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In recommender systems, the user items are offered tailored to users' requirements. Because there are multiple cloud services, recommending a suitable service for users' requirements is of paramount importance. Cloud recommender systems are qualified depending on the extent to which they accurately predict service quality values. Because no service was chosen by the user beforehand, and no record of the user's selections is available, it became challenging to recommend it to users. To promote the recommender system quality, to accurately predict service quality values by offering various procedures, including collaborative filtering, matrix factorization, and clustering. This review article first mentions the general problem and states the need for research, followed by examining and expressing the kinds of recommender systems along with their problems and challenges. In the present review, various approaches, platforms, and solutions are reviewed to articulate the pros and cons of individual approaches, simulation models, and evaluation metrics employed in the reviewed techniques. The measured values in various approaches of the papers are compared with one another in several diagrams. This review paper reviews and introduces the entire datasets applied in the studies. [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

In the domain of data mining, the extraction of frequent patterns from expansive datasets remains a daunting task, compounded by the intricacies of temporal and spatial dimensions. While the Apriori algorithm is seminal in this area, its constraints are accentuated when navigating larger datasets. In response, we introduce an avant-garde solution that leverages parallel network topologies and GPUs. At the heart of our method are two salient features: (1) the use of parallel processing to expedite the realization of optimal results and (2) the integration of the cat and mouse-based optimizer (CMBO) algorithm, an astute algorithm mirroring the instinctual dynamics between predatory cats and evasive mice. This optimizer is structured around a biphasic model: an initial aggressive pursuit by the cats and a subsequent calculated evasion by the mice. This structure is enriched by classifying agents using their objective function scores. Complementing this, our architectural blueprint seamlessly amalgamates dual Nvidia graphics cards in a parallel configuration, establishing a marked ascendancy over conventional CPUs. In amalgamation, our approach not only rectifies the inherent shortfalls of the Apriori algorithm but also accentuates the extraction of association rules, pinpointing frequent patterns with enhanced precision. A comprehensive evaluation across a spectrum of network topologies explains their respective merits and demerits. Set against the benchmark of the Apriori algorithm, our method conspicuously outperforms in terms of speed and effectiveness, heralding a significant stride forward in data mining research. [ABSTRACT FROM AUTHOR]

Introduction: Social Anxiety Disorder (SAD) as a negative emotion can be associated with specific patterns of brain behavioral system functions. The purpose of this research is to evaluate the effectiveness of Hofmann-based CBT (Cognitive Behavioral Therapy) in reducing anxiety symptoms and improving brain behavioral system functions in patients suffering from social anxiety disorder. Materials and Methods: This research is a semi-experimental study with a pretest/posttest plan. 20 SAD patients were randomly selected and divided equally into two groups (experimental and control). The experimental group underwent 12 sessions of Hofmann-based CBT, whereas the control group was placed in the waiting list. Anxiety disorder Interview Schedule for DSM-IV (ADIS-IV), Social phobia questionnaire, and Gary-Wilson's personality questionnaire were used for evaluating results at pretests and posttests. Data were analyzed with descriptive indices and multivariate analysis of covariance (MANCOVA) by SPSS software. Results: The experimental group showed significant improvement in total scores of social phobia, including fear (P<0.001), avoidance (P<0.001), and physiological discomfort (P<0.001) Also, brain behavioral systems (including behavioral activation, behavioral inhibition, and fight/flight) showed significant improvement (P<0.001) in behavioral inhibition in the experimental group. Conclusion: Cognitive behavioral therapy based on Hofmann model is effective in patients suffering from social anxiety disorder, especially in improving anxiety symptoms and behavioral inhibition functions. [ABSTRACT FROM AUTHOR]