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Note on 'RAKS: robust authentication and key agreement scheme for satellite infrastructure'

Note on 'RAKS: robust authentication and key agreement schem...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Tsai, Chun-Chieh Sun, Chin-Yu Yang, Shih-Hsuan National Taipei University of Technology Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Low Earth orbit (LEO) satellites play an important role in modern communication systems. Recently, Khan et al. [1] designed a robust authentication and key agreement scheme (RAKS) for LEO satellite communication. They also performed security analysis of RAKS to verify the declared safety. Upon careful examination, however, we discovered several security flaws in RAKS, including insufficient information for user identification, vulnerability to elaborate DoS attacks, and inaccurate estimation of computational costs. In this paper, we highlight these flaws and suggest potential modifications to improve the scheme. © 2023 IEEE.

关键词： Satellites

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Explainability to Business: Demystify Transformer Models with Attention-based Explanations 2

Explainability to Business: Demystify Transformer Models wit...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Thiruthuvaraj, Rajasekhar Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science & Engineering Kottayam India

ISBN: (纸本)9781665456302

Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations. © 2023 IEEE.

关键词： Forecasting

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Brain-computer Interfaces: A Key to Neural Communication's Limitless Possibilities 1

Brain-Computer Interfaces: A Key to Neural Communication's L...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Saxena, Shilpi Ranjan, Mritunjay Kr. Sattar, Arif Md. School of Computer Science and Engineering Sandip University Nashik India A.M. College Dept. of Computer Science & Information Technology Gaya India

ISBN: (纸本)9798350361780

Brain-computer Interfaces (BCIs) have revolutionized human-machine interaction by establishing a direct communication pathway between the brain and an external machine. These systems rely on neural signals, acquired invasively through implanted electrodes or non-invasively using scalp sensors. Signal processing algorithms extract meaningful information from these signals, such as motor intentions or cognitive states. BCIs have applications in various domains, including healthcare, neurorehabilitation, gaming, virtual reality, and augmented reality. However, BCI development faces challenges such as improving signal acquisition and decoding techniques, enhancing reliability and robustness, and ensuring user safety and privacy. The translation of BCI technology from the lab to real-world applications requires addressing ethical, legal, and societal considerations. Standardized evaluation metrics and protocols are needed to compare and validate different BCI approaches effectively. In conclusion, BCIs have the potential to revolutionize human-machine interaction across various domains, but challenges remain in technology refinement, user acceptance, and ethical considerations. Future research directions include enhancing spatial and temporal resolution of neural signals, exploring novel electrode materials, and investigating long-term implantation methods. © 2024 IEEE.

关键词： Augmented reality

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Sybil Attack Models of MM-Wave Communication for LEO Satellite Network in Cybersecurity

Sybil Attack Models of MM-Wave Communication for LEO Satelli...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Mohamed, Abdiasis Ahmed Chang, Shih Hao National Taipei University of Technology Dept. Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

With the rapid progress of six-generation (6G) wireless communication technologies, Low Earth Orbit (LEO) satellite global network technology played a very important role and promoted millimeter-wave (mm-wave) systems gained demand. However, security remains a critical concern in wireless network design and usage. In this paper, we propose a method based on an artificial intelligence technique, machine-learning algorithms in cybersecurity. This approach handles significant security dataset training, using different techniques such as decision trees and Recurrent Neural networks, which allow feature selection and Sybil attack classification. In addition, we refer to the guidelines and specifications of orbit satellite standards in ISO 16164-2015 and ISO 27852-16 to validate the satellite transmitter. Simulation results arise an improvement in security and the ability to detect and prevent Sybil attacks. Overall, this approach makes a significant contribution to the field of satellite cybersecurity by providing a standard-compliant solution © 2023 IEEE.

关键词： Recurrent neural networks

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Image to Audio Using LLM for Blind People 1

Image to Audio Using LLM for Blind People

引用

1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Kumaresan, T. Geetha, A. Rajeshkumar, G. Benita Gracia Thangam, J. Eswaran, Malathi Rajkumar, Thivya Bannari Amman Institute of Technology Dept. of Computer Science and Engineering Sathyamangalam India Presidency University Dept. of Computer Science and Engineering Bangalore India Velalar college of Engineering and Technology Dept. of Artificial Intelligence and Data science Erode India Kongu engineering College Dept. of Computer Technology Perundurai India

ISBN: (纸本)9798331532956

Blind people face many difficulties while engaging with their surroundings. This project is all about creating innovative solutions for the blind people by providing the information in the format of audio. Image-to-Audio transformation system specifically designed for natural language understanding using Large Language Model (LLM).The system will capture a picture or video of the situation present in front of the blind person and then provide descriptions about the images. The objective is to provide guidelines for blind people by understanding their surroundings through an LLM model and provide audio response. The CLIP model analyzes the objects, scenes, text etc.. and provides relevant information. The extracted visual data is then passed through GTTs (Google-Text-To-Speech) to create rich audio descriptions. It provides accurate and detailed representation of the visual content, allowing blind people to understand their surroundings. The project involves integrating convolutional neural networks and Large Language Model (e.g GPT-4) technologies to create a real-time image-to-text system for blind individuals. The CNN model is used to calculate the distance of the object from the user. We are integrating the Google Assistants to open the app automatically by the blind people with help of their voice. © 2024 IEEE.

关键词： Image retrieval

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UAV Platform for Real-time Indoor Mapping 2

UAV Platform for Real-time Indoor Mapping

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2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Kshirsagar, Spruha Nitin Rawat, Abhishek Bhandari, Ravi Visvesvaraya National Institute of Technology Dept. of Computer Science and Engineering Nagpur India Dept. of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350368949

SLAM enables Robots and UAVs to effectively understand unknown surroundings and estimate their own position within those environments. Recent advancements in 2D mapping have significantly increased its popularity for navigation tasks, primarily because it is easier to generate and requires less computational power compared to 3D mapping. This paper provides a comparison of two widely used ROS-based 2D SLAM libraries: Google Cartographer and Hector SLAM, deployed on a custom built UAV that uses 2D LiDAR, and finds Hector SLAM to be more feature rich as well as computationally efficient. © 2024 IEEE.

关键词： Robot Operating System

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Diagnosis of Parkinson’s Diseases with MDVP Features Using Machine Learning Approaches

Diagnosis of Parkinson’s Diseases with MDVP Features Using ...

引用

2024 IEEE International Conference on Biomedical engineering, computer and information technology for Health, BECITHCON 2024

作者： Sikder, Iftikhar Ahammad Uddin, Khandaker Mohammad Mohi Nur-A-Alam, Md. Based, Md Abdul Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Sunamgonj Science and Technology University Dhaka Bangladesh

ISBN: (纸本)9798331534356

Parkinson's disease is a neurodegenerative disorder that poses a significant global health challenge. Its prevalence has prompted the urgent need for fast and immediate diagnosis to enable timely intervention and treatment planning. Parkinson's disease causes both motor and non-motor symptoms necessitating a comprehensive approach to prediction. This research explores the intricacies of Parkinson's disease prediction, highlighting the importance of multidimensional data analysis. Acknowledging the evolving landscape of medical research, the study advocates for a comprehensive approach to enhance the early detection of Parkinson's disease, provides significant insights on the possibilities of machine learning techniques for improved patient outcomes, particularly focusing on the distinctive features offered by MDVP (Multidimensional Voice Program). Including MDVP features enhances the predictive capabilities of the models, offering a novel perspective for accurate early detection and applying machine learning models like AdaBoost, Multilayer Perceptron, Decision Tree, Random Forest, Logistic Regression, K-nearest Neighbors, Support Vector Machine (SVM), Gradient Boosting, & Naive Bayes. This research explores the intricacies of Parkinson's disease prediction. Results highlight the remarkable performance of the Support Vector Machine, achieving an accuracy of 98.98% & Gradient Boosting, 97.28% accuracy with the feature selection technique XGBoost and SelectKBest underscoring the significance of MDVP features in refining the predictive accuracy. This study leads to continuous efforts for the improvement of diagnostic accuracy and prognosis for Parkinson's disease, emphasizing the importance of integrating MDVP features within the machine learning framework. © 2024 IEEE.

关键词： Support vector machines

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Face Generation and Recognition in Forensic science 11

Face Generation and Recognition in Forensic Science

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ravi, Gayathri Joy, Heynes Jitto, Jeffin Joshy, Jocelyn Jose, Jisha Mary Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350379136

Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic science is a promising step in st

关键词： Convolutional neural networks

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Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach 10

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Venkatesan, R. Sangeetha, M. Nivedha, S. Selvakumar, M. Chandru, K.S. Parthasarathi, P. Bannari Amman Institute of Technology Department of Information Technology Erode India K S Rangasamy College of Technology Department of Information Technology Namakkal India Hindusthan College of Engg and Technology Dept. of Computer Science & Engineering Coimbatore India Bannari Amman Institute of Technology Dept. Computer Science & Business Systems Erode India Bannari Amman Institute of Technology Dept. Computer Science and Engineering Erode India

ISBN: (纸本)9798350384369

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients. © 2024 IEEE.

关键词： Tweets

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