Showing papers by "Islamic Azad University published in 2018"

Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas

Abstract: Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

Hydrophilic Multitemplate Molecularly Imprinted Biopolymers Based on a Green Synthesis Strategy for Determination of B-Family Vitamins.

Abstract: A novel green synthesis strategy was proposed for preparation of multitemplate molecularly imprinted biopolymers (mt-MIBP) in aqueous media with less consumption of organic solvents, which were subsequently used as sorbents of ultrasound-assisted dispersive solid-phase extraction (d-SPE) for simultaneous recognition and efficient separation of B-family vitamins in juice samples, followed by high performance liquid chromatography (HPLC) determination. The obtained mt-MIBP was fully characterized by SEM, FT-IR, TEM, and BET. It offered high binding capacity, good selectivity, and fast dynamics toward all the templates. Involved parameters in the d-SPE efficiency such as mt-MIBP mass, sonication time, and eluting/washing solvents' types and volumes were concurrently investigated by central composite design with rapidity and reliability. Under the optimum conditions, the developed mt-MIBP-d-SPE-HPLC method exhibited wide linear range, low limits of detection and quantification (LOQs) within 1.2-5.5 μg L-1 and 4.0-18.4 μg L-1, respectively, and appropriate repeatability (relative standard deviation values below 4.2%, n = 4). The high selectivity of this method makes it suitable for successful monitoring of vitamins in juice samples with satisfactory recoveries of 75.8-92.7%, 81.1-92.5%, and 84.7-93.8% for vitamins riboflavin (B2), nicotinamide (B3), and pyridoxine (B6), respectively. The present study implied highly promising perspectives of water-compatible eco-friendly mt-MIBP for highly effective multiresidue analysis in complicated matrixes.

Effects of Size and Aggregation/Agglomeration of Nanoparticles on the Interfacial/Interphase Properties and Tensile Strength of Polymer Nanocomposites

Abstract: In this study, several simple equations are suggested to investigate the effects of size and density on the number, surface area, stiffening efficiency, and specific surface area of nanoparticles in polymer nanocomposites. In addition, the roles of nanoparticle size and interphase thickness in the interfacial/interphase properties and tensile strength of nanocomposites are explained by various equations. The aggregates/agglomerates of nanoparticles are also assumed as large particles in nanocomposites, and their influences on the nanoparticle characteristics, interface/interphase properties, and tensile strength are discussed. The small size advantageously affects the number, surface area, stiffening efficiency, and specific surface area of nanoparticles. Only 2 g of isolated and well-dispersed nanoparticles with radius of 10 nm (R = 10 nm) and density of 2 g/cm3 produce the significant interfacial area of 250 m2 with polymer matrix. Moreover, only a thick interphase cannot produce high interfacial/interphase parameters and significant mechanical properties in nanocomposites because the filler size and aggregates/agglomerates also control these terms. It is found that a thick interphase (t = 25 nm) surrounding the big nanoparticles (R = 50 nm) only improves the B interphase parameter to about 4, while B = 13 is obtained by the smallest nanoparticles and the thickest interphase.

A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

Abstract: A new method is developed for recommender systemsThe recommender system is developed based on collaborative filteringScalability and sparsity issues in recommender systems are solvedMovieLens and Yahoo! Webscope R4 datasets are used for method evaluationThe method is effective in solving the sparsity and scalability problems in CF Improving the efficiency of methods has been a big challenge in recommender systems It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques Then, we use ontology to improve the accuracy of recommendations in CF part In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition(SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature The results showed that our method is effective in improving the sparsity and scalability problems in CF

A state-of-the-art survey of malware detection approaches using data mining techniques

Abstract: Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria

Abstract: A third party reverse logistic provider (3PRLP) selection and evaluation process is developed.A multiple attribute decision making (MADM) model to evaluate and select 3PRLPs in the presence of risk factors is proposed.A fuzzy step-wise weight assessment ratio analysis (SWARA) approach to weight the evaluation criteria is applied.A fuzzy (COPRAS) is proposed to rank and select the sustainable third-party reverse logistics providers in the presence of risk factors.A real world case study is used from automotive industry to validate the quality of our model. Reverse logistics is the backward process of collecting and redistributing products at the end-of-life from customers to producers and manufacturers for reuse, remanufacturing and disposal purposes. While reverse logistics brings several economic benefits, it seems to become a necessity for businesses to remain competitive in a world that environmental and social aspects of business activities are key to sustainable development. The operations and management of reverse logistics systems is a complex task that requires substantial level of infrastructure, technology, expertise and experience. Therefore, increasingly many business organizations tend to outsource their reverse logistics activities to third-party reverse logistics providers (3PRLPs). In this paper, we propose a multiple attribute decision making (MADM) model to rank and select 3PRLPs, using fuzzy step-wise weight assessment ratio analysis (SWARA) to weight the evaluation criteria. Accordingly, a developed fuzzy complex proportional assessment of alternatives (COPRAS) was proposed to rank and select the sustainable 3PRLPs in the presence risk factors. The suggested model was applied to a case study from automotive industry. Eventually, COPRAS and COPRAS-G methods were considered for the purpose of comparison and validation. As a result, the most sustainable 3PRLP was selected. While incorporating risk factors into our analysis, our study shows that environmental and social drivers are increasingly becoming dominant when selecting 3PRLPs.