A Parallel Mining Algorithm for Maximum Erasable Itemset Based on Multi-core Processor

Mining the erasable itemset is an interesting research domain, which has been applied to solve the problem of how to efficiently use limited funds to optimise production in economic crisis. After the problem of mining the erasable itemset was posed, researchers have proposed many algorithms to solve it, among which mining the maximum erasable itemset is a significant direction for research. Since all subsets of the maximum erasable itemset are erasable itemsets, all erasable itemsets can be obtained by mining the maximum erasable itemset, which reduces both the quantity of candidate and resultant itemsets generated during the mining process. However, computing many itemset values still takes a lot of CPU time when mining huge amounts of data. And it is difficult to solve the problem quickly with sequential algorithms. Therefore, this proposed study presents a parallel algorithm for the mining of maximum erasable itemsets, called PAMMEI, based on a multi-core processor platform. The algorithm divides the entire mining task into multiple subtasks and assigns them to multiple processor cores for parallel execution, while using an efficient pruning strategy to downsize the space to be searched and increase the mining speed. To verify the efficiency of the PAMMEI algorithm, the paper compares it with most advanced algorithms. The experimental results show that PAMMEI is superior to the comparable algorithms with respect to runtime, memory usage and scalability

IMPLEMENTASI ALGORITMA DATA MINING NAIVE BAYES PADA KOPERASI

One of the factors of failure in the field of credit business is the lack of accurate assessment of the ability of the debtor, thus resulting in errors in credit decisions that culminate in credit congestion. Data mining techniques can be used to assess customer ability based on past data. Debtor data that has been through the stages of data mining is then processed using Naive Bayes data mining algorithm. Naive Bayes is a simple probabilistic based prediction technique based on the application of bayes rules. Implementation using Weka 3.8 with a total of 3018 records yields a truth level of 94%

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels

Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2%  ±  2.8% measured by zero-one loss for the first evaluation session and 79.6%  ±  3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low

A Brief History of OLEDs—Emitter Development and Industry Milestones

Organic light‐emitting diodes (OLEDs) have come a long way ever since their first introduction in 1987 at Eastman Kodak. Today, OLEDs are especially valued in the display and lighting industry for their promising features. As one of the research fields that equally inspires and drives development in academia and industry, OLED device technology has continuously evolved over more than 30 years. OLED devices have come forward based on three generations of emitter materials relying on fluorescence (first generation), phosphorescence (second generation), and thermally activated delayed fluorescence (third generation). Furthermore, research in academia and industry toward the fourth generation of OLEDs is in progress. Excerpts from the history of green, orange‐red, and blue OLED emitter development on the side of academia and milestones achieved by key players in the industry are included in this report

Cell-free fat extract regulates oxidative stress and alleviates Th2-mediated inflammation in atopic dermatitis

Atopic dermatitis (AD) is a common inflammatory skin disease that significantly affects patients’ quality of life. This study aimed to evaluate the therapeutic potential of cell-free fat extract (FE) in AD. In this study, the therapeutic effect of DNCB-induced AD mouse models was investigated. Dermatitis scores and transepidermal water loss (TEWL) were recorded to evaluate the severity of dermatitis. Histological analysis and cytokines measurement were conducted to assess the therapeutic effect. Additionally, the ability of FE to protect cells from ROS-induced damage and its ROS scavenging capacity both in vitro and in vivo were investigated. Furthermore, we performed Th1/2 cell differentiation with and without FE to elucidate the underlying therapeutic mechanism. FE reduced apoptosis and cell death of HaCat cells exposed to oxidative stress. Moreover, FE exhibited concentration-dependent antioxidant activity and scavenged ROS both in vitro and vivo. Treatment with FE alleviated AD symptoms in mice, as evidenced by improved TEWL, restored epidermis thickness, reduced mast cell infiltration, decreased DNA oxidative damage and lower inflammatory cytokines like IFN-γ, IL-4, and IL-13. FE also inhibited the differentiation of Th2 cells in vitro. Our findings indicate that FE regulates oxidative stress and mitigates Th2-mediated inflammation in atopic dermatitis by inhibiting Th2 cell differentiation, suggesting that FE has the potential as a future treatment option for AD