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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]

By raising evolutionary network connections, a software‐defined network (SDN) offers a well‐managed and flexible novel network topology. The SDN efforts are provided to abstract the original network configuration for business applications and web services. Additionally, the SDN modifies the network management policies such as topology policies, deployment policies, the applicability, and infrastructure maintenance. One of the important features of the SDN is standardizing the network interfaces with the precise functional semantics in application and control flow layers. In another hand, specification and evaluation of the existing network interfaces is a main and significant challenge to prove the correctness of the configurable scenarios in the SDN approach. Recently, formal verification presents a high potential platform to evaluate the main‐layer scenarios of the SDN as the novel perspective in this area. Up to now, there is no survey and state‐of‐the‐art review on the formal verification methods in the SDN. This paper provides a systematic literature review (SLR) for the formal verification methods in the SDN in form of the SDN plans to recognize the state of the art of the open challenges. The presented SLR is classified into three main fields: application plan, data plan, and control plan approaches. The verification mechanisms are compared with each other according to the important factors such as structural properties, quality‐of‐service metrics, applied algorithms, and measurement tools. [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]

Advanced technologies such as internet of things (IoT) and clouds have significantly influenced on modern medical monitoring systems. Analytical statistics derived from massive patients' medical data via different data analysis methods, contribute in remote medical monitoring, early diagnosis of diseases, predicting clinical events, and recommending vital health/medical instructions. According to existence of the same health/medical services in functional aspect, finding appropriate composite health/medical services by the patients has been remained as a major concern in modern medical systems. Regarding this challenge, in this paper, a medical monitoring scheme for cloud‐based IoT platform is proposed, in which the patients' medical conditions are derived through predicting diseases by mining her physiological data collected from IoT devices and other medical records. A disease diagnosis model is used to analyze the patients' medical data for the aim of offering a composite health/medical prescription. After confirming the outcomes by medical team, it is sent to the patient. Then, the patient indicates her nonfunctional requirements such as location, cost and time to find the most appropriate composite health/medical service based on her preferences. Experimental results reveal that the proposed scheme is successful in achieving effective diseases diagnosis for offering composite health/medical prescriptions. [ABSTRACT FROM AUTHOR]