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COGNITIVE COMPUTATION
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Summary: With the advancement of medical technology, the utilization of big data in medicine is becoming increasingly important. Brain tumors are considered one of the deadliest and most devastating diseases due to their diverse characteristics and low survival rates. Early detection of brain tumors is challenging but crucial for patient treatment and survival. This study proposes a hybrid deep learning model (CNN-LSTM) to classify and predict brain tumors using magnetic resonance images (MRI), achieving high classification accuracy.
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Summary: With the help of smart homes and the IoT, the health industry is finding ways to monitor and manage cognitive diseases like dementia. Machine learning and deep learning algorithms can accurately analyze activity patterns and predict the first signs of cognitive decline. Deep neural networks and multilayer perceptron classifiers show the best results for classifying dementia vs. healthy individuals based on complex interwoven activities.
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Summary: Future smart cities are crucial for fulfilling increasing demands and better resource management through information and communication advancements. However, rapid population growth poses challenges in creating sustainable urban spaces. The rise of smart cities ensures citizen rights and well-being, along with evaluating urban planning from an environmental perspective. This paper surveys future technologies and requirements for smart cities, reviews existing application frameworks, discusses technological challenges, and identifies future dimensions for developing smart cities.
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2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 22)
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Summary: This study investigates the feasibility of using Electrodermal Activity (EDA) collected from wearable devices to detect people's stress. The experimental results demonstrate the potential of wearable devices with EDA sensors to predict stress status.
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)
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18TH ANNUAL INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS 2022)
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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
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Summary: The research proposes a two-layered approach to improve accurate recognition of physical activities with complex interclass variations. By clustering and using a machine learning classifier, the method achieves a 99% accuracy on a self-collected dataset and 95% accuracy on a publicly available dataset. PARCIV outperforms various state-of-the-art studies by 8%-17% for both simple and complex activities.
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Jerry Chen et al.
Summary: The review discusses the mechanism and correlation of pain and stress, as well as the use of wearable sensors for pain and stress detection. By integrating various physiological and behavioral signals, a new approach for chronic pain detection may be found.
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Abdul Rehman Javed et al.
Summary: IoT offers smart solutions for future urban communities with minimal human intervention. Smart homes assess residents' ability to perform daily activities and health condition using CA-SHR, aiming to early detect cognitive impairment. By utilizing ensemble AdaBoost classification, CA-SHR improves the reliability in labeling smart home residents.
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Mavra Mehmood et al.
Summary: Cervical cancer is the fourth most common cause of cancer death in women globally, associated with HPV infection. Early screening can prevent cervical cancer, but in developing countries, women lack access to screening programs, leading to high individual patient risk.
FRONTIERS IN PUBLIC HEALTH
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Akshi Kumar et al.
Summary: The study presents a model for detecting mental stress states using sensor-based bio-signals, employing a multi-level deep neural network with hierarchical learning capabilities to classify high-level features into stress categories, achieving a superior performance accuracy of 87.7%.
PATTERN RECOGNITION LETTERS
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Hamid Mukhtar et al.
Summary: The fast contactless spread of COVID-19 has led to difficulties in screening, but this study has developed a medical device that utilizes sensors to monitor the health status of virus-infected individuals in real-time and classifies them based on rule-based decision-making. This approach enables quick identification of infected individuals and reduces the risk of transmission in a short period of time.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
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Personal and Ubiquitous Computing
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Arash Shokouhmand et al.
Summary: This study explores the classification of mean pressure gradient in aortic stenosis patients using angular chest movements and gyroscopic readings. By extracting features and using machine learning methods, the severity of the disease can be accurately determined. The Light GBM method achieves the best performance and highlights the importance of features like IVCT and IVRT in assessing AS severity.
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
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Prerna Garg et al.
Summary: The study aims to use machine learning techniques to detect people's stress levels and improve their quality of life. By utilizing the WESAD dataset and various machine learning models, the Random Forest model was found to outperform others in stress detection.
26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES (IUI '21 COMPANION)
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Summary: Breast cancer is a deadly disease that can be detected early to increase treatment opportunities and survival rates. Deep learning plays a crucial role in extracting features from medical image datasets for accurate diagnosis. It effectively assists existing methods in examining and diagnosing breast cancer.
Review
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Taher M. Ghazal et al.
Summary: Smart city is a concept that aims to make cities more efficient, technologically advanced, greener and socially inclusive. The focus is on using digital technologies to address challenges faced by urban society, with particular potential in the healthcare sector.
Review
Computer Science, Information Systems
Shruti Gedam et al.
Summary: Stress is an escalated psycho-physiological state that emerges in response to challenging events or demanding conditions. Prolonged exposure to multiple stressors can adversely affect mental and physical health. Continuous monitoring of stress using wearable devices can help in preventing stress-related issues.
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