@inproceedings{bib_Anal_2023, AUTHOR = {Aashay Singhal, Kamalakar Karlapalem}, TITLE = {Analysis of h-index for research awards}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute h- index of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. We also compare h-index of a paper with nine h-index like metrics. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.

@inproceedings{bib_Capt_2023, AUTHOR = {Ayushman Panda, Kamalakar Karlapalem}, TITLE = {Capturing Hiders with Moving Obstacles}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2023}}

The classic hide-and-seek game is an abstraction for many real- world scenarios like capturing intruders in a closed space, locating objects, patrolling an area, etc. Since most of the present work is based on static obstacles, we address solutions for the hide-and- seek game in an environment where the obstacles are not static. We design strategies that would facilitate seekers to capture hiders in an environment with moving obstacles. We have three strategies: Baseline strategy, Set-cover strategy, and Sweep strategy, which use different surveillance techniques to be followed by the seekers. We simulate the methods and compare their performance in different scenarios. While the baseline strategy demands many seekers in large environments, the other two strategies, set-cover and sweep, are ideal for applying in large environments as they require fewer seekers in the same environment.

@inproceedings{bib_Scen_2023, AUTHOR = {Kamalakar Karlapalem}, TITLE = {Scenic Routes in Rd}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

In this work, we introduce the problem of scenic routes among points in R d . The key development is the nature of the problem in terms of both defining the concept of scenic points and scenic routes and then coming up with algorithms that meet different criteria for the generated scenic routes. The scenic routes problem provides a visual trajectory for a user to comprehend the layout of high-dimensional points. The nature of this trajectory and the visual layout of the points have applications in comprehending the results of machine learning supervised and unsupervised learning techniques. We study the problem in 2D and 3D (with two color points) before exploring the issues in R d . The red/blue points in our examples could be to be in a class or not to be in a class. The applications could include landscape design to adhere to the scenic beauty of the artifacts on the ground. The generation of equally separated layouts for designing composite hardware where interference could be an issue. Keywords: sce

@inproceedings{bib_Aspe_2023, AUTHOR = {Nikhil E, Anshul Padhi, PARIKH PULKIT TRUSHANT KUMAR, Swati Kanwal, Kamalakar Karlapalem, Natraj Raman}, TITLE = {Aspect-based Summarization of Legal Case Files using Sentence Classification}, BOOKTITLE = {International Conference on World wide web}. YEAR = {2023}}

Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.

@inproceedings{bib_Conf_2020, AUTHOR = {CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {Conformance Checking Methodology Across Discharge Summaries and Standard Treatment Guidelines}, BOOKTITLE = {Transactions on Computing for Healthcare}. YEAR = {2020}}

Conformance checking of treatment plans in discharge summary data would facilitate the development of clinical decision support system, treatment plan quality assurance, and new treatment plan discovery. Conformance checking requires extraction of medical entities and relationships among them to form a computable representation of the treatment plan present in the discharge summary. We propose a workflow representation of patient’s discharge summary that is referred to as workflow instance. We employ a multi-layer perceptron neural network to extract relationships between medical entities to construct the workflow instance. The aim of this work is to check the conformance of the workflow instance against standard treatment plan. Standard treatment plans are extracted from the treatment guidelines provided on healthcare websites such as WebMD, Mayo Clinic, and Johns Hopkins. For each disease …