Idea mining is a new and interesting field in the areas of information retrieval research. The thoughts of people are helpful to improve strategic decision making. This paper demonstrates the efficient computational methods of idea characterization based concept by extracting the interesting hidden data from unstructured texts which come in many forms and sizes. It may be stored in patents, publications, reports, documents, Internet etc. We briefly discussed a number of successful text mining tools and text classification to extract the idea with a combination of idea mining measures.

In recent years, there has been a global growing demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about a number of automated applications that ease the retrieval of knowledge from the Holy Book, being the major source of Knowledge in Islam. However, the current retrieval methods in the Quranic domain lack adequate semantic search capabilities; they are mostly based on the keywords matching approach. There is a lack of adequate linked data to provide a better description of concepts found in the Holy Quran. In this study we propose an Ontology assisted semantic search system in the Qur'an domain. The system makes use of Quran ontology and various relationships and restrictions. This will enable the user to semantically search for verses related to their query in Al-Quran. The system has improved the search capability of the Holy Quran knowledge to 95 percent accuracy level.

The Lighting Network (LN) is a network of micropayment channels that runs on top of Bitcoin. The balances of payment channels are not broadcasted to the LN network to preserve the privacy of the nodes participating in the network. A balance disclosure attack (BDA) has been proven to be successful in determining the balance of large amounts of channels in the network. In this paper we propose an improved algorithm for the BDA as well as a new type of attack that leverages the differences between LN client software implementations. Our improved algorithm extends the original BDA by performing payments from both sides of the channel. The new attack uses malformed payments to shutdown payment channels an adversary is not part of.

Nowadays, e-commerce is growing fast, so product reviews have grown rapidly on the web. The large number of reviews makes it difficult for manufacturers or businesses to automatically classify them into different semantic orientations (positive, negative, and neutral). Most existing method utilize a list of opinion words for sentiment classification. whereas, this paper propose a fuzzy logic model to perform semantic classifications of customers review into the following sub-classes: very weak, weak, moderate, very strong and strong by combinations adjective, adverb and verb to increase holistic the accuracy of lexicon approach. Fuzzy logic, unlike statistical data mining techniques, not only allows using nonnumerical values also introduces the notion of linguistic variables. Using linguistic terms and variables will result in a more human oriented querying process.

In Cross-Language Information Retrieval (CLIR) process, the translation effects have a direct impact on the accuracy of follow-up retrieval results. In dictionary-based approach, we are dealing with the words that have more than one meaning which can decrease the retrieval performance if the query translation return an incorrect translations. These issues need to be overcome using efficient technique. In this study we proposed a Cross-Language Information Retrieval (CLIR) method based on domain ontology using Quran concepts for disambiguating translation of the query and to improve the dictionary-based query translation. For experimentation, we use Quran ontology written in English and Malay languages as a bilingual parallel-corpora and Quran concepts as a resource for cross-language query translation along with dictionary-based translation. For evaluation, we measure the performance of three IR systems. IR₁ is natural language query IR, IR₂ is natural language query CLIR based on dictionary (as a Baseline) and IR₃ is the retrieval of this research proposed method using Mean Average Precision (MAP) and average precision at 11 points of recall. The experimental result shows that our proposed method brings significant improvement in retrieval accuracy for English document collections, but deficient for Malay document collections. The proposed CLIR method can obtain query expansion effect and improve retrieval performance in certain language

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Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

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