Penggunaan perkhidmatan terminal layan diri sistem perbankan dalam kalangan pengguna di Parit Raja, Johor

Perubahan teknologi yang semakin pesat telah memberikan impak yang besar ke atas perkembangan industri perbankan secara global termasuklah industri perbankan di Malaysia. Lantaran itu, wujud alternatif perkhidmatan bank melalui perbankan elektronik seperti Automated Teller Machines (ATM), phone banking, PC-banking dan perbankan internet sebagai alat pemudah cara. Sistem perbankan turut berkembang seiring dengan arus perubahan teknologi dengan menyediakan perkhidmatan terminal layan diri selain perkhidmatan kaunter bank seperti mesin juruwang automatik, mesin deposit tunai, mesin deposit cek, mesin mengemas kini buku simpanan dan terminal helpline. Sistem perbankan dahulu dan kini jauh berbeza oleh kerana peredaran masa dan teknologi hari ini yang semakin berkembang pesat. Perkhidmatan terminal layan diri kini merupakan perkara penting dalam kehidupan seharian. Perubahan ini membawa kepada satu perubahan gaya hidup yang baharu. Penerimaan atau penolakan sesuatu inovasi bermula apabila pelanggan mengetahui tentang produk tersebut (Rogers & Shoemaker, 1971). Di United Kingdom, Daniel (1999) melaporkan bahawa pelanggan biasanya akan mencari produk kewangan dan bank yang menawarkan nilai yang terbaik kepada wang yang dilaburkan. Oleh itu, para pelanggan mesti diberitahu akan kewujudan dan kelebihan produk baharu berbanding saluran sedia ada melalui aktiviti promosi yang hebat agar ia lebih membantu pihak bank untuk mencapai sasarannya (Suganthi et al., 2001)

Content-Based Image Retrieval Based on Electromagnetism-Like Mechanism

Recently, many researchers in the field of automatic content-based image retrieval have devoted a remarkable amount of research looking for methods to retrieve the best relevant images to the query image. This paper presents a novel algorithm for increasing the precision in content-based image retrieval based on electromagnetism optimization technique. The electromagnetism optimization is a nature-inspired technique that follows the collective attraction-repulsion mechanism by considering each image as an electrical charge. The algorithm is composed of two phases: fitness function measurement and electromagnetism optimization technique. It is implemented on a database with 8,000 images spread across 80 classes with 100 images in each class. Eight thousand queries are fired on the database, and the overall average precision is computed. Experimental results of the proposed approach have shown significant improvement in the retrieval performance in regard to precision

Measuring learner's performance in e-learning recommender systems

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test

How learners’ interactions sustain engagement: a MOOC case study

In 2015, 35 million learners participated online in 4,200 MOOCs organised by over 500 universities. Learning designers orchestrate MOOC content to engage learners at scale and retain interest by carefully mixing videos, lectures, readings, quizzes, and discussions. Universally, far fewer people actually participate in MOOCs than originally sign up with a steady attrition as courses progress. Studies have correlated social engagement to completion rates. The FutureLearn MOOC platform specifically provides opportunities to share opinions and to reflect by posting comments, replying, or following discussion threads. This paper investigates learners’ social behaviours in MOOCs and the impact of engagement on course completion. A preliminary study suggested that dropout rates will be lower when learners engage in repeated and frequent social interactions. We subsequently reviewed the literature of prediction models and applied social network analysis techniques to characterise participants’ online interactions examining implications for participant achievements. We analysed discussions in an eight week FutureLearn MOOC, with 9855 enrolled learners. Findings indicate that if learners starts following someone, the probability of their finishing the course is increased; if learners also interact with those they follow, they are highly likely to complete, both important factors to add to the prediction of completion model

Customer profiling using classification approach for bank telemarketing

Telemarketing is a type of direct marketing where a salesperson contacts the customers to sell products or services over the phone. The database of prospective customers comes from direct marketing database. It is important for the company to predict the set of customers with highest probability to accept the sales or offer based on their personal characteristics or behaviour during shopping. Recently, companies have started to resort to data mining approaches for customer profiling. This project focuses on helping banks to increase the accuracy of their customer profiling through classification as well as identifying a group of customers who have a high probability to subscribe to a long-term deposit. In the experiments, three classification algorithms are used, which are Naïve Bayes, Random Forest, and Decision Tree. The experiments measured accuracy percentage, precision and recall rates and showed that classification is useful for predicting customer profiles and increasing telemarketing sales

Effect of PEG on the biodegradability studies of kenaf cellulose -polyethylene composites

Several blends of cellulose derived from bast part of kenaf (Hibiscus cannabinus L.) plant, with different thermoplastics, low density polyethylene (LDPE) and high density polyethylene (HDPE), were prepared by a melt blending machine. Polyethylene glycol (PEG) was used as plasticizer. Biodegradability of these blends was measured using soil burial test in order to study the rates of biodegradation of these polymer blends. It was found that the cellulose/LDPE and cellulose/HDPE blends were biodegradable in a considerable rate. The bio-composites with high content of cellulose had higher degradation rate. In addition, biodegradability of the bio-composites made up using PEG was superior to those of the bio-composites fabricated without PEG, due to the improved wetting of the plasticizer in the matrix polymer. The results were also supported by the scanning electron microscopy (SEM)

Self regulated learning in flipped classrooms: A systematic literature review

The flipped classroom is considered an instructional strategy and a type of blended learning instruction that focused on active learning and student engagement. Over the years, flipped classroom studies have focused more on the advantages and challenges of flipped instruction and its effectiveness, but little is known about the state of self-regulation in flipped classrooms. This study investigates the self-regulation strategies as well as the supports proposed for self-regulated learning in flipped classrooms. Findings show that relatively few studies have focused on self-regulated learning in flipped classrooms compared to the overall research and publication productivity in flipped classrooms. Also, the existing solutions and supports have only focused on either self-regulation or online help-seeking, but have not focused on other specific types of self-regulation strategies. Our study proposed some future research recommendations in flipped classrooms

Skills of future workforce: skills gap based on perspectives from academicians and industry players

Apart from having specific knowledge, graduates are expected to possess a set of soft and hard skills to be employed. This study aims to identify soft and hard skills relevant to the future workforce in the electrical and electronic (E&E) industry based on two perspectives; academicians from public higher education institution (HEI) and E&E industry players. Further, the study aims to investigate skills gaps between two stakeholders. A total of 50 academicians and 31 industry players in Malaysia were surveyed using a structured questionnaire. Statistical analysis was performed using an independent t-test. In terms of soft skills, analytical thinking skills, communication skills, and discipline were more perceived by academicians, whereas decision-making skills, teamwork skills, and discipline were more favored by industry players. For hard skills, both players favored technology use, except for organizational capabilities which were perceived more by academicians while troubleshooting was favored more by industry players. This study contributes to the collaboration between public HEI and the E&E industry to address the skills gaps, which will benefit all stakeholders. This study focuses on the skills that are perceived more by both stakeholders

Decentralized blockchain network for resisting side-channel attacks in mobility-based IoT

The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods