range relationships among joints, these methods utilize multi-scale operators. However, they fail to provide equal importance to all joint combinations resulting in an incomplete realization of long-range relationships between joints and important body parts. Furthermore, only considering joint coordinates may fail to capture discriminatory information provided by the bone structures and motion. In this work, a novel multi-scale graph convolution approach, namely ‘GaitGCN++’, is proposed, which utilizes joint and bone information from individual frames and joint-motion data from consecutive frames providing a …

To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class …

Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to maintain the same strokes while writing any digit repeatedly, etc. In addition to that, the structural complexities of the digits of a particular language may lead to ambiguous scenarios of HDR. Over the years, researchers have developed numerous offline and online HDR pipelines, where different image processing techniques are combined with traditional Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures. Although evidence of extensive review studies on HDR exists in the literature for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few surveys on Bengali …

The influence of social media is one of the most dominating characteristics of the current era, and this has led cyberbullying to grow into a more serious social issue. As a result, automated cyberbullying detection systems need to be an integral part of almost all social media platforms. Past studies on this domain have primarily focused on hand-picked features and traditional machine learning approaches for cyberbullying detection from user comments on social media. Recently, transformers have been proved to be quite effective in various language-related tasks; however, their effectiveness has not been extensively explored in this particular domain. In this study, we evaluate the individual performance of several well-known transformer-based architectures and aim to contribute to the development of automated cyberbullying detection systems by proposing our own transformer-based ensemble framework. Our …