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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Intelligent Accident Management System 3

Intelligent Accident Management System

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Jayashree, Jayashree Chandana, R. Hiremath, Prerana B Kumar, Pavana Pawar, Suresh Computer Science and Engineering Nitte Meenakshi Intitute of Technology Bangalore India

ISBN: (纸本)9798331528201

With the growing population and the consequent surge in vehicular traffic, there is an urgent need for heightened safety measures. Addressing this challenge requires the integration of advanced systems equipped with highly sensitive sensors to proactively mitigate the risk of accidents. Ensuring vehicle reliability is paramount, leading to the incorporation of microcontrollers within each vehicle. These microcontrollers enable seamless data storage in cloud-based platforms and utilize SIM technology for uninterrupted communication and data transmission. Beyond data aggregation, these systems play a critical role in swiftly guiding individuals to emergency services in the event of an accident. Additionally, they provide crucial information and assistance to both drivers and passengers, both before and after an accident, thereby enhancing overall safety measures. This comprehensive approach not only prioritizes accident prevention but also ensures rapid and effective response mechanisms, thereby reducing potential risks and elevating overall road safety standards. © 2024 IEEE.

关键词： Emergency services

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Deep Learning Approaches To Image-Based Species Identification 2

Deep Learning Approaches To Image-Based Species Identificati...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Nirmala, J.S. Tharun, Kamsala Sravya, Somu Geetha Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317558

This research investigates the efficacy of deep learning and transfer learning methodologies for image species recognition, crucial for biodiversity conservation. Using TensorFlow, Keras, and OpenCV libraries, diverse species images are processed and various convolutional neural network (CNN) models are trained. Data augmentation enhances model generalization, while optimization techniques mitigate overfitting. Comparison of transfer learning models identifies Xception as most effective. Fine-tuning Xception achieves high accuracy in species recognition, demonstrating the potential of deep learning in biodiversity research. Image-based species recognition is vital for biodiversity conservation and ecological studies. Deep learning and transfer learning offer promising avenues for automating this task, but their effectiveness needs thorough investigation. Utilizing TensorFlow, Keras, and OpenCV libraries, diverse species images are processed. Data augmentation enhances diversity, while EarlyStopping and learning rate reduction optimize training. Transfer learning models including ResNet50V2, ResNet152V2, InceptionV3, Xception, and DenseNet121 are compared to identify the most effective architecture. Xception emerges as the most promising model and undergoes fine-tuning on the dataset. The resultant model achieves high accuracy in species recognition, demonstrating the potential of deep learning and transfer learning in automating and enhancing species identification tasks. This study highlights the effectiveness of advanced neural network architectures and transfer learning in image-based species recognition. The findings underscore their potential in addressing ecological challenges and advancing biodiversity research and conservation efforts. © 2024 IEEE.

关键词： Data augmentation Deep learning DenseNet121 Early stopping ResNet152V2 InceptionV3 ResNet50V2 Xception

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Spatial Analysis and Morphological Clustering of Barchan Dunes using Advanced Computational Techniques 3

Spatial Analysis and Morphological Clustering of Barchan Dun...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shobha, P. Sujay, B.R. Bennur, Swarna Girish Nalini, N. Raghunandan, K.R. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Benagaluru India Nitte India

ISBN: (纸本)9798331527549

Barchan dunes, crescent-shaped aeolian landforms shaped by unidirectional wind regimes, are integral to understanding sediment transport and landscape evolution in arid regions. This study conducts a geomorphometric analysis and clustering of barchan dunes, focusing on their spatial distribution, morphological characteristics, and environmental controls. Leveraging high-resolution digital elevation models (DEMs) and advanced clustering algorithms such as K-Means, DBSCAN, and Gaussian Mixture Models, the research identifies patterns in dune morphology and examines their relationships with local topography and climatic drivers. The methodology integrates preprocessing steps, including feature extraction and scaling, with unsupervised clustering to classify dunes based on key attributes such as width, length, height, and geographic location. Morphometric analyses reveal spatial and structural variations, while clustering results highlight patterns linked to wind regimes and sediment availability. Among the clustering techniques, DBSCAN emerges as the most effective for identifying irregularly shaped clusters, achieving higher evaluation metrics such as Silhouette Scores and Davies-Bouldin Index values. This study investigates the morphology and spatial distribution of barchan dunes using advanced clustering techniques such as DBSCAN and K-Means. The primary issue addressed is the challenge of accurately classifying and analyzing irregularly shaped dune formations under varying environmental conditions. Utilizing high-resolution digital elevation models (DEMs), the research identifies distinct morphological patterns and their correlation with wind regimes and sediment availability. The study finds that DBSCAN outperforms other clustering algorithms in handling the irregular geometries of dunes, achieving superior evaluation metrics like Silhouette Scores and Davies-Bouldin Index. These findings provide valuable insights for environmental monitoring and desertification m

关键词： K-means clustering

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Culture Craze Couture: Personalized Picks for Indian Elegance 2

Culture Craze Couture: Personalized Picks for Indian Eleganc...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Vijaya Shetty, S. Teja Kumar, M. Vivek Gowda, V. Vishal, V. Rai, Tarkeswar Ramesh Naidu, P. Computer Science and Engineering Nitte Meenakshi Institue of Technology Bengaluru India

ISBN: (纸本)9798350373110

Fashion recommendation systems have gained popularity, yet there's a noticeable gap in representing Indian ethnic fashion. This paper introduces an innovative Fashion Recommendation System tailored specifically for Indian clothing to address this gap. Employing both image-based and text-based recommendation techniques, the system provides personalized suggestions to users. Notably, it includes a virtual try-on feature, allowing users to preview Indian ethnic clothing items on their bodies before purchasing. This feature relies on a sophisticated body segmentation algorithm, extracting users' body shapes from photos or videos and overlaying selected clothing items onto them. Additionally, the system comprises a dedicated wardrobe section, enabling users to revisit and tryon previously purchased Indian ethnic clothing. By combining advanced technologies with a focus on Indian fashion, this system enhances the online shopping experience, boosting user satisfaction and confidence in their fashion choices. © 2024 IEEE.

关键词： Convolutional neural networks

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Housing Market Intelligence: Data science for Rental Price Forecasting 3

Housing Market Intelligence: Data Science for Rental Price F...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Nirmala, J.S. Sravya, Somu Geetha Tharun, Kamsala Nitte Meenakshi Institute of Technology Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

The house rental price prediction project utilizes data science techniques to forecast rental prices based on various property attributes. The dataset encompasses factors such as the number of bedrooms (BHK), property size, location, and furnishing status. Through extensive data preprocessing, feature engineering, and exploratory data analysis, insights were gained into the influential factors affecting rental prices. The temporal trends in rental prices were also investigated, providing valuable information on market dynamics. In building the predictive model, a Random Forest Regressor was selected as the algorithm of choice[1]. This decision was grounded in the algorithm's ability to handle non-linear relationships and interactions among features, making it particularly suitable for the complexity of real estate pricing. The choice of Random Forest Regression over other algorithms is justified by its robustness in handling a diverse range of features and capturing intricate patterns within the dataset. Unlike simpler models that may struggle with the complexity of real estate data, the ensemble nature of Random Forest enables it to provide more accurate predictions. The abstract concludes with an acknowledgment of the model's potential real-world applications for property owners, real estate agents, and tenants, while highlighting areas for future improvements and research. © 2024 IEEE.

关键词： Costs

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Connectivity Enhancing-Automated Lip Interpretation Using Deep Learning 4

Connectivity Enhancing-Automated Lip Interpretation Using De...

引用

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Srujana, Sn Vijaya Shetty, S. Nitte Meenakshi Institute of Technology Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350393354

The research paper exploits the advances in deep learning technology to build the convolutional deep learning model to extract text content from videos. We aim to understand the speech in the video clips by analyzing the lip movements of the person. To predict the sentence, videos are segmented into frames, and specifically, the mouth region is isolated and passed on to the convolutional neural networks to capture the important features. The Adam optimizer is used, and the learning rate is regularized. Bidirectional LSTMs are employed as they work extremely well with sequential data like speech. The Connectionist Temporal Loss Function is utilized in the back-propagation of the weights while the model is learning the outputs. Finally, the SoftMax activation is used to predict the sentence. The accuracy is 95% for the prediction during training and 92% for the test data, as the audios are synchronized with videos. The model provides higher robustness even in cases of variability in speech. © 2024 IEEE.

关键词： Convolutional neural networks

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Development and Implementation of Advanced Text Autocorrection via Probabilistic Methods Using Levenshtein Distance and Jaccard Similarity 5

Development and Implementation of Advanced Text Autocorrecti...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Saunshimath, Nirmala J Upadhyaya, Dipankar Raj Yadav, Abiskar Kumar Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331522100

The paper aims at creating an advanced autocorrection system specifically for the Nepali language. This system uses sophisticated natural language processing (NLP) techniques to automatically rectify typographical errors of Nepali texts. The autocorrection system suggests the most likely corrections for misspelt words using the Levenshtein Distance and Jaccard Similarity algorithms. This paper aims to improve the efficiency and user experience across a variety of text-based applications, such as social media, document editing, and messaging, by decreasing the amount of time needed for text editing and increasing text accuracy. The groundwork for future developments in NLP applications for the Nepali language is laid by this effort. © 2025 IEEE.

关键词： Natural language processing systems

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Unleashing the potential of Natural Language Processing for News Link Article Summarization: Comparing TF-IDF and Text Rank Algorithms

Unleashing the potential of Natural Language Processing for ...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sandhya, B.R. Chaudhary, Shusank Pandit, Basant Seth, Khem Raj Jha, Aditya Nitte Meenakshi Institute of Technology Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350375442

Summarizing news links articles has become a crucial technique in the digital age because so much information is produced daily. It can be difficult for readers to stay updated with current events due to the abundance of news sources and the rapid pace of news production. By offering succinct and pertinent summaries, summarization helps control this information overload by facilitating the reader's access to information without requiring a large time commitment. A comparative study of two algorithms TF-IDF and Text Rank has been done in this work. These algorithms are implemented to generate the summary in a concise and precise manner and are evaluated using Rouge Scores such as Rouge 1, Rouge 2, and Rouge L with recall, precision, and f-score. The Experimental results show that compared with TFIDF, the average recall scores of the Text Rank algorithm are improved by 0.6% of Rouge 1, 0.15% of Rouge 2, and 1.9% of Rouge L respectively. © 2024 IEEE.

关键词： Inverse problems

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Model Identification for Malware Detection in a File Using Machine Learning 3

Optimized Model Identification for Malware Detection in a Fi...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Laya, N. Shruthi Shetty, J. Sarojadevi, H. Sowmya, P. Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Malware remains one of the most critical digital threats today due to the rapidly evolving landscape of online activities. Detecting malware has become a pivotal aspect of safeguarding computer system, especially as polymeric malware arises by utilizing dynamic modifications to confound traditional signature-based detection methods. To combat the surging volume of malware, Machine Learning (ML) has emerged as a key strategy. Hence, ML-based detection mechanisms are imperative. This overview underscores the diverse array of machine learning-driven detection techniques. The primary objective is to construct an optimal machine learning representation for malware detection within files. Three distinct machine learning algorithms are employed: random forest, logistic regression, and a basic neural network. From these, the superior model is determined based on rigorous evaluation metrics-accuracy, f1 score, and both normalized and non-normalized confusion matrices on both training and testing data. The selection process hinges on achieving the highest accuracy and f1 score. The random forest algorithm was depicted to be having highest train accuracy with 98.28% and test accuracy with 98.38% and f1 score was found to be 97.3% which was chosen as a final model for malware detection in a file. The chosen model subsequently serves as the foundation for malware detection. © 2024 IEEE.

关键词： Logistic regression

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