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Advancing Brain-Computer Interfaces: A Deep Learning Approach for Enhanced Neural signal processing 3

Advancing Brain-Computer Interfaces: A Deep Learning Approac...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Dharmendra, Kaku Riya Verma, Deepak Kumar Vats, Satvik Marwadi University Department of Computer Engineering Rajkot India Madan Mohan Malaviya University of Technology Department of Computer Science and Engineering U.P. Gorakhpur India

ISBN: (纸本)9798331542375

Brain-to-computer interfaces (BCIs) are a developing technology that has led to a straight link among the human brain and various systems. The complexity and lack of predictability of brain signals make the development of a trustworthy, accurate BCI system. In this study, we look into how advanced deep learning approaches like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Graph Isomorphism Networks (GIN), and Convolutional Neural Network (CNN) can be used to improve neural signal processing performance in BCI. LSTM and GRU are good at discovering the temporally related EEG sequences, while CNNs extract the spatial feature of raw EEG data very well. We model and analyze graph-structured data from functional brain networks using GIN, revealing insights into spatial dynamics and inter-channel connections. Both graph-based and spatiotemporal features are integrated as proposed in the framework, and a deep learning architecture is constructed for signal decoding and classification that allows for a higher signal decoding and classification accuracy. Our experimental results on publicly available EEG datasets demonstrate that this approach outperforms standalone deep learning models and traditional machine learning approaches. It designs its solutions to attain higher accuracy, robustness, and the ability to generalize across various BCI tasks, including cognitive state recognition and motor imagery classification. The result indicates that our model LSTM is 96.92%, CNN is 95.95%, GRU is 95.40%, and GIN is 88.68%. Finally, this study demonstrates how advanced deep learning techniques can address the current limitations of BCI systems, opening the door for more reliable and successful real-world applications. © 2025 IEEE.

关键词： Convolutional neural networks

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From data to Diagnosis: A Journey with Machine Learning, Hyperparameter Tuning, and Ensemble Learning for Disease Prognostication 3rd

From Data to Diagnosis: A Journey with Machine Learning, H...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Hossain Efat, Anwar Faysal Ferdous, Shahriar Islam Nayem, Shajibul Islam Surhyth, Azmain Department of Computer Science and Engineering North Western University Khulna9100 Bangladesh

ISBN: (纸本)9789819739363

While the classification of medical data poses a formidable challenge, it remains a source of profound fascination within the research community. Its potential to enable precise preventive measures against future ailments is a driving force. Machine learning (ML) algorithms play a pivotal role in this endeavor, and the effectiveness of these algorithms is deeply intertwined with the pre-processing of data. This study seeks to evaluate a range of ML-based models with a specific focus on their utility in predicting Autism Spectrum Disorder in Toddlers (ASD-T) and Chronic Kidney Disease (CKD). The pre-processing techniques encompass a spectrum of procedures such as imputing missing values, feature selection, resampling, and feature scaling. In the pursuit of precision, hyperparameter tuning is employed to fine-tune these models. Subsequently, six classification techniques—Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Extra Tree (ET), and Random Forest (RF)—are executed using a rigorous tenfold cross-validation approach to classify the datasets. The integration of ensemble learning techniques, namely bagging and boosting, enhances performance while safeguarding against bias and overfitting. Although the literature is replete with experimental examples, room for improvement persists. This study distinguishes itself by achieving significantly superior results compared to prior research. Notably, our proposed model, the traditional ET classifier, delivers unparalleled performance, boasting a remarkable 100% accuracy in the ASD-T dataset and an impressive 99.85% accuracy in the CKD dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Performance Evaluation of NOMA Systems 3rd

Performance Evaluation of NOMA Systems

引用

3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Kanojiya, Shivaji Kumar, Arvind Department of Electronics and Communication Engineering NIT Kurukshetra Haryana Kurukshetra India

ISBN: (纸本)9789819719426

Non-orthogonal multiple access (NOMA) stands out as an auspicious technique aimed at improving throughput, enabling maximum data rates (reaching 100+ MBPS), and reducing latency within the context of fifth-generation (5G) wireless communication systems, particularly in scenarios involving massive Internet of Things (IoT) connectivity. NOMA enables users to efficiently utilize shared frequency and time resources through power domain multiplexing. NOMA operates primarily based on two fundamental techniques. The first technique is superposition coding and the second method is successive interference cancelation(SIC). Within this context, the employed modulation schemes encompass binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). An evaluation and simulation of several key metrics in the NOMA system, such as bit error rate (BER), signal-to-interference noise ratio (SINR), channel capacity, and outage probability, are conducted. Specifically, our study focuses on the power domain downlink NOMA system, and we observe its performance under different channel conditions, including Rayleigh fading, Rician fading, and Additive White Gaussian Noise (AWGN) channel, involving two users. Furthermore, our investigation includes a comparative analysis of channel capacity and outage probability through simulation, contrasting the performance of MIMO NOMA with that of NOMA. The primary goal of NOMA is to promote spectrum sharing, a concept reminiscent of cognitive radio networks. NOMA has the potential to enhance channel capacity in the context of 6G communication systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： signal to noise ratio

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Proceedings - 2023 3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

Proceedings - 2023 3rd International Signal Processing, Comm...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

ISBN: (纸本)9798350307023

The proceedings contain 153 papers. The topics discussed include: the control of dual-input DC-DC converter system based on fuzzy MPPT;research on the product automatic pricing model based on grey relation analysis;prediction of water quality of Xincheng bridge in Lanzhou based on CNN-LSTM model;research on optimization of power grid planning for comprehensive transmission and distribution tariff reform;research on the collaborative interaction between electricity and computing power for green development — taking data centers as an example;categorizing pain intensity through facial recognition using deep learning;the application of IoT+Artificial intelligence technology in adoption agriculture;a study on train schedule planning based on simulated annealing algorithm;research on digital system of collaborative cycle integration information transformation in electric power enterprises;and research and design of a deep learning-based free trade zone data analysis system.

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A Comprehensive Study: Evaluating Machine Learning Algorithms with Credit Card Transaction data 3rd

A Comprehensive Study: Evaluating Machine Learning Algorithm...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Basak, Sarnali Shanto, Shaikh Qutub Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9789819739363

The financial sector places significant emphasis on the detection of credit card fraud, and machine learning techniques have emerged as a promising solution. Nevertheless, questions regarding the interpretability of machine learning models and their resilience against adversarial attacks within the realm of credit card fraud detection persist. This study aims to bridge these gaps and underscore their pivotal role in advancing the field. It involves a comprehensive analysis of a credit card transaction dataset and the development of multiple machine learning models for fraud detection. Python scripts leverage well-known libraries such as Pandas, NumPy, Seaborn, Scikit-Learn, TensorFlow, and Matplotlib for data processing, visualization, and machine learning tasks. For fraud detection, the dataset is partitioned into training and testing sets, and three machine learning models, namely XGBoost, Random Forest, and Support Vector Machine (SVM), are applied. Performance metrics, encompassing accuracy, sensitivity (recall), F1-score, Matthews Correlation Coefficient (MCC), Balanced Classification Rate (BCR), and confusion matrices, are computed and presented. Additionally, a Long Short-Term Memory (LSTM) neural network is deployed for sequence-based fraud detection. This research offers a comprehensive roadmap for analyzing credit card transaction data and constructing machine learning models to detect fraudulent activities, underscoring the importance of data exploration and model evaluation in fraud detection systems. It emphasizes the significance of feature engineering in enhancing the effectiveness of fraud detection models and equips decision-makers with the ability to make informed choices based on model predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Healthcare Cost Patterns and Prediction: Investigating Personal datasets Using data Analytics 3rd

Healthcare Cost Patterns and Prediction: Investigating Perso...

引用

3rd international conference on signal and data processing, icsdp 2023

作者： Islam, Md. Aminul Nag, Anindya Chandra, Pretam Mishra, Bhupesh Kumar Fahim, S. M. Firoz Ahmed Hoque, Md Mozammel School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom College of Engineering and Physical Sciences University of Birmingham Birmingham United Kingdom Computer Science & Engineering Discipline Khulna University Khulna 9208 Bangladesh Department of Computer Science & Engineering Adamas University Kolkata 700126 India Centre of Excellence for Data Science AI and Modelling University of Hull Hull United Kingdom Department of Computer Science & Engineering American International University Dhaka Bangladesh Gannon University Erie United States

ISBN: (纸本)9789819795772

The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter, since the significance of health insurance as a research topic has markedly escalated following the occurrence of the pandemic. The dataset employed in this research comprises 1338 observations, 7 columns, and corresponds to individual medical expenditures in the United States which is available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers utilized machine learning models including neural network, XAI, auto modeling to determine the correlation between pricing and the aforementioned attributes. The training process involved partitioning the dataset into a 80–20 ratio for training and evaluation respectively. As a consequence, the system achieved an accuracy rate of 97% by Gradient Boosting but we corrected it as 92% by Gradient Boosting Regressor by encoding and hyper tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Insurance Machine learning Medical cost Prediction

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Research on Immersive Virtual Reality Visualization Display System Based on Big data Technology 3

Research on Immersive Virtual Reality Visualization Display ...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Liu, Hongli Feng, Rui Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798400712692

With the rapid development of virtual reality technologies, immersive experiences have become important elements for promoting interactivity and visualization, especially in education and entertainment, where their importance is forming the most obvious one. The increasing availability of big data technologies has enabled virtual reality systems to have robust and dynamic data support, unlocking the enormous potential of visualization platforms in information processing and presentation Notably, large data set processing, real -. and with increased time-analysis capabilities Virtual Reality Systems Provide New Opportunities for Innovation and Improvement By taking advantage of big data processing capabilities, virtual reality visualization systems not only provide multiple sources of information for inclusion together not only better but This also with different aspects, connectivity enables business, improving users' psychological experiences The trend emphasizes that leveraging big data technology to optimize and innovate virtual reality systems is not only critical but also an important direction for future knowledge dissemination and data communication. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Big data technology display systems immersive virtual reality visualization

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Uncovering Bullying on Social Media Platforms: A Comprehensive Study of Machine Learning Classifiers for Cyberbullying Detection 3rd

Uncovering Bullying on Social Media Platforms: A Comprehensi...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Hamim, Hasibul Uddin, Khandaker Mohammad Mohi Mim, Mst. Nishat Tasnim Mostafiz, Rafid Based, Md. Abdul Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of ComputerScience and Engineering Southeast University Dhaka1208 Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (纸本)9789819739363

One of the most delicate concerns is cyberbullying due to today’s worldwide web advancement. Bullying may contribute to severe consequences, including mental health issues, academic performance challenges, and job dropout. It can also instill hatred in individuals or communities, making cyberbullying prevention critical. Our main goal in this paper is to mitigate the impact of cyberbullying and contribute to fostering a healthier Internet environment where individuals can interact without fear of unwarranted criticism. We propose a proactive approach involving early detection mechanisms. In this study, we employ a comprehensive analysis of tweets using multiple factors. The data is preprocessed using machine learning methods and natural language processing (NLP), followed by Term Frequency Inverse Document Frequency (TF-IDF) and Count Vectorizer. Several machine learning models, including Light Gradient Boosting Machine (LightGBM) and 12 others, are evaluated. The dataset comprises over 47,000 tweets from various perspectives. Our analysis indicates that the LightGBM classifier outperforms other models with an accuracy of 94.24%, precision of 95%, recall of 94%, and an F1-score of 94%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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EpiCNN-LSTMDetect: A Combined Deep Learning Method for Identifying Epileptic Seizures 3

EpiCNN-LSTMDetect: A Combined Deep Learning Method for Ident...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kumar, Priyaranjan Upadhyay, Prabhat Kumar Birla Institute of Technology Mesra Department of Electrical & Electronics Engineering Ranchi India

ISBN: (纸本)9798331523893

Epilepsy, characterized by recurrent seizures, necessitates timely and accurate detection for effective management. This study presents EpiCNN-LSTMDetect, a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze EEG data for seizure identification. Leveraging the CHBMIT EEG dataset, the model extracts features such as mean, skewness, kurtosis, and STFT. These features effectively capture spatial and temporal brain dynamics. EpiCNN-LSTMDetect achieved exceptional performance. It recorded an average accuracy of 99.61%, sensitivity of 99.83%, specificity of 99.23%, and an F1 score of 99.64%. The results surpass existing methods. These findings highlight the potential of advanced deep learning techniques in improving epilepsy care. © 2025 IEEE.

关键词： CNN EEG Epilepsy detection LSTM signal processing

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

3rd International Conference on Signal Processing and Machin...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

The proceedings contain 64 papers. The topics discussed include: diffusion models-based data augmentation for the cell cycle phase classification;data augmentation by using GANs and image transformation in facial emotion classification;researches advanced in fine-grained image classification based on deep learning;performance analysis of attention mechanism and teacher forcing ratio based on machine translation;collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle;the advance of underdetermined linear regression optimization based on implicit bias;bird audio recognition based on convolutional neural network;the configuration optimization of the plants in vertical farming by 3D modeling;robot navigation based on multi-sensor fusion;a study on the model predictive control based on convolutional neural network;channel capacity on visible light communication;and the gap between theoretical computational theories and application models.

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