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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Vedanayagam, Sumathy Cyriloose, Pretty Diana Cyril Perumal, Govindaraj Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology TN Kattankulathur India Department of Biomedical Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819780891

In the healthcare domain revolution, emerging technologies provide innovative solutions to enhance patient care and medical research. Each new concept has potential for specific problems, and they also have some challenges. Many technologies are introduced to improve service with security and qualities in healthcare applications like Blockchain, Digital Healthcare Systems, Nano Technology, Augmented Reality, Virtual Reality, and Artificial Intelligence. Blockchain technology yields security and transparency for electronic health records. We elaborate on the benefits and challenges of blockchain in healthcare applications. Powerful technology can be applied to various healthcare domains for the betterment and contribution of innovative solutions to healthcare. Blockchain has multiple features, including distributed ledger, decentralized storage, authentication, security, and immutability, which greatly solve complex healthcare problems. Blockchain plays an essential role in healthcare applications such as preserving and sharing patient data, diagnostic labs, and pharmacy stores to effectively handle narcotic and other prescribed drugs for safe use by clinicians, handling the patient medical records and clinical trial data. Technology also improves the security of patient medical records and the efficient operation of healthcare organizations. One of the recent applications of blockchain in the healthcare system is customized function to personalized medicine in the health information technology by providing shareable private keys by the healthcare providers. In addition, blockchain technology can maintain patients’ health information confidentially, which improves remote access to the individual. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Gender-Specific Speech Enhancement Architecture for Improving Deep Neural Networks Learning

Gender-Specific Speech Enhancement Architecture for Improvin...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and technologies, 3ICT 2024

作者： Nossier, Soha A. Sharif, Mhd Saeed Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

Deep learning techniques for speech enhancement rely on training a deep neural network to process noisy speech, regardless the gender of the speaker. However, research shows that the speech of male and female stimulates different parts in human brain, and that female speech requires more complex analysis. This implies that different processing is applied on the speech, based on the speaker gender. In this work, we argue that male and female speeches have different features that can help in the learning process of speech enhancement deep neural networks if the training is performed on male and female speech data, independently, and using two different deep neural networks, specifically implemented for enhancing the noisy speech signal of the target gender. This work presents a gender-specific speech enhancement architecture, which consists of a front-end binary classifier to detect the speaker gender. Based on the classifier decision, the noisy speech is enhanced using either a male or female speech enhancement model. One-stage and two-stage speech enhancement approaches are used to process male and female speeches, respectively. The results reveal that gender-specific speech enhancement has positive impact on the enhanced speech by deep neural networks. Additionally, the developed architecture achieved classifier accuracy 96.9% and 0.11 increase in Covl speech quality metric for the test data, in comparison to other best-performing networks. ©2024 IEEE.

关键词： Deep neural networks

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Few-Shot Learning for Early Detection of Laryngeal Cancer with Explainable AI 8

Few-Shot Learning for Early Detection of Laryngeal Cancer wi...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Pradhan, Ujan Prerna Aditya, M.N. SRM Institute of Science & Technology Department of Computing Technologies Chennai India SRM Institute of Science & Technology Department of Biomedical Engineering Chennai India SRM Institute of Science & Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331520762

Early diagnosis of laryngeal cancer significantly relates to prognosis and treatment. This work presents a classification model that uses very few-shot learning where classification between healthy and early-stage cancerous tissues can be done accurately even with very limited data. The proposed approach relies on an expert-curated dataset of 1,320 image patches of size 100x100 pixels, which suggests promising potential for real-world applications, especially in strictly limited amounts of annotated medical images. This approach uses Explainable AI (XAI) to increase the transparency of the model and eventually enhance clinical trust in the predictions obtained with that model. This technique has the promise for early detection, but it also has high accuracy and further allows clinical thinking into the decision mechanism of the model. That is a future direction for developing the refined version of such a technique to develop robustness and application in different clinical settings. © 2025 IEEE.

关键词： Mammography

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Phase-Amplitude Coupling in Response to Transcutaneous Vagus Nerve Stimulation: Focus on Regions Implicated in Mood and Memory

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Neuromodulation 2025年第4期28卷 663-671页

作者： Keatch, Charlotte Lambert, Elisabeth Woods, Will Kameneva, Tatiana School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Health Sciences Swinburne University of Technology Melbourne Australia Iverson Health Innovation Research Institute Swinburne University of Technology Melbourne Australia Department of Biomedical Engineering The University of Melbourne Melbourne Australia

Objective: This study aimed to investigate whether transcutaneous vagus nerve stimulation (tVNS) at different frequencies affects phase-amplitude coupling among regions of the brain linked to mood and memory disorders using simultaneous magnetoencephalography (MEG) in healthy participants. Materials and Methods: Phase-amplitude coupling was measured among brain areas in response to different stimulation frequencies of tVNS using concurrent MEG and tVNS in 17 healthy participants. The 4 protocols were: 24 Hz cymba concha, 1 Hz cymba concha, PFM cymba concha, and 24 Hz ear lobe. A driven autoregressive method was used to estimate the coupling among brain areas in different physiological frequency bands in response to these protocols. Results: Different tVNS stimulation protocols led to alterations in phase-amplitude coupling among multiple brain regions linked to mood and memory, notably the prefrontal cortex, hippocampus, and temporal pole. Stimulation delivered at 24 Hz was observed to decrease delta-gamma coupling within the temporal pole and cingulate cortex when contrasted with 24-Hz sham stimulation. Increased alpha-gamma coupling was observed between the hippocampus and prefrontal cortex when contrasting 24 Hz with pulse-frequency–modulated stimulation. Finally, a comparison of 24-Hz with low-frequency 1-Hz stimulation showed an increase in theta-gamma coupling within the prefrontal cortex. Significance: To our knowledge, this study represents the first attempt to quantify phase-amplitude coupling in response to tVNS and suggests that different stimulation frequencies can modulate coupling between different areas of the brain. Abnormal phase-amplitude coupling has been linked to multiple mood and memory disorders. Further investigations using different stimulation frequencies of tVNS to alter phase-amplitude coupling may lead to the development of tVNS as a therapeutic option for different medical conditions. © 2025 The Authors

关键词： cross-frequency coupling magnetoencephalography (MEG) neuromodulation phase-amplitude coupling transcutaneous vagus nerve stimulation (tVNS)

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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IoT-Enabled Horticultural Lighting for Optimizing Plant Growth and Agriculture Operations 2

IoT-Enabled Horticultural Lighting for Optimizing Plant Grow...

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

作者： Srinivasan, S. Lk, Shoba Alavanthar, T. Srinivasan, C. Murugan, S. Sujatha, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

The Internet of Things (IoT) and horticulture lighting systems may improve plant growth and agricultural operations. The variation in the light spectrum, intensities, and durations affect plant physiological systems. The proposed system can dynamically adjust and control lighting settings using real-time sensor data by seamlessly integrating IoT capabilities. These sensors carefully track plant health, ambient conditions, and energy use. This dynamic feedback system allows operators to make informed choices and adjust lighting techniques for optimum growth, yields, and resource use. Plant growth and resource efficiency benefit from light parameter improvement. The connection between IoT and horticulture lighting leads to sustainable agriculture that maximizes agricultural yields and energy efficiency. Moving from static to adaptive lighting systems represents a paradigm change in agriculture, as data-driven decisions enable precision farming. Combining IoT's real-time abilities with horticulture lighting systems promises unique yields, energy savings, and sustainable agriculture practices. © 2024 IEEE.

关键词： Horticulture

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IoT -Weather Integration for Enhanced Cricket Tactics with Gradient Boosting Algorithm 8

IoT -Weather Integration for Enhanced Cricket Tactics with G...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ranganathan, Chitra Sabapathy Ramesh, S. Preetha, J. Rakesh, Nitin Mohankumar, N. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Kongunadu College of Engineering and Technology Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Nagpur Campus Maharashtra Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505462

From a strategic perspective, the pitch's state and the wind's direction are the most important factors in cricket. In this paper, an innovative method that uses the IoT to report the state of the pitch and the weather in real time is presented. The system gives updates on the state of the field and the weather continuously. The device can gather data on the pitch's hardness and humidity using sensors strategically placed around the field. It may also record information about the weather, including temperature, humidity, wind speed, and cloud cover. Cricket teams and players can have a deep understanding of the present playing conditions based on the real-time transmission and analysis. Teams may improve their results by making data-driven adjustments to their strategies and lineups. Teams may also be able to make more educated decisions on their approaches to batting, bowling, and fielding. The technology has the potential to alter the game's dynamics while also increasing audience participation by shedding light on cricket's many nuanced aspects. © 2024 IEEE.

关键词： Cricket (Sports)

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Cluster-Factors of Mobile Sensor Network Technology for Security Enhanced PEGASIS

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile technologies 2023年第18期17卷 143-155页

作者： Sivakumar, T.K. Dhyani, Saurabh Manikanthan, S.V. Kaliappan, Vishnu Kumar Baskaran, K. Kishore, Kakarla Hari Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of USCS Uttaranchal University Uttarakhand Dehradun India Melange Academic Research Associates Puducherry India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Biomedical Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile wireless sensor networks (MWSNs) have been a hot topic of research, and numerous routing methods have been developed to increase energy efficiency and extend longevity. Nodes close to the sink often use more energy to transmit data from their neighbors to the sink, which causes them to run out of energy faster. These places are also referred to as rendezvous points, and choosing the best one is a hard task. The likelihood of choosing an ideal node as the rendezvous point will be extremely low because hierarchical algorithms only use their local information to select these places. The warm spot problem is addressed from four angles in this work using the Enhanced Power-Efficient Gathering in Sensor Information Systems (EPEGASIS) technique. In order to limit the amount of energy used during transmission, the ideal communication distance is first calculated. To balance the energy consumption among the nodes, mobile sink technology is employed once a threshold value is set to safeguard the dying nodes. The node can then modify its communication range based on how far away the sink node is from it. Thorough testing has been done to demonstrate that our suggested EPEGASIS works better in terms of longevity, drive usage, and web latency. © 2023 by the authors of this article. Published under CC-BY.

关键词： Energy efficiency

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Galvanic Skin Response based Stress Detection System using Machine Learning and IoT 2

Galvanic Skin Response based Stress Detection System using M...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Raju, Ayalapogu Ratna Ramadevi, R. Babu, Padamata Ramesh Vetriselvi, D. Mahatma Gandhi Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur Chennai India

ISBN: (纸本)9798350325799

The primary objective of this study is to construct an Internet of Things (IoT)-based stress detection system that uses Galvanic Skin Response (GSR) and pulse sensors. In today's contemporary world, stress has developed into a widespread problem and early recognition of the condition is necessary for efficient intervention and treatment. The suggested system utilizes GSR and pulse sensors in conjunction with the IoT technologies to continuously monitor and analyze an individual's stress levels. The GSR sensor analyzes changes in the electrical conductance of the skin, which is known to occur during times of stress;in contrast, the pulse sensor identifies patterns in heart rate related to stress. Signal processing methods are used to process the newly collected sensor data, after which useful characteristics are retrieved. After the data has been analyzed, it is wirelessly sent to a cloud platform so that machine learning algorithms may be used for further analysis and categorization. The system displays the current amount of stress in real-time and can send warnings if the current stress level reaches a certain threshold. To guarantee that the system conforms to the rules, the system has established privacy and data security precautions. Our goal is to contribute, via this study, to the creation of an accurate and reliable stress detection system that may aid people in efficiently controlling the amounts of stress that they are experiencing in their lives. © 2023 IEEE.

关键词： Machine learning

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Design and Development of EEG Signal based Driver Activity Recognition and Monitoring System

Design and Development of EEG Signal based Driver Activity R...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Ramachandran, R. Ramya, G. Titus, Anita Balaji, M. Bharaneedharan, M. Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India Jeppiaar University Department of ECE Chennai India Department of ECE Andhra Pradesh Tirupati India S.A. Engineering College Department of ECE Chennai India

ISBN: (纸本)9798350319200

Drivers who drive commercial and personal vehicles report having a "very close to miss"owing to lack of energy, while one in five drivers confess to making a severe mistake because of drowsiness pursuant to a 2012-2013 survey by the National-Sleep-Foundation (NSF). This has been shown that drivers' mental exhaustion contributes to the occurrence of road accidents almost as much as their physical fatigue. Driving is a challenging activity that calls for one's undivided attention and a level head. An individual's ability to drive may be impaired if they are under stress or experiencing intense emotions. A system that constantly assesses the driver's well-being is always required. The brain signals of the drivers have been analyzed by researchers using electroencephalography (EEG). This study delves into the current state of the art of EEG assisted Driver surveillance System technology. The planned mechanism for tracking drivers has also been briefly described. Research have looked into brain waves across both brief and lengthy time periods to enable surveillance at varying frequencies. The suggested method achieves 97.2% accuracy in classification compared to an average human professional in a brief time frame, while requiring sophisticated computing. Additional standard warnings, including electrocardiography (ECG), fingers temperature, epidermal of the skin as well as breathing, are additionally assessed with EEG's reliability in constructing a system of this sort. The findings prove that the system makes decisions at regular one-minute intervals and can compensate for differences between users. © 2023 IEEE.

关键词： Accident Monitoring Driver Activity Recognition Driver Safety Drowsiness Detection EEG Electroencephalography Vehicle Protection

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