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Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting

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APPLIED ENERGY 2017年 208卷 845-866页

作者： Heng, Jiani Wang, Jianzhou Xiao, Liye Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116000 Peoples R China Univ Elect Sci & Technol China Sch Phys Elect Chengdu Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

Solar radiation forecasting plays a significant role in precisely designing solar energy systems and in the efficient management of solar energy plants. Most research only focuses on accuracy improvements;however, for an effective forecasting model, considering only accuracy or stability is inadequate. To solve this problem, a combined model based on nondominated sorting-based multiobjective bat algorithm (NSMOBA) is developed for the optimization of weight coefficients of each model to achieve high accuracy and stability results simultaneously. In addition, a statistical method and data mining-based approach are used to determine the input variables for constructing the combined model. Monthly average solar radiation and meteorological variables from six datasets in the U.S. collected for case studies were used to assess the comprehensive performance (both in accuracy and stability) of the proposed combined model. The simulation in four experiments demonstrated the following: (a) the proposed combined model is suitable for providing accurate and stable solar radiation forecasting;(b) the combined model exhibits a more competitive forecasting performance than the individual models by using the advantage of each model;(c) the NSMOBA is an efficient algorithm for providing accurate forecasting results and improving the stability where the single bat algorithm is insufficient.

关键词： Global solar radiation forecasting Non-dominated sorting based multi-objective bat algorithm frequent pattern growth algorithm Combined model Forecasting accuracy and stability

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Android Malware Detection by Correlated Real Permission Couples Using FP growth algorithm and Neural Networks

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IEEE ACCESS 2023年 11卷 124996-125010页

作者： Banik, Abhinandan Singh, Jyoti Prakash Natl Inst Technol Patna Patna 800005 India

In the current internet era, where mobile devices are ubiquitous and often hold sensitive personal and corporate data, Android malware analysis is crucial to protect against the increasing sophistication and prevalence of mobile-based cyberattacks. This article proposes an innovative approach to Android malware detection using real permissions features extracted from Android code. This approach is unique among existing literature because, there for most cases, the declared permissions are being used, which is different from the real permission used in Android apps. Here, step-by-step guidelines have been elaborated to perform reverse engineering in any Android application package (APK) to extract the real permission feature from the Android disassembled code. After that, the most frequent & correlated pairs of real permissions were identified using the frequent pattern (FP) growth algorithm. Thereafter, the existence of those identified real permission couples was checked and fed into a multi-layered, K-fold cross-validated neural network model to predict whether an APK is malware or benignware. Simultaneously, five other traditional machine learning models have also been applied to benchmark the results. The outcome of these models is measured with various well-known metrics like Accuracy, Precision, Recall, Loss, Specificity, F1 Score, Receiver Operating Characteristic (ROC) curve, Negative Predictive Value, and Mathew's Correlation Coefficient. The experimental evaluation of the proposed methodology shows better performance on the well-known Drebin dataset as well as on the last 5 years customized dataset with an accuracy of over 96%.

关键词： Malware Operating systems Feature extraction Codes Reverse engineering Static analysis Neural networks Androids Permission Multi-layer neural network Android malware detection reverse engineering android real permissions frequent pattern growth algorithm K-fold cross validation multi-layered neural network

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Correlation analysis between characteristics under gastroscope and image information of tongue in patients with chronic gastritis

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Journal of Traditional Chinese Medicine 2022年第1期42卷 102-107页

作者： SHANG Zimeng DU Zhengguang GUAN Bei JI Xingyu CHEN Longchang WANG Yongji MA Yun Clinical College of Traditional Chinese Medicine Capital Medical UniversityBeijing 100013China College of Traditional Chinese Medicine Capital Medical UniversityBeijing 100069China Department of Gastroenterology Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical UniversityBeijing 100010China Cooperative Innovation Center Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Computer Science Institute of Softwarethe Chinese Academy of SciencesBeijing 100190China

OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic ***:frequent pattern growth(FP-growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under ***:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/*** are a total of 9 strong correlation ***:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/*** conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue.

关键词： gastritis tongue stomach duodenum correlation of data frequent pattern growth algorithm

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Towards Enhancing the Performance of Parallel FP-growth on Spark

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IEEE ACCESS 2022年 10卷 286-296页

作者： Essam, Amr Abdel-Fattah, Manal A. Abdelhamid, Laila Helwan Univ Fac Comp & Artificial Intelligence Dept Informat Syst Cairo 12317 Egypt

frequent itemset mining (FIM) is a crucial tool for identifying hidden patterns in information. FP-growth is an FIM algorithm used to find associations. When the data size increases, the execution of FIM algorithms on a single machine suffers from computational problems, such as memory and time consumption. For these reasons, parallel and distributed processing on platforms such as Spark is essential. The parallel frequent pattern (PFP) is the implementation of FP-growth in Spark. The main problem with PFP is that it does not consider the load balancing between cluster units. This research proposes an enhanced balanced parallel frequent pattern "EBPFP" algorithm to enhance and balance the PFP. The proposed algorithm (EBPFP) proposes two ideas. First, a strategy for load balancing between groups is proposed to ensure that the items are evenly divided between the nodes, and the cluster resources are used more effectively. Second, the improved conditional pattern base (ICPB) method aims to remove infrequent items from the conditional pattern base before constructing local FP-Trees. The experimental results show that the proposed EBPFP algorithm outperforms PFP, and the difference in running time between EBPFP and PFP was 21.56% and 39.72%, respectively.

关键词： Clustering algorithms Sparks Itemsets Task analysis Data mining Load management Partitioning algorithms Big data data mining association rule analysis frequent pattern growth algorithm spark load balancing

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Speeding Up Recommender Systems Using Association Rules 14th

Speeding Up Recommender Systems Using Association Rules

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14th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Kannout, Eyad Hung Son Nguyen Grzegorowski, Marek Univ Warsaw Inst Informat Warsaw Poland

ISBN: (纸本)9783031219665;9783031219672

Recommender systems are considered one of the most rapidly growing branches of Artificial Intelligence. The demand for finding more efficient techniques to generate recommendations becomes urgent. However, many recommendations become useless if there is a delay in generating and showing them to the user. Therefore, we focus on improving the speed of recommendation systems without impacting the accuracy In this paper, we suggest a novel recommender system based on Factorization Machines and Association Rules (FMAR). We introduce an approach to generate association rules using two algorithms: (i) apriori and (ii) frequent pattern (FP) growth. These association rules will be utilized to reduce the number of items passed to the factorization machines recommendation model. We show that FMAR has significantly decreased the number of new items that the recommender system has to predict and hence, decreased the required time for generating the recommendations. On the other hand, while building the FMAR tool, we concentrate on making a balance between prediction time and accuracy of generated recommendations to ensure that the accuracy is not significantly impacted compared to the accuracy of using factorization machines without association rules.

关键词： Recommendation system Association rules Apriori algorithm frequent pattern growth algorithm Factorization machines Prediction's time Quality of recommendations

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Privacy Protection and Data Security Governance Strategies for Association algorithms in the Digital Era of Enterprises

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Procedia Computer Science 2025年 261卷 589-597页

作者： Shengxin Song Xia Lei Guangxi University of Finance and Economics Nanning China

Aiming at the problems of low governance efficiency and high risk of information leakage in dynamic association analysis of large-scale data security protection in enterprises, this paper studies the privacy protection and governance strategies by combining the FP-growth (frequent pattern-growth) association algorithm to improve data security and governance efficiency. First, the sensitive data of enterprises was classified and preprocessed into three types: high sensitivity, medium sensitivity and low sensitivity, and unified format and standardization were performed. Then, the FP-growth method was used to mine frequent patterns in the data, identify potential security threats and the relationship between sensitive information; finally, the data compression optimization method based on the tree structure was used, combining block partitioning and global indexing to improve memory utilization and algorithm operation efficiency. Experiments show that when processing large data scales, compared with the two advanced association algorithms Apriori and Eclat, FP-growth’s running time is reduced by 24.8% and 14.5% respectively; memory consumption is reduced by 9.4% and 4.2% respectively. The conclusion shows that the FP-growth algorithm helps to improve the level of security governance and provide effective support for enterprise digitalization.

关键词： Privacy protection data security governance association algorithm frequent pattern growth algorithm enterprise digital transformation

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Parallel extraction of association rules from genomics data

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APPLIED MATHEMATICS AND COMPUTATION 2019年 350卷 434-446页

作者： Agapito, Giuseppe Guzzi, Pietro Hiram Cannataro, Mario Magna Graecia Univ Catanzaro Data Analyt Res Ctr Dept Med & Surg Sci Viale Europa I-88100 Catanzaro Italy

High-throughput experimental platforms like microarrays produce massive amounts of omics data for each analyzed sample. As an example, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform can discover Single Nucleotide Polymorphisms (SNPs) from 225 human genes involved in absorption, distribution, metabolism, and excretion (ADME) of drugs, enabling large pharmacogenomics studies. Moreover, the application of such platforms to large populations of subjects is further increasing the size of experimental datasets produced in clinical studies. Thus, the production of big omics datasets is a first reason to use parallel computing in bioinformatics. Such omics datasets are usually analyzed with classical statistical analysis and, more recently, by using data mining methods that can extract knowledge hidden in the data, e.g. by highlighting multiple associations among features of the data. However, the use of standard off-the-shelf data mining algorithms to large omic datasets, especially when considering association rule mining, poses two main issues: (i) huge requests of central memory that may prevent the execution of data mining software on personal/desktop computers;and (ii) very long response time, that may increase the time requested for completing extensive pharmacogenomics studies. To overcome the limits of standard association rule mining algorithms when applied to omics datasets, we propose PARES (Parallel Association Rules Extractor from SNPs), a novel parallel algorithm for the efficient extraction of association rules from omics datasets. PARES is implemented as a multi-thread version of an optimized version of the frequent pattern growth (FP-growth) algorithm. Moreover, it includes a customized SNPs datasets preprocessing strategy based on a Fisher's Test Filter to discard the trivial transactions from the input dataset, reducing the search space from which to build many independent FP-Trees. The experimental results show that

关键词： frequent pattern growth algorithm Association rules mining Data mining Genomics Parallel computing

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Strategizing Game Playing Using Evolutionary Approach 18th

Strategizing Game Playing Using Evolutionary Approach

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18th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Nagpal, Abhinav Gabrani, Goldie Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India BML Munjal Univ Dept Comp Sci & Engn Gurugram India

ISBN: (纸本)9783030209124;9783030209117

Since the inception of evolutionary algorithms, the capabilities of genetic algorithms is showcased by games. This paper proposes the use of a genetic algorithm for the game, Tetris. An evolutionary approach is used to design a Tetris bot. The proposed approach uses a novel set of parameters to decide which move needs to be taken by the Tetris bot for each falling Tetromino. These parameters represent the various genes present in the chromosome. Each individual is being allowed to play the game once. Once the entire population has played, the population undergoes crossover and mutation. In this way, the parameters are evolved to get a better bot. The most evolved bot as per the fitness is allowed to simulate 200 rounds of Tetris during which its actions are recorded. Further, frequent pattern growth algorithm, a data mining technique, is used to extract knowledge from the given stored actions. The extracted knowledge is used for mining association rules and identifying strategies used by the evolved bot to play the game.

关键词： Tetris Artificial Intelligence Genetic algorithms Data mining frequent pattern growth algorithm

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