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Enhancing Security and Privacy in Cloud - Based Healthcare Data Through Machine Learning 1

Enhancing Security and Privacy in Cloud - Based Healthcare D...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Shukla, Aasheesh Pokhariya, Hemant Singh Michaelson, Jacob Srivastava, Arun Pratap Narayanamma, Laxmi Srivastava, Amit Gla University Department of Electronics and Communication Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

It is becoming more and more important for healthcare providers to protect the integrity and security of sensitive medical data as they use cloud computing for data processing and storage. This work explores the field of machine learning algorithms that are secure and privacy-preserving when applied to healthcare information in cloud environments. We investigate sophisticated cryptography, federated learning, and differentiating privacy techniques using an interpretive philosophy and a method based on deduction. Our results highlight the computational expense associated with cryptographic protocols, while also revealing their nuanced performance and potential for enabling secure calculations. Federated learning is shown to be effective in collaborative model training, providing a workable approach to privacy-preserving data analysis over-dispersed healthcare datasets. Differential privacy systems require careful parameter calibration because they demonstrate a delicate balance between data value and privacy preservation. © 2023 IEEE.

关键词： computation federated learning protocols Technique vast amounts

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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MentalHealthAI: Utilizing Deep Learning for Accurate Stress Level Prediction 3

MentalHealthAI: Utilizing Deep Learning for Accurate Stress ...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bharathi, S.T. Raj, J. Relin Francis Krishnan, R. Santhana Rajalakshmi, V. Kumar, V. Vinoth Anchitaalagammai, J.V. Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

MentalHealthAI is an advanced deep learning system designed to accurately predict stress levels by integrating various data sources, including user surveys, heart rate metrics, sentiment scores from social media interactions, and demographic information. This innovative approach employs a Fully Connected Neural Network (FCNN) architecture, enabling the model to uncover complex relationships among multiple stress-related factors, such as PHQ-9 and GAD-7 questionnaire scores, heart rate variability, sleep patterns, and calculated sentiment scores. By utilizing Natural Language Processing (NLP) techniques, specifically the VADER model, the system analyzes social media text data to derive sentiment scores, providing insights into users' emotional states. This holistic assessment empowers individuals to understand the interplay between their mental health and daily activities, fostering proactive stress management. The advantages of MentalHealthAI include its capacity to deliver timely and personalized stress assessments, allowing users to make informed decisions about their mental well-being. By combining objective data from wearables with subjective sentiment analysis, the system offers a comprehensive perspective on stress dynamics. Ultimately, MentalHealthAI aims to enhance mental health support, providing valuable insights that encourage healthier lifestyles and improved emotional resilience. © 2024 IEEE.

关键词： Electronic health record

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ADOD: Adaptive Density Outlier Detection 24

ADOD: Adaptive Density Outlier Detection

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Qian, Li Qian, Jing Sun, Xin Guo, Wengang Bohm, Christian Institute of Informatics Ludwig Maximilian University of Munich Munich Germany University of Vienna Faculty of Computer Science Vienna Austria Carbon Neutral R&D Center China Railway Hi-Tech Industry Corporation Limited Beijing China City University of Macau Faculty of Data Science China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798331506681

Outlier detection plays a dual role in data analysis: cleansing data to optimize the performance of downstream tasks and identifying potentially rare valuable events or patterns. Proximity-based methods, which are independent of data distribution assumptions, are plagued by parameter selection and performance challenges when handling data with varying densities. This study proposed a novel unsupervised algorithm named Adaptive Density Outlier Detection (ADOD) to address these challenges. The core innovation of ADOD involves two main aspects: adaptive neighborhood boundaries and density consistency scoring. First, instead of relying on a predefined fixed radius, ADOD employs perplexity to calculate the local scale of each data point. It then dynamically adjusts the neighborhood boundaries according to this scale to adapt to data with varying densities. Second, ADOD estimates local density using a mutual neighbor graph and combines the density differences between data points and their neighbors to compute outlier scores, effectively distinguishing outliers that significantly deviate from their surroundings. This study evaluated ADOD on one synthetic and 32 real datasets, and compared it with 14 classical and state-of-the-art algorithms from different categories. Extensive experimental results demonstrated the superior performance of ADOD, achieving the highest average accuracy across ROC, P@N, and AP metrics. This study promotes the development of outlier detection techniques and expands their potential for real-time applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Cloud Computing Approach for Electronic based Health Record System 5

Smart Cloud Computing Approach for Electronic based Health R...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Kanna, R. Kishore Anitha, R. Sundaramoorthy, K. Salau, Ayodeji Olalekan Danquah-Amoah, Archibald Banappagoudar, Sudharani B. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Information Technology Chennai India Afe Babalola University Department of Electrical/ Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Accra Technical University Medical Laboratory Technology and Biomedical Engineering Ghana Itm University School of Nursing Science MP Gwalior India

ISBN: (纸本)9798331542559

This study provides a comprehensive examination of the potential benefits of speed and substantial change from integrating health measurement with cloud-based electronic health records (cloud EHRs) to expedite health care Many challenges a traditional health record systems meet include data fragmentation, limited accessibility, security. There were some concerns. The use of cloud computing in healthcare, which integrates patient data and provides data security and universal access, addresses these issues. The healthcare industry has rapidly embraced cloud computing, dramatically changing the way patient care is delivered and delivered. This study looks at the benefits of using a cloud-based electronic health record (Cloud EHR) in conjunction with the generation of health metrics, providing a detailed analysis of how this integration leads to improved health care delivery increase The article also discusses how cloud EHR systems use context -the-art technologies such as data analytics and machine learning and provide relevant data for prediction and analysis. They also review the security measures used in cloud systems to ensure that confidential health information is accessible, intact and private. © 2024 IEEE.

关键词： Patient treatment

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A computer-aided diagnosis system for breast pathology: a deep learning approach with model interpretability from pathological perspective

A computer-aided diagnosis system for breast pathology: a de...

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作者： Hsu, Wei-Wen Wu, Yongfang Chen, Chung-Hao Hao, Chang Hou, Yu-Ling Gao, Xiang Shao, Yun Zhang, Xueli He, Tao Tai, Yanhong Department of Computer Science and Information Technology National Taitung University Taitung Taiwan Department of Pathology The Fifth Medical Center of Chinese PLA General Hospital Beijing China Department of Electrical and Computer Engineering Old Dominion University NorfolkVA United States Institute of Big Data Technology Bright Oceans Corporation Beijing China

In this study, a computer-aided diagnosis (CAD) system for whole-slide images (WSIs) with breast cancer was proposed to achieve lesion detection and classification. Different from the previously proposed works that performed segmentation directly and applied the segmentation metrics for performance evaluation, here we computed the accuracy in patch-level, region-level, and slide-level to access our proposed framework to meet the clinical requirements, instead of simply regarding it as an AI segmentation task. To achieve that for WSIs, lesion detection was conducted at high magnification (× 40) first for region proposal by patching using the modified AlexNet model;and then, several patches were sampled from the detected regions at lower magnification (× 10) to observe cells’ patterns for lesion classification by the model of ResNet50. With such fashion, the deep features being distinguishing in lesion classification from the convolutional neural networks (CNN) were further analyzed in this work to provide comprehensive interpretability for the proposed CAD system based on pathological knowledge. For our experiments, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). The slide-level accuracy rate for the three-category classification reached 90.8% (99/109) through a fivefold cross-validation and achieved 94.8% (73/77) on the testing set. In addition, the learned features reflect the clinical insights in lesion classification for explainable AI, which enhanced the reliability and validity of our proposed system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： computer aided diagnosis

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

引用

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Artificial Intelligence based Models to Support Water Quality Prediction using Machine Learning Approach

Artificial Intelligence based Models to Support Water Qualit...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： William, P. Oyebode, Oluwadare Joshua Ramu, Gandikota Gupta, Manish Bordoloi, Dibyhash Shrivastava, Anurag Sanjivani College of Engineering Department of Information Technology Savitribai Phule Pune University Pune India Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India Lovely Professional University Division of Research and Development Phagwara India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350333244

The purpose of this study is to simulate the propagation of bio-contamination risk in Water Distribution System (WDS). This will be accomplished by putting an emphasis on source identification and response modelling. This will be accomplished in the WDS via the process of simulating the bio-contamination risk propagation under real environmental circumstances. This will be done in order to accomplish this goal. Due to the failure to take into consideration the ability for the bulk of the pollutants to undergo chemical degradation and the neglecting of dynamic responses, an overestimation of the population that is exposed has resulted. The study presented here takes into account the connected change in a variety of water quality indicators that are caused by the mixing of the pollutants. These measurements include the total organic carbon content, pH, and alkalinity. In practise, the baseline requirements for each of the aforementioned metrics are set at the beginning of the regular WDS monitoring. Contamination is then indicated by considerable deviations from the base line. The study's goal was to determine whether it is feasible to develop a smart monitoring system powered by artificial intelligence (AI) that would successfully enable water operators to ensure near real-time quality control for the early detection of chemical and/or biological pollution and risk management. The study's goal was to see whether it was possible to develop an artificial intelligence (AI)-based smart monitoring system. The study's purpose was to investigate the viability of building a sophisticated monitoring system that would allow water operators to perform quality control in near real time. Artificial Neural Networks (ANN) and other sophisticated pattern recognizers like Support Vector Machines (SVMs) are examples of cutting-edge sensor technologies that have been implemented as solutions to identify anomalies and estimate the severity of such irregularities. © 2023 IEEE.

关键词： Support vector machines

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Deploying Deep Learning for Real-Time Tsunami Monitoring: A CNN-LSTM Model for Sensor-based Prediction 8

Deploying Deep Learning for Real-Time Tsunami Monitoring: A ...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Karthihadevi, M. Francis Raj, J. Relin Sumi, N. Vanitha, S. Begum, A. Benazir Krishnan, R. Santhana Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India

ISBN: (纸本)9798331512248

Early detection of tsunamis is essential for disaster risk reduction and minimizing loss of life. This research introduces an advanced deep learning-based tsunami detection model that integrates Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. The dataset comprises seismic waveform signals, oceanographic sensor data, and satellite imagery, incorporating critical earthquake attributes such as magnitude, depth, epicenter location, and past tsunami occurrences. To enhance feature extraction, Short-Time Fourier Transform (STFT) is applied, converting time-domain seismic signals into frequency-domain spectrograms. The CNN component captures spatial characteristics in seismic spectrograms, while the LSTM component processes sequential oceanic sensor readings to analyze long-term dependencies in water level fluctuations and seismic activity patterns. The model is optimized using binary cross-entropy loss and the Adam optimizer, ensuring efficient learning while minimizing false alarms. Evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, confirm that the CNN + LSTM framework surpasses standalone deep learning models such as CNN, LSTM, and GRU. Experimental findings highlight the model's capability to provide accurate tsunami predictions with reduced false positives. This approach can be implemented in real-time tsunami early warning systems, IoT-based monitoring platforms, and disaster risk assessment tools to improve preparedness and response strategies. © 2025 IEEE.

关键词： CNN deep learning disaster management early warning systems LSTM oceanographic sensors seismic data STFT Tsunami detection

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Analysis of the Performance of Software Defined Networks (SDN) Versus Networks with TCP/IP Architecture 2

Analysis of the Performance of Software Defined Networks (SD...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tanguturi, Rama Chaithanya Amutharaj, J. Prakash, N. Subramani, K. Taqui, Syed Noeman Sree Southry, S. Pace Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Raja Rajeswari College of Engineering Department of Information Science and Engineering Bengaluru560074 India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Department of Physics Tamil Nadu Dindigul India Saveetha School of Engineering Department of VLSI Microelectronics Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India

ISBN: (纸本)9798350327533

It is expected that the transfer speeds on average would rise from 39Mbps to 75.4Mbps. For this reason, the idea of implementing centralized networks, with easy administration and traffic management through policies, has become essential in recent years. Software-defined networks (SDN) and traditional TCPIIP architecture networks are two distinct approaches to network design and management. While TCPIIP architecture networks have been the norm for decades, SDN is a relatively new concept that promises to revolutionize network performance, flexibility, and scalability. In this work, the performance of SDN versus networks with TCPIIP architecture is analyzed and compared. In the analysis of the performance of software-defined networks and networks with TCPIIP architecture, it is predicted to have a favourable response in the evaluation of different metrics such as latency, jitter and bandwidth in both networks;but with better performance in the case of SDN networks. © 2024 IEEE.

关键词： Flexibility and Adaptability Integration with Cloud Services Management and Configuration Security Software-defined networks (SDN)

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