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Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. If left untreated, stroke can lead to death. In most cases, patients with stroke have been observed to have abnormal bio-signals (i.e., ECG). Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can receive appropriate treatment quickly. However, most diagnosis and prediction systems for stroke are image analysis tools such as CT or MRI, which are expensive and difficult to use for real-time diagnosis. In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. EMG (Electromyography) bio-signals were collected in real time from thighs and calves, after which the important features were extracted, and prediction models were developed based on everyday activities. Prediction accuracies of 90.38% for Random Forest and of 98.958% for LSTM were obtained for our proposed system. This system can be considered an alternative, low-cost, real-time diagnosis system that can obtain accurate stroke prediction and can potentially be used for other diseases such as heart disease.

With the rapid advancement of network technologies, cyberthreats have become increasingly sophisticated, posing significant challenges to traditional intrusion detection systems. Conventional machine learning and deep learning approaches frequently experience performance degradation when confronted with imbalanced datasets and novel attack vectors. To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. The proposed architecture integrates convolutional neural networks (CNNs) with an attention mechanism to extract and aggregate salient features, thereby enhancing the model’s discriminative capacity between normal traffic and various network attack categories. Experimental results demonstrate that the proposed model achieves a detection accuracy of 94.21% even when trained on only 33% of the available data, outperforming conventional models. These findings underscore the effectiveness of the proposed feature fusion strategy via transfer learning in improving detection capabilities within dynamic and evolving cyberthreat environments.

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.

Suicide among older people is one of the most rapidly emerging healthcare issues. The objective of this study was to identify the factors associated with suicide ideation in the aged population in South Korea. The study recruited 684 subjects older than 65 years old (males = 147, females = 537, mean age = 78.20±7.02 years), and trained interviewers performed the interviews. The study was performed as part of a community mental health suicide prevention program. The subjects’ socio-demographic data, physical health, alcohol problems, social relationships, psychological well-being, and depression severity were all considered. The Korean version of the Beck Scale for Suicide Ideation (K-BSI) was used to evaluate the intensity of suicide ideation. Correlation and hierarchical multiple regression analyses were performed to identify the factors associated with the K-BSI. The study results were tested using a path analysis. Depression severity was positively correlated with suicide ideation, and economic status, psychological well-being, and social relationships were negatively correlated with suicide ideation. Depression severity had the largest direct impact, and economic status and social relationships had indirect impacts on suicide ideation. Psychological well-being exerted both direct and indirect influences. Depression severity was the most important predictor of suicide ideation among older people. Other direct and indirect factors played secondary roles. Effective suicide prevention strategies should focus on early detection and active intervention for depression. Socio-economic programs may also indirectly reduce suicide ideation among the aged population.

Recently, excellent by Internet of Things (IoT), the era of connected everything device is coming. However, the devices hardly show the manner to autonomous connectivity on it and the self-cooperation for applied to real-world environments. In this paper, we proposed a smart building on IoT and cloud-based technology that can perform collaboration and efficient operation with various sensing devices in building and facilities. The smart building is very important to reduce on a huge amount of building energy is consumed by the management system of buildings. The proposed system selects an optimum device feature subset from the computing resources and storages by our cloud-based building management system. The performance of our proposed system is tested via experiments which verify that its measures are satisfactory.

Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. If left untreated, stroke can lead to death. In most cases, patients with stroke have been observed to have abnormal bio-signals (i.e., ECG). Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can receive appropriate treatment quickly. However, most diagnosis and prediction systems for stroke are image analysis tools such as CT or MRI, which are expensive and difficult to use for real-time diagnosis. In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. EMG (Electromyography) bio-signals were collected in real time from thighs and calves, after which the important features were extracted, and prediction models were developed based on everyday activities. Prediction accuracies of 90.38% for Random Forest and of 98.958% for LSTM were obtained for our proposed system. This system can be considered an alternative, low-cost, real-time diagnosis system that can obtain accurate stroke prediction and can potentially be used for other diseases such as heart disease.

In this paper, we propose a real-time cooling load forecasting system in order to overcome the problems of the conventional methods. The proposed system is a new load forecasting model that hierarchically combines Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset by our cooling load forecasting system that enables real-time load data generation and collection. The system is composed of two layers: The first layer predicts the time slots in three representative forms: morning, midday and afternoon. The second layer performs specialized prediction of each individual time slot. Since the proposed system enables both coarse-and fine-grained forecasting, it can efficient cooling load management. Moreover, even when a new time slot emerges, it can be easily adapted for incremental updating and scaling. The performance of the proposed system is validated via experiments which confirm that the recall and precision measures of the method are satisfactory.

In this paper, we propose an efficient one-pass algorithm for shot boundary detection and a cost-effective anchor shot detection method with search space reduction, which are unified scheme in news video story parsing. First, we present the desired requirements for shot boundary detection from the perspective of news video story parsing, and propose a new shot boundary detection method, based on singular value decomposition, and a newly developed algorithm, viz., Kernel-ART , which meets all of these requirements. Second, we propose a new anchor shot detection system, viz., MASD , which is able to detect anchor person cost-effectively by reducing the search space. It consists of skin color detector, face detector, and support vector data descriptions with non-negative matrix factorization sequentially. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

Background: Several studies of patients with mild cognitive impairment (MCI) have revealed that this population, like people with dementia, have neuropsychiatric symptoms (NPS) as well as memory impairment. No study has reported on the natural history and course of NPS in MCI although this is important in terms of management. We aimed to determine the persistence of NPS over six months in participants with MCI. Method: The Neuropsychiatric Inventory (NPI) was used to rate the severity of NPS in 241 consecutive referrals with MCI from a Korean clinic at baseline and in 220 patients at 6-month follow-up. We also collected information about the cognition and quality of life of patients and their caregivers. Results: Ninety-seven (44.1%) MCI participants who completed the 6-month follow-up exhibited at least one NPS at baseline; 60 (27.3%) were clinically significant NPS. Seventy (72.1%) of those with any symptom had at least one persistent NPS at 6-month follow-up, and 44 (73.3%) of those with clinically significant symptoms had at least one significant and persistent NPS at 6-month follow-up. Those with persistent symptoms had more severe baseline symptoms. Both patients and caregivers had a poorer quality of life when the patient had at least one clinically significant symptom. Conclusions: NPS were highly persistent overall in older people with MCI. Persistence was predicted by having more severe symptoms at baseline. Clinically significant levels of NPS were associated with decreased quality of life. We conclude that clinicians should be aware that NPS symptoms in MCI usually persist.