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A Novel Approach for Phishing Website Detection using Deep Learning Models 8

A Novel Approach for Phishing Website Detection using Deep L...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Palaniswamy, Suja Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331505462

Phishing website detection is a major task in cybersecurity, aimed at saving users from sites that steal personal information. The traditional approaches can hardly minimize false positives and cope with the dynamic strategies of phishing. In this work, a novel approach has been proposed to detect phishing websites using deep learning models, such as CNN, RNN, RNN with attention layers, CRNN, LSTM, and GRU, to enhance accuracy in detection. This work has addressed one vital domain challenge, which includes very rapid changes of techniques and phishing activities, and the real-time detection feature that conventional models generally may not have. The proposed approach will leverage these advanced models for automatic feature extraction and adaptability to new strategies for significant improvement of detection accuracy and reduction of false positives. The proposed method gives a robust and real-time solution for the task of phishing website detection and improving cybersecurity in general, after its evaluation on a comprehensive dataset. © 2024 IEEE.

关键词： Phishing

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Exploring the Techniques for Option Pricing: An Empirical Study and Performance Assessment

Exploring the Techniques for Option Pricing: An Empirical St...

引用

2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Anand, R.N. Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378092

The accurate pricing of financial options is crucial for effective risk management and decision-making in financial markets. Traditional option pricing models, such as the Black-Scholes model, rely on simplifying assumptions that may not fully capture the complexities of real-world market dynamics. This study aims at exploring machine learning (ML) and deep learning (DL) models in different scenarios and evaluating their performances against the conventional methods. The methodology harnesses the power of neural networks within deep learning models to discern and learn intricate patterns and relationships inherent in historical market data. By training on a diverse dataset comprising stock prices, strike prices, time to expiration, and volatility, DL models aim to capture the nuanced interactions among these variables, thus enabling more accurate and robust option pricing compared to traditional methods. Moreover, the analysis is extended to incorporate ML models, which complement DL techniques by leveraging a variety of algorithms to discern patterns and make predictions based on historical data. Through the evaluation and comparison of both DL and ML models, we aim to ascertain the efficacy and suitability of each approach for option pricing tasks. Among the models evaluated, the Multi-Layer Perceptron (MLP) demonstrated superior performance in option pricing tasks, achieving high accuracy and robustness across synthetic and real-world datasets. Additionally, ensemble learning techniques such as XGBoost and Gradient Boosting Tree Regression (GBTR) exhibited strong predictive capabilities, offering valuable insights into option pricing dynamics with R2 values of 0.9694 and 0.9474 in S&P 500 dataset respectively. Overall, the comparative analysis highlights the efficacy of machine learning and deep learning models in option pricing, with MLP emerging as a standout performer. © 2024 IEEE.

关键词： Long short-term memory

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Continuous Sign Language Recognition using Convolutional Neural Network 2

Continuous Sign Language Recognition using Convolutional Neu...

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Shankara Narayanan, V. Sneha Varsha, M. Padmavathi, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Automatic Continuous Sign Language Recognition is a task that aims at associating words/phrases for sign language gestures. The scope of building a CSLR framework extends beyond bridging the communication barrier between the deaf community and the hearing majority but includes profound benefits such as bootstrapping translation systems, human-computer interaction, virtual reality environments, etc. In this study, a continuous sign language recognition framework based on a CNN architecture is proposed. Utilizing the Argentina Sign Language dataset, sign language gestures are preprocessed to extract training and testing frames. Employing transfer learning with the ResNet-50 model, a CNN is trained to classify each frame into one of 64 different words present in the dataset. The paper also conducts a thorough post-training data analysis, offering insights into areas where the model performed less effectively, identifying commonly confused pairs of sign language gestures. Additionally, a heatmap overlay technique is employed to elucidate the black-box CNN model. © 2024 IEEE.

关键词： computer vision

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Detecting AI-generated images with CNN and Interpretation using Explainable AI 2

Detecting AI-generated images with CNN and Interpretation us...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Bharathi Mohan, G. Rangarajan, Prasanna Kumar Akilesh Rao, S. Sukesh, Mandava Abinandhini, M. Jaikanth, Y. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

The rapid proliferation of digitally manipulated images in various media outlets has necessitated the development of robust and reliable methods for distinguishing between authentic and synthetic visual content. In response to this imperative, this research leverages state-of-the-art deep learning techniques and an extensive dataset of 'real' and 'fake' images to create an AI model for image identification. The study encompasses a meticulous data collection process, preprocessing steps, the selection of the DenseNet121 model architecture, model training, and evaluation of the model's performance through a range of metrics, including accuracy (93.42), precision, recall, and the Area Under the ROC Curve (AUC). To enhance the transparency and interpretability of the model, employed the Gradient-weighted Class Activation Mapping (Grad-CAM) technique, enabling to visualize the regions within images that influenced the model's decision-making. The research provides valuable insights into the development and evaluation of AI systems for image identification, particularly in the context of distinguishing between real and fake content. Furthermore, the interpretability techniques shed light on the model's decision-making process. This research contributes to the field of image authentication and paves the way for enhanced image classification in the era of synthetic media. © 2024 IEEE.

关键词： Cams

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Image Super-Resolution by Augmentation of Region Information by Rapid Segmentation

Image Super-Resolution by Augmentation of Region Information...

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International Conference on Applied Soft computing and Communication Networks, ACN 2023

作者： Satish, M. Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819720033

Spatial Super-Resolution (SR) of images finds practical applications in multiple domains like resolution enhancement of old images, improvement of resolution where imaging systems are limited, like microscopy, surveillance and remote sensing. Out of the many techniques available, deep learning-based models have shown improvement performance when augmentation methods like geometric transformations and information-based fusion augmentation techniques are used. In the current work, the technique chosen is Single Image Super-Resolution (SISR) using segmentation information augmentation, a form of fusion technique on the Caltech University Birds 200 (CUB) dataset, which contains images of birds. The deep learning model, which was used for performing SR is based on EDSR technique. It was found that the best performing hyper-parameters were 64 filters with 8 residual blocks. Also, the performance of region augmented EDSR was around 0.5 dB better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Birds

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Comparative Analysis of Deep Learning Models for Currency Recognition and Value Detection 15

Comparative Analysis of Deep Learning Models for Currency Re...

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

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

ISBN: (纸本)9798350370249

Accurate currency recognition and conversion are essential for smooth cross-border transactions in today's globalized economy. Yet, the wide variety of cash denominations and variances, combined with the complexities of deep learning and image processing, make this work extremely difficult. A comparative comparison of deep learning models is helpful in addressing these issues by identifying the best method for value identification and currency recognition. The objective of this research is to examine how well ResNet50V2, VGG16, and MobileNet perform in terms of precisely identifying and valuing currencies. Utilizing a sample of Thai and Indian banknotes from IEEE data port that include notes with differences in orientation (rotated at 180-degree angles), lighting conditions, and other environmental factors., the study assesses how well each model handles the complexity of money data. The purpose of this study is to give practitioners in the banking and finance industries useful insights. © 2024 IEEE.

关键词： Banking Industry Currency Recognition Deep Learning Financial Transactions Value Detection

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An Explainable AI Regression Model For Gold Price Prediction 9

An Explainable AI Regression Model For Gold Price Prediction

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bhadula, Shivam Kartik, Devashish Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Gold is an important commodity that has a significant influence on important business sectors and the world economy. Its impact on the dynamics of the market reverberates through multiple channels, making it a highly desirable asset. Central banks in many nations utilize gold as a reserve asset, which they then exchange for national currencies to inject into markets. Its inherent value is recognized by both companies and gold traders, and it is especially evident during times of inflation and market turbulence. The development of a machine learning model to anticipate the price of gold has many potential applications for traders, businesses, investors, and even laypeople who want to make money on gold investments. By utilizing a variety of significant factors that affect gold prices throughout the world, a strong prediction model based on a ten-year market trend can be developed. This research paper aims to democratize gold investing opportunities by providing end users with feasible options so they may maximize profits while minimizing risks and losses. Positive results were obtained from our investigation into a variety of machine learning models. When Explainable AI approaches are applied, it becomes possible to fully comprehend how various algorithms rank different features as key contributors during model training. © 2024 IEEE.

关键词： Investments

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Enhanced Diabetic Retinopathy Grading Using a Deep Hybrid Approach 15

Enhanced Diabetic Retinopathy Grading Using a Deep Hybrid Ap...

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

作者： Laharesh, Gali Chandra, G.V.S. Sarath Sakthi Vikkraman, R. Kumar, S.B.S. Mani Suchithra, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

In recent times there have been significant achievements in deep learning methodologies, the task of classifying retinal diseases is still challenging because of their minute pathological regions. The retina's blood vessels can be compromised by diabetic retinopathy, a complication arising from diabetes falls under the distinct category of retinal diseases. Despite many attempts with classification models such as CNN and other deep learning approaches detection encounters several challenges. These include existing deep learning models are susceptible to biases towards either local or global perspectives. These biases can impact semantic-level understanding of these representations. This work proposes a hybrid framework for Diabetic retinopathy grading. Inspired from the achievements of vision transformers in image classification and its ability to capture spatial relationships in fundus images related to Diabetic retinopathy features. Augmenting this vision transformer with traditional multiple instance learning forming a hybrid model ViT-MIL. By combining ViT's global learning capabilities with MIL's effective local feature extraction this hybrid model achieves enhanced performance. © 2024 IEEE.

关键词： Diabetic retinopathy Hybrid Multiple Instance Learning Vision Transformer

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Tweeting the Blues: Leveraging NLP and Classification Models for Depression Detection 2

Tweeting the Blues: Leveraging NLP and Classification Models...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Chereddy, Spandana Geetha, K. Sreedevi, A.G. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350371314

Depression is a global mental health problem while Twitter is a valuable medium on which content indicating emotional affect is generated. Our research draws upon NLP to analyze tweets by the application of Naive Bayes, Support Vector Machines, and Logistic Regression classifiers. The first objective will be the tweets' classification into positive and negative sentiment categories with a secondary goal to explore the possibility of the detection of community characteristics including gender, age, and location. We investigate the tweet content by using NLP in an attempt to pinpoint the sensing patterns and the department that can reliably be used to detect depression. Results of the experiments proved comprehensive model accuracy with Support Vector Machines taking a better position on the matter of correctly classifying depression tweets. Especially, Support Vector Machines gave an accuracy of 85%, and values for precision, recall, and f1 were 0.89, 0.63, and 0.63, respectively. Therefore, the remaining ones for attaining higher accuracy and recall are difficult to achieve in models that are depressed but there is still the necessity for more data generation. This research thus clears the way for NLP in the diagnosis of mental health by providing vital assessment data that can be used by psychologists, researchers, health authorities, and policymakers to develop targeted intervention strategies and advancement of public health goals. © 2024 IEEE.

关键词： Support vector machines

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E-Sangrahan: A Sustainable Paradigm and comprehensive framework for efficient E-Waste Management towards a Greener Future

E-Sangrahan: A Sustainable Paradigm and comprehensive framew...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Malagati, Nischal S. Rajesh, M. Sreevidya, B. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350309140

According to experts, E-Waste is one of the most dangerous environmental concern India is facing. According to ASSOCHAM an industrial body predicts that by 2020, India will generate 5.2 million tons of E-Waste. E-waste stands for Electronic waste that has been discarded. computers, monitors, and other electronic appliances contain electronic items harmful to nature if not recycled properly. E-waste plays a major role in polluting soil, and air which is caused due to irregular ways of disposing of E-Waste. There are many governments driven initiatives for E-Waste management, but the awareness and the significance of W-Waste management is still not reached to the larger community. The proposed work - E-Sangrahan is an effort to tackle the problems faced while dealing with E-Waste. A web application is developed to handle the E-Waste management process in a much ease and user-friendly way. E-Sangrahan is the future of recycling E-Waste, which helps create a sustainable future. E-Sangrahan offers a dynamic website that is simple to use and everybody can access. The website includes a live user-friendly E-Waste warehouse locator, that helps dispose of E-Waste by yourself. The locator even tells you about the directions to be taken and how to get there by road. The novelty or uniqueness of E-Sangrahan is the fact that the website also consists of a machine learning model that helps people to identify the type of E-Waste it belongs to. The objective of this machine learning model is to remove the confusion between E-Waste items and non-E-Waste items because if any E-Waste item is not recycled, the environmental impact is large. E-Sangrahan would be the next revolution in the theme of E-Waste. © 2024 IEEE.

关键词： Waste management

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