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Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs. Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility—a MOF key performance indicator—of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host–guest interaction studies. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs. Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility—a MOF key performance indicator—of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host–guest interaction studies. [ABSTRACT FROM AUTHOR]

Matrix factorization (MF) is one of the most important regression analysis methods used in recommendation systems. Max margin matrix factorization (MMMF) is a variant of MF which transforms the regression analysis problem into a single multi‐class classification problem, and then learns a multi‐class max margin classifier to achieve to a better error rate. One drawback of multi‐class MMMF is its bias towards class with small sample size. Therefore, hierarchical MMMF (HMF) which uses some two‐class MMMF problems in a hierarchical manner for multi‐class classification was proposed. Each two‐class MMMF of HMF is learned on the basis of thresholded training data which is too imbalanced for some two‐class MMMFs. Meanwhile, all training data is used in each two‐class MMMF. In the test phase of HMF, an imbalanced tree is used to estimate rating. Each node of this tree is a learned two‐class MMMF. In this paper, we propose a balanced HMF, which constructs a balanced tree with minimum depth. Each node of this tree is a learned two‐class MMMF on the basis of a part of data which is selected such that to be more balanced than that of the traditional HMF. Moreover, each part of data in our proposed balanced HMF does not have overlap with all previous parts of data. Therefore, the overall training data used in each step of balanced HMF is smaller than that of in the traditional HMF. Experimental results on real datasets show that training time, test time and error rate of our proposed balanced HMF is better than those of the traditional HMF. [ABSTRACT FROM AUTHOR]

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems. [ABSTRACT FROM AUTHOR]

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1186/s13321-023-00764-2.Correction: Journal of Cheminformatics (2023) 15:94https://doi.org/10.1186/s13321-023-00764-2Following publication of the original article [[1]], we have been informed that Fig. 4 is missing, and instead of Fig. 4, Fig. 3 has been repeated as Fig. 4.The original article [[1]] has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Mehrdad Jalali; A. D. Dinga Wonanke and Christof WöllReported by Author; Author; Author [Extracted from the article]

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS-based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click-through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click-through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called 'Swallow' creates a graph-based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms. [ABSTRACT FROM AUTHOR]

Social networks are generally sets of individuals or organizations that are connected with one or more links. Usually, social networks are presented by undirected graphs, where the set of vertices V and the set of edges E state the individuals and relation between them respectively. One of the most applicable problems in these networks is the centrality values allocation problem to the vertices and edges. Recently, a new evaluation criterion for the edge centrality so called centrality index of k paths has been proposed which is based on intranet issuing the messages along with random paths composed of k edges. From the other side, it has been vivid the importance of computing the edges centrality through these years. In this study, by referring to message propagation along random paths, a new diffusion model was reached by applying heat diffusion algorithm. This model was based such that the vertex on the way of heat diffusion of most of vertices could be considered as an important node and it could obtain centrality edges by scoring the edges on the heat diffusion path. The proposed technique was compared with centrality drawing method by means of random paths of length k and the results of analyzing the algorithm performance on online large social networks' data set show a remarkable efficacy of the proposed method to the mentioned method. Utilizing known large social networks in evaluation proves the efficiency of the proposed method for analyzing the large scale network. [ABSTRACT FROM AUTHOR]

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks. [ABSTRACT FROM AUTHOR]

Abstract: Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an online prediction using Web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy. [Copyright &y& Elsevier]

Two previously proposed heuristic algorithms for solving penalized regression‐based clustering model (PRClust) are (a) an algorithm that combines the difference‐of‐convex programming with a coordinate‐wise descent (DC‐CD) algorithm and (b) an algorithm that combines DC with the alternating direction method of multipliers (DC‐ADMM). In this paper, a faster method is proposed for solving PRClust. DC‐CD uses p × n × (n − 1)/2 slack variables to solve PRClust, where n is the number of data and p is the number of their features. In each iteration of DC‐CD, these slack variable and cluster centres are updated using a second‐order cone programming (SOCP). DC‐ADMM uses p × n × (n − 1) slack variables. In each iteration of DC‐ADMM, these slack variables and cluster centres are updated using ADMM. In this paper, PRClust is reformulated into an equivalent model to be solved using alternating optimization. Our proposed algorithm needs only n × (n − 1)/2 slack variables, which is much less than that of DC‐CD and DC‐ADMM and updates them analytically using a simple equation in each iteration of the algorithm. Our proposed algorithm updates only cluster centres using an SOCP. Therefore, our proposed SOCP is much smaller than that of DC‐CD, which is used to update both cluster centres and slack variables. Experimental results on real datasets confirm that our proposed method is faster and much faster than DC‐ADMM and DC‐CD, respectively. [ABSTRACT FROM AUTHOR]

• Trojan malware detection in the cloud-computing infrastructure. • Using semantic algorithms to detect malware, in particular unknown ones. • Ability to detect malicious mobile malware with very high accuracy. • Using subgraphs semantic homeomorphism coefficient innovation to classify Trojans. • Combining DNN GMDH algorithm and semantic methods to detect malware automatically. There are several approaches for preventing mobile devices from malware intrusion, but most of them suffer from the insufficient accuracy required for detecting Trojan malware. A combination of semantic and machine learning techniques can be effective in preventing intrusions. In this paper, we have used a hierarchical semantic approach to convert numerical and string data to meaningful values, Subgraph Semantic Homomorphism Coefficient (SSHC) to select optimal features, and Group Method of Data Handling (GMDH) deep neural network (DNN) algorithm to detect malware via a cloud-computing infrastructure. To evaluate our model, Android Trojan Dataset has been used. After evaluation, the accuracy reached 99.91%, which was improved by about 5.25% compared to StormDroid, Drebin, and KuafuDet models. Also, the accuracy was improved by about 10.4% and 31.9% compared to machine learning based approaches of Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), in the state-of-the-art KuafuDet model, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

There are report regarding therapeutic effects for Allium cepa L. (A. cepa) in Iranian traditional medicine and the plant has showed anti-inflammatory, anti-allergic, anti-hyperglycemic, antioxidant, anti-cancer, anti-hypertension, anti-hypercholesterolemia and anti-asthmatic activities in previous studies. In this study, the contribution of β2 adrenergic, muscarinic and histamine (H1) receptors, calcium and potassium channels, and cyclooxygenase pathway in the relaxant effect of A. cepa extract on tracheal smooth muscle (TSM) was assessed. TSM was contracted by KCl (60 mM) or methacholine (10 μM) for 5 min and cumulative concentrations of A. cepa extract (2, 4, 8, 16, 32 and 64 mg/ml) were added to organ bath every 5 min. Theophylline (0.2, 0.4, 0.6 and 0.8 mM) as positive control, and saline (1 ml) as negative control were also examined in non-incubated tissues. The relaxant effect of A. cepa extract was examined on non-incubated and incubated TSM with propranolol, chlorpheniramine, diltiazem, atropine, glibenclamide and indomethacin. A. cepa showed concentration-dependent relaxant effects on non-incubated TSM contracted by KCl (60 mM) or methacholine (10 μM), (P < 0.01 to p < 0.001). There was no significant difference in the relaxant effects of A. cepa between non-incubated and incubated tissues with glibenclamide, atropine, chlorpheniramine and indomethacin. The plant extract showed significant lower relaxant effects in incubated TSM with propranolol and diltiazem compared to non-incubated tissues. EC50 values of A. cepa extract in incubated TSM with propranolol and diltiazem were significantly lower than non-incubated tissues (p < 0.001 and p < 0.05, respectively). The relaxant effects of different concentrations of the extract of A. cepa were not significantly different with those of theophylline. The concentrations of A. cepa extract and theophylline were significant correlated with their relaxant effects (p < 0.05 to p < 0.001). In incubated TSM with propranolol and diltiazem, concentration ratio minus one (CR-1) values was positive (2.65 ± 0.63 and 1.28 ± 0.43 respectively). The A. cepa extract showed relatively potent relaxant effect on TSM which was comparable to the effect of theophylline. The results showed that β2-adrenergic stimulatory and/or calcium channel blockade are the possible mechanisms for the relaxant effects of the plant. Image 1 [ABSTRACT FROM AUTHOR]