Dibya Jyoti Bora

Sagar Tamang, Dibya Jyoti Bora

Training of a tokenizer plays an important role in the performance of deep learning models. This research aims to understand the performance of tokenizers in five state-of-the-art (SOTA) large language models (LLMs) in the Assamese language of India. The research is important to understand the multi-lingual support for a low-resourced language such as Assamese. Our research reveals that the tokenizer of SUTRA from Two AI performs the best with an average Normalized Sequence Length (NSL) value of 0.45, closely followed by the tokenizer of GPT-4o from Open AI with an average NSL value of 0.54, followed by Gemma 2, Meta Llama 3.1, and Mistral Large Instruct 2407 with an average NSL value of 0.82, 1.4, and 1.48 respectively.

Sagar Tamang, Dibya Jyoti Bora

As autonomous agents become more powerful and widely used, it is becoming increasingly important to ensure they behave safely and stay aligned with system goals, especially in multi-agent settings. Current systems often rely on agents self-monitoring or correcting issues after the fact, but they lack mechanisms for real-time oversight. This paper introduces the Enforcement Agent (EA) Framework, which embeds dedicated supervisory agents into the environment to monitor others, detect misbehavior, and intervene through real-time correction. We implement this framework in a custom drone simulation and evaluate it across 90 episodes using 0, 1, and 2 EA configurations. Results show that adding EAs significantly improves system safety: success rates rise from 0.0% with no EA to 7.4% with one EA and 26.7% with two EAs. The system also demonstrates increased operational longevity and higher rates of malicious drone reformation. These findings highlight the potential of lightweight, real-time supervision for enhancing alignment and resilience in multi-agent systems.

Rupjyoti Chutia, Dibya Jyoti Bora

Digital image processing involves the systematic handling of images using advanced computer algorithms, and has gained significant attention in both academic and practical fields. Image enhancement is a crucial preprocessing stage in the image-processing chain, improving image quality and emphasizing features. This makes subsequent tasks (segmentation, feature extraction, classification) more reliable. Image enhancement is essential for rice leaf analysis, aiding in disease detection, nutrient deficiency evaluation, and growth analysis. Denoising followed by contrast enhancement are the primary steps. Image filters, generally employed for denoising, transform or enhance visual characteristics like brightness, contrast, and sharpness, playing a crucial role in improving overall image quality and enabling the extraction of useful information. This work provides an extensive comparative study of well-known image-denoising methods combined with CLAHE (Contrast Limited Adaptive Histogram Equalization) for efficient denoising of rice leaf images. The experiments were performed on a rice leaf image dataset to ensure the data is relevant and representative. Results were examined using various metrics to comprehensively test enhancement methods. This approach provides a strong basis for assessing the effectiveness of methodologies in digital image processing and reveals insights useful for future adaptation in agricultural research and other domains.

Dibya Jyoti Bora, Mrinal Kanti Mishra

Segmentation of brain tumors from Magnetic Resonance Imaging (MRI) remains a pivotal challenge in medical image analysis due to the heterogeneous nature of tumor morphology and intensity distributions. Accurate delineation of tumor boundaries is critical for clinical decision-making, radiotherapy planning, and longitudinal disease monitoring. In this study, we perform a comprehensive comparative analysis of two major clustering paradigms applied in MRI tumor segmentation: hard clustering, exemplified by the K-Means algorithm, and soft clustering, represented by Fuzzy C-Means (FCM). While K-Means assigns each pixel strictly to a single cluster, FCM introduces partial memberships, meaning each pixel can belong to multiple clusters with varying degrees of association. Experimental validation was performed using the BraTS2020 dataset, incorporating pre-processing through Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). Evaluation metrics included the Dice Similarity Coefficient (DSC) and processing time, which collectively demonstrated that K-Means achieved superior speed with an average runtime of 0.3s per image, whereas FCM attained higher segmentation accuracy with an average DSC of 0.67 compared to 0.43 for K-Means, albeit at a higher computational cost (1.3s per image). These results highlight the inherent trade-off between computational efficiency and boundary precision.

Dibya Jyoti Bora

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.

Dibya Jyoti Bora, Anil Kumar Gupta

In computer vision, image segmentation is always selected as a major research topic by researchers. Due to its vital rule in image processing, there always arises the need of a better image segmentation method. Clustering is an unsupervised study with its application in almost every field of science and engineering. Many researchers used clustering in image segmentation process. But still there requires improvement of such approaches. In this paper, a novel approach for clustering based image segmentation is proposed. Here, we give importance on color space and choose lab for this task. The famous hard clustering algorithm K-means is used, but as its performance is dependent on choosing a proper distance measure, so, we go for cosine distance measure. Then the segmented image is filtered with sobel filter. The filtered image is analyzed with marker watershed algorithm to have the final segmented result of our original image. The MSE and PSNR values are evaluated to observe the performance.

Dibya Jyoti Bora, Anil Kumar Gupta, Fayaz Ahmad Khan

Color image segmentation is a very emerging topic for image processing research. Since it has the ability to present the result in a way that is much more close to the human yes perceive, so todays more research is going on this area. Choosing a proper color space is a very important issue for color image segmentation process. Generally LAB and HSV are the two frequently chosen color spaces. In this paper a comparative analysis is performed between these two color spaces with respect to color image segmentation. For measuring their performance, we consider the parameters: mse and psnr . It is found that HSV color space is performing better than LAB.

Dibya Jyoti Bora, Anil Kumar Gupta

Image processing is an important research area in computer vision. Image segmentation plays the vital rule in image processing research. There exist so many methods for image segmentation. Clustering is an unsupervised study. Clustering can also be used for image segmentation. In this paper, an in-depth study is done on different clustering techniques that can be used for image segmentation with their pros and cons. An experiment for color image segmentation based on clustering with K-Means algorithm is performed to observe the accuracy of clustering technique for the segmentation purpose.

Dibya Jyoti Bora, Anil Kumar Gupta

Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of fuzzy logic based algorithm, the parameter like exponent for the partition matrix that we have to fix for the clustering task plays a very important rule on the performance of the algorithm. In this paper, an experimental analysis is done on FCM algorithm to observe the impact of this parameter on the performance of the algorithm.

Dibya Jyoti Bora, Dr. Anil Kumar Gupta

Data clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important clustering technique based on fuzzy logic. Also we have some hard clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy clustering algorithm and hard clustering algorithm