A Named-Entity Recognition (NER) is part of the process in Text Mining and it is a very useful process for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. However, different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities; Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development.

Manual time and attendance systems are still being practiced within a considerable number of universities, colleges, government departments and companies. This traditional system requires the user to fill in their timesheets or sign on the attendance sheet manually. The practice inefficient and ineffective for university’s student attendance recording due to, i) lecturer may lose the attendance sheet, ii) student may sign for their friends, iii) student may forget to sign on the attendance sheet, and iv) challenging to monitor attendance rate and analysis. Thus, there is a necessity to change these traditional attendance recording methods to a more efficient one. The attendance rate is significant because students are more likely to succeed in academics when they attend classes consistently. As such, a mobile app, namely Smart-Hadir, is designed to overcome problems as mentioned above. Smart-Hadir is a smartphone application developed by UMS aimed to record student attendance digitally during a class session. The technology used is slightly different from the existing applications, where Smart-Hadir is not using RFID technology but rather covers NFC, QR code, and beacon technologies. Attendance could be taken either using NFC or QR code displayed on the wall using a projector or scan the available beacon. Lecturers can create class session using the Smart-Hadir anywhere and anytime. Students are then required to take their attendance either scan the QR code, search the beacon, or swiped their student card on the lecturer’s phone. The students’ attendance will be immediately captured and saved into the database. Lecturers will be able to view the statistic report, identify and e-mail absentees, as well as monitor students’ attendance behaviour. A statistical report can be generated. It shows the percentages of students’ attendance rate, absent rate, and status. The Smart-Hadir is advantageous as it can be used to capture meeting or event attendance record as well.

The large volume of online and offline information that is available today has overwhelmed users’ efficiency and effectiveness in processing this information in order to extract relevant information. The exponential growth of the volume of Internet information complicates information access. Thus, it is a very time consuming and complex task for user in accessing relevant information. Information retrieval (IR) is a branch of artificial intelligence that tackles the problem of accessing and retrieving relevant information. The aim of IR is to enable the available data source to be queried for relevant information efficiently and effectively. This paper describes a robust information retrieval framework that can be used to retrieve relevant information. The proposed information retrieval framework is designed to assist users in accessing relevant information effectively and efficiently as it handles queries based on user preferences. Each component and module involved in the proposed framework will be explained in terms of functionality and the processes involved

As Internet continues to grow, user tends to rely heavily on search engines. However, these search engines tend to generate a huge number of search results and potentially making it difficult for users to find the most relevant sites. This has resulted in search engines losing their usefulness. These users might be academicians who are searching for relevant academic papers within their interests. The need for a system that can assist in choosing the most relevant papers among the long list of results presented by search engines becomes crucial. In this paper, we propose Document Recommender Agent, that can recommend the most relevant papers based on the academician’s interest. This recommender agent adopts a hybrid recommendation approach. In this paper we also show that recommendation based on the proposed hybrid approach is better that the content-based and the collaborative approaches