First-order logic based data structure have knowledge representations in Prolog-like syntax. In an agent based system where beliefs or knowledge are in FOL ground fact notation, such representation can form the basis of agent beliefs and inter-agent communication. This paper presents a formal model of classification rules in first-order logic syntax. In the paper, we show how the conjunction of boolean [Passed, Failed] decision predicates are modelled as Passed(N) or Failed(N) formulas as well as their implementation as knowledge in agent oriented programming for the classification of students’ skills and recommendation of learning materials. The paper emphasizes logic based contextual reasoning for accurate diagnosis of students’ skills after a number of prior skills assessment. The essence is to ensure that students attain requisite skill competences before progressing to a higher level of learning.

One of problems that can increase the risk of forest fire occurrences in Indonesia is drought which is affected by weather conditions. Therefore, weather conditions and forest fire are strongly related. Spatial co-location pattern can be applied to identify the weather conditions that are vulnerable to fires based on the distance between weather observation points and hotspot occurrences. The purpose of this study is to apply the co-location miner algorithm on the weather and hotspot data in Rokan Hilir Riau Indonesia and to analyze the generated co-location patterns. Experimental results show that precipitation which co-located with hotspot occurrences are 0.08–6.69 mm/day. In addition, the temperature which co-located with hotspot occurrences are 22°C–29.17°C. Inside the intervals, hotspots will occur in the radius of 9.724 km from the precipitation and temperature observation points. In 2008, many hotspots were found on the three areas in the study area with the average of precipitation around 3.65–3.71 mm/day and temperature around 24.44°C–25.23°C.

Private organizations like offices, libraries, hospi-tals make use of computers for computerized database, when computers became a most cost-effective device.After than E.F Codd introduced relational database model i.e conventional database. Conventional database can be enhanced to temporal database. Conventional or traditional databases are structured in nature. But always we dont have the pre-organized data. We have to deal with different types of data. That data is huge and in large amount i.e Big data. Big data mostly emphasized into internal data sources like transaction, log data, emails etc. From these sources high-enriched information is extracted by the means of process text data mining or text analytics. Entity Extraction is a part of Text Analysis. An entity can be anything like people, companies, places, money, any links, phone number etc. Text documents, bLogposts or any long articles contain large number of entities in many forms. Extracting those entities to gain the valuable information is the main target. Extraction of entities is possible in natural language processing(NLP) with R language. In this research work we will briefly discuss about text analysis process and how to extract entities with different big data tools.

The suggested system aims to develop the level of security for the Egyptian police ambushes and to decrease the overcrowding because the traditional police ambushes was depressing in the last decade in Egypt due to the actions of terrorism and the threatening of our nation security, which was causing a waste of human casualties and will decrease the income of the tourism and reputation of our nations. The direct interaction with the vehicles inside the police ambushes may cause an unnecessary overcrowding. The introduced system try to minimize the human element in the police ambush through utilizing RFID technology by specifying an RFID card for each vehicle and vehicle driver which will be like the electronic passport at the RFID Sensing check point before the ambush. After checking for the information saved on the RFID card supposed inside the vehicle and also registered previously on the system server results in giving the vehicle the “PASS” signal or “ARRESSTED” signal which required a capture photo shot for the vehicle. So that system aims to increase the security level and reduce casualties of police ambushes anywhere even if it is fixed or non-fixed.

This paper proposes a Microsoft Kinect-based real-time 3D virtual dressing room system. The system has two advantages. First, by using Kinect and 3D modeling customization, the user’s real-time movement is tracked and displayed, which provides the user a realistic feeling during trying on the items virtually. Second, unlike other virtual dressing room where the user has to use it physically at the boutiques, the system can be used at home, which provides the users more convenient shopping experience. Our main goal is focusing on the garment physical movement, by different techniques, we try to make the physical movement of the garment as real as we can, because we think that the physical movement of the garment is one of the most important standard to classify a good virtual dressing system. The paper is divide into seven parts, include introduction, important techniques, system description, result and conclusion etc., also we included some related work that we have searched to improve our system.

The paper presents a method and tools for consistency checking in UML design of an object-oriented software system. The proposed method uses graph representation of UML diagrams and first-order predicate logic to specify consistency rules mostly on the cross-diagram level. Classification of consistency rules is presented. Two approaches to implementation of con-sistency checking are discussed and compared.

Plants are important to human life since plants provide the food, shelter, rain, building material, medicine, fuel such as coal, wood, etc. Therefore, planting, growing, and protecting the plants is essential for sustainable development of any nation. The plant disease can affect the growth of the plats that is caused by pathogens, living microorganisms, bacteria, fungi, nematodes, viruses, and living agents. Hence, identifying the plant disease is very essential to protect the plants in the early stage. Moreover, the plant diseases are identified from the symptoms that appear in stem, fruit, leaf, flower, root, etc. The common symptom of the plant disease can be predicted from the appearance of leaf since the appearance of leaves highly depends on the healthiness of the plant. Therefore, this paper presents a system to identify the lesion leaf from the plants in order to detect the disease occurred in the plant. This system is developed using the bag of visual words model. Moreover, the real time images are collected for various plants and tested with this system and the system produces better results for the given set of images.

In today’s world tremendous amount of unstructured data, especially text, is being generated through various sources. This massive amount of data has lead the researchers to focus on employing data mining techniques to analyse and cluster them for an efficient browsing and searching mechanisms. The clustering methods like k-means algorithm perform through measuring the relationship between the data objects. Accurate clustering is based on the similarity or dissimilarity measure that is defined to evaluate the homogeneity of the documents. A variety of measures have been proposed up to this date. However, all of them are not suitable to be used in the k-means algorithm. In this paper, an extensive study is done to compare and analyse the performance of eight well-known similarity and dissimilarity measures that are applicable to the k-means clustering approach. For experiment purpose, four text document data sets are used and the results are reported.