Journal of Advances in Computer Research
Volume:9 Issue: 4, Autumn 2018

Providing banking services, especially online banking and electronic payment systems, has always been associated with high concerns about security risks.
In this paper, customer authentication for their transactions in electronic banking has been discussed, and a more appropriate way of using biometric fingerprint data, as well as encrypting those data in a different way, has been suggested. Using fingerprint biometrics increases the security of online payment systems.Biometrics is used in a database in the banking system. The fingerprint biometrics is more reliable and easier to use than other biometrics and can be obtained from anyone with an easy access. In this thesis, according to needs analysis, validation is performed not only by the user but also by the bank itself, according to the standards of the banking system.More precisely, a new protocol, known as Stream Cipher, is developed to generate a one-time password from biometric data, to ensure that security and privacy are maintained. In the suggested system, Ciphering and deciphering user information by issuer bank provides security.The results of the research indicate its proper function compared to other authentication methods. The protocol security analysis also demonstrates the benefits of enhancing security by employing the accelerated encryption methods in the proposed method. The results of the research show The Errors rate (EER, FRR and SFAR) is very low and can be ignored. This method is highly resistant to all kinds of electronic banking attacks, such as phishing and password theft

Distributed systems such as Grid- and Cloud Computing provision web services to their users in all of the world. One of the most important concerns which service providers encounter is to handle total cost of ownership (TCO). The large part of TCO is related to power consumption due to inefficient resource management. Task scheduling module as a key component can has drastic impact on both user response time and underlying resource utilization. Such heterogeneous distributed systems have interconnected different processors with different speed and architecture. Also, the user application which is typically presented in the form of directed acyclic graph (DAG) must be executed on this type of parallel processing systems. Since task scheduling in such complicated systems belongs to NP-hard problems, existing heuristic approaches are no longer efficient. Therefore, the trend is to apply hybrid meta-heuristic approaches. In this paper, we extend a meta-heuristic shuffled genetic-based task scheduling algorithm to minimize total execution time, makespan, of user application. In this regard, we take benefit of other heuristics such as Heterogeneous Earliest Finish Time (HEFT) approach to generate smart initial population by applying a new shuffle operator which makes a fortune to explore feasible and promising individuals in the search space. We also conduct other genetic operators in right way to produce final near to optimal solution. To reach concrete results we have conducted several scenarios. Our proposed algorithm outperforms in term of average makespan compared with other existing approaches such as HEFT versions and QGARAR.

In recent years, wireless sensor networks have drawn great attention. This type of network is composed of a large number of sensor nodes which are able to sense, process and communicate. Besides, they are used in various fields such as emergency relief in disasters, monitoring the environment, military affairs and etc. Sensor nodes collect environmental data by using their sensors and send them to the base station. Localization in the sensor nodes is an important operation of wireless sensor networks. Thus, data generated by the sensor nodes should also show the position of the node. Hence, a reliable localization algorithm is always necessary. Regarding the localization methods, range-based methods are fairly accurate to estimate the nodes and they estimate the node location. To ensure the accuracy of the obtained range, a range-free method namely distance vector routing has been investigated in this study. Some nodes which benefit from conscious coordinates and help other nodes to estimate their coordinates are called anchor nodes. The present study have used zoning and estimated coordinates of the nodes in each zone to improve localization and it tried to upgrade the accuracy of vector routing localization. The proposed method consists of two steps which in the first step, localization of each node are calculated by DV-hop. In the next step, the estimated coordinates is reformed using zoning method. Simulation results will show that the proposed algorithm is efficient and robust.The proposed method increases the localization accuracy.

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural network training, but training a neural network can involve thousands of computers for months. In the present study, basic optimization algorithms in deep learning were evaluated. First, a performance criterion was defined based on a training dataset, which makes an objective function along with an adjustment phrase. In the optimization process, a performance criterion provides the least value for objective function. Finally, in the present study, in order to evaluate the performance of different optimization algorithms, recent algorithms for training neural networks were compared for the segmentation of brain images. The results showed that the proposed hybrid optimization algorithm performed better than the other tested methods because of its hierarchical and deeper extraction.

Due to ever increasing of medical images data in the world’s medical centers and recent developments in hardware and technology of medical imaging, necessity of medical data software analysis is needed. Equipping medical science with intelligent tools in diagnosis and treatment of illnesses has resulted in reduction of physicians’ errors and physical and financial damages. In this article we propose a computer – aided diagnosis system framework in order to automatic classification and annotation of histological and bone marrow images. The proposed method has been tested on two data set including cytological and histological images. Images context features are used to train support vector machine classifier and the accuracy of classifier is 96%. Results show that the proposed framework can be a software model in order to classify and annotate microscopic images in clinical routine functions.
Due to ever increasing of medical images data in the world’s medical centers and recent developments in hardware and technology of medical imaging, necessity of medical data software analysis is needed. Equipping medical science with intelligent tools in diagnosis and treatment of illnesses has resulted in reduction of physicians’ errors and physical and financial damages. In this article we propose a computer – aided diagnosis system framework in order to automatic classification and annotation of histological and bone marrow images

In today’s competitive, dynamic, and changing business environment, being able to collaborate globally within and beyond the enterprise borders is critical. Inter-Organizational Collaborations (IOCs) have been proposed as a response to the characteristics of highly competitive global business environments. So far, a number of reference models, frameworks, and ad hoc architectures related to some manifestations of inter-organizational collaborations have emerged. However, less research attention has been focused on concrete cases of IOCs establishment. This paper derives a distributed service-oriented reference architecture for inter-organizational process-oriented collaboration support system from the conceptual architectures of three examined research projects in collaborative networks domain: CrossWork, ECOLEAD, and eSRA. The reference architecture covers both the IOCs’ establishment and management systematically. The proposed reference architecture is then evaluated using three other research projects: SUDDEN, MISE and Pilarcos projects. The reference architecture can serve as a foundation for analysis and design of concrete architectures of IOC systems.

Graphs have enormous usage in software engineering, network and electrical engineering. In fact graphs drawing is a geometrically representation of information. Among graphs, trees are concentrated because of their ability in hierarchical extension as well as processing VLSI circuit. Many algorithms have been proposed for drawing binary trees within polygons. However these algorithms generate binary trees with edge intersections and bends. Likewise, they have limitations in drawing binary trees in any arbitrary polygon with uniform distribution. The proposed algorithm which is based on calculating center of gravity in polygons draws a binary tree inside any kinds of polygon. besides center of gravity as a heuristic the segmentation process has already been used. The results which have been implemented with Microsoft Visual Basic reveal that the proposed heuristic algorithm had no edge intersection and no bend. In addition, not only the symmetry of drawing has been improved but also the computational complexity becomes less.

Encryption is an important issue in information security which is usually provided using a reversible mathematical model. Digital image as a most frequently used digital product needs special encryption algorithms. This paper presents a new encryption algorithm high security for digital gray images using genetic algorithm and Lattice Map function. At the first the initial value of Logistic Map function from a 120 bits key is offered, and then by using the produced chaos series moves original picture pixels. In third step, the original image with Lattice Map function series create by sequence of Logistic Map function from latest level to encrypt the image. This process goes under evolution through the generation of the genetic algorithm until the algorithm converges to an encrypted image with a highest entropy and lowest correlation coefficient among pixels. The results reveal the highest level of resistance and security against statistical attacks. With obtained entropy results from the proposed method were 7.9993 which shows its proficiency compared to the counterpart methods.

This paper, based on the Viterbi algorithm, selects the most likely combination of different wording from a variety of scenarios. In this regard, the Bi-gram and Unigram tags of each word, based on the letters forming the words, as well as the bigram and unigram labels After the breakdown into the composition or moment of transition from the decomposition to the combination obtained from the types of sentences, the educator is used in 194 different wording types, and the sum of them is obtained by the amount of the advance of each wording state and the MAX value is considered as the output of the system. And at the end, the success rate of these methods and the effectiveness of these two types of labeling are compared with each other.