Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
8 results
Search Results
Item Categorizing Relations via Semi-supervised Learning Using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach(Springer Science and Business Media Deutschland GmbH, 2022) Agrawal, S.; Ahmed, R.; Anand Kumar, M.; Ramanna, S.In the last few decades, we have seen a tremendous increase in the amount of data available on the web. There have been significant advances in constructing knowledge bases consisting of relations from the text data. These relations are words in the text often represented as pairs (Noun, Context), for example (Disease, Symptom), which can be classified into some predefined category to give us some useful information. Categorization of relations using tolerance-rough set based semi-supervised learning algorithm (TPL) have been successfully demonstrated in several works. However, an unexplored problem is the automatic selection of hyper parameters of the TPL algorithm. This paper proposes a genetic algorithm-based approach (TPL-GA) for optimizing the hyper-parameters that are fundamental to the TPL algorithm. The proposed approach was tested on two standard datasets drawn from different domains representing two different languages: English and Hindi text. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item An Effective Diabetic Retinopathy Detection Using Hybrid Convolutional Neural Network Models(Springer Science and Business Media Deutschland GmbH, 2023) Kumar, N.; Ahmed, R.; Venkatesh, B.H.; Anand Kumar, M.Loss of vision in the present era of the developing world is mainly caused by diabetic retinopathy. More than 103 million people are believed to be affected. It is estimated that around 40 million beings have diabetes in the United States, and according to the World Health Organization (WHO), 347 million people are living with the disease globally. Diabetic retinopathy (DR) is a long-term diabetes-related eye condition. Roughly, 45–50% of the American citizens suffering from diabetes undergo some unique stages that can be categorized. When DR is diagnosed on a timely basis, the possibility of it extending to the course of vision impairment can be delayed and stopped, though this is not entirely true and a very daunting task because it seldom reveals any symptom before it escalates to a stage of no return to effectively treat it. The paper uses convolutional neural network models to achieve an effective classification for diabetic detection of retinal fundus images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item An Image Transmission Technique using Low-Cost Li-Fi Testbed(Institute of Electrical and Electronics Engineers Inc., 2021) Salvi, S.; Geetha, V.; Maru, H.; Kumar, N.; Ahmed, R.Visible Light Communication (VLC) or Light Fidelity (Li-Fi) with Light Emitting Diodes (LEDs) as transmitter and light sensor as receiver will turn the present lightening system into a communication system. Li-Fi based data communication provides secure communication within the luminous coverage of the light source. Thus, it has several applications in places where Radio Frequency interference is not desirable. Similar to other wireless communication techniques even Li-Fi is used for transmission and reception of digital data. Li-Fi system can also be used to transfer images from one device to another. In this paper, a preliminary study is discussed by proposing and implementing an encoding and decoding scheme for transmission of the binary image using Li-Fi. The proposed system is evaluated based on the light intensity, distance, accuracy, size of the image, image resolution, and transmission time. © 2021 IEEE.Item Sketch-Based Image Retrieval Using Convolutional Neural Networks Based on Feature Adaptation and Relevance Feedback(Springer Science and Business Media Deutschland GmbH, 2022) Kumar, N.; Ahmed, R.; B Honnakasturi, V.; Kamath S․, S.; Mayya, V.Sketch-based Image Retrieval (SBIR) is an approach where natural images are retrieved according to the given input sketch query. SBIR has many applications, for example, searching for a product given the sketch pattern in digital catalogs, searching for missing people given their prominent features from a digital people photo repository etc. The main challenge involved in implementing such a system is the absence of semantic information in the sketch query. In this work, we propose a combination of image prepossessing and deep learning-based methods to tackle this issue. A binary image highlighting the edges in the natural image is obtained using Canny-Edge detection algorithm. The deep features were extracted by an ImageNet based CNN model. Cosine similarity and Euclidean distance measures are adopted to generate the rank list of candidate natural images. Relevance feedback using Rocchio’s method is used to adapt the query of sketch images and feature weights according to relevant images and non-relevant images. During the experimental evaluation, the proposed approach achieved a Mean average precision (MAP) of 71.84%, promising performance in retrieving relevant images for the input query sketch images. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Deep Learning Framework Based on Audio–Visual Features for Video Summarization(Springer Science and Business Media Deutschland GmbH, 2022) Rhevanth, M.; Ahmed, R.; Shah, V.; Mohan, B.R.The techniques of video summarization (VS) has garnered immense interests in current generation leading to enormous applications in different computer vision domains, such as video extraction, image captioning, indexing, and browsing. By the addition of high-quality features and clusters to pick representative visual elements, conventional VS studies often aim at the success of the VS algorithms. Many of the existing VS mechanisms only take into consideration the visual aspect of the video input, thereby ignoring the influence of audio features in the generated summary. To cope with such issues, we propose an efficient video summarization technique that processes both visual and audio content while extracting key frames from the raw video input. Structural similarity index is used to check similarity between the frames, while mel-frequency cepstral coefficient (MFCC) helps in extracting features from the corresponding audio signals. By combining the previous two features, the redundant frames of the video are removed. The resultant key frames are refined using a deep convolution neural network (CNN) model to retrieve a list of candidate key frames which finally constitute the summarization of the data. The proposed system is experimented on video datasets from YouTube that contain events within them which helps in better understanding the video summary. Experimental observations indicate that with the inclusion of audio features and an efficient refinement technique, followed by an optimization function, provides better summary results as compared to standard VS techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Continuous Sign Language Recognition Using Leap Motion Sensor(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, N.; Ahmed, R.; Venkatesh, B.H.; Salvi, S.; Panjwani, Y.A vital communication tool that connects persons with hearing and speech impairments worldwide is sign language. Sign language involves mostly hand movements plus face gestures, which are interpreted by recognizing these gestures to form meaningful sentences. In this study, we use two machine learning models: Long Short-Term Memory (LSTM) and Support Vector Machines (SVM), to predict signs. A dataset of 42 unique sign words and 28 sentences was used to train and evaluate our models. Our method uses depth sensors, like the Leap Motion gadget, to improve sign language recognition (SLR).Worldwide, sign language is an essential communication tool for people with speech and hearing impairments. Sign languages are primarily made up of hand gestures and facial expressions, and their meaning is communicated through precise gesture interpretation. Our models were trained on a dataset containing 42 distinct sign words and 28 sentences, achieving an accuracy of 90.35% for word prediction and 98.21 for sentence prediction. The LSTM model outperformed the SVM model, which had accuracies of 85.96% and 89.58% for words and sentences, respectively. By using depth sensors like the Leap Motion device, our approach aims to enhance sign language recognition (SLR). © 2024 IEEE.Item Forest Vulnerability to Climate Change: A Review for Future Research Framework(MDPI, 2022) Roshani; Sajjad, H.; Kumar, P.; Masroor, M.; Rahaman, M.H.; Rehman, S.; Ahmed, R.; Sahana, M.Climate change has caused vulnerability not only to the forest ecosystem but also to forest-dependent communities. Therefore, its management is essential to increase forest ecosystem services and reduce vulnerability to climate change using an integrated approach. Although many scientific studies examined climate change impact on forest ecosystems, forest vulnerability assessment, including forest sensitivity, adaptability, sustainability and effective management was found to be scant in the existing literature. Through a systematic review from 1990 to 2019, this paper examined forest vulnerability to climate change and its management practices. In this paper, descriptive, mechanism and thematic analyses were carried out to analyze the state of existing research, in order to understand the concept of vulnerability arising from climate change and forest management issues. The present study proposed a framework for integrated forest assessment and management for addressing such issues in future research. The conversion of forest land into other land uses, forest fragmentation, forest disturbance and the effects of climate change on the forest ecosystem are the existing problems. Forest vulnerability, effective adaptation to forest ecosystems and long-term sustainability are priority areas for future research. This study also calls for undertaking researchers at a local scale to involve communities for the effective management of forest ecosystems. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Item Assessing forest health using remote sensing-based indicators and fuzzy analytic hierarchy process in Valmiki Tiger Reserve, India(Institute for Ionics, 2023) Roshani; Sajjad, H.; Rahaman, M.H.; Rehman, S.; Masroor, M.; Ahmed, R.Anthropogenic activities, climate variability and environmental stresses have greatly affected forest ecosystems globally. Thus, monitoring of forest health is essential for proper planning and effective management. The present study employed an integrated approach of remote sensing and fuzzy analytic hierarchy process to assess the forest health in the Valmiki Tiger Reserve in India. Advanced vegetation index, normalized difference vegetation index, normalized difference moisture index, forest fragmentation, rainfall and soil types were derived from remote sensing data. Multiple buffer zones of villages, roads, railways and canals were also determined for analyzing the forest health status. These layers were prepared in the geographical information system. These layers were given weightage using fuzzy analytic hierarchy process. These layers were integrated to prepare forest health map using weighted overlay method. The results revealed that the largest forest area was found under moderately healthy forest (37%) followed by healthy forest (31%) and unhealthy forest (13%). Of the total area of the Reserve, 19% area was under non-forest category. Human-induced disturbances such as encroachment, illegal sand mining, livestock grazing and forest conversion to agriculture have been attributed to the unhealthy forest in the Reserve. The receiver operating characteristic curve value and area under curve (0.792) show reliability of forest health map. The findings of this study may be helpful for forest managers, conservationists and local communities in devising sustainable strategies for effective management of the forest. The methodological framework adopted in this study may be utilized in other geographical regions interested in assessing forest health. © 2022, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.