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A Deep Learning Approach to Automated Sleep Stages Classification Using Multi-Modal Signals

Procedia computer science 2023年 218卷 867-876页

作者： Santosh Kumar Satapathy Hari Kishan Kondaveeti S R Sreeja Hiral Madhani Nitinsingh Rajput Debabrata Swain Information and Communication Technology Pandit Deendayal Energy University Gandhi Nagar Gujarat 382007 India VIT-AP University Amaravati Andhra Pradesh 522237 India Assistant Professor CSE Group IIIT Sri City Computer Science and Engineering Pandit Deendayal Energy University Gandhi Nagar Gujarat 382007 India

Sleep is one of the elements most vital to human life. However, the modern lifestyle continues to push people to neglect this critical requirement. With a vast majority of people falling victim to various sleep disorders, it has become increasingly essential to have a robust system for diagnosing and treating such ailments. Sleep stage classification is one of the primary steps for identifying sleep-related anomalies. Sleep stages are classified according to the frequency and nature of signals received during a polysomnography test. Since the early days, this has been performed manually with the help of trained technicians. However, manual scoring is often prone to error and subjectivity and requires tremendous time and effort. It is, therefore, essential to automatize this process. Several challenges from the correct selection of features remain to be faced in the machine learning-based sleep stage classification system. As an alternative, Deep Learning, capable of automatic feature extraction, proves far more reliable for this task. This experimental study analyses both techniques to compare and decide on a better approach. Three popular Machine Learning classifiers, namely Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), and a neural network comprising CNN and LSTM, have been trained on a vast base of diverse data. The proposed model reported an accuracy of 87.4% with CNN + LSTM.

关键词： Sleep Staging Electroencephalogram Machine Learning Deep Learning

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Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization

arXiv

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arXiv 2021年

作者： Thaler, Franz Payer, Christian Urschler, Martin Štern, Darko Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Austria School of Computer Science University of Auckland New Zealand

In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotations. To model the annotation ambiguities of the training dataset, we propose to learn anisotropic Gaussian parameters modeling the shape of the target heatmap during optimization. Furthermore, our method models the prediction uncertainty of individual samples by fitting anisotropic Gaussian functions to the predicted heatmaps during inference. Besides state-of-the-art results, our experiments on datasets of hand radiographs and lateral cephalograms also show that Gaussian functions are correlated with both localization accuracy and observer variability. As a final experiment, we show the importance of integrating the uncertainty into decision making by measuring the influence of the predicted location uncertainty on the classification of anatomical abnormalities in lateral cephalograms. © 2021, CC BY-SA.

关键词： Anisotropy

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Data-Driven Healthcare Management, Analysis, and Future Trends

Data-Driven Healthcare Management, Analysis, and Future Tren...

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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Lipsa Das Ankesh Kumar Vikrant Sharma Devang Bhatnagar Sumit Singh Nandita Tripathi Amity School of Engineering & Technology Amity University Greater Noida UP India Amity School of Engineering & Technology (Applied Science) Amity University Greater Noida UP India Computer Science and Engineering Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun India Amity Institute of Information Technology Amity University Greater Noida UP India Allied Sciences Amity University Greater Noida UP India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Data-driven approaches are revolutionizing healthcare management and analysis, offering insights into patient care, operational efficiency, and future trends. The sum of information Big data’ is, can do miracles. In the last 20 years, it has become a topic of interest because the potential lies within is enormous. Big Data is produced, stored and examined by several industries of public and private domain with the main aim to enhance their services. Big Data comes from variety of resources in medical industry such as medical patient records, test reports, and IOT medical devices. The large amount of data is generated by medical research which is vital to public healthcare. The data must be appropriately organised and analysed to obtain useful information. Else, finding solution through big data analysis will be like looking for needle in haystack. Set of difficulties frequently occur at every phase of managing Big Data which can only be overcome by installing advanced computer systems for its analysis. This paper examines the relationship between data science and healthcare with an intent to use complex metrics to make intelligent and accurate decisions. Important areas are data-driven approaches for enhancing medical administration, recognizing new developments influencing healthcare evolution, and the methods for evaluating large sets of medical data. By incorporating the data-driven techniques, the medical industry might able to improve the patient health, optimize operations and adjust to current as well as to continuously evolving situations.

关键词： Industries Measurement Medical devices Accuracy Big Data Data science Market research

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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Low-error Reconstruction of Directional Functions with Spherical Harmonics

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IEEE Transactions on Visualization and computer graphics 2025年 PP卷 PP页

作者： Vlnas, Michal Milet, Tomas Zemcik, Pavel Brno University of Technology Department of Computer Graphics and Multimedia Faculty of Information Technology Czech Republic Lappeenranta-Lahti University of Technology School of Engineering Science Lappeenranta53850 Finland

This paper proposes a novel approach for the low-error reconstruction of directional functions with spherical harmonics. We introduce a modified version of Spherical Gaussians with adaptive narrowness and amplitude to represent the input data in an intermediate form. This representation is then projected into spherical harmonics using a closed-form analytical solution. Because of the spectral properties of the proposed representation, the amount of ringing artifacts is reduced, and the overall precision of the reconstructed function is improved. The proposed method is more precise comparing to existing methods. The presented solution can be used in several graphical applications, as discussed in this paper. For example, the method is suitable for sparse models such as indirect illumination or reflectance functions. © 1995-2012 IEEE.

关键词： Visualization

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Speculative Vulnerability: Uncovering the Temporalities of Vulnerability in People s Experiences of the Pandemic

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Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1-27页

作者： Seberger, John S. Obi, Ike Loukil, Mariem Liao, William Wild, David J. Patil, Sameer Department of Information Science College of Computing and Informatics Drexel University 3675 Market Street PhiladelphiaPA19104 United States Department of Computer Graphics Technology Purdue Polytechnic Institute Purdue University 401 N. Grant St. West LafayetteIN47907 United States Mediterranean Institute of Technology Les Jardins du Lac 2 Tunis1053 Tunisia Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University Bloomington 901 E. Tenth Street BloomingtonIN48104 United States School of Computing University of Utah 50 Central Campus Dr. Salt Lake CityUT84112 United States

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-computer Interaction (HCI) and for the oversight of app-based public health surveillance. © 2022 Owner/Author.

关键词： Public health

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The Technical Ethics used in Bad and SD Methods: A Deep Review

The Technical Ethics used in Bad and SD Methods: A Deep Revi...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Aadithyan Ammar Hameed Shnain Vikrant Sharma Alaa M. Lafta Mustafa Mudhafar Ali Ghobash Ahmed Qusay Jawad Department of computer applications Hindustan institute of technology and science Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Medical Physics Faculty of Mediscal Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Departmen of Electrical Engineering Technologies Kut University College/Wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Artificial intelligence (AI) is the process by which human-designed systems analyse available data and make judgements based on predetermined criteria in order to accomplish particular objectives. The incorporation of artificial intelligence (AI) has sparked a deep ethical conversation among key players, including tech companies, businesses, educational institutions, and legislators. AI’s ethical problems have prompted the creation of ethical frameworks that aim to control its application for the good of society, the defence of human rights, and the maintenance of individual autonomy and privacy. This paper provides a critical evaluation and summary of the fundamental concepts behind the use of artificial intelligence, with a focus on the GDPR’s (General Data Protection Regulation) approach. The rights of data subjects, data protection legislation, and data subjects’ permission are crucial considerations in the context of “privacy by design.”

关键词： Ethics Privacy Reviews Oral communication Companies Systems engineering and theory Maintenance

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Deep Learning and its Applications: A Novel Approach to Machine Learning with Encoding Algorithms

Deep Learning and its Applications: A Novel Approach to Mach...

引用

Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Kamalesh Thoppaen Suresh Babu Bhagyashree Ashok Tingare Vijayshri Khedkar Tarun Dhar Diwan P. William Abhishek Badholia Globallogic Inc Santa Clara CA USA Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Deaprtment of Information Technology Sanjivani College of Engineering Kopargaon Amity University Dubai UAE Department of Data Science Shri Shankaracharya Institute of Professional Management and Technology Raipur

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Deep learning has revolutionized data processing by recognizing complicated patterns using multi-layered neural networks. Encoding complex data like hyperspectral photos may help deep learning models perform better. Encoding shapes raw data for compression, dimensionality reduction, and feature extraction. These methods matter. This research found that higher-dimensional encodings can improve deep learning model accuracy and convergence. Complex encoding algorithms like FE-16D and BLOSUM-20D outperform simpler ones in validation accuracy and AUC. To prove this, extensive testing and comparison were done. The study emphasizes the necessity to combine deep learning frameworks with improved encoding techniques to improve machine learning and large-scale data processing.

关键词： Deep learning Dimensionality reduction Machine learning algorithms Computational modeling Neural networks Feature extraction Data processing Encoding Data models Hyperspectral imaging

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Intrusion Detection in Cloud Architecture Using Machine Learning 4

Intrusion Detection in Cloud Architecture Using Machine Lear...

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Govinda Rajulu, G. Santhoshkumar, R. Venkatesan, D. Kalvikkarasi, S. Santosh Kumar Patra, P. St. Martin's Engineering College Department of Computer Science and Design India St. Martin's Engineering College Department of Artificial Intelligence and Data Science India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE India Podhagai College of Engineering & Technology Professor in Ece Department India St. Martin's Engineering College India

ISBN: (纸本)9781665474368

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ. © 2022 IEEE.

关键词： Cloud computing architecture

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The Maximum (k, m)-Subsets Problem is in the Class NEXP

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-5页

作者： Challita, Khalil Abdo, Jacques Bou An Associate Professor in the Department of Computer Science Notre Dame University-Louaize Zouk Mosbeh Lebanon An Assistant Professor in the College of Business and Technology University of Nebraska at Kearney United States

In this paper we define and solve the following new problem: Given a set of n elements, we are interested in determining the largest number of subsets of size k that have at most m elements in common. We prove that in the worst-case scenario, a brute force solution requires a double exponential time with respect to n. Afterwards, we show that our problem is in the class NEXP by reducing it to the succinct K-coloring problem. We use in our proof ordered binary decision diagrams to determine whether there is an edge between two nodes of a graph that corresponds to an instance of our problem. We also show that it is at least EXP-hard. © 2021. All Rights Reserved.

关键词： Binary decision diagrams

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