Publication Date: 2017
Telecommunication Systems (10184864)64(2)pp. 367-390
Designing QoS-aware medium access control (MAC) scheme is a challenging issue in vehicular ad hoc networks. Proportional fairness and bandwidth utilization are among the significant requirements that should be taken into account by a MAC scheme. In this paper, a bandwidth-efficient and fair multichannel MAC protocol is proposed to address these two requirements, specifically in vehicle-to-vehicle communications. The proposed scheme is based on clustering of vehicles and exploits time division multiple access (TDMA) method alongside the carrier sense multiple access with collision avoidance mechanism to allocate DSRC-based resources in a different manner from IEEE 802.11p/IEEE 1609.4 protocols. It divides each channel into aligned dynamic-sized time frames. In each time frame, in a fully TDMA-based period, transmission opportunities are assigned to vehicles letting them have dedicated transmissions on the service and control channels. The maximum number of transmission opportunities per each frame is determined by the cluster head (CH) based on a defined optimization problem which aims at maximizing both proportional fairness and bandwidth utilization. Furthermore, the bandwidth utilization is assumed to be enhanced more through reallocation of unused transmission opportunities in each time frame, using a proposed reallocation algorithm. The proposed MAC protocol is treated as a lightweight scheme such that various types of unicast, multicast and broadcast communications are possible within the cluster without involving the CH. Evaluation results show that the proposed scheme has more than 90 % achievement in terms of proportional fairness and bandwidth utilization simultaneously, and in this case, has a considerable superiority over TC-MAC. In addition, using the proposed scheme, the satisfaction level of vehicles is preserved appropriately. © 2016, Springer Science+Business Media New York.
Combinatorial auctions are auctions in which bidders bid on combinations of items, bundles, instead of on individual items. In these auctions, bidders always tend to construct and bid on the most beneficial bundles of items, while facing a substantial number of items. Since there are a huge number of items available in a combinatorial auction, deciding on which items to put in bundles is a challenge for bidders. In combinatorial auctions, bundling of items and bidding on the best possible bundles are of great importance and developing an efficient bidding strategy can increase quality of the auctions considerably. In this paper, we have proposed an efficient bidding strategy. Performance of the proposed strategy in various markets has been simulated and compared with the bidding strategies already available in the literature. The obtained results show that in comparison with the previously available bidding strategies, the proposed strategy is more beneficial to both bidders and auctioneer, especially in markets where there is a considerable difference between values of items. © 2011 IEEE.
Publication Date: 2020
Iranian Conference on Machine Vision and Image Processing, MVIP (21666776)2020
Two-dimensional (2-D) convolution is a common operation in a wide range of signal and image processing applications such as edge detection, sharpening, and blurring. In the hardware implementation of these applications, 2d convolution is one of the most challenging parts because it is a compute-intensive and memory-intensive operation. To address these challenges, several design techniques such as pipelining, constant multiplication, and time-sharing have been applied in the literature which leads to convolvers with different implementation features. In this paper, based on design techniques, we classify these convolvers into four classes named Non-Pipelined Convolver, Reduced-Bandwidth Pipelined Convolver, Multiplier-Less Pipelined Convolver, and Time-Shared Convolver. Then, implementation features of these classes, such as critical path delay, memory bandwidth, and resource utilization, are analyticcally discussed for different convolution kernel sizes. Finally, an instance of each class is captured in Verilog and their features are evaluated by implementing them on a Virtex-7 FPGA and reported confirming the analytical discussions. © 2020 IEEE.
Publication Date: 2013
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 252-257
Collaborative filtering has been considerably successful in improving recommender systems both in the literature and commercial applications. Most of the algorithms designed up to now consider users' ratings equally and do not pay attention to the fact that users' interests or requirements might change over the time. In this paper a collaborative filtering based recommender system is designed which tries to find each user's interests to each group of items, thus resulting to a better prediction of ratings a user will give to an item in the near future. This goal is achieved through using the ratings' timestamp, predefined groups of items, and defining a new similarity measure among users. Unlike standard collaborative filtering methods and many new ones in which similarity between users is defined as a single number, in this research we define similarity between users as "group similarity" which is an array of similarity values between items of each group rated by two users. Predefined groups for items e.g. genres for movies, are used as groups for items. Also for calculating similarity, different weights will be dedicated to ratings of each user based on the ratings' timestamp, i.e. a rating with higher timestamp will receive a higher weight. Empirical tests show that our proposed algorithm works better than standard User-based and Item-based collaborative filtering methods in the case of predicting users' interests in the near future with higher precision. Also it is empirically shown that our algorithm works considerably well for cold-start users. © 2013 IEEE.
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.
Rahimi, S.K.,
Lano, K.,
Sharbaf, M.,
Karimi, M.,
Alfraihi, H. Publication Date: 2020
Journal of Systems and Software (01641212)169
The quality of model transformations (MT) has high impact on model-driven engineering (MDE) software development approaches, because of the central role played by transformations in MDE for refining, migrating, refactoring and other operations on models. For programming languages, a popular paradigm for code quality is the concept of technical debt (TD), which uses the analogy that quality flaws in code are a debt burden carried by the software, which must either be ‘redeemed’ by expending specific effort to remove its flaws, or be tolerated, with ongoing additional costs to maintenance due to the flaws. Whilst the analysis and management of quality flaws and TD in programming languages has been investigated in depth over several years, less research on the topic has been carried out for model transformations. In this paper we investigate the characteristics of quality flaws and technical debt in model transformation languages, based upon systematic analysis of over 100 transformation cases in four leading MT languages. Based on quality flaw indicators for TD, we identify significant differences in the level and kinds of technical debt in different MT languages, and we propose ways in which TD in MT can be reduced and managed. © 2020 Elsevier Inc.
Publication Date: 2021
Amirkabir Journal of Mechanical Engineering (20086032)53(4 Special Issue)pp. 577-580
In this paper deep neural controller is evaluated in self-driving car application which is one of the most important and critical among human-in-the-loop cyber physical systems. To this aim, the modern controller is compared with two classic controllers, i.e. proportional–integral–derivative and model predictive control for both quantitative and qualitative parameters. The parameters reflect three main challenges: (i) design-time challenges like dependency to the model and design parameters, (ii) implementation challenges including ease of implementation and computation workload, and (iii) run-time challenges and parameters covering performance in terms of speed, accuracy, control cost and effort, kinematic energy and vehicle depreciation. The main objective of our work is to present comparison and concrete metrics for designers to compare modern and traditional controllers. A framework for design, implementation and evaluation is presented. An end-to-end controller, constituting six convolution layers and four fully connected layers, is evaluated as the modern controller. The controller learns human driving behaviors and is used to drive the vehicle autonomously. Our results show that despite the main advantages of the controller i.e. being model free and also trainable, in terms of important metrics, this controller exhibits acceptable performance in comparison with proportional–integral–derivative and model predictive controllers. © 2021, Amirkabir University of Technology. All rights reserved.
Auctions have been as a competitive method of buying and selling valuable or rare items for a long time. Single-sided auctions in which participants negotiate on a single attribute (e.g. price) are very popular. Double auctions and negotiation on multiple attributes create more advantages compared to single-sided and single-attribute auctions. Nonetheless, this adds the complexity of the auction. Any auction mechanism needs to be budget balanced, Pareto optimal, individually rational, and coalition-proof. Satisfying all these properties is not so much trivial so that no multi-attribute double auction mechanism could address all these limitations. This research analyzes and compares the GM, timestamp-based and social-welfare maximization mechanisms for multi-attribute double auctions. The analysis of the simulation results shows that the algorithm proposed by Gimple and Makio satisfies more properties compared to other methods for such an auction mechanism. This multi-attribute double auction mechanism is based on game theory and behaves fairer in matching and arbitration. © 2013 IEEE.
Publication Date: 2025
Journal of Supercomputing (15730484)81(14)
Steganography is a technique to hide the presence of secret communication and can be used when one of the communication elements is under the enemy’s influence. The primary measure to evaluate steganography methods in a specific capacity is security. Therefore, in a certain capacity, reducing the number of changes in the cover media leads to a higher embedding efficiency and, thus, higher security of a steganography method. Generally, security and capacity conflict and the increase of one lead to the decrease of the other. A single criterion representing security and capacity simultaneously can help compare steganography methods. Exploiting modification direction (EMD) and methods based on it are a type of steganography techniques that optimize the number of changes resulting from embedding (security). Despite their effectiveness, existing evaluation metrics for EMD-based methods lack precision and comprehensiveness. The present study aims to provide an evaluation criterion for this group of steganography methods. In this study, after a general review and comparison of EMD-based steganography techniques, a method is presented for their precise comparison from the perspective of embedding efficiency. Initially, we conduct a thorough review and comparative analysis of existing EMD-based steganography methods to identify their strengths and limitations. Building on this foundation, we introduce an enhanced embedding efficiency formula that accurately quantifies the impact of one or more-pixel changes, providing a more nuanced assessment of embedding performance compared to traditional metrics. Our results demonstrate that the proposed embedding efficiency formula offers superior performance evaluation, particularly in scenarios involving multiple pixel alterations. Furthermore, we establish an upper bound analysis to determine the theoretical maximum embedding efficiency achievable for any given capacity. This upper bound serves as a benchmark for assessing the optimal performance of EMD-based methods. Finally, leveraging the upper bound, we present an additional evaluation criterion that facilitates a more precise and meaningful comparison of EMD-based steganography methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Publication Date: 2024
Automated Software Engineering (09288910)31(2)
The Inter-Component Communication (ICC) model in Android enables the sharing of data and services among app components. However, it has been associated with several problems, including complexity, support for unconstrained communication, and difficulties for developers to understand. These issues have led to numerous security vulnerabilities in Android ICC. While existing research has focused on specific subsets of these vulnerabilities, it lacks comprehensive and scalable modeling of app specifications and interactions, which limits the precision of analysis. To tackle these problems, we introduce VAnDroid3, a Model-Driven Reverse Engineering (MDRE) framework. VAnDroid3 utilizes purposeful model-based representations to enhance the comprehension of apps and their interactions. We have made significant extensions to our previous work, which include the identification of six prominent ICC vulnerabilities and the consideration of both Intent and Data sharing mechanisms that facilitate ICCs. By employing MDRE techniques to create more efficient and accurate domain-specific models from apps, VAnDroid3 enables the analysis of ICC vulnerabilities on intra- and inter-app communication levels. We have implemented VAnDroid3 as an Eclipse-based tool and conducted extensive experiments to evaluate its correctness, scalability, and run-time performance. Additionally, we compared VAnDroid3 with state-of-the-art tools. The results substantiate VAnDroid3 as a promising framework for revealing Android inter-app ICC security issues. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Publication Date: 2015
Computers and Electrical Engineering (00457906)46pp. 303-313
Content-based image retrieval systems are designed to retrieve images based on the high-level desires and needs of users. However, due to the use of low-level features, image retrieval systems are faced with the so-called semantic gap problem in describing high-level concepts. In order to address this critical problem, a new concept-based model is proposed in this paper. The proposed model retrieves images based on two conceptual layers. In the first layer, the object layer, the objects are detected using the discriminative part-based approach. The second layer, on the other hand, is designed to recognize visual composite, a higher level concept to specify the related co-occurring objects. In the proposed model, this concept is recognized by a new template structure including the appearance filters, constraints, and a set of parameters trained by latent SVM. Experiments are carried out on the well-known Pascal VOC dataset. Results show that the proposed model significantly outperforms the existing content-based approaches. © 2015 Elsevier Ltd.
Publication Date: 2009
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 131-138
Anonymity is an important issue in information security, which its main goal is to protect entities privacy in the systems. Different methods and protocols (with different types of anonymity services) have been developed so far to provide special anonymity requirements of applications. Each of these systems has been developed with different ad hoc approaches. In this paper we present a conceptual framework that makes specification, analysis and design of anonymity applications more systematic. To do this, first we go toward presenting a conceptual model of anonymity which can be used in clear description of different aspects of anonymity. Then we extract a list of anonymity primitives from the existing anonymity providing methods. These primitives are base functions which can be composed to form anonymity services to provide specified anonymity requirements of the system. Copyright 2009 ACM.
Publication Date: 2026
Frontiers in Computer Science (26249898)7
Introduction: In the software development life cycle, collaborative modeling through multiple projective views of a single, shared model is a critical activity that enables effective collaboration among experts and stakeholders. Real-time optimistic collaboration in multi-view modeling allows concurrent modifications but often introduces inconsistencies that must be resolved to achieve an integrated and valid model. Existing inconsistency management methods frequently focus on isolated repairs or offer limited alternatives, lacking support for collaborative dynamics and configurable resolution strategies. This study aims to develop a configurable framework for managing intra-model inconsistencies in real-time multi-view collaborative modeling environments. Methods: We propose a novel framework for inconsistency management tailored to multi-view collaborative modeling, based on Model-Driven Engineering (MDE) principles. The framework supports real-time modeling scenarios and enables change propagation according to the online collaboration mode. Key components include a consistency oracle and incremental consistency checking, which together manage the integration of model changes and overlaps. We introduce the COMIM approach, which assists collaborators in handling inconsistencies by considering team interactions, individual ownership, and configurable repair strategies. Results: The framework was evaluated through a case study involving multi-view collaborative modeling sessions. Empirical results demonstrate the feasibility and effectiveness of the COMIM approach in maintaining consistency during concurrent modeling activities. The system performed efficiently for teams of up to seven concurrent users, successfully managing change propagation, detecting inconsistencies incrementally, and supporting configurable resolution aligned with collaborative priorities. Discussion: The proposed framework effectively addresses the complexities of repairing inconsistencies across diverse software models in a collaborative setting. By emphasizing collaborative dynamics, our approach advances traditional inconsistency management methods, which often lack personalization and configurability. Future work may explore scalability to larger teams and adaptation to additional modeling paradigms. Copyright © 2026 Alsharuee, Sharbaf and Tork Ladani.
Publication Date: 2021
Peer-to-Peer Networking and Applications (19366450)14(2)pp. 781-793
Internet of Things (IoT) is expected to empower all aspects of the Intelligent Transportation System (ITS), the main goal of which is to improve transportation safety. However, due to high demands by the increasing number of associated vehicles, the allocated bandwidth of ITS is inadequate. Cognitive Radio (CR) technology can be used as a solution for this high demand level. In CR, the pre-allocated spectrum bands are sensed to find the existing holes, caused by the absence of primary users. Cooperative spectrum sensing is an efficient tool for the detection of free spectrum bands that increase the probability of correct detection. In this paper, a distributed cooperative spectrum sensing technique is proposed using the consensus algorithm which is a distributed data aggregation mechanism whereby each vehicle combines the results received from its neighbors’ spectrum sensing. The combined results are repeatedly shared and combined such that all vehicles reach the same results. In vehicular networks, due to the vehicle’s movement, the number of its neighbors changes dynamically. Therefore, considering the vehicle’s mobility is essential in the spectrum sensing process. The consensus algorithm which is a data aggregation method is used to increase the probability of correct detection, and thus to reduce the number of collisions in the spectrum acquisition process. In our method, each vehicle accurately selects a number of its neighbors dynamically, and involves them in the decision-making process. Moreover, separate weights determined based on the entropy of their information are assigned to the sensing results of the selected neighbors. In this way, even if the vehicles are affected by fading or shadowing, they can make more accurate decisions using the sensing results received from other vehicles. The simulation results of the proposed method show that it increases the probability of correctly detecting free spectrum bands as well as convergence speed. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which employs user context and semantic concepts to discover new words for obtaining accurate results. Experimental results show an enhancement on information retrieval performance comparing to the traditional approaches. © 2011 IEEE.
Publication Date: 2011
Wireless Communications and Mobile Computing (discontinued) (15308677)11(6)pp. 723-741
Growing demands for pervasive and ubiquitous services over wireless mobile networks and evolution of such networks towards heterogeneous solutions have emphasized the necessity of more intelligent handoff decisions. The existing handoff management methods in the literature are mostly using signal strength measurements and other link quality evaluations not addressing the knowledge about context of mobile devices, users and networks. Recently, context-aware handoff management has been considered as a novel candidate for fourth generation (4G) wireless technology. In this paper, user perceived quality of service has been considered in addition to traditional contexts such as user preferences, application requirements, network parameters and link quality for decision making. User perceived quality (UPQ) has been employed as a trigger source, in addition to link layer triggers which are emerged using media independent handover (MIH) event service. This paper presents a policy based mechanism for handoff decision making where fuzzy petri nets (FPNs) have been utilized as its evaluation algorithm. A case study has been provided by simulations to show the usability and user level satisfaction. Simulation results show superior performance in terms of UPQ, jitter and packet delivery measures. Copyright © 2009 John Wiley & Sons, Ltd.
Publication Date: 2011
ADVANCES IN ARTIFICIAL INTELLIGENCE (03029743)6657pp. 301-312
In this paper we have proposed a context-aware reputation-based trust model for multi-agent environments. Due to the lack of a general method for recognition and representation of context notion, we proposed a functional ontology of context for evaluating trust (FOCET) as the building block of our model. In addition, a computational reputation-based trust model based on this ontology is developed. Our model benefits from powerful reasoning facilities and the capability of adjusting the effect of context on trust assessment. Simulation results shows that an appropriate context weight results in the enhancement of the total profit in open systems.
Publication Date: 2007
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743)4749pp. 404-409
In this paper we have proposed an approach to extend the existing service-oriented architecture reference model by taking into consideration the hierarchical human needs model, which can help us in determining the user's goals and enhancing the service discovery process. This is achieved by enriching the user's context model and representing the needs model as a specific ontology. The main benefits of this approach are improved service matching, and ensuring better privacy as required by users in utilizing specific services like profile-matching. © Springer-Verlag Berlin Heidelberg 2007.
Publication Date: 2009
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 29-35
In this paper, we introduce a continuous double auction method for grid resource allocation in which resources are considered as provider agents and users as consumer agents. In each time step, each provider agent determines its requested value based on its workload and each consumer agent determines its bid value based on two constraints: the remaining time for bidding, and the remaining resources for bidding. We study this method in terms of economic efficiency and system performance. Experimental results show that the proposed method is better than Earliest Deadline First (EDF) method, which is a default strategy in many schedulers.
Publication Date: 2024
Pervasive and Mobile Computing (15741192)105
In recent years, one type of complex network called the Social Internet of Things (SIoT) has attracted the attention of researchers. Controllability is one of the important problems in complex networks and it has essential applications in social, biological, and technical networks. Applying this problem can also play an important role in the control of social smart cities, but it has not yet been defined as a specific problem on SIoT, and no solution has been provided for it. This paper addresses the controllability problem of the temporal SIoT network. In this regard, first, a definition for the temporal SIoT network is provided. Then, the unique relationships of this network are defined and modeled formally. In the following, the Controllability problem is applied to the temporal SIoT network (CSIoT) to identify the Minimum Driver nodes Set (MDS). Then proposed CSIoT is compared with the state-of-the-art methods for performance analysis. In the obtained results, the heterogeneity (different types, brands, and models) has been investigated. Also, 69.80 % of the SIoT sub-graphs nodes have been identified as critical driver nodes in 152 different sets. The proposed controllability deals with network control in a distributed manner. © 2024 Elsevier B.V.
Publication Date: 2024
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 253-259
This study presents a method for the automatic identification of micro-cracks in photovoltaic solar modules using deep learning techniques. The main challenge in this research is the lack of labeled data and class imbalance for the detection of micro-cracks. The proposed method employs a multi-stage approach. Initially, 10% of the dataset is manually labeled to train a simple convolutional neural network model. This model is then used to generate pseudo-labels for the unlabeled data using a semi-supervised approach. The pseudo-labels are manually reviewed to increase the number of micro-crack samples in the training set. Data augmentation techniques are also applied to increase the size and diversity of the training dataset. Finally, the pre-trained ResNet-50 model is fine-tuned on the expanded labeled dataset for accurate detection of microcracks. Advanced preprocessing steps, including solar cell segmentation, cropping, and data augmentation, have been performed. The class imbalance problem is addressed through undersampling and weighted loss functions. The experimental results demonstrate the effectiveness of the proposed method, achieving an accuracy of 0.978 and an F1-score of 0.797 in the detection of micro-cracks in electroluminescence images of solar panels. This study provides insights into the use of limited labeled data for training robust deep learning models for the identification of defects in solar modules. © 2024 IEEE.
Publication Date: 2023
Computer Networks (13891286)224
The traditional method of saving energy in Virtual Machine Placement (VMP) is based on consolidating more virtual machines (VMs) in fewer servers and putting the rest in sleep mode, which may lead to the overheating of servers resulting in performance degradation and cooling cost. The lack of an accurate and computationally efficient model to describe the thermal condition of the data center environment makes it challenging to develop an effective and adaptive VMP mechanism. Although recently, data-driven approaches have acted successfully in model construction, the shortage of clean, adequate, and sufficient amounts of data put limits their generalizability. Moreover, any change in the data center configuration during operation, makes these models prone to error and forces them to repeat the learning process. Thus, researchers turn to applying model-free paradigms such as reinforcement learning. Due to the vast action-state space of real-world applications, scalability is one of the significant challenges in this area. In addition, the delayed feedback of environmental variables such as temperature give rise to exploration costs. In this paper, we present a decentralized implementation of reinforcement learning along with a novel state-action representation to perform the VMP in the data centers to optimize energy consumption and keep the host temperature as low as possible while satisfying Service Level Agreements (SLA). Our experimental results show more than 17% improvement in energy consumption and 12% in CPU temperature reduction compared to baseline algorithms. We also succeeded in accelerating optimal policy convergence after the occurrence of a configuration change. © 2023 Elsevier B.V.
One of the attacks in the RPL protocol is the Clone ID attack, that the attacker clones the node's ID in the network. In this research, a Clone ID detection system is designed for the Internet of Things (IoT), implemented in Contiki operating system, and evaluated using the Cooja emulator. Our evaluation shows that the proposed method has desirable performance in terms of energy consumption overhead, true positive rate, and detection speed. The overhead cost of the proposed method is low enough that it can be deployed in limited-resource nodes. The proposed method in each node has two phases, which are the steps of gathering information and attack detection. In the proposed scheme, each node detects this type of attack using control packets received from its neighbors and their information such as IP, rank, Path ETX, and RSSI, as well as the use of a routing table. The design of this system will contribute to the security of the IoT network. © 2021 IEEE.
Publication Date: 2018
Reliability Engineering and System Safety (9518320)pp. 13-24
This paper presents an approach to optimize the reliability and makespan of hardware task graphs, running on FPGA-based reconfigurable computers, in space-mission computing applications with dynamic soft error rates (SERs). Thus, with rises and falls of the SER, the presented approach dynamically generates a set of solutions that apply redundancy-based fault tolerance (FT) techniques to the running tasks. The set of solutions is generated by decomposing the task graph into multiple subgraphs, applying a multi-objective optimization algorithm to the subgraphs separately, and finally combining and filtering out the obtained solutions of the subgraphs. In this regard, a heuristic has been proposed to decompose task graphs in such a way that a high coverage of the true Pareto set is attained. The experiments show that the presented approach covers 97.37% of the true Pareto set and improves the average computation time of generating the Pareto set from 6.29 h to 81.86 ms. In addition, it outperforms the NSGA-II algorithm in terms of the Pareto set coverage and computation time. Additional experiments demonstrate the advantages of the presented approach over the state-of-the-art adaptive FT techniques in dynamic environments. © 2018 Elsevier Ltd
Question-answering systems, characterized by their fundamental functions of question classification, information retrieval, and answer selection, demand refinement to enhance precision in retrieving exact answers. Question classification, a cornerstone task, anticipates the probable answer to a posed query. However, the performance of question classification algorithms is hampered, particularly in agglutinative languages with complex morphology like Persian, where linguistic resources are limited. In this study, we propose a novel multi-layer Long-short-term memory (LSTM) Attention Convolutional Neural Network (CNN) (LACNN) classifier, tailored to extract pertinent information from Persian language contexts. Notably, this model operates autonomously, obviating the need for prior knowledge or external features. Moreover, we introduce UIMQC, the first medical question dataset in Persian, derived from the English GARD dataset. The inquiries within UIMQC are inherently intricate, often pertaining to rare diseases necessitating specialized diagnosis. Our experimental findings demonstrate a notable enhancement over baseline methods, with a 9% performance increase on the UTQC dataset, and achieving 67.08% accuracy on the UIMQC dataset. Consequently, we advocate for the adoption of the LACNN model in various morphological analysis tasks across low-resource languages, as in Question Answering systems it improves the performance for retrieving accurate answers to the users’ queries. ©2024 IEEE.
Publication Date: 2017
Computers in Biology and Medicine (00104825)91pp. 181-190
Background and objective To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Methods Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. Results The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. Conclusions In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. © 2017 Elsevier Ltd
Publication Date: 2010
International Journal of Innovative Computing, Information and Control (13494198)6(9)pp. 4219-4234
Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature of grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be looked for. This paper represents a discrete Particle Swarm Optimization (DPSO) approach for grid job scheduling. PSO is a population based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or nearoptimal solutions. In this paper, the scheduler aims at minimizing makespan and flowtime simultaneously in grid environment. Experimental studies illustrate that the proposed method is more efficient and surpasses those of reported metaheuristic algorithms for this problem. © 2010 ICIC INTERNATIONAL.
