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

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An Adaptive Threat Defence Mechanism Through Self Defending Network to Prevent Hijacking in WiFi Network

An Adaptive Threat Defence Mechanism Through Self Defending ...

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Advances in Electronics, Communication, computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Datta Sai Manikanta Narayana Sai Bharadwaj Nookala Shraddha Chopra Udhayakumar Shanmugam Department of Computer Science and Engineering (Cyber Security) School of Computing Chennai

IEEE 802.11 generally known as the Wi-Fi network, is our backbone of the present communication system delivering the best effort service to the end users. Access control in wireless network is an intrinsic security problem which creates possibilities for malevolent manipulation by third parties. Hackers make the use of free software to easily execute wireless attacks to create denial of service (DOS) and man in the middle attack and thereby the existing defense becomes passive for new types of network attacks. The paper proposes a self-defending network that can quickly identify the attackers, prevent them from doing further damage and then get itself adapt to the threat behavior. To implement the mechanism a pre-connection attack is performed by targeting a network and analyzing the packets. The proposed work demonstrates how a hacker gains access to a Wi-Fi network by cracking different types of encryptions like Wired Equivalent Privacy (WEP), Wireless Protected Access (WPA)/WPA2 using a simple wireless network adapter. Our proposed adaptive threat defense model identifies the hacker and creates a diverse strategy for self-defense. The proposed model would be effective against dynamic network configuration like Wi-Fi and further prevents hijacking.

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Reinforcement Learning Based Control Domain Division in LEO Satellite Networks 25

Reinforcement Learning Based Control Domain Division in LEO ...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Tang, Feilong Li, Xue Chen, Long Liu, Jiacheng Gao, Ming Yang, Yanqin Xu, Wenchao Zhang, Heteng School of Data Science and Engineering East China Normal University Shanghai China School of Computing Science Simon Fraser University Vancouver Canada The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong School of Communication and Electric Engineering East China Normal University Shanghai China Suzhou All-Space-Time Information Technology Co. Ltd. Suzhou China

ISBN: (纸本)9798350330014

The performance of SDN-based LEO satellite networks significantly depends on the control domain division approaches. The dynamical topology in LEO networks results in the time-varying delay for network management. Therefore, the control domain division is very challenging and should be dynamical. In this paper, we propose a control domain division approach based on reinforcement learning (RL), which aims at reducing the average management delay of controllers during the control period, and as well as minimizing the number of control domain handovers. Firstly, we analyze the factors that need to be paid attention to in controller deployment, such as delay, load and controller switching cost, and formulate the controller deployment. By relaxing the constraints, we prove that the problem is NP-hard. Secondly, we propose an approach that considers future motion trajectory based on reinforcement learning (FMT _ RL) to deploy controllers. Since the dimension of the neural network in reinforcement learning is fixed, we use the improved K-Means algorithm to select the set of mechanisms to determine the dimension of the neural network. Particularly, in order to obtain the node composition control domain, the observation including both future motion trajectory and history controller selection information are input into the neural network to calculate the control domain division results. The experimental results demonstrate that our approach significantly outperforms related approaches, with better stability and average control cost. © 2023 IEEE.

关键词： Reinforcement learning

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Deep Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) Images

Deep Convolution Neural Network-Based Classification and Dia...

引用

International Conference for Convergence of Technology (I2CT)

作者： Thanu Kurian Thangam. S Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. The ECG image can be taken by scanning the image using a smart phone. This would be very helpful in small healthcare centers where there are no experts for diagnosis. The proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. The work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset.

关键词： Heart Heart beat Convolution Medical services Electrocardiography Myocardium Data models

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User Impersonation Detection and Authentication Using Image Based OTP

User Impersonation Detection and Authentication Using Image ...

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Innovations in Power and Advanced computing Technologies (i-PACT)

作者： A Raja Rajendran Gokul Raj J Sharath Kumar K Mukeshvar A Radhika G Department of Computer Science and Engineering Amrita School of Computing Coimbatore India

In this age of the internet and technology, it is very important to be vigilant with one's devices and have safe and secure passwords to avoid several types of crimes. In this paper, we concentrate on two types of attacks: user impersonation and shoulder surfing. User impersonation is the act where one person pretends to be you or someone else to create confusion or obtain information that is private to you. To prevent this, we discuss a method to classify a person as authorised or unauthorised, utilising data from the user's mobile device's accelerometer and gyroscope sensors [1]. One of the simplest methods scammers use to acquire your personal information is by shoulder surfing, which may be as easy as simply looking over your shoulder. It is extremely common and hard to prevent in crowded areas. We proposed a method of authentication that uses OTPs and graphical password techniques and would prove effective against shoulder surfing attacks.

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OCEAN - Ovarian Cancer subtypE clAssification and outlier detectioN using DenseNet121

OCEAN - Ovarian Cancer subtypE clAssification and outlier de...

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International Conference on Image Information Processing

作者： P Paayas R Annamalai Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In our exploration, we tackle the significant hurdle of diagnosing ovarian cancer, a formidable challenge within the field of oncology. This cancer type often remains undetected until its advanced stages due to inconspicuous symptoms and the absence of reliable early detection methods. To combat this issue, we propose a method utilizing DenseNet121, a deep learning approach, for the multi-classification of ovarian cancer sub-types based on histopathology images. We leverage the UBC Ovarian Cancer data-set obtained from Kaggle, encompassing whole slide images and tissue microarrays with various dimensions. Among the sub-types covered in this dataset are clear cell (CC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC) and mucinous (MC). To address the challenge posed by large image sizes up to 100,000 x 50,000 pixels and the diverse morphological characteristics present in the images, we have devised comprehensive data augmentation techniques. Our DenseNet121 model demonstrates robust generalization capabilities by effectively learning features from this varied data-set. A thorough experiment with challenging cases of ovarian cancer sub-types resulted in 99.6% accuracy and 100% F1-score in the multi class classification. Ovarian cancer detection systems based on this approach may be developed to detect the disease early and accurately.

关键词： Deep learning Representation learning Histopathology Computational modeling Sensitivity and specificity Data augmentation Oncology

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Optimizing University Time Table Schedules for Smart Space Utilization

Optimizing University Time Table Schedules for Smart Space U...

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Global Conference for Advancement in Technology (GCAT)

作者： Chetan Aditya Dasari Solasa Manikanta Kumar Arpitalaxmi Arpitalaxmi M G Thushara Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Manual university course timetable scheduling is a time-consuming and error-prone process, often resulting in conflicts and inefficiencies. An optimized timetable is crucial for smooth academic operations, ensuring efficient use of resources and fostering a positive learning environment for students and faculty. We address the challenge of university course scheduling by proposing an optimized time-table scheduling algorithm. This paper will identify suitable approaches to automate timetable generation, considering factors like lecturer availability, student groups, room capacity, and course requirements. Keeping track of all the different factors that affect things like course requirements, teacher availability, room capacity, and fair task distribution is hard. By working on slot-based scheduling methods, we hope to find ways to make the schedule work best for both students and teachers, which will make everyone happier and help them learn more.

关键词： Schedules Scheduling algorithms Scalability Manuals Software Scheduling Resource management Smart spaces Optimization

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Deep Learning Based Image Fusion Applied on Landsat-8 Archival Data Using UVCGAN

Deep Learning Based Image Fusion Applied on Landsat-8 Archiv...

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Innovations in Power and Advanced computing Technologies (i-PACT)

作者： Suhas Preetham Kambham D Bharathi Department of Computer Science and Engineering Amrita School of Computing Coimbatore India

Image fusion is required to maximise the utilisation of the different satellite sensors and enhance the usage and quality of the images obtained from them. One can get over obstacles like varied geographical resolutions, spectral bands, and temporal coverage by fusing images from multiple satellites, such as Landsat 8 and Sentinel 2. For various applications that involve observation of the Earth like land cover classification, monitoring of vegetation, disaster monitoring, and urban planning, the fusion process aims to produce composite images that integrate the spectral richness, spatial detail, and temporal frequency of the input images. The creation of Sentinel-like imagery from Landsat8 data enhances its potential for Earth observation applications, making the problem of Landsat 8 and Sentinel 2 imagery fusion very important to solve. A deep learning methodology is required to solve this problem because of its capacity to discover complex correlations and patterns within the data. In this paper, a U-Net Vision Transformer Cycle-consistent Generative Adversarial Network (UVCGAN) - based image fusion method that effectively transforms Landsat 8 images into Sentinel-like imagery is pro- posed. The results obtained from the experiments illustrate the efficiency and effectiveness of the proposed method in generating high-quality composite images with improved spectral and spatial characteristics.

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Tele-Rheumatology-Advanced Neural Network Model for Remote Assessment and Management of Rheumatic Conditions 10

Tele-Rheumatology-Advanced Neural Network Model for Remote A...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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Mood-Based Movie Recommendation System Using Sentiment Analysis 4th

Mood-Based Movie Recommendation System Using Sentiment Analy...

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4th International Conference on Machine Learning, Advances in computing, Renewable Energy and Communication, MARC 2023

作者： Bhushan, Shashi Singh, Anuj Kumar Kumar, Sandeep Sharmila Bhuyan, Bikram Pratim Universiti Teknologi PETRONAS Seri Iskandar Malaysia School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Tulas Institute Dehradun India Department of Electronics and Communication Engineering Raj Kumar Goel Institute of Technology Ghaziabad India LISV Laboratory University Paris-Saclay 10-12 Av.de l’Europe Paris France

ISBN: (纸本)9789819752300

This research project aims to develop an Android application that uses voice command sentiment analysis to recommend movies based on a user’s current mood. Movies are an essential part of people’s lives, offering a necessary disconnect from the outside world and reducing stress and anxiety. While online streaming platforms like Netflix and Amazon Prime Video provide access to movies, they do not have access to a user’s current mood, limiting the ability to make personalised recommendations. By analysing voice attributes, including pitch, tone, and gender, our application aims to provide personalised genre suggestions to reduce stress and anxiety based on a user’s current sentiment. The project will utilise Android Studio and IntelliJ Idea as IDEs and incorporate ML algorithms. The end product will be an Android application that recommends movies based on a user’s mood. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Video streaming

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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