http://localhost:8081/jspui/handle/123456789/15072 2026-05-07T20:44:30Z 2026-05-07T20:44:30Z Tukaram, Shingade Sandip http://localhost:8081/jspui/handle/123456789/20451 2026-04-20T06:37:34Z 2024-09-01T00:00:00Z

Title: A FRAMEWORK FOR METAHEURISTIC BASED ALGORITHMS FOR TEAM FORMATION Authors: Tukaram, Shingade Sandip Abstract: Effective collaboration in networks is crucial for successful team formation. The team forma tion problem involves selecting a subset of agents, referred to as a team, from a larger pool, ensuring the team meets certain desirable properties. This research focuses on selecting agents with the necessary skills, previous communication, and shared abilities, thereby minimizing communication costs. A practical application of the proposed approach is in team formation for IT projects and other team selection scenarios. In this study, we use real-world datasets, ACM,Academia Stack Exchange, DBLP and Players_20 football team dataset to evaluate our methods. Wesuggest a single-objective heuristic approach based on the Grey Wolf Optimizer (GWO) with a modified swap operation to improve upon previous team formation work. This method effectively minimizes communication costs while selecting agents with the required skills. Ex perimental results show that the Improved GWO significantly outperforms traditional methods in terms of both performance metrics and communication cost reduction. Building on this, we propose a hybrid metaheuristic approach that combines Particle Swarm Optimization (PSO) and the Jaya algorithm with a modified swap operator(PSO-Jaya). The third approach focuses on improving algorithm efficiency by integrating state space re duction techniques into the metaheuristic framework to address the increasing complexity and computational demands of the previous methods. The Employee Bee Algorithm (EBA) is en hanced with state space reduction, speeding up the computation while maintaining or improving result quality(IEB). Lastly, we consider a multi-objective optimization context for team formation. For this, we compare several metaheuristic approaches, including NSGA-II, NSGA-II with Simulated Annealing (NSGA-II-SA), NSGA with PSO (NSGA-II-PSO), and our approach Differential Evolution-based NSGA-II (NSGA-II-DE).

2024-09-01T00:00:00Z Ali, Tofik http://localhost:8081/jspui/handle/123456789/20447 2026-04-20T06:33:08Z 2024-07-01T00:00:00Z

Title: INFORMATION RETRIEVAL AND LOCALIZATION IN DOCUMENT IMAGE Authors: Ali, Tofik Abstract: Information retrieval and localization in document images involve extracting, identi fying, and making accessible the relevant information within digital or digitized visual representations of traditional paper-based documents. This process is crucial for manag ing a vast and diverse range of documents encountered in various sectors such as legal, medical, academic, and corporate environments. Document images, which preserve the content, format, and sometimes the texture of the original documents, play a significant role in maintaining the integrity and authenticity of information. Effective retrieval and localization of information from these images require sophisticated techniques in image processing, machine learning, and deep learning to address challenges such as varying image quality, diverse document formats, and the need for accurate and efficient text recognition and interpretation. The goal is to transform static document images into dynamic, actionable data sources, enhancing their utility and accessibility in real-world applications. The digital transformation has revolutionized how information is stored, accessed, and managed across various sectors. Digitized documents offer numerous advantages over their physical counterparts, including ease of access, improved storage efficiency, and enhanced security. However, the real challenge lies in making this digitized information accessible and intelligible to users. Advanced technologies are required to bridge the gap between digitized information and its practical utility, necessitating the development of robust models and algorithms for efficient processing and interpretation. This research addresses the challenges inherent in document image analysis, such as i Abstract variability in image quality, diverse document formats, and complex layouts. It aims to develop advanced computational models for document image analysis to improve the accuracy and efficiency of character recognition, text segmentation, and image understanding. The study focuses on employing multi-task pre-training strategies to enhance the accuracy and efficiency of these technologies. The research methodology involves breaking down the problem into manageable components and systematically addressing each challenge using convolutional neural networks (CNNs), advanced text segmentation and recognition algorithms, and image understanding techniques. Key contributions of this research include the development of high-accuracy character recognition systems, particularly for handwritten scripts, leveraging advanced CNNs; the introduction of the Gated Multiscale Input Feature Fusion (GMIF) scheme for scale-invariant text detection; the development of Fast&Focused-Net (FFN) for small object feature encoding using the Volume-wise Dot Product (VDP) layer; and the introduction of a multi-task pre-training approach that combines text, image, and layout information to enhance document information analysis. The proposed models and techniques have been evaluated on various datasets, demonstrating significant improvements in the accuracy and efficiency of document image analysis tasks. The real-world applications of these advanced technologies are vast and varied, spanning academic institutions, corporate environments, legal industries, and the medical field. This research contributes to transforming static document images into dynamic, actionable data sources, supporting automated workflows, facilitating decision-making, and promoting knowledge discovery. Keywords: Document Image Analysis, Information Retrieval, Text Localization, Machine Learning, Deep Learning, Convolutional Neural Networks (CNNs), Multi-Task Pre-Training, Image Processing, Text Segmentation, Character Recognition, Gated Multiscale Input Feature Fusion (GMIF), Fast&Focused-Net (FFN), Volume-wise Dot Product (VDP) Layer, Entity Recognition, Relationship Extraction, Layout Analysis.

2024-07-01T00:00:00Z Naik, Swapnil Pramod http://localhost:8081/jspui/handle/123456789/20350 2026-04-09T08:00:56Z 2022-05-01T00:00:00Z

Title: NAMED ENTITY RECOGNITION IN BIOMEDICAL DOMAIN Authors: Naik, Swapnil Pramod Abstract: The key task of the Named Entity Recognition (NER) in the biomedical domain is to identify named entities like genes, proteins, diseases, chemicals etc. Compared to normal NER, Biomedical Named Entity Recognition (BIONER) has some additional complexities. As the field is rapidly developing and new information in the field of medicines and biology continues to evolve there has been more development in a lot of new words which need to be constantly added to identity named entities and types. Also, these names are typically longer and combination of many individual terms, many abbreviations are used, and also combinations of letters, symbols and punctuation e.g., BRCA1.

2022-05-01T00:00:00Z Mohammad, Taj http://localhost:8081/jspui/handle/123456789/20349 2026-04-09T08:00:34Z 2022-05-01T00:00:00Z

Title: REAL-TIME VEHICLE IDENTIFICATION, TRACKING AND COUNTING SYSTEM USING YOLO AND DEEPSORT Authors: Mohammad, Taj Abstract: The count of motor vehicles on the road are continuously on the rise since industrial revolution took place. Due to this, quick and reliable vehicle detection, tracking and vehicle counting, on the road are required. However, the present approach focuses on counting the total number of vehicles without considering the direction and heterogeneity of vehicles. Therefore, there is a pressing need to design an efficient method to detect vehicle’s type, track, and count in up and down direction. Considering this in view, we design an efficient method to conquer the aforementioned task by utilizing deep learning methods, You Only Look Once (YOLO) and DeepSORT that helps to identify vehicle’s type, tracking the vehicle and estimate traffic density using YOLOv4 and DeepSORT. Accuracy metric is employed to find the effectiveness of the proposed model . Moreover, the accuracy metric of the presented framework is compared with the latest techniques which demonstrates that the presented framework performs better than the latest approaches: YOLOv3-tiny, YOLOv3, YOLOv4-tiny, DeepSORT by improving the detection accuracy by 12.16 %, 3.07%, and 7.69%, respectively and counting accuracy by 27.50%, 4.40%, and 11.00%, respectively.

2022-05-01T00:00:00Z