A. Mahmood

Effective Classification and Categorization for Categorical Sets: Distance Similarity Measures

Measuring the similarity between objects is considered one of the main hot topics nowadays and the main core requirement for several data mining and knowledge discovery task. For better performance most organizations are in need on semantic similarity and similarity measures. This article presents different distance metrics used for measuring the similarity between qualitative data within a text. The case study represents a qualitative data of Faculty of medicine Cairo University for theses. The dataset is about 5,000 thesis document with 35 departments and about 16,000 keyword. As a result, we are able to better discover the commonalities between theses data and hence, improve the accuracy of the similarity estimation which in return improves the scientific research sector. The experimental results show that Kulczynksi distance yields better with a 92.51 % without normalization that correlate more closely with human assessments compared to other distance measures.

An intelligent recommender system for drinking water quality

This article presents a recommender system based on rough mereology for the prediction of water quality in Louisiana, a state located in the southern region of the United States. The proposed system firstly maps the water dataset into a normalized dataset of potential of hydrogen (PH) levels prediction. Then, rough mereology and rough inclusion techniques are applied for clustering and classifying the normalized PH dataset into sets of granules with different radius. Voting by objects approach is subsequently applied in order to select the optimized granules. Finally, normalized rating matrix is acquired, then the predicted PH level will be recommended. The data, tested by the proposed recommender system, was collected from stations of the United states Environmental Protection Agency (EPA). The obtained results demonstrate the effectiveness and the reliability of the proposed recommender system. Based on the data resulted, the average PH level prediction in a certain time is characterized by a mean absolute error of 0.34. In addition, both experimentally resulted and actual dataset values existed in the healthy region of the PH level for drinking water, which is within the range 6.5 to 8.0 according to the World Health Organization (WHO) drinking water guidelines.

An Intelligent Multi-agent Recommender System

This article presents a Multi-Agent approach for handling the problem of recommendation. The proposed system works via two main agents; namely, the matching agent and the recommendation agent. Experimental results showed that the proposed rough mereology based Multi-agent system for solving the recommendation problem is scalable and has possibilities for future modification and adaptability to other problem domains. Moreover, it succeeded in reducing the information overload while recommending relevant decisions to users. The system achieved high accuracy in ranking using users profile and information system profiles. The resulted value of the Mean Absolute Error (MAE) is acceptable compared to other recommender systems applied other computational intelligence approaches.

Climate Recommender System for Wheat Cultivation in North Egyptian Sinai Peninsula

In this paper, a recommender system based on rough mereology was presented for predicting best cultivation dates for wheat in Egyptian Sinai Peninsula according to the required mean temperature for germination stage during October to December. The weather data was hourly collected from El-Arish weather station in north Sinai government from 1985 to 2013. As far as we know, this was the first study in Egypt regarding a wheat recommender expert system for predicting temperature and sowing dates in North Sinai. In current study, accurate weather forecasting information and suitable cultivation dates of wheat were obtained from the proposed recommender system. The results show that the proposed system can forecast different temperatures with minimum Mean Absolute Error (MAE). These findings may allow decision makers to take their proper decisions on managing agricultural development in new reclaimed areas like the Egyptian Sinai Peninsula.

Federated cloud computing security using forward-secure broadcast encryption HIBE

In recent years, many countries, companies, and research groups have launched their own cloud systems. Due to security issues, there is a limitation for a single-provider of a cloud to cooperate with other cloud systems to maximize the function of available resources. In addition, each cloud computing system provides a digital identity for a user. Hence, a user has many digital identities for difference clouds. Therefore, in hybrid cloud the user has many digital identities. The broadcast media market is discovering the business advantages in using a cloud system in its communication. In cloud computing environments, we propose federated layered broadcast cloud security architecture guarantee efficiency and secure authenticated of multiple broadcasters. However, each broadcaster can be dynamically broadcast messages into an authorized group of receivers. A secure mutual authentication protocol for federated broadcast cloud (MAFBC) is presented. In this paper, we combine a federated identity management mechanism and a forward secure broadcast encryption scheme using HIBE to have a unique digital identity for a user, which will handle all the above problems. Moreover, the key distribution and the mutual authentication problems will be handled.

A new efficient algorithm for executing queries over encrypted data

Outsourcing databases into cloud increases the need of data security. The user of cloud must be sure that his data will be safe and will not be stolen or reused even if the datacenters were attacked. The service provider is not trustworthy so the data must be invisible to him. Executing queries over encrypted data preserves a certain degree of confidentiality. In this paper, we propose an efficient algorithm to run computations on data encrypted for different principals. The proposed algorithm allows users to run queries over encrypted columns directly without decrypting all records.

Intelligent road surface quality evaluation using rough mereology

The road surface condition information is very useful for the safety of road users and to inform road administrators for conducting appropriate maintenance. Roughness features of road surface; such as speed bumps and potholes, have bad effects on road users and their vehicles. Usually speed bumps are used to slow motor-vehicle traffic in specific areas in order to increase safety conditions. On the other hand driving over speed bumps at high speeds could cause accidents or be the reason for spinal injury. Therefore informing road users of the position of speed bumps through their journey on the road especially at night or when lighting is poor would be a valuable feature. This paper exploits a mobile sensor computing framework to monitor and assess road surface conditions. The framework measures the changes in the gravity orientation through a gyroscope and the shifts in the accelerometers indications, both as an assessment for the existence of speed bumps. The proposed classification approach used the theory of rough mereology to rank the modified data in order to make a useful recommendation to road users.

Classification Approach Based on Rough Mereology

This article presents a classification approach based on granular computing combined with rough set. The proposed classification approach used the theory of rough mereology and fuzzification in order to classify input datasets into sets of optimized granules. The proposed approach was applied to five datasets of the UC Irvine Machine Learning Repository. The Abalone dataset that consists of 4177 objects and eight attributes was selected as an illustrative example. Empirically obtained experimental results demonstrated that the proposed rough mereology based classification approach obtained better performance compared to other experienced proposed classification approaches.