Ibrahim F. Moawad

A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems

Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.

A semantic-based Software-as-a-Service (SaaS) discovery and selection system

With the proliferation of Software-as-a-Service in the cloud environment, users find it tiresome and time consuming to search for the right service that satisfies all their requirements. In addition, services may provide the same functionality but differ in their characteristics or the Quality of Service attributes (QoS) they offer. In this paper, we propose a semantic-based system that facilitates the SaaS publication, discovery and selection processes. To achieve these goals, we developed a unified ontology that combines services domain knowledge, SaaS characteristics, QoS metrics and real SaaS offers. A hybrid service matchmaking algorithm is introduced based on the proposed ontology. It integrates semantic-based metadata and ontology-based matching. Prototypical implementation results demonstrate the effectiveness of the proposed system.

Semantic Trajectories: A Survey from Modeling to Application

Trajectory data analysis has recently become an active research area. This is due to the large availability of mobile tracking sensors, such as GPS-enabled smart phones. However, those GPS trackers only provide raw trajectories (x, y, t), ignoring information about the activity, transportation mode, etc. This information can contribute in producing significant knowledge about movements, which transforms raw trajectories into semantic trajectories. Therefore, research lately has focused on semantic trajectories; their representation, construction, and applications. This paper investigates the current studies on semantic trajectories so far. We propose a new classification schema for the research efforts in semantic trajectory construction and applications. The proposed classification schema includes three main classes: semantic trajectory modeling, computation, and applications. Besides, we discuss the current research gaps found in this research area.

Cloud Services Discovery and Selection: Survey and New Semantic-Based System

With the proliferation of Software-as-a-Service (SaaS) in the cloud environment, it is difficult for users to search for the right service that satisfies all their needs. In addition, services may provide the same functionality but differ in their characteristics or Quality of Service attributes (QoS). In this chapter, we present a comprehensive survey on cloud services discovery and selection research approaches. Based on this survey, a complete system with efficient service description model, discovery, and selection mechanisms is urgently required. Therefore, we propose a semantic-based SaaS publication, discovery, and selection system, which assists the user in finding and choosing the best SaaS service that meets his functional and non-functional requirements. The basic building block of the proposed system is the unified ontology, which combines services domain knowledge, SaaS characteristics, QoS metrics, and real SaaS offers. A hybrid service matchmaking algorithm is introduced based on the proposed unified ontology. It integrates semantic-based meta data and ontology-based matching. Ontology-based matching integrates distance-based and content-based concept similarity measures. The matchmaking algorithm is used in clustering the SaaS offers into functional groups to speed up the matching process. In the selection process, the discovered services are filtered based on their characteristics, and then they are ranked based on their QoS attributes. Case studies, prototypical implementation results, and evaluation are presented to demonstrate the effectiveness of the proposed system

Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness.

Extracting N-gram terms collocation from tagged Arabic corpus

Information Extraction (IE) is one of the most important Natural Language Processing (NLP) applications, which extracts information such as Named-Entities (NE) and collocation of terms from the corpus. Collocation is a sequence of terms that co-occur together in the corpus. In Arabic Information Extraction, there are many problems because of the complex of Arabics grammar and ambiguity. In general, in linguistics research, the more efficient corpus is the one annotated by Part of Speech Tagging (POST). In this paper, we propose a prototype that extracts collocation of N-gram words (from 2-6 gram) based on the sequence of POST from Arabic Quran corpus. This approach extracts the collocation of N-gram words by matching the input structured pattern of Arabic language versus the Part of Speech Tagging of Quran corpus. The system enables users to select a sequence of tags (2-6 gram) and scope of the corpus source (whole Quran Corpus or specific Surah). To show how the system is beneficial for linguistic research, a set of experiments has been conducted in different scenarios.

A personalized recommender system for SaaS services

In this paper, we propose the Software‐as‐a‐Service (SaaS) Recommender (SaaSRec), a personalized reputation‐based QoS‐aware recommender system (RS) for SaaS services. SaaSRec semantically processes user requests in order to find business‐oriented matching services, which are then filtered to satisfy the user QoS requirements and service characteristics. Subsequently, hybrid filtering is utilized to validate the services set on the basis of services metadata, reputation and user interests. Finally, the recommended set of services is ranked using a unique combination of factors: Relevance to user profile, service reputations and service cost. Moreover, we propose a new method for calculating the service reputation from the objective time‐weighted user feedbacks. SaaSRec addresses many challenges faced by the generic RSs: User cold‐start problem, limited content analysis and low performance. In respect of service RSs, SaaSRec tackles the disregarding of relevant factors to services recommendation: Cloud service characteristics, user physical location, service reputation and user interests. Moreover, SaaSRec provides a hybrid justification for the recommended services to increase the users acceptance. Experimental evaluation against a real‐world services dataset has been carried out, and the results show that the proposed recommendation approach surpasses other collaborative filtering‐based recommendation approaches in respect of both precision and recall. This performance improvement was verified using different matrix density levels and number of recommendations. Copyright

Concept Recommendation System for Cloud Services Advertisement

Cloud computing is a major trend in Information Technology (IT), which has witnessed high adaption rate for cloud solutions. Software-as-a-Service (SaaS) providers compete to address nearly every business and IT application needs. Heterogeneous cloud service advertisements make it difficult for potential users to discover the required service offers. To overcome this problem, the cloud service registry is fundamental for both the cloud users and providers. It provides detailed information about SaaS offers from different providers in one place, which increases the services reachability. In this paper, we focus on the business perspective of the SaaS services, which has not received eligible consideration by existing literature. We propose a semantic-based system for unified SaaS service advertisements. We introduce a template for the service registration, a guided registration model, and a registration system. Moreover, we present a semantic similarity model for services metadata matchmaking. Prototypical implementation and evaluation proved the proposed system effectiveness.

Generic opponent modelling approach for real time strategy games

One of the fundamental and challengeable research areas in Real Time Strategy (RTS) games is opponent modelling. Most current approaches to opponent modelling pretended inefficiency. They are either computationally expensive or required a numerous amount of online gameplays to start learn successful models. Unfortunately, most successful approaches also were game specific. They mainly depend on the experts knowledge of the game. In this paper, a generic and adaptive opponent modelling approach for RTS games is proposed. It is a completely automated approach for learning the highly informative features of the opponents behavior of any RTS game. Inspired by the case-based reasoning technique, a case base of different opponent models is constructed in the approach offline phase. The online phase (during gameplay) utilizes only this model base for opponent classification. To better cope with opponents that switch strategies, the approach keeps track of the performance after classification. To show how the proposed approach is beneficial, a case study called SPRING game case-study is presented.

Enhanced similarity measure for personalized cloud services recommendation

Cloud users are overwhelmed with great numbers of cloud services. Service recommender systems evaluate the services that provide same functionalities according to the user requirements. A key enabler to accurate recommendation in recommender systems is the appropriate determination of similar users. This paper contributes to the personalized cloud services recommendation area. In specific, we introduce a user‐based similarity measure that integrates relevant similarity aspects: user demographic information, service ratings, and user interest. The proposed similarity measure is used in a hybrid collaborative filtering (CF) approach that leverages the advantages of both model‐ and memory‐based approaches to improve the recommendation process. Experimental evaluation on real‐world services data set shows that the proposed approach outperforms other CF approaches in respect of the prediction accuracy and recommendation time while maintaining better or same coverage.