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Visualization of DDoS Attack using Python Libraries 2

Visualization of DDoS Attack using Python Libraries

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Subbulakshmi, T. Mohith, G.K. Santhosh R A, Arun Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

With companies relying more and more on online services, the risk posed by Distributed Denial of Service (DDoS) attacks has notably increased. These attacks have the potential to cause significant damage to the systems they target, leading to disruptions in service, loss of data, and economic losses. To mitigate the impact of DDoS attacks, effective detection mechanisms are necessary. This research paper focuses on DDoS Attack prediction and visualisation techniques using Scapy functions of Python and its libraries to create graphs of various parameters that helps us analyse the of a given "pcap"file of which stores the packets captured in the real time network using WireShark Application. The paper uses various modules of Python to avoid human error and to give various features for the User to view the results plotted. © 2024 IEEE.

关键词： Python

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Dual-Model Approach for Diabetic Retinopathy and Macular Edema Detection

Dual-Model Approach for Diabetic Retinopathy and Macular Ede...

引用

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

作者： Micheal, A. Ancy Sai, Ladi Jeevan Vellore Institute of Technology Chennai School of Computer Science and Engineering India

ISBN: (纸本)9798350378092

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are major causes of vision loss in diabetic patients. Early detection is crucial for preventing permanent vision impairment. This research paper proposes a novel dual-model architecture for detecting Diabetic Retinopathy and Diabetic Macular Edema. The proposed framework integrates preprocessing techniques such as circular cropping and Ben's preprocessing, followed by data augmentation. The Diabetic Retinopathy model is trained on the APTOS2019 dataset, while the Diabetic Macular Edema model is trained using the IDRid dataset. InceptionV3 outperforms other models with an accuracy of 88% for Diabetic Retinopathy classification, while MobileNetV2 outperforms other models with an accuracy of 83% for DME classification. The dual-model architecture enables simultaneous evaluation of retinal images for DR and DME diseases. The Experimental results demonstrate promising outcomes, suggesting the potential effectiveness of the proposed dual-model architecture in diagnosing diabetic eye diseases. © 2024 IEEE.

关键词： Ophthalmology

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Prediction of Common Diseases Lurking in Smokers and Alcoholics and its Impact on their Health

Prediction of Common Diseases Lurking in Smokers and Alcohol...

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Jeevan, B. Koushik Vishnu, D. Jagadish, R. Praveen Joe, R.I. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Smoking and alcohol intake have become very common in modern society, leading to many complications and health problems, including respiratory illnesses and cardiovascular diseases. Early intervention and targeted healthcare interventions are very much needed to mitigate these risks and improve patient outcomes. This research addresses the need by using patient data to predict potential future health risks. By analysing 19 health and lifestyle factors, including smoking, alcohol consumption, diabetes, hypertension, haemoglobin and such factors, the study developed a predictive model to identify diseases that patients may currently have or develop in the near future. After extensive hyperparameter tuning and analysis, the study utilized a deep learning model optimized with the ReduceLROnPlateau technique, Adam as the optimizer and binary cross-entropy as the loss function. With the best parameters, including a learning rate of 0.001, 200 epochs, and a batch size of 64, the model achieved the highest accuracy of 94.69%. Additionally, to assess and compare the efficiency of the deep learning model, the study explored Random Forest algorithm due to its robustness in handling complex datasets. Initially focused on smokers and alcoholics, the study expanded to include non-smokers and non-alcohol consumers, demonstrating the model's broad applicability. These findings highlight the model's strong predictive capability and its potential for widespread use in preventive healthcare. Further, the current study uses dashboard visualization that reveals common disorders that affect smokers and drinkers, providing the valuable insights on targeted treatments to improve patient care and outcomes in population. © 2024 IEEE.

关键词： Network security

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Detection of AI-Generated Image Content in News and Journalism 15

Detection of AI-Generated Image Content in News and Journali...

引用

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

作者： Jagadish, Tarun Graceline Jasmine, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

This research paper explores the application of deep learning techniques for the detection of AI-generated images within the context of news and journalism. In response to the escalating risk posed by the pervasive use of sophisticated AI-generated content, particularly in the dissemination of fake news and disinformation, this study endeavors to develop a robust solution. The proposed approach involves the deployment of a high-accuracy deep learning model trained on a diverse dataset encompassing both authentic and synthetic images, with a focus on content generated by advanced AI tools such as diffusion models. The ultimate goal is to equip journalists with a tool that enables them to identify and avoid the incorporation of AI-generated images, thereby fortifying media integrity and bolstering public trust. © 2024 IEEE.

关键词： AI-generated Images Convolutional Neural Networks Deep Learning Diffusion Models Image Processing News and Journalism Synthetic Image Detection

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Smart Alarm System for Crime Hotspot Detection using Deep Learning 15

Smart Alarm System for Crime Hotspot Detection using Deep Le...

引用

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

作者： Pawar, Rohan Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

The increasing number of surveillance cameras to watch over human activities necessitates the development of a system that can automatically identify violent and suspicious events. This is an essential piece of equipment for smart buildings. The average yearly death toll from acts of human aggression worldwide is 7.9 per 10,000 individuals. The majority of these acts of violence against people happen suddenly or in remote places. One of the main obstacles to halting these activities is the information delay in this case. In this work, the real time violence detecting technique is employed to thrive on this issue. Certain deep learning algorithms are applied in computer vision to anticipate and identify actions and attributes in videos. When police arrive at dangerous locations in real time, they analyze CCTV footage and begin an investigation before moving further. In smart buildings, this technology is mandatory. Violence Detection in real time will prevent any mis happenings from taking place by raising alerts to the security guards. Our model also has YOLO object detection inculcated in it in order to recognize any weapons or tools that an attacker may be using against people and the security guards will act accordingly. © 2024 IEEE.

关键词： CCTV surveillance technique Deep Learning MobileNetV2 Violence Detection

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Flowing Asanas: Enhancing Yoga Posture Detection With BiLSTM Attention and MediaPipe 2

Flowing Asanas: Enhancing Yoga Posture Detection With BiLSTM...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Noel Jeygar Robert, V. Aakash Kumar, P. Rani, Abha Pandey, Shraddha Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Yoga is an ancient practice that the combines harmony of mind, body, and soul with discipline, relaxation, and happiness. Besides its breathing and meditation exercises, a central aspect of yoga is its asanas (postures). Keeping the importance of asanas of yoga in mind, this research paper aims to describe a novel and compact posture detection system for yoga that integrates MediaPipe for real-time skeletal landmark placement and deep learning models for posture analysis and accuracy evaluations. The architecture acquires live yoga poses and hand gestures directly from a webcam through an RGB camera where MediaPipe recognizes key body points such as the knees, eyes, hands, etc., to create a skeleton representation of the pose taken by the user. Next, these skeletal structures are evaluated by deep learning algorithms against correct yoga postures that have been pre-trained and can tell how aligned or accurate the user's postures may be. It automatically stops the timer once it identifies the wrong posture to ensure enhanced safety and efficiency. The technique helps expand the horizons of yoga to greater potential in physical training and rehabilitation while enhancing the practice itself. © 2024 IEEE.

关键词： Deep learning

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ResNet50 Integrated Vision Transformer for Enhanced Plant Disease Classification 3

ResNet50 Integrated Vision Transformer for Enhanced Plant Di...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohnish Kumaar, D. Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This study introduces an innovative hybrid model that integrates the powerful feature extraction capabilities of ResNet 50 with the advanced pattern recognition and contextual understanding afforded by the Vision Transformer (ViT) for the classification of plant diseases. By embedding a class token into the input feature sequence and leveraging a novel multi-head attention mechanism, this model captures both local and global image features, leading to a significant enhancement in diagnostic accuracy and efficiency. Our approach involves a meticulous configuration process that adjusts hyperparameters such as the number of transformer encoder layers and attention heads to optimize performance. Rigorous testing on a meticulously curated and pre-augmented dataset underscores the model's superiority over existing methodologies, demonstrating its readiness for real-world agricultural applications. The findings from this research not only mark a substantial advancement in plant disease diagnostics but also set a new benchmark for the application of hybrid deep learning models in precision agriculture, paving the way for sustainable farming practices in the face of escalating global food demands. © 2024 IEEE.

关键词： Deep learning

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Integrative Framework for Heart Failure Risk Assessment and Tailored Mitigation Strategies 3

Integrative Framework for Heart Failure Risk Assessment and ...

引用

3rd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Yuthish Kumar, V. Goutham, S. Ir, Praveen Joe Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

Heart failure is a major problem which affects many people across the *** paper proposes an integrated architecture combining advanced predictive modeling for the risk assessment of heart failure and medication recommendations concerning metabolism-related factors. By using classification algorithms, including decision trees and gradient boosting, the paper aims at accurately predicting heart failure risk. Moreover, the model will suggest remedial actions concerning diabetes stabilization, creatinine levels, and anemia management, striving to provide a complete approach to the prevention of heart failure. © 2024 IEEE.

关键词： Heart

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Liver disease Prediction using Ensemble Learning 3

Liver disease Prediction using Ensemble Learning

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Pilankar, Atharva Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The increasing prevalence of liver disease necessitates efficient identification methods to alleviate the diagnostic burden on healthcare providers. Machine learning offers a promising solution by evaluating vital parameters to predict liver disease, thereby reducing the risk of incorrect diagnoses and subsequent financial burdens on patients. Ensuring that predictive models generalize well across diverse populations and demographics is crucial for their wide applicability. Standardized evaluation metrics for liver disease prediction models are essential for unbiased comparisons and benchmarking. Demonstrating the superiority of new models over existing clinical methods is key to their adoption in clinical practice. This research explores the use of ensemble learning techniques to enhance the accuracy of liver disease predictions. By combining various supervised machine learning models, including Support Vector Machines (SVM), logistic regression, gradient boosting, decision trees, random forests, and artificial neural networks, the predictive outcomes have been improved. Each model undergoes extensive hyperparameter tuning to identify the optimal parameters for the given dataset. Additionally, feature selection techniques are employed to determine the most significant features contributing to the prediction. The study implements both stacking and bagging classification methodologies, achieving an accuracy rate of 79%. This hybrid approach demonstrates significant potential in improving the reliability and accuracy of liver disease predictions, offering a robust tool for early diagnosis and management of liver disease in diverse patient populations. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Leukemia Diagnosis through Machine Learning: A Customized ResNet50 Approach 2

Enhancing Leukemia Diagnosis through Machine Learning: A Cus...

引用

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

作者： Narendra, Modigari Negi, Sumit School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350371314

Leukemia is a type of blood and bone marrow cancer that continues to pose a major challenge in the field of healthcare. This is predominantly caused by the wide range of leukemia subtypes and the complexity of the classification process. Therefore, the current research aims to propose an approach to classify leukemia - more specifically, acute lymphoblastic leukemia - using a comprehensive methodology that integrates various machine learning techniques. A thorough data preprocessing is conducted and utilized Otsu's Thresholding for image segmentation and ResNet50 convolutional neural network for feature extraction. By applying the Lazy Predict library, the proposed work is further streamlined, allowing to finalize the offline model selection process. Finally, a custom ResNet50 CNN model is designed with extra layers in the feature extraction and classification resulting better performance compared to other state of the methods with an accuracy of 0.87 and a balanced accuracy of 0.83. Besides, the proposed model has faster inference times, making its application feasible for real-time leukemia diagnosis. © 2024 IEEE.

关键词： Image segmentation

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