HA users, other individuals, and organizations interested in HAs leave their digital footprint on a wide variety of social media sources. The community connects, offers support, and shares information on a variety of HA-related issues. The HA community is as active in social media utilization as other groups, such as the cochlear implant community, even though the patterns of their social media use are different because of their unique needs.
Alzheimer’s disease (AD) and Alzheimer’s disease-related disorders (ADRD) are late-onset, age-related progressive neurodegenerative disorders, characterized by memory loss and multiple cognitive impairments. Current research indicates that Hispanic Americans are at an increased risk for AD/ADRD and other chronic conditions such as diabetes, obesity, hypertension, and kidney disease, and given their rapid growth in numbers, this may contribute to a greater incidence of these disorders. This is particularly true for the state of Texas, where Hispanics are the largest group of ethnic minorities. Currently, AD/ADRD patients are taken care by family caregivers, which puts a tremendous burden on family caregivers who are usually older themselves. The management of disease and providing necessary/timely support for patients with AD/ADRD is a challenging task. Family caregivers support these individuals in completing basic physical needs, maintaining a safe living environment, and providing necessary planning for healthcare needs and end-of-life decisions for the remainder of the patient’s lifetime. Family caregivers are mostly over 50 years of age and provide all-day care for individuals with AD/ADRD, while also managing their health. This takes a significant toll on the caregiver’s own physiological, mental, behavioral, and social health, in addition to low economic status. The purpose of our article is to assess the status of Hispanic caregivers. We also focused on effective interventions for family caregivers of persons with AD/ADRD involving both educational and psychotherapeutic components, and a group format further enhances effectiveness. Our article discusses innovative methods and validations to support Hispanic family caregivers in rural West Texas.
Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of several distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that can explicitly model a problem containing continuous variables. Nevertheless, state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.
Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on Support Vector Machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called Dynamic Feature Selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.
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