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A Brain-computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition 2

A Brain-Computer Interface Augmented Reality Framework with ...

2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Mustafa, Yasmine Elmahallawy, Mohamed Luo, Tie Eldawlatly, Seif Missouri University of Science and Technology Computer Science Department MO United States German University Digital Media Engineering and Technology Department Cairo Egypt Ain Shams University Computer and Systems Engineering Department Egypt The American University Computer Science and Engineering Department Cairo Egypt

ISBN: (纸本)9798350300802

Brain-computer Interface (BCI) initially gained attention for developing applications that aid physically impaired individuals. Recently, the idea of integrating BCI with Augmented Reality (AR) emerged, which uses BCI not only to enhance the quality of life for individuals with disabilities but also to develop mainstream applications for healthy users. One commonly used BCI signal pattern is the Steady-state Visually-evoked Potential (SSVEP), which captures the brain's response to flickering visual stimuli. SSVEP-based BCI-AR applications enable users to express their needs/wants by simply looking at corresponding command options. However, individuals are different in brain signals and thus require per-subject SSVEP recognition. Moreover, muscle movements and eye blinks interfere with brain signals, and thus subjects are required to remain still during BCI experiments, which limits AR engagement. In this paper, we (1) propose a simple adaptive ensemble classification system that handles the inter-subject variability, (2) present a simple BCI-AR framework that supports the development of a wide range of SSVEP-based BCI-AR applications, and (3) evaluate the performance of our ensemble algorithm in an SSVEP-based BCI-AR application with head rotations which has demonstrated robustness to the movement interference. Our testing on multiple subjects achieved a mean accuracy of 80% on a PC and 77% using the HoloLens AR headset, both of which surpass previous studies that incorporate individual classifiers and head movements. In addition, our visual stimulation time is 5 seconds which is relatively short. The statistically significant results show that our ensemble classification approach outperforms individual classifiers in SSVEP-based BCIs. © 2023 IEEE.

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Exploring The Potential of Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study 4

Exploring The Potential of Machine Learning for Early Diagno...

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4th International Conference on Intelligent engineering and Management, ICIEM 2023

作者： Devi Padma, K. Harshitha, P. Nafiza, R. Lilian, J. Felicia Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350341126

The neurological ailment Parkinson's disease affects millions of individuals globally. Contrarily, an early detection of the illness will aid in its efficient treatment. With machine learning, which is still in its infancy, there are a number of prospects for computer-aided identification and diagnosis that may lessen inescapable medical issues and intrinsic clinical uncertainty, offer direction, and enhance decision-making. In an effort to improve the technique for diagnosing Parkinson's disease, we have proposed a model for early detection of the condition. Performance and speed were taken into consideration when XGBoost, a new machine learning technique, was developed. A decision tree-based technique called Extreme Gradient Boosting, or XGBoost, was developed. © 2023 IEEE.

关键词： Decision trees

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Leveraging Machine Learning to Predict Asthma following Vaccination 5

Leveraging Machine Learning to Predict Asthma following Vacc...

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

作者： Gull, Seema C. NaveenKumar, C.G. Gull, Karuna C. Sanskruti, V.B. Laddi, Mahantesh Karnataka State Open University Department of Studies in Computer Science Mysuru India S.G.Balekundri Institute of Technology Department of Computer Science & Business Systems Belagavi India M.S. Sheshgiri College Engineering and Technology Department of Biomedical Engineering Belagavi India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350376685

This project seeks to employ more sophisticated approaches to machine learning to determine asthma occurrences after vaccine administration. As vaccine distribution becomes more widespread around the world, it is important to monitor and prevent issues such as asthma. The goals for future research include the creation of risk-adjusted models that may predict the risk of asthma following vaccination, depending on patients' demographic information and the characteristics of the vaccines. The research uses several sophisticated techniques such as Artificial-Neural-Networks (ANN), Random-Forests (RF), Naïve-Bayes (NB), and logistic regression (LR) to improve the ability to predict and identify prevention measures and healthcare management. Thus, a significant innovation of this work is the comprehensive assessment of these algorithms when exposed to noise, which is a common issue in many medical data sets. The study also showed that Random-Forest and ANN models were significantly more accurate and yielded larger ROC AUC scores than other models;moreover, they were much less sensitive to noise with noise values ranging from 0 to 0.5. This places RF and ANN on a better pedestal to offer a better prediction of post-vaccination asthma than the other models, especially in a scenario where there is the possibility of data contamination. The present findings can thus help create better and more resistant models of prediction that are useful in better health management and treatment of asthma over and after vaccination. © 2024 IEEE.

关键词： Logistic regression

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Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization

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National science Review 2023年第5期10卷 240-250页

作者： Sijie Zhou Yi Wu Yu Zhao Zhen Zhang Limin Jiang Lin Liu Yan Zhang Jijun Tang Ying-Jin Yuan Frontiers Science Center for Synthetic Biology (Ministry of Education) Tianjin University Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin University Institute for Systems Genetics NYU Langone Health School of Computer Science and Technology College of Intelligence and ComputingTianjin University Epigenetic Group Frasergen Bioinformatics Co.Ltd. Department of Computer Science University of South Carolina

Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary *** Chromosome Rearrangement and Modification by LoxP-mediated Evolution(SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural ***,we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes(synⅡ,synⅢ,synⅤ,circular synⅥ,synⅨR and synⅩ).Remarkably,we find that the rearrangement events exhibit a specific landscape of *** further reveal that the landscape is shaped by the combined effects of chromatin accessibili and spatial contact *** rearrangements tend to occur in 3D spatially proximal and chromatin-accessible *** enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution,and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.

关键词： synthetic biology genome rearrangement hierarchical chromatin organization synthetic yeast genome SCRaMbLE Saccharomyces cerevisiae

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A Hybrid Neural Network-based Approach for Forecasting Water Demand

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computers, Materials & Continua 2022年第10期73卷 1365-1383页

作者： Al-Batool Al-Ghamdi Souad Kamel Mashael Khayyat Department of Computer Science and Artificial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia Department of Computer and Network Engineering College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia Department of Information Systems and Technology College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia

Water is a vital *** supports a multitude of industries,civilizations,and ***,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is ***,it is crucial to forecast water demand to provide it to sectors either on regular or emergency *** study aims to develop an accurate model to forecast daily water demand under the impact of climatic *** forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the *** on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’*** on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series ***,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand.

关键词： Water demand forecasting artificial neural network multivariate time series climatic conditions particle swarm optimization hybrid algorithm

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Security Threat and Vulnerability Assessment and Measurement in Secure Software Development

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computers, Materials & Continua 2022年第6期71卷 5039-5059页

作者： Mamoona Humayun NZ Jhanjhi Maram Fahhad Almufareh Muhammad Ibrahim Khalil Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufKSA School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Computer Science Bahria UniversityIslamabadPakistan

Security is critical to the success of software,particularly in today’s fast-paced,technology-driven *** ensures that data,code,and services maintain their CIA(Confidentiality,Integrity,and Availability).This is only possible if security is taken into account at all stages of the SDLC(Software Development Life Cycle).Various approaches to software quality have been developed,such as CMMI(Capabilitymaturitymodel integration).However,there exists no explicit solution for incorporating security into all phases of *** of the major causes of pervasive vulnerabilities is a failure to prioritize *** the most proactive companies use the“patch and penetrate”strategy,inwhich security is accessed once the job is *** cost,time overrun,not integrating testing and input in SDLC,usage of third-party tools and components,and lack of knowledge are all reasons for not paying attention to the security angle during the SDLC,despite the fact that secure software development is essential for business continuity and survival in today’s ICT *** is a need to implement best practices in SDLC to address security at all *** fill this gap,we have provided a detailed overview of secure software development practices while taking care of project costs and *** proposed a secure SDLC framework based on the identified practices,which integrates the best security practices in various SDLC phases.A mathematical model is used to validate the proposed framework.A case study and findings show that the proposed system aids in the integration of security best practices into the overall SDLC,resulting in more secure applications.

关键词： Security secure software development software development life cycle(SDLC) confidentiality integrity availability

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BnGraphemizer: A Grapheme-based Tokenizer for Bengali Handwritten Text Recognition 9

BnGraphemizer: A Grapheme-based Tokenizer for Bengali Handwr...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Basher, Mohammad Jahid Ibna Noor, Mohammad Raghib Afroze, Sadia Ahmed, Ikbal Hoque, Mohammed Moshiul Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Dhaka Department of Management Information Systems Dhaka Bangladesh CCN University of Engineering & Technology Computer Science and Engineering Cumilla Bangladesh

ISBN: (纸本)9798350319651

Bengali Handwritten Text Recognition (HTR) is a challenging task due to the text's cursive disposition and significant variations in the graphemic unit, the smallest written unit in alphasyllabary languages. These graphemic units, or simply graphemes, may consist of multiple characters and diacritics, and the order of characters or diacritics between textual and visual representations is only sometimes preserved. Moreover, Bengali conjunct characters, a subset of the graphemes, often have unique visual representations vastly different from the characters they comprise. Because of these inherent characteristics, a character-based tokenizer fails to tokenize the label text appropriately for BHTR in a supervised setting, making it challenging to adapt the existing sequence-to-sequence frameworks often used for Handwritten Text Recog-nition (HTR). To address this, this paper proposes a trie- based text tokenizer (called BnGraphemizer) that allows one to tokenize text into a set of graphemes instead of characters. Experimental investigations show that the BnGraphemizer-based BHTR system performs better regarding higher Word Recognition Rate (WRR) than its character-based counterpart in two of three Bengali handwritten datasets. © 2023 IEEE.

关键词： Character recognition

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"Risky or Trustworthy?": User Behavior towards Biometric Authentication Method 7

"Risky or Trustworthy?": User Behavior towards Biometric Aut...

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7th Information systems International Conference, ISICO 2023

作者： Kusyanti, Ari Catherina, Harin Puspa Ayu Effendrik, Popong Santoso, Nurudin Ekowati, Nurul Syamsi Department of Computer Science Universitas Brawijaya Indonesia Department of Information Systems Universitas Brawijaya Indonesia Department of Electrical Engineering Politeknik Negeri Malang Indonesia

Smartphones as a means of communication are not only used to send and receive messages and make phone calls. Today's smartphones are also intended to store personal data and sensitive information hence it is vulnerable to be illegally accessed by others. Fingerprint as a biometric lock screen for smartphones can be trusted to ensure the security and privacy of smartphone users. A fingerprint is closely related to a user's identity. When using fingerprints, users provide their personal biometric pattern, unknowingly giving their privacy and making them vulnerable to unauthorized use of their biometric pattern. This study is designed to reveal the factors that influence users when using fingerprints as a smartphone lock, using eight variables adapted from previous research. The data analysis used is Partial Least Squares (PLS) analysis. The results of this study suggest that trust, attitudes, and perceived risk are the factors that influence users when using their fingerprint as a smartphone lock. © 2023 The Authors. Published by Elsevier B.V.

关键词： Smartphones

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Rule and Encryption based Secure Routing for Internet of Vehicle Networks 2

Rule and Encryption based Secure Routing for Internet of Veh...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Anand, M. Muthurajkumar, S. Selvi, M. Thangaramya, K. Svn, Santhosh Kumar Kannan, A. Anna University Madras Institute of Technolog Department of Computer Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication devices and hence it helps in information exchange to vehicle drivers and the passengers. In IoV, Cybersecurity threats including Denial-of-Service (DoS) attacks are present and it compromises the security in communication among the vehicles and the passengers. Therefore, it is needed to develop a robust security mechanism that can protect the data communicated through routing in IoV. The inclusion of Encryption and Signature verification techniques in the routing algorithm will enhance the security. This paper proposes a Rule with Encryption-Oriented Secure Routing Protocol (REO-SRP) is proposed using clustering and RSA encryption with DSA Digital signature verification with rules for routing. Here, routing is performed after encryption of data and analysis of other vehicles using rules for reliable and secured data delivery. © 2024 IEEE.

关键词： Vehicles

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