Osinga, S.A.,
Kramer, M.R.,
Hofstede, G.J.,
Roozmand, O.,
Beulens, A.J. Publication Date: 2010
Lecture Notes in Economics and Mathematical Systems (00758442)645pp. 177-188
This paper investigates the effect of a selected top-down measure (whatif scenario) on actual agent behaviour and total system behaviour by means of an agent-based simulation model, when agents' behaviour cannot fully be managed because the agents are autonomous. The Chinese pork sector serves as case. A multilevel perspective is adopted: the top-down information management measures for improving pork quality, the variation in individual farmer behaviour, and the interaction structures with supply chain partners, governmental representatives and peer farmers. To improve quality, farmers need information, which they can obtain from peers, suppliers and government. Satisfaction or dissatisfaction with their personal situation initiates change of behaviour. Aspects of personality and culture affect the agents' evaluations, decisions and actions. Results indicate that both incentive (demand) and the possibility to move (quality level within reach) on farmer level are requirements for an increase of total system quality. A more informative governmental representative enhances this effect. © Springer-Verlag Berlin Heidelberg 2010.
Roozmand, O.,
Ghasem-aghaee, N.,
Hofstede, G.J.,
Nematbakhsh, M.A.,
Baraani, A.,
Verwaart, T. Publication Date: 2011
Knowledge-Based Systems (09507051)24(7)pp. 1075-1095
Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede's model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae's five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries. © 2011 Elsevier B.V. All rights reserved.
Publication Date: 2011
Scientia Iranica (23453605)18(6)pp. 1460-1468
Self-Organizing Maps (SOMs) are among the most well-known, unsupervised neural network approaches to clustering, which are very efficient in handling large and high dimensional datasets. The original Particle Swarm Optimization (PSO) is another algorithm discovered through simplified social model simulation, which is effective in nonlinear optimization problems and easy to implement. In the present study, we combine these two methods and introduce a new method for anomaly detection. A discussion about our method is presented, its results are compared with some other methods and its advantages over them are demonstrated. In order to apply our method, we also performed a case study on forest fire detection. Our algorithm was shown to be simple and to function better than previous ones. We can apply it to different domains of anomaly detection. In fact, we observed our method to be a generic algorithm for anomaly detection that may need few changes for implementation in different domains. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
Sentiment analysis is the process of analyzing the characteristics of opinions, feelings, and emotions which are expressed in textual data. This paper presents a novel approach for generation of a lexical resource named PersianClues used for sentiment analysis in Persian language. Moreover, a novel unsupervised LDA-based sentiment analysis method called LDASA is proposed. In order to generate the PersianClues, at the first phase, an automatic translation approach is used to translate the existing English clues to Persian. Next, iterative refinement approach is used to correct the erroneous clues resulted from previous step. Then, topic-based polar sets are achieved from these clues and finally, each document is categorized into its related polarity using a classification algorithm. To evaluate this method, three resources about hotels, cell phones and digital cameras have been manually gathered from the e-shopping websites and the results of sentiment analysis on these resources are compared with a baseline named SVM-Unigrams. The experimental results demonstrate an improvement of 9% on average in polarity classification accuracy of the base system. © 2012 IEEE.
Shams, M.,
Saffar, Mohammadtaghi,
Shakery, Azadeh,
Faili, Heshaam Publication Date: 2012
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (16113349)
The idea of applying a conjunction of sentiment and social network analysis to improve the performance of applications has recently attracted attention of researchers. In widely used online shopping websites, customers can provide reviews about a product. Also a number of relations like friendship, trust and similarity between products or users are being formed. In this paper a combination of sentiment analysis and social network analysis is employed for extracting classification rules for each customer. These rules represent customers' preferences for each cluster of products and can be seen as a user model. The combination helps the system to classify products based on customers' interests. We compared the results of our proposed method with a baseline method with no social network analysis. The experiments on Amazon's meta-data collection show improvements in the performance of the classification rules compared to the baseline method. © 2012 Springer-Verlag.
Clustering is the task of grouping related and similar data without any prior knowledge about the labels. In some real world applications, we face huge amounts of unstructured textual data with no organization. In these situations, clustering is a primitive operation that needs to be done to help future e-commerce tasks. Clustering can be used to enhance different e-commerce applications like recommender systems, customer relationship management systems or personal assistant agents. In this paper we propose a new method for text clustering, by constructing a term correlation graph, and then extracting topic word sets from it and finally, categorizing each document to its related topic with the help of a classification algorithm like SVM. This method provides a natural and understandable description for clusters by their topic word sets, and it also enables us to decide the cluster of documents only when needed and in a parallel fashion, thus significantly reducing the offline processing time. Our clustering method also outperforms the well-known k-means clustering algorithm according to clustering quality measures. © 2013 IEEE.
Publication Date: 2014
Simulation Series (07359276)46(1)pp. 77-83
In this paper we propose an agent-based model approach to determining the effects of consumer choice on aggregate demand (CCAD). Our overall goal is to better understand how the availability of information, heuristic decision making, and social norms affect: 1) total aggregate demand and 2) the aggregated demand for disposable vs. more durable goods. In the preliminary model presented here, consumer agents select among baskets of goods with different combinations of quality and disposability. Consumer choices are based on individual agent preferences and subject to a discretionary income constraint. Agents may be either maximizing, which means that they choose the best basket of goods that they can afford, or satisficing, which means that they choose the first affordable basket of goods that they can find with utility greater than their satisfaction threshold. When run at different price levels, the resulting models can be used to generate aggregated demand curves for each group of consumers. We also demonstrate that, satisficers buy more than maximizers overall. Further analysis shows that this is because maximizers focus their trading on more durable products to gain the highest utility, however satisficers purchase more disposable products because they shop for convenience rather than utility maximization.
Publication Date: 2014
Proceedings of the Institution of Civil Engineers: Water Management (17517729)167(4)pp. 187-193
Experimental and numerical studies were performed to examine the relationship between the hydraulic properties and physical dimensions of a subsurface weir system and the discharge over it under steady-state conditions. Experimental modelling was based on a laboratory-scale physical sand-tank model, within which a subsurface dam and weir structure was constructed. To evaluate the effect of the dominant parameters on the discharge coefficient, numerical modelling was also carried out. A number of physical and numerical models of subsurface dams and weirs were constructed and the effect of each parameter on the discharge coefficient was studied. The results indicate that the hydraulic conductivity of the aquifer, the head above the weir and the effective width of the aquifer, in addition to the geometry of the subsurface weir, significantly affect discharge efficiency. It was also shown that the Reynolds and Weber numbers only have a small effect on the discharge coefficient. Based on the experimental results, a threshold value is suggested for the subsurface weir submergence. Finally, according to the results of both experimental and numerical models, an equation is derived to estimate the discharge coefficient of subsurface weirs.
Publication Date: 2014
Wireless Personal Communications (1572834X)79(3)pp. 2155-2170
Recently user quality of experience (QoE) is employed in evaluating end user satisfaction in communications systems. Generally, current approaches for QoE assessment are obtrusive, laboratory based and offline. Estimation of user satisfaction in static manner based on mean opinion score is not directly related to instantaneous individual end user contentment. In this paper, based on correlations between user’s physiological signals and her/his feelings about the service quality, a non-intrusive and user centric QoE assessment system for voice communications is developed. The findings of this study indicate that the emotional patterns in response to the changes in channel quality can be adapted to estimate the level of satisfaction in a QoE assessment system in a live manner. Based on experimental results, two categories of users are identified: sensitive and insensitive towards quality degradations. The results indicate that for the sensitive users, our non-intrusive subjective quality assessment method outperforms ITU-T P.563 standard with respect to root mean square error; while, the results are much better among the insensitive users. © 2014, Springer Science+Business Media New York.
Publication Date: 2016
Intelligent Data Analysis (1088467X)20(1)pp. 199-218
Influence maximization in a social network involves identifying an initial subset of nodes with a pre-defined size in order to begin the information diffusion with the objective of maximizing the influenced nodes. In this study, a sign-aware cascade (SC) model is proposed for modeling the effect of both trust and distrust relationships on activation of nodes with positive or negative opinions towards a product in the signed social networks. It is proved that positive influence maximization is NP-hard in the SC model and influence function is neither monotone nor submodular. For solving this NP-hard problem, a particle swarm optimization (PSO) method is presented which applies the random keys representation technique to convert the continuous search space of the PSO to the discrete search space of this problem. To improve the performance of this PSO method against premature convergence, a re-initialization mechanism for portion of particles with poorer fitness values and a heuristic mutation operator for global best particle are proposed. Experiments establish the effectiveness of the SC in modeling the real-world cascades. In addition, PSO method is compared with the well-known algorithms in the literature on two realworld data sets. The evaluation results demonstrate that the proposed method outperforms the compared algorithms significantly in the SC model. © 2016 - IOS Press and the authors. All rights reserved.
Publication Date: 2016
Multimedia Tools and Applications (13807501)75(2)pp. 903-918
Measuring end user Quality of Experience (QoE) is currently performed by subjective or objective standard methods each with its own deficiencies. The subjective quality assessment is laboratory based, costly and offline; while, the objective estimation of user satisfaction is obtained through a static manner, not directly related to end user contentment. The attempt is made here to measure user QoE based on an online, user-aware and non-intrusive method. This is investigated by identifying the measurable objective indicators of user satisfaction/dissatisfaction and assigning them to the subjective nature of QoE concept. Proposing an architectural model, the extent of modalities for implicit sensing of the user QoE is explored with respect to the real-time measurement of her/his experiences. The vocal and interactional signs of VoIP service users on their smartphone devices are applied to estimate their satisfaction/dissatisfaction levels as a case study. The obtained results are compared to the users’ self-report in order to evaluate the accuracy of this proposed method. © 2014, Springer Science+Business Media New York.
Publication Date: 2017
Journal of Information Science (01655515)43(2)pp. 204-220
One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks. © Chartered Institute of Library and Information Professionals.
Publication Date: 2017
Journal of Biomedical Informatics (15320480)68pp. 167-183
Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm. © 2017
Publication Date: 2018
Briefings in Bioinformatics (14774054)19(5)pp. 878-892
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
Mohammadi, F.,
Bina, B.,
Amin, M.M.,
Pourzamani, H.R.,
Yavari, Z.,
Shams, M. Publication Date: 2018
Canadian Journal of Chemical Engineering (00084034)96(8)pp. 1762-1769
Alkylphenols (APs) received great attention in the past decade because they were on the priority hazardous substances list. The kinetics of a lab-scale moving bed biofilm reactor (MBBR) that was fed with synthetic wastewater containing 4-NonylPhenol (4-NP) and 4-tert-OctylPhenol (4-t-OP) was investigated in this paper. The MBBR reactor was evaluated under different APs, organic loading rates, and hydraulic retention times (HRT). The substrate removal rate was predicted with the first-order, second-order, Stover-Kincannon, and Monod substrate removal models. 4-NP and 4-t-OP pollutants were removed in the different percentages of 87.1 to 99.9 % and 83.2 to 99.9 %, respectively. Biokinetic parameters, like Y, KS, k, μmax, and kd, that would be favourable to design an MBBR were evaluated. Based on the results, the second-order (Grau), Stover-Kincannon, and Monod models were observed to be the most suitable for this reactor. These models showed high correlation coefficients of about 99.6, 99.1, and 92.9 %, respectively. Consequently, these models could be utilized in anticipating the performance and design of MBBR reactors. © 2017 Canadian Society for Chemical Engineering
Publication Date: 2019
Expert Systems with Applications (09574174)119pp. 476-490
Influence maximization is an important issue in social network analysis domain which concerns finding the most influential nodes. Determining the influential nodes is made with respect to information diffusion models. Most of the existing models only contain trust relationships while distrust exist in social networks as well. There exist some drawbacks in limited studies where distrust relationship is involved. The most outstanding drawback is the lack of assessment on the validity of the schemes presented on how influence propagates through distrust relationships in comparison with real word propagation in social networks. In this paper, two schemes are proposed, where based on each, some new models are proposed in two classes: cascade-based and threshold-based. All models of concern here are evaluated in comparison with the benchmark models through two real data sets, the Epinions and Bitcoin OTC. Results obtained indicate the superiority of one of the proposed schemes: when a distrusted user performs an action or adopts an opinion, the target users may tend not to do it. © 2018 Elsevier Ltd
Publication Date: 2019
Methods in Molecular Biology (19406029)1903pp. 291-316
Using existing drugs for diseases which are not developed for their treating (drug repositioning) provides a new approach to developing drugs at a lower cost, faster, and more secured. We proposed a method for drug repositioning which can predict simple and complex relationships between drugs, drug targets, and diseases. Since biological networks typically present a suitable model for relationships between different biological concepts, our primary approach is to analyze graphs and complex networks in the study of drugs and their therapeutic effects. Given the nature of existing data, the use of semi-supervised learning methods is crucial. So, in our research, we have developed a label propagation method to predict drug-target, drug-disease, and disease-target interactions (Heter-LP), which integrates various data sources at different levels. The predicted interactions are the most prominent relationships among the millions of relationships suggested to the related researchers for further investigation. The main advantages of Heter-LP are the effective integration of input data, eliminating the need for negative samples, and the use of local and global features together. The main steps of this research are as follows. The first step is the construction of a heterogeneous network as a data modeling task, in which data are collected and prepared. The second step is predicting potential interactions. We present a new label propagation algorithm for heterogeneous networks, which consists of two parts, one mapping and the other an iterative method for determining the final labels of the entire network vertices. Finally, for evaluation, we calculated the AUC and AUPR with tenfold cross-validation and compared the results with the best available methods for label propagation in heterogeneous networks and drug repositioning. Also, a series of experimental evaluations and some specific case studies have been presented. The result of the AUC and AUPR for Heter-LP was much higher than the average of the best available methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2020
Scientific Reports (20452322)10(1)
Rare or orphan diseases affect only small populations, thereby limiting the economic incentive for the drug development process, often resulting in a lack of progress towards treatment. Drug repositioning is a promising approach in these cases, due to its low cost. In this approach, one attempts to identify new purposes for existing drugs that have already been developed and approved for use. By applying the process of drug repositioning to identify novel treatments for rare diseases, we can overcome the lack of economic incentives and make concrete progress towards new therapies. Adrenocortical Carcinoma (ACC) is a rare disease with no practical and definitive therapeutic approach. We apply Heter-LP, a new method of drug repositioning, to suggest novel therapeutic avenues for ACC. Our analysis identifies innovative putative drug-disease, drug-target, and disease-target relationships for ACC, which include Cosyntropin (drug) and DHCR7, IGF1R, MC1R, MAP3K3, TOP2A (protein targets). When results are analyzed using all available information, a number of novel predicted associations related to ACC appear to be valid according to current knowledge. We expect the predicted relations will be useful for drug repositioning in ACC since the resulting ranked lists of drugs and protein targets can be used to expedite the necessary clinical processes. © 2020, The Author(s).
Publication Date: 2020
Expert Systems with Applications (09574174)159
Background and objective: Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. Methods: In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network. Results: We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy. © 2020 Elsevier Ltd
Publication Date: 2020
International Journal of Ad Hoc and Ubiquitous Computing (17438225)34(1)pp. 35-44
Wireless sensor networks have shown to be a promising technology for industrial automation in which continuous monitoring is a critical requirement. Deploying an energy-aware sensor network permits increasing the network lifetime and prolonging the monitoring operation. IEEE 802.15.4e and RPL have been used as de-facto protocols at the access and network layer in low power and low-rate wireless networks. Specifically, the time slotted channel hopping (TSCH) of 802.15.4e has been designed to provide a reliable access method in low power and lossy networks. More importantly, the combination of TSCH and RPL facilitates providing load-balancing together with energy-saving in such networks. This paper proposes schedule aware RPL (SA-RPL) which aims at prolonging the network lifetime while improving load balancing. It periodically collects scheduling matrix information form TSCH to compute a new measure for selecting the next hop at the network layer. More precisely, the parent with minimum number of occupied cells is more likely to be chosen as the preferred parent. To evaluate the performance of SA-RPL, we modified a distributed management scheme already developed in NS2 simulator. Simulation results show that SA-RPL, compared with other methods, prolongs the network lifetime up to two times and achieves a more uniform energy consumption distribution without decreasing other performance metrics. © 2020 Inderscience Enterprises Ltd.
Publication Date: 2021
Journal of Biomedical Informatics (15320480)115
One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein networks could overcome this drawback, causing a network with fewer false positive interactions. The integration method plays a crucial role in the quality of the constructed network. In this paper, we integrate several sources to build a reliable heterogeneous network, i.e., a network that includes nodes of different types. Due to the different gene/protein sources, four gene-gene similarity networks are constructed first and integrated by applying the type-II fuzzy voter scheme. The resulting gene-gene network is linked to a disease-disease similarity network (as the outcome of integrating four sources) through a two-part disease-gene network. We propose a novel algorithm, namely random walk with restart on the heterogeneous network method with fuzzy fusion (RWRHN-FF). Through running RWRHN-FF over the heterogeneous network, disease-related genes are determined. Experimental results using the leave-one-out cross-validation indicate that RWRHN-FF outperforms existing methods. The proposed algorithm can be applied to find new genes for prostate, breast, gastric, and colon cancers. Since the RWRHN-FF algorithm converges slowly on large heterogeneous networks, we propose a parallel implementation of the RWRHN-FF algorithm on the Apache Spark platform for high-throughput and reliable network inference. Experiments run on heterogeneous networks of different sizes indicate faster convergence compared to other non-distributed modes of implementation. © 2021 Elsevier Inc.
Publication Date: 2021
Journal of Biomedical Informatics (15320480)116
Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we propose a domain-specific method that models a document as a multi-layer graph to enable multiple features of the text to be processed at the same time. The features we used in this paper are word similarity, semantic similarity, and co-reference similarity, which are modelled as three different layers. The unsupervised method selects sentences from the multi-layer graph based on the MultiRank algorithm and the number of concepts. The proposed MultiGBS algorithm employs UMLS and extracts the concepts and relationships using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BERTScore shows increased F-measure values. © 2021 Elsevier Inc.
Publication Date: 2022
Physica A: Statistical Mechanics and its Applications (03784371)604
Community detection is one of the most essential issues in social networks analysis field. Among the available categories of algorithms, the label propagation algorithm-based (LPA-based) methods, due to their proper time complexity, are of high concern. As all the social networks explicitly or implicitly include signed relationships, the attempt here is to suggest an LPA-based approach for community detection in the directed signed social networks. The direction of edges is not addressed in available LPA-based community detection methods for signed social networks. In this respect, 1) a weighting method is suggested in order to utilize the direction information that converts the network into an undirected weighted signed social network, 2) this weight is combined with a second weight obtained from the sign information of the edges, and 3) the LPA is extended, where the combined weights are applied in label propagation. Moreover, the directed signed modularity and the directed signed flow-based capacity measures are proposed. The findings of the run experiments indicate that the proposed method as to the directed signed modularity, directed signed flow-based capacity, and frustration measures on real-world and synthetic data sets, outperforms its counterparts. © 2022 Elsevier B.V.
Publication Date: 2022
Knowledge and Information Systems (02191377)64(12)pp. 3293-3324
Patient similarity assessment, which identifies patients similar to a given patient, is a fundamental component of many secondary uses of medical data. The assessment can be performed using electronic medical records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate distance. This study presents a new data representation method for EMRs that considers the information in clinical narratives. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised manner is proposed for building a patient representation, which integrates unstructured and structured data extracted from patients' EMRs. We employed a tree structure to model the extracted data that capture the temporal relations of multiple medical events from EMR. We processed clinical notes to extract medical concepts using Python libraries such as MedspaCy and ScispaCy and mapped entities to the Unified Medical Language System (UMLS). To capture temporal aspects of the extracted events, we developed two new relabeling methods for the non-leaf nodes of the tree. To create an embedding vector for each patient, we traversed the tree to generate sequences that the Doc2vec algorithm would use. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean-squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Sharifi, S.,
Lotfi shahreza, M.,
Pakdel, A.,
Reecy, J.M.,
Ghadiri, N.,
Atashi, H.,
Motamedi, M.,
Ebrahimie, E. Publication Date: 2022
Animals (20762615)12(1)
Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the leading agents causing acute severe infection with clinical signs. E. Coli, environmental mastitis pathogens, are the primary etiological agents of bovine mastitis in well-managed dairy farms. Response to E. Coli infection has a complex pattern affected by genetic and environmental parameters. On the other hand, the efficacy of antibiotics and/or anti-inflammatory treatment in E. coli mastitis is still a topic of scientific debate, and studies on the treatment of clinical cases show conflicting results. Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing. In the current research, a novel, semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration, was used to potentially identify novel therapeutic avenues for the treatment of E. coli mastitis. Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for E. coli mastitis, including critical genes with robust bio-signatures, drugs, and related disorders, were used as input data for analysis with the Heter-LP algorithm. Our research identified novel drugs such as Glibenclamide, Ipratropium, Salbutamol, and Carbidopa as possible therapeutics that could be used against E. coli mastitis. Predicted relationships can be used by pharmaceutical scientists or veterinarians to find commercially efficacious medicines or a combination of two or more active compounds to treat this infectious disease. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Publication Date: 2023
Journal Of Optimization In Industrial Engineering (24233935)16(2)pp. 257-273
Agent-based modelling and simulation (ABMS) is one of the topics which has been extensively studied by researchers in the field of marketing and consumer behaviour. However, no such analysis has been conducted on using Agent-based modelling and simulation in marketing and consumer behaviour. An extensive bibliometric analysis, as well as a thorough visualization and science mapping, was carried out in this field from 1995 to 2022, in response to capturing recent ABMS development in this field. A total of 1210 documents from the WOS and Scopus databases were analyse d using bibliometrix R-Tool and VOS viewer. The results showed the 20 documents with the most citations were in the area of energy consumption (55%) and innovation diffusion behaviour (20%). The USA has the most publications in this field, with the production of 188 documents. The “EXPERT SYSTEMS WITH APPLICATIONS” is a productive journal publishing in this field. Generally, the major journals that publish research on the use of ABM in marketing and consumer behaviour are multidisciplinary or interdisciplinary. 6 clusters were identified based on the analysis of the most frequent key-words: Cluster 1 (multi-agent systems and consumer behaviour), Cluster 2 (agent-based simulation and SCM), Cluster 3 (ABM and energy consumption), Cluster 4 (AMB and innovation diffusion), Cluster 5 (complex system and Simulation) and Cluster 6 (ABM and TAM). Prediction is one of the goals that has attracted the most attention of ABMS researchers among many goals such as optimization, description, self-organization, and adaptability, and there are many recent works in this field. These results show that many topics that were of interest in the past, such as the ontology of ABMS, are no longer of much interest to researchers, and the attention of researchers has been directed toward issues such as the diffusion of innovation, energy consumption, and pricing in recent years. This topic can determine the appropriate approach for other researchers to research in this field. © 2023 Qazvin Islamic Azad University. All rights reserved.
