Papers by ramesh kumar ayyasamy
PloS one, Apr 5, 2024
Semantic segmentation of cityscapes via deep learning is an essential and game-changing research ... more Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.
CRC Press eBooks, Aug 4, 2023
IEEE access, 2024
There is a substantial worldwide effect, both in terms of disease and death, that is caused by pe... more There is a substantial worldwide effect, both in terms of disease and death, that is caused by pediatric pneumonia, which is a disorder that affects children under the age of five. Even while Streptococcus pneumoniae is the most prevalent agent responsible for this sickness, it may also be brought on by other bacteria, viruses, or fungi. An efficient approach utilizing deep-learning methods to forecast pediatric pneumonia reliably using chest X-ray images has been developed. The current study presents an updated version of the DenseNet-121 deep-learning model developed for identifying scans of pediatric pneumonia. The batch normalization, maximum pooling, and dropout layers introduced into the standard model were done so to improve its accuracy. The activations of the preceding layers are scaled and normalized using batch normalization, leading to a mean value of zero and a variance of one. This helps to decrease internal variability during training, which in turn speeds up the training process, promotes model stability, and improves the model's overall capacity to generalize. Max pooling is a beneficial technique for cutting down on the number of model parameters, making the model more computationally effective. Meanwhile, dropout is a preventative measure against overfitting by decreasing the co-dependence of neurons. As a result, the network acquires more durable and adaptive features. Classifying instances of pediatric pneumonia with the help of the proposed model resulted in an exceptional accuracy rate of 97.03%.
Education and Information Technologies, May 2, 2023
CRC Press eBooks, Aug 4, 2023
IEEE Access, 2022
Numerous earlier studies focused on the term weighting scheme to increase examination question cl... more Numerous earlier studies focused on the term weighting scheme to increase examination question classification accuracy based on Bloom's Taxonomy (BT). While determining the cognitive level of the examination question, all the terms present in the question are not equally significant. Verbs are the most important parts of speech while assigning weights to the terms. However, two types of verbs may be present in the questions: BT and supporting. BT verbs have a higher impact on determining the cognitive level of a question than supporting verbs. Nevertheless, the proposed schemes of past studies assigned equal weight to both types of verbs. Therefore, this study aims to introduce the term weighting scheme ETFPOS-IDF, which assigns BT a higher weight than supporting verbs. The BT verbs were identified based on their position in the questions. Three datasets and three classifiers: Support Vector Machine, Artificial Neural Network, and Random Forest, were used in this study. Two evaluation metrics: accuracy and F1 score, were used to evaluate the performance of the proposed model. The experiment results showed that the proposed ETFPOS-IDF outperformed all the schemes introduced by earlier studies in examination question classification and achieved 0.749 in accuracy and 0.746 in F1 score. The finding of this study demonstrates that distinguishing between different verb types is significant in reducing the misclassification of examination questions. This research contributed by introducing a novel term weighting scheme in classifying examination questions based on BT. Future work may involve identifying the optimal weight for both types of verbs, evaluating the proposed scheme with a larger dataset, and comparing the performance with deep learning.
Computer systems science and engineering, 2023
IEEE Access, 2022
Social media users use words and phrases to convey their views or opinions. However, some people ... more Social media users use words and phrases to convey their views or opinions. However, some people use idioms or proverbs that are implicit and indirect to make a stronger impression on the audience or perhaps catch their attention by utilizing funny, sarcastic, or metaphorical phrases. Idioms and proverbs are figurative expressions with a thematically coherent totality that cannot be understood literally. In previous work, the extension of IBM's Sentiment Lexicon of Idiomatic Expressions was proposed to include around 9,000 idioms; a crowdsourcing service manually annotates both lexicons. Therefore, in this research, we provide a knowledge-based expansion approach to avoid human annotation of idioms. For sentiment classification, the proposed method has the advantage that it does not require any fine-tuning for the BERT model. Experimental comparisons show that automated idiom enrichment and annotation are very beneficial for the performance of the sentiment classifier. The expanded annotated lexicon will be made available to the general public.
Work
BACKGROUND: Fear of losing psychological resources can lead to stress, impacting psychological he... more BACKGROUND: Fear of losing psychological resources can lead to stress, impacting psychological health and behavioral outcomes like burnout, absenteeism, service sabotage, and turnover. OBJECTIVE: The study examined the impact of job stressors (time pressure, role ambiguity, role conflict) on employee well-being and turnover intentions. The study also investigated the mediating role of employee well-being between job stressors and turnover intention based on the conservation of resources (COR) theory. METHODS: Data from 396 IT executives in Malaysian IT firms were analyzed using the Partial Least Squares - Structural Equation Modeling (PLS-SEM) technique. RESULTS: Results confirmed a significant negative correlation between time pressure (–0.296), role ambiguity (–0.423), role conflict (–0.104), and employee well-being. Similarly, employee well-being showed a significant negative relationship with turnover intentions (–0.410). The mediation analysis revealed that employee well-being ...
Business Ethics and Leadership
Small and medium-sized enterprises (SMEs) are considered to be the world’s largest. They play a v... more Small and medium-sized enterprises (SMEs) are considered to be the world’s largest. They play a vital role as they create jobs and improve the living conditions of their local communities as they contribute to the country’s GDP growth. Due to their contributions to the nation’s economy, they have been given much attention in entrepreneurship. This paper aims to study the impact of entrepreneurial education and entrepreneurial competencies on small and medium enterprises’ performance. Entrepreneurship is a vital component of any successful business strategy. It can be used to overcome the uncertainties of today’s business environment. The roles of these two independent factors in the business environment are also more relevant to small businesses. The goal of this paper is to develop a framework that explores the performance of firms from the perspective of their entrepreneurial education and entrepreneurial competencies. The proposed framework aims to provide a comprehensive view of...
Education and Information Technologies
2023 International Conference on Computer Communication and Informatics (ICCCI)
2023 International Conference on Computer Communication and Informatics (ICCCI)
2023 International Conference on Computer Communication and Informatics (ICCCI)
2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS)
One of the benefits of recognizing a slang, an idiom or an abbreviation in a tweet is the ability... more One of the benefits of recognizing a slang, an idiom or an abbreviation in a tweet is the ability to help in finding certain sentiment in a concise and understandable manner. However, a lack of adequate annotated "idiomatic tweets" makes classification challenging. We propose a pliable augmentation technique to improve the classification of idiomatic tweets with tiny training samples. For classification, we evaluate the performance of fine-tuning version of the pre-trained embedding model at different flavors. During the augmentation process, we deduce the intrinsic propositional meaning of the idiomatic expression from IBM's SliDE (Sentiment Lexicon of IDiomatic Expressions) and another lexicon we built. The empirical results show that the proposed method is beneficial in concealing the actual intent of the tweet and advantageous to tackle the problem of overfitting caused by smaller training sets.
2023 International Conference on Computer Communication and Informatics (ICCCI)
IEEE Access
Social media users use words and phrases to convey their views or opinions. However, some people ... more Social media users use words and phrases to convey their views or opinions. However, some people use idioms or proverbs that are implicit and indirect to make a stronger impression on the audience or perhaps catch their attention by utilizing funny, sarcastic, or metaphorical phrases. Idioms and proverbs are figurative expressions with a thematically coherent totality that cannot be understood literally. In previous work, the extension of IBM's Sentiment Lexicon of Idiomatic Expressions was proposed to include around 9,000 idioms; a crowdsourcing service manually annotates both lexicons. Therefore, in this research, we provide a knowledge-based expansion approach to avoid human annotation of idioms. For sentiment classification, the proposed method has the advantage that it does not require any fine-tuning for the BERT model. Experimental comparisons show that automated idiom enrichment and annotation are very beneficial for the performance of the sentiment classifier. The expanded annotated lexicon will be made available to the general public.
2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
Uploads
Papers by ramesh kumar ayyasamy