Fog computing is known as a new computing technology where it covers cloud computing shortcomings in term of delay. This is a potential for running IoT applications containing multiple services taking benefit of closeness to fog nodes near to devices where the data are sensed. This article formulates service placement issue into an optimization problem with total power consumption minimization inclination. It considers resource utilization and traffic transmission between different services as two prominent factors of power consumption, once they are placed on different fog nodes. On the other hand, placing all of the services on the single fog node owing to power reduction reduces system reliability because of one point of failure phenomenon. In the proposed optimization model, reliability limitations are considered as constraints of stated problem. To solve this combinatorial problem, an energy-aware reliable service placement algorithm based on whale optimization algorithm (ER-SPA-WOA) is proposed. The suggested algorithm was validated in different circumstances. The results reported from simulations prove the dominance of proposed algorithm in comparison with counterpart state-of-the-arts. پرونده مقاله

انتخاب سبد سهام مناسب همواره از اساسی ترین مسائل سرمایه گذاران است. اساساً پیش بینی روند قیمت با استفاده از آنالیز فنی یا آنالیز اساسی انجام می شود. آنالیز فنی بر عملکرد بازار تمرکز دارد، در حالیکه تمرکز آنالیز اساسی مبتنی بر مکانیزم عرضه و تقاضا است و این سبب تغییر قیمت ها می شود. وجود راهکاری که بتواند رشد یا کاهش سهام را با استفاده از آن پیش بینی نماید، بعنوان یک نیاز اساسی در این تحقیق به آن پرداخته شده است. در پژوهش حاضر، به کمک دیتاسِت نظارت شده از راهکاری مبتنی بر الگوریتم های مجموعه راف و تحلیل سلسله مراتبی برای کاهش ویژگی و از الگوریتم های درخت تصمیم، ماشین بردار پشتیبان و شبکه بیزین برای پیش بینی استفاده شده است. این راهکار پیشنهادی با استفاده از زبان سی شارپ پیاده سازی و با راهکارهای مختلفی مقایسه شده و نتایج تحقیق نشان داده است که روش پیشنهادی با 80 درصد دقت صحت پیش بینی و 20 اشتباه در پیش بینی دارای بیشترین دقت و کمترین میزان اشتباه در میان روش های مورد مقایسه را دارد. پرونده مقاله

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models. پرونده مقاله

پژوهش حاضر، با هدف بررسی رابطه استفاده از بازی‌های رایانه‌ای با افت تحصیلی دانش‌آموزان دختر دوره متوسطه انجام شده است. روش پژوهش، توصیفی از نوع همبستگی و جامعه آماری این پژوهش دانش‌آموزان دختر دوره متوسطه شهر بابل به تعداد 7542 نفر در سال تحصیلی 93-1392 بودند. حجم نمونه بر اساس جدول کرجسی و مورگان، 364 نفر تعیین شد. نمونه‌گیری به روش تصادفی خوشه‌ای انجام پذیرفت. ابزار گردآوری اطلاعات دو پرسش‌نامه محقق‌ساخته بازی‌های رایانه‌ای و وضعیت تحصیلی، مجموعاً 25 سؤالی به صورت بسته پاسخ در طیف پنج‌ گزینه‌ای لیکرت بوده است. روایی صوری و محتوایی ابزار توسط متخصصان و صاحب‌نظران رشته تکنولوژی آموزشی و روان‌شناسی مورد تأیید واقع شده است و ضریب پایایی آن با استفاده از آزمون آلفای کرونباخ 83/0 محاسبه شد. داده‌ها در دو سطح توصیفی با استفاده از جداول و نمودارها، میانگین و انحراف معیار و سطح استنباطی با استفاده از آزمون کولموگروف- اسمیرونف و آزمون‌های پارامتریک تی تک نمونه و ضریب همبستگی پیرسون مورد توصیف و تحلیل واقع شد. نتایج نشان داد که اکثریت دانش‌آموزان دختر دوره متوسطه شهر بابل از بازی‌های رایانه‌ای استفاده می‌کنند. هم‌چنین، یافته‌ها حاکی از آن بود که بین بازی‌های رایانه‌ای با افت تحصیلی و انگیزه تحصیلی رابطه قوی و با یادگیری دانش‌آموزان دختر دوره متوسطه شهر بابل رابطه ضعیف وجود دارد. پرونده مقاله

The wireless sensor network (WSN) is typically comprised many tiny nodes equipped with processors, sender/receiver antenna and limited battery in which it is impossible or not economic to recharge. Meanwhile, network lifespan is one of the most critical issues because of limited and not renewal used battery in WSN. Several mechanisms have been proposed to prolong network lifespan such as LEACH, HEED and CHEF, but in all of them nodes consume energy continuously. One of the promising technique is to apply dynamic sleep/wake up scheduling. In this paper, a novel sleep/wake up scheduling algorithm is proposed so-called FT-ECCKN . Each node executes sleep/wake up scheduling right after sending/receiving data where a node changes its status to sleep mode if it has at least k neighbors awake in its radius neighborhood with more residual energy in comparison with the node executing scheduler. Whenever the number of nodes is more than 2k, fuzzy TOPSIS method is used to rank nodes based on residual energy and coverage distance to select k out of number of nodes in ranking list. To evaluate the proposed algorithm, 25 scenarios are conducted in the experimental field 800X600 m^2 between 100 through 500 nodes increasing with 100 numbers and k belongs to {1,2,…5}. Totally, our proposed algorithm outperforms 23.27 percent in term of network life time in comparison with EC-CKN method for overall scenarios. Remarkable results show that the proposed algorithm is beneficial for large scale fields with dense nodes along with smallest k. پرونده مقاله

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. پرونده مقاله

Grid computing is an emerging computing paradigm that will have significant impact on the next generation information infrastructure. Due to the largeness and complexity of grid system, its quality of service, performance and reliability are difficult to model, analyze and evaluate. This paper proposes a virtual tree-structured model of the grid service. This model simplifies the physical structure of a grid service because there are several links between RMS and resources. In this paper, the task scheduling by RMS and the task execution within grid resources considering data dependence and failure correlation are modeled using colored Petri nets (CPNs). We have also proposed a method for evaluation the grid service reliability based on the analysis of the model. In addition, an instance of the proposed model for a sample grid environment is constructed and analyzed using CPN Tools. پرونده مقاله

This paper surveys parallel scheduling problems and metrics correlated to and then applys metrics to make decision in comparison to other policy schedulers. Parallel processing is new trend in computer science especially in embedded and multicore systems whereas needs more power consumption to reach speed up. The QOS requirement for users is to have good responsiveness and for service providers or system owners to have high throughput and low power consumption in parallel processing or embedded multicore systems. Moreover, fairness is vital issue to make decision wether the scheduler is good or not. Using the metrics is very intricate because misleadling metrics will cause to lose performance and system utility that is why the metrics has been opted cautiously in this paper. However, satisfying all of the objects in which have potentially conflicts is computationally NP-Hard. So, tradeoff between metrics is needed. This paper indicates DEA FDH model based on linear programming that will select the optimal scheduling near to exact solution پرونده مقاله

Visual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still important challenges. The target coverage problem in such networks, in which all of the specified targets must be continuously observed and monitored by administrators is formulated as an integer linear programming problem (ILP) that is an NP-Hard problem. Although several approaches have been presented in the literature to solve the aforementioned problem, the majority of them suffer from getting stuck in the local trap and low exploration in search space. To address the issue, a discrete cuckoo-search optimization algorithm (DCSA) is extended to solve this combinatorial problem. The discrete operator of the proposed algorithm is designed in such a way that explore search space efficiently and lead to balancing in the local and global search process. The proposed algorithm was examined in different conducted scenarios. The returned results of simulations of numerous scenarios show the dominance of the proposed algorithm in comparison with other existing approaches in terms of network lifetime maximization. In other words, the proposed DCSA has 19.75% and 13.75% improvement in terms of network average lifetime expansion against HMNLAR and GA-based approaches respectively in all scenarios. پرونده مقاله