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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

引用

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

作者： Upadhyay, Deepak Gupta, Mridul Sharma, Kunj Bihari Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Stealth Encoding - Concealing data with the Least Significant Bit Method

Stealth Encoding - Concealing data with the Least Significan...

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Kantapalli, Bhaskar Meruga, Siva Gnani Polagani, Prasanthi Pingali, Venu Babu Seelam, Surya Teja Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Mandavalli India Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Vijayawada India Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Machilipatnam India

ISBN: (纸本)9798350396485

Data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message's content and the fact that it is being sent. In order to make it challenging to Figure out that a hidden message is present in a specific film, this technique involves concealing secret data within a conventional, non-secret file. At its destination, the secret data is subsequently removed. Several uses of steganographic technology have distinct requirements. Here, we suggest a method for concealing data, including reverse data hiding for data extraction. The textual data will be secured in different frames of the video and audio using the Least Significant Bit (LSB) and related algorithms. We can conclude that the original data will never change the characteristics of video or audio even after applying steganography techniques. © 2023 IEEE.

关键词： Steganography

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Optimal Channel Selection for EEG-Based Enhanced Brain Robot Interface for Autonomous Wheelchair 12

Optimal Channel Selection for EEG-Based Enhanced Brain Robot...

引用

12th International Conference on Awareness science and Technology, iCAST 2023

作者： Sibli, Sabbir Ahmed Kabir, Raihan Jannat, Nuray Watanobe, Yutaka Islam, Md Rashedul Dept. of Computer Science and Engineering Fareast International University Banani Dhaka1213 Bangladesh Dept. of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan Dept. of Computer Science and Engineering BRAC University Dhaka1212 Bangladesh Dept. of Computer Science and Engineering University of Asia Pacific Dhaka1216 Bangladesh

ISBN: (纸本)9798350324693

Creating an autonomous wheelchair system based on EEG (electroencephalogram) signals is a fascinating and challenging concept. In non-invasive Brain robot Interface (BRI), scalp EEG signals acquired from the multiple channels come with different artifact types, majorly as an electrooculogram (EoG). Also, all the channels don't carry equally important information regarding the particular motor activities. The primary contribution of this paper is to denoise the EEG signal and then find the optimal channels to achieve the most informative EEG signals for the motor activity recognition. Thus, this research proposes a model where firstly t he E oG a rtifacts h ave b een removed from EEG signals with the regression method and other noises with Discrete Wavelet Transform (DWT). Secondly, It proposes a model of selecting optimal channels using a hybrid feature selection process, which includes a filter a nd w rapper method using sequential floating forward search (SFFS). Maximum Class Separability (MCS) and Support Vector Machine (SVM), both have been used for evaluating the channels. Then again SVM has been applied for classifying the motor activities by the signals recorded from the optimal channels. A dataset with four classes of human motor activities has been used for evaluating the whole proposed model. This proposed model shows the highest accuracy of 84.15% for the optimal channel set with Standard Deviation (STD) feature among other optimal channel sets of features. © 2023 IEEE.

关键词： Support vector machines

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Detecting Ransomware Threats Using Machine Learning Approach 8

Detecting Ransomware Threats Using Machine Learning Approach

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shetty, Sanmitha Chitharanjan Bastian, Rose Khader, Sarah Abdul St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Biomedical Literature Analysis to Predict Drug Interactions Using Multi Criteria Decision Making and LSTM-CNN Model

Enhancing Biomedical Literature Analysis to Predict Drug Int...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798350315127

The extraction of information from unstructured text has become a crucial task in the biomedical literature field. Specifically, the analysis of various relationships such as Drug Food, Drug Disease, Drug non-prescription and Drug Drug interactions has gained significant importance. To accomplish this, a combination of Multi Criteria Decision Making (MCDM) and machine learning techniques has been employed. A dataset has been curated and utilized to train a model based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The trained model enables accurate predictions and identification of relevant information pertaining to drug interactions in biomedical literature. This approach enhances the understanding and knowledge extraction from vast amounts of unstructured text data, leading to improved insights and potential advancements in the field of biomedical research. © 2023 IEEE.

关键词： Long short-term memory

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Eye Tracking System Using Convolutional Neural Network 5

Eye Tracking System Using Convolutional Neural Network

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Iskandar, Shatha Jaara, Zahraa Ali, Mohamad Abou Sbeity, Fatima Kasem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Lebanese University Dept. of Physics & Electronics Lebanon Notre Dame University Dept. of Electrical & Computer Engineering Lebanon

ISBN: (纸本)9798350303254

Several diseases and disabilities, including cerebral palsy (CP), cause patients to lose their ability to communicate easily with the outside world. In this paper we aim to detect the movement of the iris that can be used to move a wheelchair for CP patients, write sentences by simply looking at an interactive screen, or even by moving an artificial arm. Thus, the two-dimensional CNN model and the coding method, we proposed, tracks the eye movement, specifically the movement of the iris, using VGG image annotator software and a dataset of several people's eyes images A dataset of 500 images from several patients, where only the eyes were cropped and studied, was taken to train the model about eye movement and detection. The proposed model achieved a mean squared error drop of about 49,720 from the original we started with, which was 50,000. © 2023 IEEE.

关键词： Neural network models

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content 2

Deep Analysis of Machine Learning Techniques for the Effecti...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Ashish Varshney, Sudeep Set Sharda University Dept. of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms. © 2024 IEEE.

关键词： CBIR classifier feature extraction Random Forest SVM

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Detecting Retinal Damage From OCT Images 15

Detecting Retinal Damage From OCT Images

引用

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

作者： Vineetha, Kundrapu Ali, Md Jaffer Prathima, Narisetty Singh, Tripty Duraisamy, Prakash Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Wisconsin-Green Bay Dept. of Computer Science and Engineering Wisconsin United States

ISBN: (纸本)9798350370249

Optical coherence tomography (OCT) is a crucial imaging modality in ophthalmology, offering detailed retinal images essential for diagnosing various retinal disorders. In this study, we investigate the application of convolutional neural networks (CNNs) and transfer learning to classify retinal OCT images. We compare a custom CNN model with two pretrained models, MobileNet and VGG16, to explore their effectiveness in categorizing retinal diseases. Our study focuses on improving classification accuracy through image enhancement techniques, training methodologies, and model evaluation using confusion matrices. The results highlight the VGG16-based CNN model as the most effective, demonstrating its potential for accurate classification of retinal disorders based on OCT images. Furthermore, we delve into strategies to further enhance the performance of these models, such as data augmentation, finetuning, and hyperparameter tuning. We discuss the importance of these techniques in optimizing model performance and reducing overfitting. Additionally, we emphasize the significance of evaluating models using confusion matrices to gain insights into their performance across different classes. Overall, our findings suggest that CNNs, particularly the VGG16based model, hold promise for improving the automated diagnosis of retinal diseases using OCT imaging, which could lead to better patient outcomes in ophthalmology. © 2024 IEEE.

关键词： Convolutional neural networks

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System 6

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Liao, Jing Wu, Yadong Wang, Guijuan Wang, Fupan Chen, Huarong Long, Li Southwest University of Science and Technology Dept. Computer Science and Technology Mianyang China Sichuan University of Science and Engineering Dept. Computer Science and Engineering Yibin China

ISBN: (纸本)9798350325485

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint. © 2023 IEEE.

关键词： Decision trees

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