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Flex Sensor Data Analysis for Hand Rehabilitation using Wearable Glove 3

Flex Sensor Data Analysis for Hand Rehabilitation using Wear...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Thangam, S. Reddy, Anusha C. Varun, Ginnaram Hemasri, M. Virupaxappa Kumari, J. Jesy Janet Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

Every day we encounter many people with unexpected situations where they suffer from many neuropathological diseases that cause a severe impact on the daily lives of individuals, especially about hand movements, thereby directly affecting the quality of their lives. Traditional methods often lack insights into personalized progress and optimal medication outcomes, which take longer to recover. We have designed an approach based on a novel concept using wearable technology to monitor and assess finger movements. The proposed solution of wearable device integrated flex sensor technology to enhance the rehabilitation process by providing real-time insights by the input of the flex sensors captured by the finger movements of the patients. By sensor data analysis, people get a chance to know their progress and the professionals gain accurate tracking of the progress so that they enhance identifying the areas of improvement and customize the treatment. This innovative approach to designing a wearable device serves as a helping aid to improve patient outcomes by categorizing the stages of improvement into Normal, Mild, and Severe Impairment for a better understanding and helps improve the rehabilitation experience. © 2024 IEEE.

关键词： Personalized medicine

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Comparative Deep Learning Approach for Intrusion Detection 15

Comparative Deep Learning Approach for Intrusion Detection

引用

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

作者： Sah, Nabin Kumar Kolli, Mahi Dharmaraj, Kadam Prajwal Vishwas, H.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the modern world, the security of computer networks from harmful activities is very important since cyber threats keep changing from time to time. However, Intrusion Detection Systems (IDS) have not been able to match this pace using traditional methods. This research is on checking how well deep learning models which are Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) help in enhancing Intrusion Detection capabilities. The NSL-KDD dataset available on Kaggle provides various attack types and normal activities through diverse network traffic data. Rigorous data preprocessing has been done during the study through feature engineering, encoding categorical variables, and normalizing numerical attributes. After that, custom deep learning architectures are created and evaluated with accuracy being one of the standard metrics used for testing their performance among others like recall, precision, or F1-score so as to improve intrusion detection efficiency. © 2024 IEEE.

关键词： CNN cyber threats DNN GRU Intrusion detection LSTM network traffic

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Enhanced Alzheimer's Disease Classification: A Stacked Model Fusion with Brain MRI Imaging 2

Enhanced Alzheimer's Disease Classification: A Stacked Model...

引用

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

作者： Chaitanya, Tekumudi Vivek Sai Surya Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

Alzheimer's disease (AD) is still a major global health issue that requires sophisticated staging and classification techniques. This paper introduces a novel approach to AD categorization, employing a stacked network architecture and feature extractions from InceptionV3, EfficientNetB0, and MobileNet models. The primary objective is to address the challenge of imbalanced datasets while minimizing the risk of overfitting. The proposed method utilizes brain MRI scans, extracting features and incorporating a Global Average Pooling (GAP) layer to improve the identification of critical features that correspond to AD phases. Combining the retrieved features results in a single set that helps with stage discrimination, followed by finishing via a SoftMax classifier. Model evaluation and validation are essential steps in determining the ideal model to minimize the risk of overfitting. A comprehensive evaluation is ensured by cross-validation (CV) using a 3-fold technique, which shows that the suggested Stacked Model has a better accuracy of 94.58%, closely followed by EfficientNetB0 at 90.70%. Both InceptionV3 and MobileNet exhibit competitive results, with accuracy rates of 89.46% and 87.60%, respectively. By presenting a strong approach to AD classification, this study advances the field of early detection and staging of AD by demonstrating the effectiveness of the Stacked network in identifying complex features that are indicative of different stages of AD progression within brain MRI datasets. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Efficiency Analysis of Conventional and Weighted Grid-Based Pathfinding Algorithms: A Performance Comparative Study 3

Efficiency Analysis of Conventional and Weighted Grid-Based ...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Yasasree Lasya, A. Kuushalie, Asi Yerukola, Gayatri Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350369083

In many computing applications, grid-based algorithms are essential, particularly in the gaming industry where performance, shortest path and efficiency are crucial. In order to find the best algorithm for gaming settings, this research emphasizes the importance of grid-based algorithms. The primary objective is to evaluate and contrast the various grid-based algorithms' performances while taking into account both weighted and conventional grid configurations. The implementation of the Dijkstra's, A∗, and Breadth-First Search (BFS) algorithms for both normal and weighted grids will provide insights into their applicability for gaming applications. Also, in weighted grid factors, the method known as Jump Point Search with weight will be incorporated for additional comparison. A number of measures, such as cost analysis, time complexity, space complexity, and weight complexity, will be used to conduct the evaluation process. By comparing these algorithms to one another through the process of benchmarking, the aim is to identify which algorithm offers the highest performance across all of these measures. The results of this investigation will offer significant recommendations for game designers looking to improve the performance of pathfinding algorithms in their works. © 2024 IEEE.

关键词： Peer to peer networks

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Insider threat detection of ransomware using AutoML

Insider threat detection of ransomware using AutoML

引用

International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Bhuvaneswari, R. Kumar, Enaganti Karun Padmasini, Annadanam Varma, K. V. Priyanka Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9781032669663

Insider threats are one of the biggest issues that modern-day organizations and many large-scale companies face. The inside threats are caused by the insiders who are authorized individuals, also may have proper access to sensitive and secret information, and may be aware of the weaknesses in the implemented systems and operational procedures. And, it is confirmed that these insider threats have already shown their great damaging power in securing important information and because of this, organizations require more proactive and modern-day solutions to support existing cybersecurity tools in order to detect and prevent them. Conventional interruption recognition frameworks neglect to be powerful in insider threats because of the absence of a wide range of information for insider ways of behaving. All things considered;a more complex and standard technique is expected to have a more profound comprehension of the ways that insiders speak with the information framework. Ransomware hackers appear to be adopting new methods in response to tightening security. Automated machine learning has been utilised in this work for detection of ransomware and we also included the ensembling method for a few algorithms that are suitable for the given dataset. © 2024 The Author(s).

关键词： Machine learning

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Modelling and Design of a Finite State Machine Based Elevator Control System for Efficient Vertical Transportation 5

Modelling and Design of a Finite State Machine Based Elevato...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Anreddy, Shanmukha Reddy Charan, K. Sriram Sai, N. Naga Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

The elevator control system serves as a crucial element in modern infrastructures, facilitating the vertical movement of individuals within buildings efficiently and securely. Finite State Machines (FSMs) offer a method to model the behavior of elevators, capturing transitions between distinct states such as stationary, ascending, descending, and door operations. This paper presents the design of a simplified elevator control system employing FSM principles, accompanied by python code for intuitive visualization of the elevator's status and functionalities. The FSM model encompasses states including Stopped, Moving Up, Moving Down, Opening Doors, and Closing Doors, each characterized by specific transitions triggered by user commands or operational requirements. Through this approach, the system achieves robustness, responsiveness, and adaptability to varying usage scenarios, ensuring safe and efficient elevator operations in diverse building environment. © 2024 IEEE.

关键词： Elevator codes

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Interpretable AI Insights in Fake News Detection: A Comparative Analysis of CNN and LSTM 15

Interpretable AI Insights in Fake News Detection: A Comparat...

引用

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

作者： Kumar, Kanderi Johith Shreya, Kuthati Divakarla, Lasya Priya Nair, Priyanka C. Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

One of the major concerns in today's world is fake news when the majority of people spend most of their time online on social media. People come across various fake news and false information. It is important to acknowledge how such fake news spreads, and it must be prevented. This project combines text processing with the utilization of relevant machine learning techniques, like categorization based on various text features. The model has undergone training using both traditional machine learning algorithms and deep learning techniques, including Logistic Regression, Decision Tree Classifier, Naïve Bayes Classifier, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). Logistic Regression and CNN have exhibited the highest model accuracy, achieving 95%. Evaluations have been conducted to make comparisons about the efficacy of the machine learning and deep learning approaches utilized. Explainability techniques like Local Interpretable Model Agnostic Explanations (LIME) have been applied to discern the keywords influencing the classification of data into fake or genuine categories. © 2024 IEEE.

关键词： Explainable AI Techniques (XAI) Fake News Text Classification TFIDF Vectorization Word Embeddings

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A Secure Approach for Strengthening Session Management with Custom Session Handlers 2

A Secure Approach for Strengthening Session Management with ...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumaran, U. Gurupriya, M. Reddy, Annem Gnaneswara Nithin, G. Bhanusri, Kodati Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

Session management is the underlying security mechanism that is instrumental in protecting the security and integrity of web applications' user sessions. The research is dedicated to the examination of session administration solutions in web applications to provide secure user sessions and minimize the risks of session hijacking and session fixation. It touches on session management before and after user login, breaking it down into session maintenance. The custom session handler offers passive protection against session hijacking via session management using a MySQL database. This approach provides a safe way of creating and managing sessions, validating them as well as handling session expiration and performance optimization. The implementation intends to strengthen session securities of users, therefore, increasing the safety and reliableness of web applications © 2024 IEEE.

关键词： Enhancing security Session management Vulnerabilities Web applications

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A Scalable Machine Learning Model for Sales Forecasting using PySpark

A Scalable Machine Learning Model for Sales Forecasting usin...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Harshitha, Talluri Tanya, Simhadri Jaswanthi, Thadakaluru Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Sales forecasting is a useful tool used for estimating future demand and sales. It provides insightful information for strategic decision-making, resource allocation, and inventory control. This research aims to utilize various machine learning algorithms for constructing a robust sales forecasting model, with the help of the Machine Learning library in PySpark. PySpark is chosen for its scalability, parallel processing framework, and seamless integration with machine learning libraries, making it an ideal choice for handling large datasets. MLlib is a powerful machine learning library within PySpark, offering a comprehensive set of scalable and distributed algorithms that enable efficient processing and analysis of large datasets. The research also incorporates time-series analysis. The performance of the research is evaluated using evaluation metrics R-Square(R2) value Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), Mean Absolute Error (MAE). Out of all experimented models, Gradient Boosting Regressor stands out as the best-performing model with R-squared value of 0.93, showcasing its effectiveness in accurate sales predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Augmented Data Protection: Fortifying Confidentiality through CBBPH approach 15

Augmented Data Protection: Fortifying Confidentiality throug...

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

作者： Tripathy, Aayushi Jeeban Tiwari, Utkarsh Anirudh, S. Ashwani, Siddhant Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The foundation of contemporary data security, guaranteeing integrity and confidentiality in digital communications, is referred to as cryptography. On the other side, image steganography strengthens data security by providing a stealthy way to hide information within seemingly same images. A new level of data camouflage is added by the tactile writing system Braille, whose small representation makes effective use of pixel components for storage. Columnar transposition is also applied to improve security by further obscuring the contained data. To improve data security, this paper presents the Columnar Binary Braille Pixel Hiding (CBBPH) approach, a revolutionary technology that combines Braille encoding, image steganography, encryption, and columnar transposition synergistically. The proposed method involves encoding plaintext messages into Braille, transposing them using columnar transposition, and then embedding them into the pixels in the least important portions of the image. The CBBPH strategy represents a significant breakthrough in data security techniques as experimental results show that it generates higher values in Peak Signal-to-Noise Ratio (PSNR) metric computations, indicating superior visual fidelity. ©2024 IEEE.

关键词： Steganography

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