ISMAIL AL-HADI - Academia.edu (original) (raw)

Papers by ISMAIL AL-HADI

International journal of economics and financial issues, Jul 3, 2024

Journal of informatics and web engineering, Feb 14, 2024

One of the main challenges boutique hotels and resorts face is the direct outreach to tourists an... more One of the main challenges boutique hotels and resorts face is the direct outreach to tourists and customers. As a result, these independent hotels often resort to online platforms such as Agoda and Airbnb to expand their customer base. However, this approach comes at the cost of losing revenue to Online Travel Agencies (OTAs) that solely focus on room sales, hindering the establishment of a strong brand image for boutique hotels and resorts. Considering the heavy reliance on OTAs, this paper focuses on the development of GoHoliday, a cross-platform mobile app prototype that aims to bridge the gap between boutique hotels and users. This mobile application seamlessly integrates a booking engine, an AI assistant for trip planning, and an experience-sharing platform, enhancing the app's capabilities alongside other features. By implementing the GoHoliday mobile application, boutique hotels can maximize their reach and establish a distinct brand identity by directly serving their valuable guests with more personalized arrangements.

2023 IEEE 13th International Conference on System Engineering and Technology (ICSET)

2023 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

International Journal of Innovative Computing, 2012

Backpropagation algorithm is used to solve many real world problems using the concept of Multilay... more Backpropagation algorithm is used to solve many real world problems using the concept of Multilayer Perceptron. However, the main disadvantages of Backpropagation are its convergence rate is relatively slow, and it is often trapped at the local minima. To solve this problem, in literatures, evolutionary algorithms such as the Particle Swarm Optimization algorithm has been applied in feedforward neural network to optimize the learning process in terms of convergence rate and classification accuracy but this process needs longer training time. To provide alternative solutions, in this study, Bacterial Foraging Optimization Algorithm has been selected and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy. One of the main processes in Bacterial Foraging Optimization algorithm is the chemotactic movement of a virtual bacterium that makes a trial solution of the optimization problem. This process of chemotactic movement is guided to make the learning process of Artificial Neural Network faster. The developed Bacterial Foraging Optimization Algorithm Feedforward Neural Network is compared against Particle Swarm Optimization Feedforward Neural Network. The results show that Bacterial Foraging Optimization Algorithm gave a better performance in terms of convergence rate and classification accuracy compared to Particle Swarm Optimization Feedforward Neural Network.

PeerJ, Jul 14, 2021

Background: Virtual reality is utilised in exergames to help patients with disabilities improve o... more Background: Virtual reality is utilised in exergames to help patients with disabilities improve on the movement of their limbs. Exergame settings, such as the game difficulty, play important roles in the rehabilitation outcome. Similarly, suboptimal exergames' settings may adversely affect the accuracy of the results obtained. As such, the improvement in patients' movement performances falls below the desired expectations. In this paper, a recommender system is incorporated to suggest the most preferred movement setting for each patient, based on the movement history of the patient. Method: The proposed recommender system (ResComS) suggests the most suitable setting necessary to optimally improve patients' rehabilitation performances. In the course of developing the recommender system, three methods are proposed and compared: ReComS (K-nearest neighbours and collaborative filtering algorithms), ReComS+ (k-means, K-nearest neighbours, and collaborative filtering algorithms) and ReComS++ (bacterial foraging optimisation, k-means, K-nearest neighbours, and collaborative filtering algorithms). The experimental datasets are collected using the Medical Interactive Recovery Assistant (MIRA) software platform. Result: Experimental results, validated by the patients' exergame performances, reveal that the ReComS++ approach predicts the best exergame settings for patients with 85.76% accuracy.

IEEE Access

The pervasive proliferation of embedded, mobile, and IoT devices continue to change our lifestyle... more The pervasive proliferation of embedded, mobile, and IoT devices continue to change our lifestyle dramatically. However, the huge increase in these devices has come with critical breaches to data resting inside them. Many types of such data are considered to be sensitive and confidential. Because the most sensitive data of such devices are resting in databases, focusing on encrypting SQLite databases will be more efficient than full disk encryption (FDE). While SQLite is a very popular, lightweight, and easy-to-use relational database suitable for embedded and mobile devices, its stored data suffers serious security risks. If an attacker can gain access to higher system privileges or find a way to access the database plain file, he can tamper with the database files and user-sensitive data, which breaches the security CIA triad of SQLite. To ensure data confidentiality in SQLite databases of embedded devices, we present a design and implementation of a parallel database encryption system, called SQLite-XTS. The developed system encrypts the database pages on-the-fly in a transparent manner without user intervention. Because performance is a critical issue, SQLite-XTS utilizes multi-core processors coming with most current mobile and embedded devices. The developed parallel SQLite-XTS was successfully implemented and integrated into a testbed device. To assess the performance and feasibility of this system, it was compared to three other SQLite implementations: plain SQLite, serial XTS SQLite, and SQLCipher-CBC. The results show that SQLite-XTS reduces the overhead of database encryption from 30.8% with serial implementation to 17.8% when SQLite-XTS is used. This provides the developed system with an efficiency of 73% compared with its serial counterpart. The results clarify that SQLite-XTS introduces significant performance improvements compared to other implementations. Experiments also show that the system has a very low impact on the memory of these resource-limited devices. INDEX TERMS Storage security, embedded devices, SQLite RDBMS, performance evaluation, mobile devices, multi-core processors, low-power devices, XTS encryption. The associate editor coordinating the review of this manuscript and approving it for publication was Diana Gratiela Berbecaru .

Journal of Mathematical Sciences and Informatics

In this note, we consider the product indefinite integral of the form ... more In this note, we consider the product indefinite integral of the form An automatic quadrature scheme (AQS) is constructed for evaluating Cauchy principal singular integrals in two cases. In the first case c∈ [y,z] ⊂ [-1,1] where -1 < y < z < 1, density function h(t) is approximated by the truncated sum of Chebyshev polynomials of the first kind. Direct substitution does not give solutions so we have used the AQS and reduced problems into algebraic equation with unknown parameters bk which can be found in terms of the singular point with some front conditions. In the second case c ∈ [-1,1], the application of the AQS reduced the number of calculations twice and accuracy is increased. As a theoretical result, the convergence theorem of the proposed method is proven in a Hilbert space. Numerical examples with exact solutions and comparisons with other methods are also given, and they are in the line with theoretical findings.

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)

A survey by Malaysian Communication and Multimedia Commission (MCMC) has shown that Internet user... more A survey by Malaysian Communication and Multimedia Commission (MCMC) has shown that Internet users in Malaysia has increased up to 88.7% in year 2020 compared to 76.9% in year 2016 which is quite high increase in percentage. The pervasive use of smartphones and computers nowadays as well as the availability of high-speed Internet access has brought a new way of promoting products. E-commerce is a buy and sell system that can be accessed globally around the world. This system can provide efficient strategies for promoting products and services. It is widely agreed that even small enhancements in promotion techniques can increase the profitability of any e-commerce system. In this paper, a research has been conducted to study methods to enhance product promotion among Malaysians. The study investigates through a survey the factors affecting users’ WTP (willing-to-pay) during performing e-commerce transaction as well as factors what attract/repel them more. To achieve the study aim, a group of 385 respondents throughout Malaysia have been involved in this research through questionnaire study. Detailed analysis has been introduced to clarify the results. The study has identified the top e-commerce platforms that are used by Malaysians to execute product promotion. The results show that promotion through e-commerce platforms could increase profitability and help businesses to expand rapidly. This is due to the fast market penetration of online promoting compared to conventional one.

Journal of Computer Science, 2016

Proceedings of International Conference on Emerging Technologies and Intelligent Systems, 2021

Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, 2017

A recommender system provides users with personalized suggestions for items based on the user&#39... more A recommender system provides users with personalized suggestions for items based on the user's behaviour history. This system often uses the collaborative filtering for analysing the rating scores of users for items in the scoring matrix. The scoring matrix of a recommendation system contains a high percentage of data sparsity which lowers the quality of the prediction based on the collaborative filtering. Recently, the temporal with matrix factorization is one of the successful collaborative-based approaches which address data sparsity. However, the user's rating scores have drifted over time and the predicted rating scores are over-fitted which are the significant challenges in the temporal based factorization approaches. Therefore, the ShortTemporalMF approach has proposed to address these challenges. The ShortTemporalMF uses the bacterial foraging optimization algorithm (BFOA) and the k-means algorithm to minimize the over-fitting by exploiting several latent features. ...

Collaborative filtering (CF) is one of the most popular techniques of the personalized recommenda... more Collaborative filtering (CF) is one of the most popular techniques of the personalized recommendations, where CF generates personalized predictions in the rating matrix. The rating matrix typically contains a high percentage of unknown rating scores which is called the sparsity problem. The matrix factorization approach through temporal approaches has the accurate performance in addressing the sparsity issue but still with low accuracy. However, there are four issues when a factorization approach is adopted which are latent feedback learning, score overfitting, user’s interest drifting and item’s popularity decay over time. Therefore, this work introduces the temporal based factorization approach named TemporalMF++ to address all the issues. The experimental results show the TemporalMF++ approach has a higher prediction accuracy compared to the benchmark approaches. In summary, the TemporalMF++ approach has a superior effectiveness in improving the accuracy prediction of the CF by l...

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

International Journal of Advanced Computer Science and Applications, 2020

Collaborative Filtering (CF) is a widely used technique in recommendation systems. It provides pe... more Collaborative Filtering (CF) is a widely used technique in recommendation systems. It provides personal recommendations for users based on their preferences. However, this technique suffers from the sparsity issue which occurs due to a high proportion of missing rating scores in a rating matrix. Several factorization approaches have been used to address the sparsity issue. Such techniques have also been considered to tackle other challenges such as the overfitted predicted scores. Nevertheless, they suffer from setbacks such as drift in user preferences and items’ popularity decay. These challenges can be solved by prediction approaches that accurately learn the long-term and short-term preferences integrated with factorization features. Nonetheless, the current temporal-based factorization approaches do not accurately learn the convergence of the assigned k clusters due to a lower number of short-term periods. Additionally, the use of optimization algorithms in the learning process...

Recommendation systems suggest peculiar products to customers based on their past ratings, prefer... more Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based T...

The temporal recommendation system (TRS) is designed for providing users with an accurate predict... more The temporal recommendation system (TRS) is designed for providing users with an accurate prediction based on the history of their behaviour during a precise time. Most TRS approaches use matrix factorization and collaborative filtering, which are primarily based on the distribution of the user preferences. Recently, TRS has gained significant attention because it improves the accuracy of prediction. This is because since the temporal drift in the user preferences is observed, users' preferences within the short term and long term can be utilized to predict the best item to be recommended. Several existing review papers have focused on the general problems of the recommendation system (RS) and similarity measures, and they refer to recent improvements based on three recommendation strategies which are user rating, tagging and trust values. However, there is a lack of recent review papers of TRS with rating score strategy, especially in terms of learning factorization features of...