The development of an ontology model for early identification of children with specific learning disabilities

Ontology-based knowledge representation is explored in special education environment as not much attention has been given to the area of specific learning disabilities such as dyslexia, dysgraphia and dyscalculia. Therefore, this paper aims to capture the knowledge in special education domain, represent the knowledge using ontology-based approach and make it efficient for early identification of children who might have specific learning disabilities. In this paper, the step-by-step development process of the ontology is presented by following the five phases of ontological engineering approach, which consists of specification, conceptualization, formalization, implementation, and maintenance. The details of the ontological model’s content and structure is built and the applicability of the ontology for early identification and recommendation is demonstrated

A computational analysis of short sentences based on ensemble similarity model

The rapid development of Internet along with the wide use of social media applications produce huge volume of unstructured data in short text form such as tweets, text snippets and instant messages. This form of data rarely contains repeated word. It presents challenge in sentences similarity analysis as the standard text similarity models merely rely on the number of word occurrence, often resulting unreliable similarity value. Besides, the use of abbreviation, acronyms, slang, smiley, jargon, symbol or non-standard short form also contributes to the difficulty in similarity analysis. Thus, an extended ensemble similarity model approach is proposed. An experimental study has been conducted using datasets of English short sentences. The findings are very encouraging in improving the similarity value for short sentences

Pengekstrakan dan perwakilan semantik dokumen web berorientasikan domain ontologi

Internet menjadi pilihan sebagai prasarana asas bagi mendapatkan maklumat digital pelbagai topik dari seluruh dunia. Namun demikian kebanyakan dokumen web dalam internet ini adalah tidak berstruktur dan tidak mempunyai maklumat semantik dokumen. Sistem pengekstrakan maklumat yang ada lebih memfokuskan kepada pengekstrakan konsep penting dalam mewakili kandungan dokumen tanpa mengambil kira aspek semantik. Perwakilan kandungan maklumat dalam bentuk kaya semantik merupakan salah satu visi web semantik. Kertas kerja ini membincangkan pengaplikasian pendekatan ontologi dan pemprosesan bahasa tabii dalam menyokong pengekstrakan dan perwakilan maklumat semantik dokumen web. Memandangkan penganotasian maklumat semantik secara manual daripada dokumen web adalah tidak praktikal dan pembangunan sistem automatik sepenuhnya masih terlalu awal untuk diimplementasikan, maka pendekatan separa-automatik telah diusulkan. Dalam hal ini, sistem berfungsi untuk memandu pengguna dalam pemodelan semantik dokumen web yang seterusnya menghasilkan kandungan dokumen web atau set dokumen web yang lebih kaya semantik. Model semantik yang dijana diwakilkan dalam format XML

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques