Hesham A. Hefny

Fuzzy based approach for discovering crops plantation knowledge from huge agro-climatic data respecting climate changes

Climate change has noticeable significant impacts on development of most countries because of its direct negative effect on the production and revenue of most crops plantation process. In reality, the ongoing changes in climate variables affect the suitability of planting some crops in their traditional places at their traditional dates. Furthermore, the availability of huge volumes of agro-climatic data that almost incorporates uncertainty increases the complexity of managing and discovering the crops suitable plantation patterns from such data. Accordingly, a need appeared to an efficient approach to handle such uncertainty and to exploit such huge data volume to manage the crops plantation process accurately. This paper presents a fuzzy approach based on Hadoop for discovering crops plantation knowledge from the agro-climatic historical database of the years from 1983 to 2016 of Egypt. Commonly, the proposed approach provides a set of scenarios for plantation dates of each crop with a suitability degree for each scenario. Also, it helps managing crops plantation process from some other aspects such as harvesting dates, candidate diseases and follow up for crops water requirements respecting the data streaming of the prevailing weather data. The proposed approach has been tested on a set of crops with cooperation of researchers from Cairo University and Agricultural Research Center. The results show the added value of the proposed approach against other works respecting the more suitable crops plantation dates, harvesting dates, expected diseases and follow up for crops water requirements. Furthermore, the proposed approach benefits from Hadoop framework capabilities of handling huge amounts of data streamed from weather stations.

An Enhanced Ant Colony-based Approach for Query Optimization

One of the mandatory processes for all those types of applications is the inquiry process of the stored huge amounts of data. Such process is either a predefined or an ad-hoc query. From the logical point of view, the query process depends mainly on many algebraic operations, including selection, projection and joining operations. The most important one of them is the join operation, which represents the key factor of the inquiry process to retrieve the related information from different data tables. Many approaches have been proposed aiming to reduce the cost of join operations. Yet, there is still a need for more query optimizing processes in order to reduce the query response time. This paper proposes an enhanced optimal query processing approach for inner and outer join, where the proposed model exploits an adopted Ant Colony Optimization.

Predicting Algae Growth in the Nile River Using Meta-learning Techniques

This paper presents Meta-learning techniques for predicting algae growth in River Nile through the selection of inuence environmental variables such as water temperature, ph, silica, and the nitrogen group. Feature selection has been performed using several algorithms to identify the variables relevant to the growth. Then, genetic classier and CFS with the random search algorithms were adapted for predicting algae growth. The proposed predicting algae growth approach was tested on the algae data of the Nile River which collected from 14 stations in Cairo, Egypt for the sequence of twelve months started in January and ended in December from 2012 to 2015. The experimental results demonstrated that the accuracy of algae growth prediction based on feature selection which was superior by using all the features.

Moth-flame Optimization Based Segmentation for MRI Liver Images

One of the most important aims in computerized medical image processing is to find out the anatomical structure of the required organ. The hepatic segmentation is very important for surgery planning and diagnosis. The difficulty of segmentation rises from the different volumes, the different lobes and the vascular arteries of liver. This paper proposes a successful approach for liver segmentation. The proposed approach depends on Moth-flame optimization (MFO) algorithm for clustering the abdominal image. The user picks up the required clusters that represent the liver to get the initial segmented image. Then the morphological operations produce the final segmented liver. A set of 70 MRI images, was used to segment the liver and test the proposed approach. Structural Similarity index (SSI) validates the success of the approach. The experimental results showed that the overall accuracy of the proposed approach, results in 95.66% accuracy.

Improving Multiple Routing in Mobile Ad Hoc Networks Using Fuzzy Models

Nowadays, the use of Mobile Ad-Hoc Network (MANET) devices increases extremely. Dynamic Source Routing protocol (DSR) is a reactive routing protocol designated for MANET, whenever a route is demanded, DSR checks route cache, if no route exists; it floods the network with Route Request packets (RREQ). Route discovery process may result in multiple RREQ packets traversing the network until a Route Reply packet (RREP) is sent back. However, the discovered route may contain nodes with no backup routes to the next hop then path failure may occur. Moreover, due to users’ mobility and limited transmission ranges, the routing protocol should consider routes stability. Therefore, improving routing will enhance the whole network performance. In this paper, two fuzzy models were proposed to enhance routing decisions by Fuzzy Stability models (FSDSR1 & FSDSR2). The proposed models consider the following parameter: route discovery time, total route replies, delivery ratio, and the number of retransmission attempts, MANET delay, and throughput. The results showed that FSDSR2outperformed the state of art protocol DSR and FSDSR1.

EEG-Based Emotion Recognition Using a Wrapper-Based Feature Selection Method

Emotions are important part of the daily communication process between people. The need for embed emotion recognition in the human-computer interaction systems became an important issue recently. Researchers addressed the use of internal physiological signals for emotion observation. Electroencephalography (EEG) has a great attention recently and it is now the most used method for observing brain activities. This paper presents a method for EEG-based emotion recognition. Addressing the high dimensionality of the EEG features, recursive feature elimination (RFE) as a wrapper-based feature selection method is used to select the most important features. Then, many classifiers are evaluated to classify emotions using the selected features. The presented method has been tested on a public dataset, and the results demonstrate the robustness of this method and its superiority compared to other studies on the same dataset.