Biswajit Banik

Maisha Haque, Israt Jahan Ayshi, Sadaf M. Anis et al.

This research proposes "ForCM", a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 imagery. The study explores several DL models, including UNet, UNet++, ResUNet, AttentionUNet, and ResNet50-Segnet, applied to high-resolution Sentinel-2 Level 2A satellite images of the Amazon Rainforest. The datasets comprise three collections: two sets of three-band imagery and one set of four-band imagery. After evaluation, the most effective DL models are individually integrated with the OBIA technique to enhance mapping accuracy. The originality of this work lies in evaluating different deep learning models combined with OBIA and comparing them with traditional OBIA methods. The results show that the proposed ForCM method improves forest cover mapping, achieving overall accuracies of 94.54 percent with ResUNet-OBIA and 95.64 percent with AttentionUNet-OBIA, compared to 92.91 percent using traditional OBIA. This research also demonstrates the potential of free and user-friendly tools such as QGIS for accurate mapping within their limitations, supporting global environmental monitoring and conservation efforts.

Pavan Kumar Polu, Amjad Al Najjar, Biswajit Banik et al.

This paper presents an analysis of the architectural design of two distributed open source systems (OSS) developed in Java: Distributed Modular Audio Recognition Framework (DMARF) and General Intensional Programming System (GIPSY). The research starts with a background study of these frameworks to determine their overall architectures. Afterwards, we identify the actors and stakeholders and draft a domain model for each framework. Next, we evaluated and proposed a fused DMARF over GIPSY Run-time Architecture (DoGRTA) as a domain concept. Later on, the team extracted and studied the actual class diagrams and determined classes of interest. Next, we identified design patterns that were present within the code of each framework. Finally, code smells in the source code were detected using popular tools and a selected number of those identified smells were refactored using established techniques and implemented in the final source code. Tests were written and ran prior and after the refactoring to check for any behavioral changes.

Ajay Kumar Thakur, Biswajit Banik, Pankaj Kumar Pant et al.

Metrics are used mainly to predict software engineering efforts such as maintenance effort, error Prone ness, and error rate. This document emphasis on experimental study based on two open source systems namely MARF and GIPSY. With the help of various research papers we were able to analyze and give priorities to various metrics that are implemented with JDeodrant. LOGISCOPE and McCabe tools are used to identify problematic classes with help of Kiviat graph and average Cyclomatic Complexity that further are implemented with highest priority metric with JDeodrant. To obtain accurate results we collected data using different tools. The analysis of the two systems is done as a conclusion of study using different tools.