Het Patel

Rutuja Gurav, Het Patel, Zhuocheng Shang et al.

Climate change is increasingly disrupting worldwide agriculture, making global food production less reliable. To tackle the growing challenges in feeding the planet, cutting-edge management strategies, such as precision agriculture, empower farmers and decision-makers with rich and actionable information to increase the efficiency and sustainability of their farming practices. Crop-type maps are key information for decision-support tools but are challenging and costly to generate. We investigate the capabilities of Meta AI's Segment Anything Model (SAM) for crop-map prediction task, acknowledging its recent successes at zero-shot image segmentation. However, SAM being limited to up-to 3 channel inputs and its zero-shot usage being class-agnostic in nature pose unique challenges in using it directly for crop-type mapping. We propose using clustering consensus metrics to assess SAM's zero-shot performance in segmenting satellite imagery and producing crop-type maps. Although direct crop-type mapping is challenging using SAM in zero-shot setting, experiments reveal SAM's potential for swiftly and accurately outlining fields in satellite images, serving as a foundation for subsequent crop classification. This paper attempts to highlight a use-case of state-of-the-art image segmentation models like SAM for crop-type mapping and related specific needs of the agriculture industry, offering a potential avenue for automatic, efficient, and cost-effective data products for precision agriculture practices.

Mario Camarena, Het Patel, Fatemeh Nazari et al.

This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a fine-tuned vision foundation model for semantic segmentation in autonomous driving (AD). AD-SAM extends the Segment Anything Model (SAM) with a dual-encoder and deformable decoder tailored to spatial and geometric complexity of road scenes. The dual-encoder produces multi-scale fused representations by combining global semantic context from SAM's pretrained Vision Transformer (ViT-H) with local spatial detail from a trainable convolutional deep learning backbone (i.e., ResNet-50). A deformable fusion module aligns heterogeneous features across scales and object geometries. The decoder performs progressive multi-stage refinement using deformable attention. Training is guided by a hybrid loss that integrates Focal, Dice, Lovasz-Softmax, and Surface losses, improving semantic class balance, boundary precision, and optimization stability. Experiments on the Cityscapes and Berkeley DeepDrive 100K (BDD100K) benchmarks show that AD-SAM surpasses SAM, Generalized SAM (G-SAM), and a deep learning baseline (DeepLabV3) in segmentation accuracy. It achieves 68.1 mean Intersection over Union (mIoU) on Cityscapes and 59.5 mIoU on BDD100K, outperforming SAM, G-SAM, and DeepLabV3 by margins of up to +22.9 and +19.2 mIoU in structured and diverse road scenes, respectively. AD-SAM demonstrates strong cross-domain generalization with a 0.87 retention score (vs. 0.76 for SAM), and faster, more stable learning dynamics, converging within 30-40 epochs, enjoying double the learning speed of benchmark models. It maintains 0.607 mIoU with only 1000 samples, suggesting data efficiency critical for reducing annotation costs. These results confirm that targeted architectural and optimization enhancements to foundation models enable reliable and scalable AD perception.

Het Patel, Umair Rehman, Farkhund Iqbal

Phishing, a prevalent cybercrime tactic for decades, remains a significant threat in today's digital world. By leveraging clever social engineering elements and modern technology, cybercrime targets many individuals, businesses, and organizations to exploit trust and security. These cyber-attackers are often disguised in many trustworthy forms to appear as legitimate sources. By cleverly using psychological elements like urgency, fear, social proof, and other manipulative strategies, phishers can lure individuals into revealing sensitive and personalized information. Building on this pervasive issue within modern technology, this paper aims to analyze the effectiveness of 15 Large Language Models (LLMs) in detecting phishing attempts, specifically focusing on a randomized set of "419 Scam" emails. The objective is to determine which LLMs can accurately detect phishing emails by analyzing a text file containing email metadata based on predefined criteria. The experiment concluded that the following models, ChatGPT 3.5, GPT-3.5-Turbo-Instruct, and ChatGPT, were the most effective in detecting phishing emails.

Yiran Luo, Het Patel, Yu Fu et al.

Recent research has shown that pruning large-scale language models for inference is an effective approach to improving model efficiency, significantly reducing model weights with minimal impact on performance. Interestingly, pruning can sometimes even enhance accuracy by removing noise that accumulates during training, particularly through matrix decompositions. However, recent work has primarily focused on single matrix decompositions or lower precision techniques, which may fail to fully capture structural patterns. To address these limitations, we introduce TRAWL (Tensor Reduced and Approximated Weights for Large Language Models), a technique that applies tensor decomposition across multiple weight matrices to effectively denoise LLMs by capturing global structural patterns. Our experiments show that TRAWL improves model performance by up to 16% over baseline models on benchmark datasets, without requiring additional data, training, or fine-tuning.