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检索条件"机构=Department of Computer Science and Engineering School of Computing"

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Comparative Analysis of Neural Network Models for Spam E-mail Detection 4

Comparative Analysis of Neural Network Models for Spam E-mai...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Mohan, G Bharathi Prasanna Kumar, R. Sarrvesh, N. Poorani Ayswariya, P.S. Yagnitha, P. Pranav Raj, S. Boopalamani, J. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

With increasing number of internet users and the fast growth of digital communication have made email one of the most important ways of communication. Along with the growing number of users, spam emails also start rising. It has become one of the common problems on the internet. Spam emails have a huge impact on users as spammers' deceptive emails can trick people into sharing personal information or unknowingly downloading harmful software by pretending to be genuine messages. The consequences of falling for scams can vary from losses to the exposure of personal data. To address this issue, numerous types of machine learning and deep learning models have been proposed in various research papers. This paper presents a comparative study of Neural network models such as [deep learning models] Capsule Networks (CAPSNET), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and [Transformer model] RoBERTa to determine the best model. The capacity of each model was evaluated by assessing various performance metrics, including F1 score, recall, precision, accuracy, and support. © 2024 IEEE.

关键词： CAPSNET CNN Neural Network RNN RoBERTa Spam

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Preserving Data Integrity in Image Encryption: Comparing Deep Learning Models and integrating Gaussian Noise and Chaotic Baker Map 15

Preserving Data Integrity in Image Encryption: Comparing Dee...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ramesh, Sreyas Sukanth, B.N. Jaswanth, Sathyavarapu Sri Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the world of growing technology secure transformation and storage of visual data have become very important. Cryptographic techniques continue to play a vital role in various ways ensuring the confidentiality and integrity of sensitive and crucial information. This study talks about an image cryptosystem by using chaotic baker map and Gaussian noise. The chaotic baker map exhibits unpredictable characteristics making it very suitable for encryption processes. Instead of relying solely on chaotic maps the addition of Gaussian noise can impact the imagequality by adding unwanted artifacts or noise which simulates real-world flaws during image transmission. The main objective of the DL models is to reconstruct the original images from the noisy images. DL models can learn patterns from the noisy data as well as from the clean data based on the train and test dataset thereby improving the quality of reconstruction. This process ensures that only authorized users can recover the image accurately. This project has the potential to maintain the confidentiality and integrity of visual information. © 2024 IEEE.

关键词： Chaotic Baker Map Confidentiality Cryptography Data Security Decryption Deep Learning Encryption Gaussian Noise Image Cryptosystem Key Generation Visual Data

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Deep Learning Based Fetal Brain Ventricle Segmentation and Size Estimation 15

Deep Learning Based Fetal Brain Ventricle Segmentation and S...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Prakhalbh Santhosh, M. Surendran, Simi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Medical diagnostics with the segmentation and analysis of fetal brain ventricles assumes paramount importance in the prediction and management of neurological disorders. This paper focus on the segmentation and analysis of fetal brain ventricles using four deep-learning models: U-Net, VNet, ResNet, and DeepMedic, on the Feta 2022 dataset of T2weighted MRI scans. Saliency maps were used to visualize key areas in the decision-making processes for these models, which helped clinicians verify the AI output to ensure that it focused on the relevant anatomical features. The results showed very distinct strengths and trade-offs among the models, where ResNet and V-Net generally provided the best segmentation accuracy. This work not only enhances the fetal brain imaging analysis through the integration of and underlines their potential to improve diagnostic workflows for the early detection of fetal brain abnormalities. © 2024 IEEE.

关键词： Deep Learning Medical Image Analysis Ventricle Segmentation

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Author Identity Unveiled: Gender and Age Prediction from Textual Patterns using BERT 4

Author Identity Unveiled: Gender and Age Prediction from Tex...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sulochana, Balla Charishma Pragada, Bhavya Sri Kiran, Boya Chaitanya Reddy, Gaddam Anvith Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

The research delves into author profiling, aiming to identify writers' age groups and genders using extensive textual data. This involves utilizing BERT embeddings to understand sentence structure, word selection, and the overall tone of the writing. The primary goal is to employ supervised machine learning models to categorize authors based on age and gender, aiding marketers in tailoring campaigns for diverse demographics. As for text vectorization methods, BERT embeddings stand out for their ability to capture contextual information efficiently. The objective of this research is to develop a robust solution for author profiling. Additionally, techniques like TF-IDF is also considered effective for text representation, each offering unique advantages depending on the specific task and dataset characteristics. The outcome of this study strongly indicates that the AdaBoost with TF-IDF stands out as the most precise machine learning models for age group prediction with an F1 score of 0.58. The K-neighbours with TF-IDF is the best model for gender prediction with an F1 score of 0.58. © 2024 IEEE.

关键词： Embeddings

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A Comparative Analysis of ML & DL Models for Fetal Health Classification Using Cardiotocographic Data

A Comparative Analysis of ML & DL Models for Fetal Health Cl...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Kaushik, Tata Sri Vani, Tunuguntla Aasritha Vithyatharshana, N. Khare, Sangita Amrita Vishwa Vidyapeetam Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798350361537

Traditional diagnostic methods in fetal health are frequently hindered by class imbalance and complex data, which puts early intervention and optimal perinatal outcomes at risk. This study fills this important gap by conducting a thorough comparative analysis of several ML and DL models that have been effective in fetal health research. This project goes beyond the obvious applications and investigates how model performance is affected by Principal Component Analysis (PCA) and Chi-Square for feature extraction and feature selection respectively and Conditional Generative Adversarial Networks (CGANs) for data augmentation. This project seeks to determine the best model or ensemble for dependable and accurate fetal health prediction by methodically assessing model performance across all its forms, CGAN-augmented versions, integrated CGAN - PCA and CGAN - Chi-Square configurations. This will provide medical professionals with a data-driven tool for early diagnosis and well-informed clinical decisions. This thorough study has the potential to greatly enhance perinatal outcomes and improve the health of moms and their unborn children. It is positioned to transform prenatal care. In addition to identifying the best model, this comparative analysis lays the foundation for future research examining the integration of explainable AI techniques to gain deeper insights into fetal health dynamics and to understand the internal decision-making processes of the model. © 2024 IEEE.

关键词： Generative adversarial networks

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Automated Classification and Size Estimation of Fetal Ventriculomegaly from MRI Images: A Comparative Study of Deep Learning Segmentation Approaches 5

Automated Classification and Size Estimation of Fetal Ventri...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Gopikrishna, K. Niranjan, N.R. Maurya, Siddharth Uday Krishnan, V.G. Surendran, Simi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Fetal ventriculomegaly is one of the major risks in prenatal diagnosis, which is an enlargement of the ventricles of the developing fetus's brain. Timely prediction of these brain disorders helps patients and healthcare providers make informed decisions and treatment plans. This paper investigates the classification of ventriculomegaly from MRI images using a deep learning approach. Segmentation of ventricles is performed using DeeplabV3+ and U-Net architectures and the accuracy, dice coefficient, and loss of these two approaches are compared. Additionally, a novel approach for size estimation was developed, utilizing contour detection to delineate ventricle boundaries and compute their areas by enclosing them within rectangles. A threshold-based classification method was then applied to differentiate between abnormal ventriculomegaly and normal cases. This research contributes to the assessment and comparison of the segmentation algorithms for aiding in the identification and classification of ventriculomegaly cases, offering potential implications for clinical diagnosis and treatment planning. © 2024 The Authors. Published by Elsevier B.V.

关键词： Clinical research

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Can Technical Festivals Help in Attaining Undergraduate engineering Programme Outcomes? 13

Can Technical Festivals Help in Attaining Undergraduate Engi...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dhanalakshmy, Dhanya M Murali, Ritwik Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376234

The curriculum framework of an undergraduate engineering programme contains clearly defined learning outcomes. It is expected that students who graduate from a specific degree / diploma are able to attain these goals. Towards this, while not mandatory, every institution is encouraged to also host technical events such as technical festivals, workshops, technical competitions, hackathons, etc. This work explores the challenges and merits in leveraging undergraduate technical festivals to enable an evaluation of students towards the attainment of qualitative Programme Outcome. Using qualitative analysis strategies including surveys and one-on-one interviews, this work shows that technical festivals provide unparalleled hands-on experience in medium to large-scale project management, encompassing crucial disciplines that are often overlooked in the classroom evaluation rubrics. The major takeaways from organizing a technical festival can be summarized as large-scale organizing of events, better time and people management, improved communication skills, and the positive attitude / ability to learn new things. This work also mapped the specific programme outcomes to the actual learning that could be attained though the organization of a technical festival. This work also highlights the importance of such events in order to bridge the gap between industry & academia while being able to assess qualitative outcomes effectively. © 2024 IEEE.

关键词： Project management

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Enhancing Credit Card Fraud Detection: A Comparative Analysis of Anomaly Detection Models

Enhancing Credit Card Fraud Detection: A Comparative Analysi...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Sherwin Akshay, J.G. Vinusha, T. Sharon Bianca, R. Sarath Krishna, C.K. Radhika, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376876

Financial fraud poses a major threat to financial service institutions and clients, necessitating advanced anomaly detection capabilities. This paper delves into related deep learning models which can be be used to differentiate and detect fraudulent transactions from normal transactions and flag anomalies. Challenges include evolving attack strategies, class imbalance, and model interpret-ability. The proposed methods utilize of a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like autoencoders and recurrent neural networks. The outcomes exhibit reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security. © 2024 IEEE.

关键词： Contrastive Learning

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A Detailed Analysis of Machine Learning Models to Predict Water Potability 15

A Detailed Analysis of Machine Learning Models to Predict Wa...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreekumar, Vishnu Ihsan, Faraz Reghuram, Surya Sarath, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Water is one of the most indispensable things in our life. However, getting clean drinking water has become more difficult for a lot of people lately. This research paper uses machine learning techniques to tackle the crucial problem of predicting the potability of water. Reliability in water potability assessment is critical, as concerns about water quality and its effects on public health are becoming more widespread worldwide. We present a novel predictive model to assess the potability of water sources by utilizing an extensive dataset that includes various water quality parameters. In order to examine intricate relationships within the dataset, the study uses a cutting-edge machine learning framework that integrates algorithms like Random Forest, KNN, SVM, Decision tree, and Gradient boosting. To extract pertinent information, feature engineering is used, and model performance is rigorously tuned using hyper parameters. To determine the best method for predicting water potability, a comparative analysis of several models is carried out. The proposed model is validated on different datasets, which guarantees its robustness and generalizability in a range of environmental conditions. The outcomes show that the predictions of water potability have a high degree of sensitivity, specificity, and accuracy. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Computation Offloading In Wireless-Powered Mobile-Edge computing Networks With Deep Reinforcement Learning For Online Optimization 4

Enhancing Computation Offloading In Wireless-Powered Mobile-...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, R. Prasanna Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sai, Gudivada Dhyana Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Edge computing at the mobile frontier, enhanced by the integration of wireless energy, represents a cutting-edge strategy to boost processing performance in networks with limited energy resources, such as wireless sensor networks and the Internet of Things (IoT). This study investigates a mobile edge computing (MEC) framework powered by wireless energy, employing a dual-mode offloading scheme. In this paradigm, tasks from wireless devices (WD) may either be processed on-device or entirely shifted to an MEC server. In this approach, tasks from a wireless device (WD) are either processed locally or completely transferred to an MEC server. The objective is to create an online algorithm that can adjust task offloading and wireless resource allocation adaptively according to the variable conditions of the wireless channel. Conventional numerical optimization methods are inadequate due to the swift variations within the channel's coherence time. Our aim is to develop an algorithm that operates online and can dynamically adjust both offloading and resource distribution in response to the fluctuating state of the wireless channel. Traditional numerical optimization approaches fall short because they cannot swiftly adapt to the rapid changes in the channel's coherence. The solution we propose is a framework based on Deep Reinforcement Learning for Online Offloading that utilizes deep neural networks to incrementally learn from offloading decisions, thereby circumventing the need for complex combinatorial optimization. This leads to a significant reduction in the computational load, particularly in expansive networks. We've further enhanced this system with a method that enables real-time modification of the DROO algorithm's parameters. Our experiments demonstrate that this novel algorithm nearly achieves optimal efficiency and significantly reduces computation times - by more than ten times relative to existing techniques. For example, in a network with 30 users, DROO achiev

关键词： access points component Mobile-edge computing radio-frequency reinforcement learning resource allocation Wireless Devices WPT

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