Hesham A. Hefny

One of the most harmful viruses is Tomato yellow leaf curl virus (TYLCV), which is widespread over the world with tomato yellow leaf curl disease (TYLCD). It causes some symptoms to tomato leaf such as upward curling and yellowing. This paper introduces an efficient approach to detect and identify infected tomato leaves with these two viruses. The proposed approach consists of four main phases, namely pre-processing, image segmentation, feature extraction, and classification phases. Each input image is segmented and descriptor created for each segment. Some geometric measurements are employed to identify an optimal feature subset. Support vector machine (SVM) algorithm with different kernel functions is used for classification. The datasets of a total of 200 infected tomato leaf images with TSWV and TYLCV were used for both training and testing phase. N-fold cross-validation technique is used to evaluate the performance of the presented approach. Experimental results showed that the proposed classification approach obtained accuracy of 90 % in average and 92 % based on the quadratic kernel function.

Cultivate the best crop from many suitable crops is a complex process that faces the decision makers (e.g. farmers, their advisors, and others in the agricultural sector). Their goal is to select a crop which maximizes the resource utilization and in the same time ensures the sustainability for natural agricultural resources. Selecting such crop for cultivating among many suitable alternatives crops is a Multiple Criteria Decision Making (MCDM) problem. Since, the selection for the best decision is dependent in many criteria and having dependence and feedback among them. In this paper Linguistic Fuzzy Decision Network (LFDN) method is developed and applied to a real case study to decide the cultivate crop among four crops-namely: Wheat, Corn, Rice, and Fababean w.r.t given multiple criteria.

This article introduces an efficient approach to detect and identify unhealthy tomato leaves using image processing technique. The proposed approach consists of three main phases; namely pre-processing, feature extraction, and classification phases. Since the texture characteristic is one of the most important features that describe tomato leaf, the proposed system system uses Gray-Level Co-occurrence Matrix (GLCM) for detecting and identifying tomato leaf state, is it healthy or infected. Support Vector Machine (SVM) algorithm with different kernel functions is used for classification phase. Datasets of total 800 healthy and infected tomato leaves images were used for both training and testing stages. N-fold cross-validation technique is used to evaluate the performance of the presented approach. Experimental results showed that the proposed classification approach has obtained classification accuracy of 99.83%, using linear kernel function.

The paper proposes a linguistic based multi-view fuzzy ontology information retrieval model. It deals with multi-view linguistic based queries in multi domains. Such linguistics are user defined, reflecting his subjective view. The model also proposes a ranking algorithm that ranks the set of relevant documents according to some criteria such as their relevance degree, confidence degree, and updating degree.

Crops plantation dates is considered one of the most important factors in the success of crops plantation process. Obviously, each crop needs suitable values of climate variables to be planted in a certain soil. In other words, a crop plantation in different soils needs different suitable values of the climate variables. Consequently, each soil has a range of suitable values of climate variables to ensure a productive and profitable plantation process for a specific crop. Unfortunately, the ongoing changes in climate variables affect mainly the suitability of planting most crops in the same soil at the same traditional dates. So, such changes in climate variables like temperature and humidity make the traditional plantation dates of some crops in a given place became not suitable. Instead, it becomes more suitable to plant at other different dates that were not suitable before. This paper presents a fuzzy set based approach for adjust crops plantation dates reflecting climate changes. The proposed approach has three main phases, one for defining a crop suitability membership functions, the second for calculating the suitability degrees of year days, and the last one for selection and optimization of most perfect plantation dates. It has been applied on the potatoes crop with temperature and humidity as climate variables in Alexandria governorate in Egypt. In consequent, the final results are presented to a set of agriculture researchers in the Agriculture Research Center (ARC) in Egypt and they admired the results.

This paper presents an enhanced fuzzy gesturing technique for the interaction between humans and a computerized system. Commonly, gestures are used in order to facilitate the interactive capabilities between humans and computerized systems. Unfortunately, most of current gesturing techniques don't deal at the same level of inaccuracy of gesturing, resulted from the nature of human fingers and hands movements. This paper aims, in a more flexible manner, to tackle the inaccuracy problem existed with gesture-based interactions between humans and a computerized system.

Cloud computing is the concept of using maximum remote services through a network using various minimum resources, it provides these resources to users via internet. There are many critical problems appeared with cloud computing such as data privacy, security, and reliability etc. But we find that security is the most important between these problems. In this research paper, the proposed approach is to eliminate the concerns regarding data security using bio hash function for biometrics template security to enhance the security performance in cloud as per different perspective of cloud customers. Experiments using well know benchmark CASIA fingerprint-V5 data sets show that the obtained results proved that using Bio-hash function approach is more efficient in protecting the biometric template compared to Crypto-Biometric Authentication approach and the error rate is minimized by 25%.

In this paper, we present a robust image watermarking approach to hide DNA sequence data into fingerprint templates for copyright protection based on the discrete wavelet transform. A multi-bit watermark, based on DNA data, is embedded into the low frequency sub-band of the fingerprint image. During the inverse process, we employ DNA decoding to extract the image signature from the watermarked image. We evaluate our proposed method using statistical measures including mean squared error, peak signal-to-noise-ratio, and structural similarity index, and the experimental results confirm that the watermarks generated using our approach are invisible.

Commonly, climate changes in both of temperature and humidity have significant impacts on agriculture. Obviously, the ongoing changes in such climate variables affect directly the plantation dates of most crops. In other words, many crops become not suitable to plant in its traditional places at the traditional plantation dates. Instead, such crops become more suitable to plant at other new or adjusted periods that were not suitable before. This paper provides a fuzzy query approach for adapting crops plantation dates with climate changes in a given place. The proposed approach consists of three phases, one for fuzzy clustering of the year days, the second one for defining the crop suitability membership functions and the third for fuzzy-based selection of suitable plantation periods. The proposed approach based mainly on the spatial agro-climatic database. It proved that more of traditional plantation dates become not suitable and it provides new and/or adjusted more suitable periods with a suitability degree for each one. The proposed approach has been applied on the squash crop respecting climate data changes of temperature and humidity in Alexandria governorate in Egypt. In consequent, the final results are presented to a set of agriculture researcher in the Agriculture research center (ARC) and they were agreed with the results.

It is a well-known fact that the number of software requirements from customers usually exceeds the number of features that can be implemented within a given time and available resources. Therefore, requirements prioritization (RP) is essential during the requirements elicitation activity of the requirements engineering phase of software development. In addition, not all requirements can be implemented within one release, so RP is also essential for release planning. There are many RP techniques available in the literature, which are mostly attempting to solve a multi-criteria decision making problem. However, most of them work well on a small number of requirements, and many still suffer from different shortcomings such as scalability, uncertainty, a lot of stakeholders' time consumption, and complexity. According to many studies, none of the RP techniques can be considered the best; the best RP technique depends on the situation. In addition, most of the RP techniques don't take into account the effects of the required goals on the final alternatives' ranking. In this paper, we present a brief survey of the most popular decision making RP techniques, before presenting our new goal-based RP technique. Our goal-based RP technique is based on generating a relative weight for the requirements with respect to the identified goals by stakeholders. It is inspired from prioritization decision making techniques in an attempt to enhance reported RP problems of time consumption, scalability and complexity.

MapReduce is a famous model for data-intensive parallel computing in shared-nothing clusters. One of the main issues in MapReduce is the fact of depending its performance mainly on data distribution. MapReduce contains simple load balance technique based on FIFO job scheduler that serves the jobs in their submission order but unfortunately it is insufficient in real world cases as it missed many factors that impact the performance
such as heterogeneity factor and data skewness, so Load
balancing is important to make all resources utilized evenly
and more efficiently. There are two main schemes in load balancing
a- Static Load Balancing Schemes b- Dynamic load
balancing. The main aim of this work is to study and compare
existing Load Balance algorithms also to illustrate the features
of Load Balance algorithms