- Maseud Rahgozar received his B.Sc. degree in electrical engineering from Sharif University of Technology, Tehran, in 1979edit
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It has been found that, if a (negative) bias is applied to a substrate during the sputtering thereto of Alfesil, selective re-sputtering from the substrate film of aluminum and silicon will leave that film rich in iron and, attendantly,... more
It has been found that, if a (negative) bias is applied to a substrate during the sputtering thereto of Alfesil, selective re-sputtering from the substrate film of aluminum and silicon will leave that film rich in iron and, attendantly, of higher saturation magnetization (17,000 gauss) than the starting material Alfesil (10,000 gauss). Such being the case, the invention provides that the sputtering of Alfesil-type material during the manufacture of a magnetic head be performed in two phases, first, while applying a bias of a first sense to a substrate to be sputtered upon, and, second, while applying a bias of different sense (e.g. a zero bias) to the substrate, thereby to cause a composite thin film to be formed on the substrate. The composition of the thin film in question is: 1. a (generally thin) region of material of high saturation magnetization layered with 2. a (generally thicker) region of lesser saturation magnetization.
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ABSTRACT Selecting optimal locations for new facilities is a critical decision in organizations that provide field-based services such as delivery, maintenance and emergency services. The total logistics cost and facility establishment... more
ABSTRACT Selecting optimal locations for new facilities is a critical decision in organizations that provide field-based services such as delivery, maintenance and emergency services. The total logistics cost and facility establishment cost are the main objectives of the location selection procedure. With the increasing size of this problem in today's applications, the aspects of efficiency and scalability have developed into major challenges. In this paper, we study the use of spatial clustering methods to solve this problem and propose two new algorithms. The new algorithms determine the optimal locations of the new facilities plus their optimal total count during the search process. We have conducted many experiments for empirical comparative study on the application of several spatial clustering algorithms for optimal facility establishment. The benchmarks are conducted with both real world and synthetic data sets. The results reveal advantages of the proposed algorithms and confirm that these algorithms have better performance in terms of efficiency and objectives in the field-based services. Hence, the higher scalability and effectiveness of the proposed algorithms make them suitable solutions for the problem of optimal facility establishment with large databases.
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Abstract Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine... more
Abstract Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
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Protein data patterns which are discriminative can be used in many beneficial applications if they are defined correctly such as molecular medicine, agriculture, and microbial genome applications. Prediction of protein folding patterns by... more
Protein data patterns which are discriminative can be used in many beneficial applications if they are defined correctly such as molecular medicine, agriculture, and microbial genome applications. Prediction of protein folding patterns by which the function of a protein ...
Research Interests: Computer Science, Artificial Intelligence, Protein Folding, Radial Basis Function, Neural Networks, and 14 moreFuzzy Systems, Particle Swarm Optimization, Neural Network, Protein structure, Data Structures, Protein Fold Recognition, classifier Fusion, Probabilistic Neural Network, Artificial Neural Network, Particle Swarm Optimizer, Database theory, Ordered Weighted Average, Particle Swarm Optimization Algorithm, and Neural nets
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Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach... more
Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users.
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Abstract Latest generations of catalogs for comparative shopping connect to multiple Web sites and collect the information requested by the user. It's necessary for these models to get access grant from vendors before to access... more
Abstract Latest generations of catalogs for comparative shopping connect to multiple Web sites and collect the information requested by the user. It's necessary for these models to get access grant from vendors before to access their databases and retrieve the requested ...
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The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database... more
The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method-s query answering.
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Research Interests: Computer Science, Information Retrieval, Algorithm, Persian, TIME, and 5 moreProperties, Weblog, Queries, Time, and Retrieval
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Since accurate migrations of data fragments in distributed database systems, known dynamic fragment allocation, play an important role in amendment of distributed database performance, several algorithms each of which shows different... more
Since accurate migrations of data fragments in distributed database systems, known dynamic fragment allocation, play an important role in amendment of distributed database performance, several algorithms each of which shows different performance in various conditions, have been proposed to improve dynamic fragment allocation in distributed database systems. In this paper, we are going to propose a novel algorithm which is
Research Interests: Computer Science, Distributed Database, Data Mining, Database Systems, Distributed Algorithms, and 12 moreFuzzy Logic, Clustering Algorithms, Fuzzy Systems, Fuzzy Inference, Fuzzy Control, Data Engineering, Cluster Analysis, Redundancy, Oscillations, Data Clustering, Distributed Databases, and Distributed Database System
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Tree structures have gained popularity for storing data from different domains such as XML documents, bio informatics and so on. Clustering these data can facilitate different operations. In this paper, we propose TreeCluster, a novel and... more
Tree structures have gained popularity for storing data from different domains such as XML documents, bio informatics and so on. Clustering these data can facilitate different operations. In this paper, we propose TreeCluster, a novel and heuristic algorithm for clustering tree structured data. This algorithm considers a representative tree for each cluster. For each input tree T, TreeCluster computes the composition of the tree T and each of the clusters. Tree T belongs to the cluster which its composed tree gains the best score. After adding a tree to a cluster the representative tree of that cluster is updated. We evaluate the accuracy of the TreeCluster algorithm in comparison to the previous works
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Rank-aggregation or combining multiple ranked lists is the heart of meta-search engines in web information retrieval. In this paper, a novel rank-aggregation method is proposed, which utilizes both data fusion operators and reinforcement... more
Rank-aggregation or combining multiple ranked lists is the heart of meta-search engines in web information retrieval. In this paper, a novel rank-aggregation method is proposed, which utilizes both data fusion operators and reinforcement learning algorithms. Such integration enables us to use the compactness property of data fusion methods as well as the exploration and exploitation capabilities of reinforcement learning techniques. The proposed algorithm is a two-steps process. In the first step, ranked lists of local rankers are combined based on their mean average precisions with a variety of data fusion operators such as optimistic and pessimistic ordered weighted averaging (OWA) operators. This aggregation provides a compact representation of the utilized benchmark dataset. In the second step, a Markov decision process (MDP) model is defined for the aggregated data. This MDP enables us to apply reinforcement learning techniques such as Q-learning and SARSA for learning the best ranking. Experimentations on the LETOR4.0 benchmark dataset demonstrates that the proposed method outperforms baseline rank-aggregation methods such as Borda Count and the family of coset-permutation distance based stage-wise (CPS) rank-aggregation methods on P@n and NDCG@n evaluation criteria. The achieved improvement is especially more noticeable in the higher ranks in the final ranked list, which is usually more attractive to Web users.
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Abstract Mining frequent tree patterns has many practical applications in areas such as XML document mining, Web mining, bioinformatics, network routing and so on. Most of the previous works used an apriori-based approach for candidate... more
Abstract Mining frequent tree patterns has many practical applications in areas such as XML document mining, Web mining, bioinformatics, network routing and so on. Most of the previous works used an apriori-based approach for candidate generation and frequency ...
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Allocating data fragments in distributed database systems is an important issue in distributed database (DDB) systems. In this paper, we are going to improve the effectiveness of current NNA algorithm using a Fuzzy inference engine.... more
Allocating data fragments in distributed database systems is an important issue in distributed database (DDB) systems. In this paper, we are going to improve the effectiveness of current NNA algorithm using a Fuzzy inference engine. Results indicate that, our fuzzy based NNA algorithm leads 5% gain in some of systems performance metrics. This algorithm, providing a data clustering mechanism, which
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This paper addresses the problem of determining the optimal location to place a fragment (object) in a distributed non-replicated database. The algorithm defined takes into consideration a changing environment with changing access... more
This paper addresses the problem of determining the optimal location to place a fragment (object) in a distributed non-replicated database. The algorithm defined takes into consideration a changing environment with changing access patterns. This paper contributes by allocating data fragments to their optimal location, in a distributed network, based on the access patterns for that fragment. The mechanism for achieving
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In this article we propose a supervised method for expanding tweet contents to improve the recall of tweet filtering task in online reputation management systems. Our method does not use any external resources. It consists of creating a... more
In this article we propose a supervised method for expanding tweet contents to improve the recall of tweet filtering task in online reputation management systems. Our method does not use any external resources. It consists of creating a K-NN classifier in three steps. In these steps the tweets labeled related and unrelated in the training set are expanded by extracting and adding the most discriminative terms, calculating and adding the most frequent terms, and re-weighting the original tweet terms from training set. Our experiments in RepLab 2013 data set show that our method improves the performance of filtering task, in terms of F criterion, up to 13% over state-of-the-art classifiers such as SVM. This data set consists of 61 entities from different domains of automotive, banking, universities, and music.
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Research Interests: Computer Science, Voting, Persian, Clustering, Results, and 4 moreCluster Analysis, Weblog, Domain, and Retrieval
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Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not... more
Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancem...
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Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing... more
Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, thi...
Research Interests: Computer Science, Algorithms, Programming Languages, Computational Biology, Biology, and 13 moreProteomics, Medicine, Multidisciplinary, Protein-Protein Interaction, Software, Humans, Support vector machine, Amino Acids, Protein Sequence Analysis, Reproducibility of Results, Protein Function Prediction, Base Sequence, and Sensitivity and Specificity
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The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new... more
The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. We explain our model within multi-steps from data preparation to rule generation.
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The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database... more
The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method-s query answering.
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... 1. Des1 (Arg) → Des2 (Arg) : γdep, α 2. Arg1 ISA Arg : γisa1, δ1 3. Arg2 ISA Arg : γisa2, δ2 4. Des1 (Arg2) = Ref1 : γstat1, φ1 5. Des1 (Arg1) = Ref3 : γstat2, φ2 6. Ref1 SIM Ref3 , CX: Any : γsim, σ 7. Des2 (Arg1) = Ref2 : γstat3, φ3... more
... 1. Des1 (Arg) → Des2 (Arg) : γdep, α 2. Arg1 ISA Arg : γisa1, δ1 3. Arg2 ISA Arg : γisa2, δ2 4. Des1 (Arg2) = Ref1 : γstat1, φ1 5. Des1 (Arg1) = Ref3 : γstat2, φ2 6. Ref1 SIM Ref3 , CX: Any : γsim, σ 7. Des2 (Arg1) = Ref2 : γstat3, φ3 ...
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Hadi Amiri1, Abolfazl AleAhmad1, Masoud Rahgozar1, Farhad Oroumchian2 1Database Research Group (DBRG) School of Electrical and Computer Engineering, University College of Engineering, University of Tehran {h.amiri,... more
Hadi Amiri1, Abolfazl AleAhmad1, Masoud Rahgozar1, Farhad Oroumchian2 1Database Research Group (DBRG) School of Electrical and Computer Engineering, University College of Engineering, University of Tehran {h.amiri, a.aleahmad}@ece.ut.ac.ir, rahgozar@ut.ac.ir, ...