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ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis
Authors:
Maisha Haque,
Israt Jahan Ayshi,
Sadaf M. Anis,
Nahian Tasnim,
Mithila Moontaha,
Md. Sabbir Ahmed,
Muhammad Iqbal Hossain,
Mohammad Zavid Parvez,
Subrata Chakraborty,
Biswajeet Pradhan,
Biswajit Banik
Abstract:
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 comp…
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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.
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Submitted 28 December, 2025;
originally announced December 2025.
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A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Authors:
Shahana Yasmin Chowdhury,
Bithi Banik,
Md Tamjidul Hoque,
Shreya Banerjee
Abstract:
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achie…
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Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for CREMA-D, and a perfect 100% on both TESS and EMO-DB. For the combined (R+T+S) datasets, it achieves 98.82% accuracy, outperforming previously reported results. To our knowledge, no existing study has evaluated a single SER model across all five benchmark datasets (i.e., R+T+S+C+E) simultaneously. In our work, we introduce this comprehensive combination and achieve a remarkable overall accuracy of 93.76%. These results confirm the robustness and generalizability of our DCRF-BiLSTM framework across diverse datasets.
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Submitted 14 January, 2026; v1 submitted 9 July, 2025;
originally announced July 2025.
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Towards Refactoring of DMARF and GIPSY Case Studies -- A Team 5 SOEN6471-S14 Project Report
Authors:
Pavan Kumar Polu,
Amjad Al Najjar,
Biswajit Banik,
Ajay Sujit Kumar,
Gustavo Pereira,
Prince Japhlet,
Bhanu Prakash R.,
Sabari Krishna Raparla
Abstract:
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…
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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.
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Submitted 23 December, 2014;
originally announced December 2014.
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Case Study Of GIPSY and MARF
Authors:
Ajay Kumar Thakur,
Biswajit Banik,
Pankaj Kumar Pant,
Dhanashree Sankini,
Dipesh Walia,
Renuka Milkoori
Abstract:
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 u…
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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.
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Submitted 12 July, 2014;
originally announced July 2014.