An experimental comparison of unsupervised keyphrase extraction techniques for extracting significant information from scientific research articles

The automatic extraction of key information from an article that expresses all of the document’s main elements is referred to as keyphrase extraction. The number of scientific research articles each year is growing. Finding a research article on relevant topics or summarizing a particular research article using important information has become time-consuming by going through the entire article. Therefore, the textual information processing task involves the automatic keyphrase extraction from a document that expresses all of the document’s main elements. This article aims to make an experimental comparison of different unsupervised keyphrase extraction approaches, namely statistical-based, graph-based, and tree-based. The experiment is conducted upon 120 research articles from different subject areas of the computer science. The comparison between different techniques is made by calculating the precision, recall, and Fl-score. The overall performance of the experimental result shows that KP-Miner, a statistical-based technique, outperforms all the other graph-based and tree-based techniques. Among the other techniques, the tree-based technique TeKET performs better after KPMiner. The statistical-based and tree-based approach performs better than the graph-based approach

Blockchain network model to improve supply chain vsibility based on smart contract

Abstract—Due to the increasing complexity of supply chains over the past years, many factors significantly contribute to lowering the supply chains performance. Poor visibility is one of the major challenging factors that lowers supply chains performance. This paper proposes a Blockchain-based supply chain network model to improve the supply chain visibility. The model focuses in improving the visibility measurements properties: information sharing, traceability, and inventory visibility. The proposed model consists of information sharing, traceability, and inventory visibility platforms based on Blockchain technology smart contract. The model built with Hyperledger platform and extend the Hyperledger Composer Supply Chain Network (HCSC) model. The research is designed to three main phases. First phase: the preliminary phase which is the literature review phase to identify the existing challenges in the domain. The second phase: the design and implementation phase which is the development steps of the proposed research model. The third phase: the evaluation phase which represent the performance evaluation of the proposed model and the comparisons between the proposed model and the existing models. In the evaluation performance, the common performance metrics Lead time and average inventory levels will be compared in the proposed model, Cloud-based information system, and the traditional supply chain. These proposed platforms offer an end-to-end visibility of products, orders, and stock levels for supply chain practitioners and customers within supply chain networks. Which helps managers’ access key information that support critical business decisions and offers essential criteria for competitiveness and therefore, enhance supply chain performance

Comparison of accuracy performance based on normalization techniques for the features fusion of face and online signature

Feature level fusion in multimodal biometrics system is able to produce higher accuracy compared to score level and decision level of fusion due to the richer information provided. Features from multi modalities are fused prior to a classification phase. In this paper, features from face (image based) and online signature (dynamic based) are extracted using Linear Discriminant Analysis (LDA). The aim of this research is to recognize an authorized person based on both features. Due to the different domain, the features of one modality might have dominant values that will superior in classification phase. Thus, that aim is unable to be achieved if the classification will rely more on one modality rather than both. To overcome the issue, features normalization is deployed to the extracted features prior to the fusion process. The normalization is performed to standardize the range of features value. A few normalization techniques have been focused in this paper, namely min–max, z-score, double sigmoid function, tanh estimator, median absolute deviation (MAD) and decimal scaling. From those techniques, which normalization technique is most applicable to this case is observed based on best accuracy performance of the system. After the classification phase, the highest accuracy is 98.32% that is obtained from the decimal scaling normalization. It shows that technique is able to give an outperform result compared to other techniques

Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user’s interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use

Aplikasi Latihan Spiritual Islam Secara Digital Dalam Program Pembangunan Pelajar Di Universiti Malaysia Pahang

Kajian ini adalah untuk menilai tahap kebolehgunaan dan keberkesanan aplikasi latihan spiritual Islam berbantukan modul digital latihan tazkiyah al-nafs dalam program pembangunan spiritual pelajar di Universiti Malaysia Pahang (UMP). Reka bentuk kajian ini berbentuk kajian penerokaan berasaskan pendekatan kajian kualitatif iaitu data-data kajian dikumpul dari laporan refleksi program dan kajian temubual dengan peserta-peserta program latihan spiritual Islam. Seramai 15 orang peserta telah terlibat dalam program latihan ini dan responden kajian telah dipilih secara persampelan tujuan bagi tujuan penilaian aplikasi latihan. Data-data kualitatif telah dianalisis secara induktif bagi menjawab soalan-soalan kajian apakah, bagaimanakah dan sejauhmanakah dalam konteks kajian kualitatif. Secara keseluruhannya, dapatan kajian menunjukkan aplikasi latihan spiritual Islam berbantukan modul digital ini dapat diaplikasi dan memberi impak yang sangat positif kepada setiap peserta. Akhirnya, kajian ini merumuskan bahawa aplikasi latihan spiritual Islam berbantukan modul digital amat sesuai diaplikasikan dalam program pembangunan spiritual pelajar bagi mempertingkatkan nilai kualiti spiritual dan kesejahteraan diri pelajar

A combined weighting for the feature-based method on topological parameters in semantic taxonomy using social media

The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1WordNet 3.1 in these tasks are how to integrate the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification

Resolusi anafora artikel Bahasa Melayu berasaskan pengetahuan terhad dan kelas semantik

Anaphora resolution (AR) is a process to resolve reference entity of pronoun anaphora. It is a phenomenon that occur in every languages and requires human experts or specific rules in order to resolve it. AR able to improve language processing applications such as question-answering, text mining, document summarizations, and information extraction. There has been various research carried out on AR, but the majority of them were meant for languages such as English, Japanese and Norwegian. Very few and almost no research effort have been focussed on AR for Malay language. Therefore, the aim of this research is to resolve the phenomena of AR for Malay text by using knowledge poor approach and semantic class labelling model. In order to achieve the aim, a framework of the Malay AR has been developed as a guide to solve this phenomenon in Malay language. Meanwhile, the process to determine the type of usage for pronoun nya has been solved by using a set of rules, a set of similar words, and word filtering that has been generate from semantic class labelling model. This process is important because the use of pronoun nya in Malay text is the highest, amounting to 68% as compared to other pronouns that mostly depend on the sociological status of referring entity or antecedent. The antecedent candidate determination is an important process that should be considered. The antecedent candidates can be in the form of proper noun or nouns. In order to determine proper nouns as suitable candidates, two main processes need to be done: (1) the entity recognition for proper noun that has the word 'dan' and comma symbol (,); and (2) the process to determine the semantic label for each retrieved candidate in order to determine their sociological status. The research used part of the name gazetteers for people, organization, location and position. Testing has been conducted on 60 Malay articles with different classes of proper nouns. The results were compared with the benchmark data tagged by a Malay linguist. The result shows an average precision and recall values of 85% and 90% respectively. The proposed framework of AR by using knowledge poor approach for Malay text shows increased success rate by 18.79% as compared to the generic approach proposed by Mitkov and Lappin

Waspada terhadap serangan Kejuruteraan Sosial

Sejak kebelakangan ini kita sering mendengar tentang panggilan “tipu daya” atau scammer. Panggilan seperti ini digunakan bagi “mencuri” duit mangsa dengan paksa rela. Dalam bidang komputer serangan seperti ini dinamakan “social engineering” atau kejuruteraan sosial. Serangan secara ini merupakan serangan yang paling "untung" dan merangkumi semua peringkat pengguna. Ia sukar untuk dikesan kerana melibatkan kepercayaan yang mana kepercayaan merupakan lumrah semula jadi manusia

The assimilation of multi-type information for seasonal precipitation forecasting using modular neural network

The rainfall occurrences are triggered by different types of climate sources not restricted to past precipitation values but may include climate indices such as El Nino/Southern Oscillation, Indian Ocean Dipole, and Madden Julian Oscillation. In this paper, we investigated the effectiveness of assimilating two sources of inputs for heavy precipitation forecasting using modular neural network. The assimilated input was obtained by merging two input variable sources (climate indices and precipitation records) according to their individual weighting factor determined by correlation test. To simulate the hydrologic response using merged product, a modular neural network model was developed. The modular concept was implemented by separating the precipitation events based on seasonal monsoon and trained the subset of seasonal data using modular neural network. Four subsets of monthly precipitation data were sampled to evaluate modular neural network model at 1-month lead-time with single precipitation neural network model and multiple linear regression as benchmark models. The results show that the merging method can effectively assimilate information from two sources of inputs to improve the accuracy of heavy precipitation forecasting

A Case Study of an Analysis for E-Government Web Accessibility for the Disabled in Malaysia

The internet and World Wide Web has become important source of information. Graphical user interface, are designed for visual interaction through web for the user.Normal peoples can easily process the visual data and locate the information that is most relevant to them. However, this task can be time-consuming and difficult for people with disabilities. This paper will address the issue of designing accessibility government websites that substantially improves web browsing experience of people with disabilities. The study explores the web accessibility standards and guidelines, the web accessibility evaluation tools and then analyzing the government website in Malaysia. It investigates the issue of creating accessible website and the importance of implementing web accessible features to e-government websites. Then the evaluation of government websites using automated accessibility evaluation tools and manual testing according to the guidelines had been done to determine the adaption of W3C Web Content Accessibility Guideline