No abstract

The aim of this paper is to proposed software architecture for ontology-driven advisory systems. The architecture reflects the situation in our current agricultural advisory systems where farmers as a client request an advice from the experts to help them in decision making process in their cultivating. The architecture consists of three components, users, module and knowledge/database. Each component complies with the basic process in advisory systems, knowledge acquisition, cognition, and interface. The architecture embeds three approaches in this systems, personalization, knowledge management and ontology. In semantic web architecture all information is spread over the Internet. By using these technologies, we can easily share and reused the information via Internet.Keywords-Advisory system, decision making process in agricultural, ontology-driven system, semantic web, knowledge modeling, processes in advisory system

Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87% using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93% and GWAR rule 92% at rank 5.

A search for experts is a search for individuals with skills and knowledge in a particular field. Early studies studied profiling of experts in the expert search application by using keyword query. Kajian lampau mengkaji penggunaan profil pakar dalam aplikasi pencarian pakar mengguna kata kunci sebagai kueri. Penggunaan kueri berasaskan konsep yang diketengah mampu mendapat padanan pakar atau kepakaran yang berkaitan dengan bidang yang dicari. Sistem pencarian pakar kini hanya melakukan carian berdasarkan maklumat penerbitan pakar sahaja lantas menyebab tidak ada hubungan dengan maklumat lain yang boleh menjadi sumber sokongan bagi menghubung kepada bidang yang berkaitan. Kaedah pencarian pakar berdasarkan maklumat penerbitan mengecil skop carian pakar kerana terdapat aspek lain yang berkaitan dengan pakar seperti pendidikan, pengajaran, penyeliaan, perundingan, persidangan, khidmat masyarakat, kelayakan dan badan

The Holy Qur'an is the main religious text of Islam. The Qur'an has its own methods of Targhib (encouragement) and Tarhib (warning), which are important features of the Qur'an. Most of the Quranic verses would urge and encourage people to do right and good deeds, and also warn them from committing evil and bad deeds. The method of classifying a text into two opposing opinions has been applied previously in solving the problem of sentiment analysis. Currently, it is applied in identifying between Targhib (encouragement) and Tarhib (warning) verses in the Qur'an. Each verse of the Qur'an can be treated as either an encouragement, warning or neutral. The language of the Holy Qur'an is one of the most challenging natural languages in sentiment analysis. The aim of this work is to classify the verses of encouragement and warning using sentiment analysis and NLP techniques. Several approaches are used in the Sentiment Analysis classification, such as the machine learning approach, the lexicon-based approach and the hybrid approach. In carrying out this aim, the applied machine learning approach was used, where the impact of the use of different techniques such as POS tagging, N-Gram and Feature selection with correlation based were evaluated and investigated. 95.6% accuracy was achieved using Naïve Bayes (NB) and 91.5% accuracy was achieved using the Support Vector Machines (SVM). This study is a significant study in extracting information and knowledge from the Holy Qur'an. It is significant for both researchers in the field of Islamic studies as well as non-specialized researchers.