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All Journal Palmyra Fiber as Additional Materials on Solid Concrete Brick of Aggregate International Journal of Multidisciplinary Sciences and Arts The Indonesian Journal of Computer Science
Abdullah Khan
Software and Multimedia Centre, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM)
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology

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Differences Between Self-Perceived Multiple Intelligences of Urban & Rural Schools Students Shahzada, Gulap; Ghazi, Safdar Rehman; Ali, Riasat; Khan, Umar Ali; Khan, Abdullah
Mediterranean Journal of Social Sciences Vol. 2 No. 2 (2011): May 2011
Publisher : Richtmann Publishing

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Abstract

The purpose of this study was to investigate the differences between self-perceived multiple intelligences of urban and ruralschools students. Measurement of central tendency, mean score, SD for the measurement of self-perceived multiple intelligences and onesample–t test was used for mean comparison of urban and rural schools students. Result showed that there is a significant differencebetween self-perceived verbal/linguistic, logical/mathematical, visual/ spatial and intrapersonal intelligence of urban and rural studentsand there is no significant difference between self-perceived, musical, bodily/kinesthetic, interpersonal and naturalistic intelligence of urbanand rural students.
Causes of Absenteeism from the Schools at Secondary Level Shahzada, Gulap; Ghazi, Safdar Rehman; Nawaz, Habib; Khan, AbdUllah
Mediterranean Journal of Social Sciences Vol. 2 No. 2 (2011): May 2011
Publisher : Richtmann Publishing

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Abstract

The study aimed at to find out different causes of students’ absenteeism at secondary level in district Bannu, to sort out social,political and economic factors involved in the students’ absenteeism at secondary level. All secondary level students’ of district Bannuconstituted population of the study.100 students who frequently remained absent from the schools were conveniently selected as a sample ofthe study. Percentages have been used as statistical technique to analyze the data. Results of the study showed that most frequent causes ofthe absence of the students from the schools were their families’ participation in social activities, students help their parents in their work,too much home work and too many tests and examinations. It was recommended that by the care and concentration of parents, childrencan be encouraged to go to school. Availability of the school building, furniture, adequate libraries and clean and healthy environment,introducing modern methods and techniques, keeping in view the interests and needs of the children, providing financial aid to the studentswho are unable to afford the educational expenses, teachers’ sympathetic attitude are factors which can motivate and attract studentstowards schools.
Transforming Healthcare through AI: Unleashing the Power of Personalized Medicine khan, Abdullah
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 2 (2023): International Journal of Multidisciplinary Sciences and Arts, Article April 202
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i1.2424

Abstract

The healthcare sector now places a lot of emphasis on providing patients with personalized care that is catered to their unique requirements and features. A significant force behind the development and use of customized healthcare is artificial intelligence (AI). This essay examines the use of AI in personalized healthcare and how it can affect several facets of healthcare provision. The study starts out by talking about how AI is being used in diagnostics, emphasizing how machine learning algorithms and the examination of various datasets can improve diagnostic accuracy. It goes into more detail about how AI can be used to create tailored treatment plans, utilizing patient-specific data and predictive analytics to maximize therapeutic results and reduce side effects. Examined are the ethical issues surrounding AI in customized healthcare, such as data privacy, algorithmic bias, and the value of responsibility and openness. The integration of AI into clinical practice is also covered in the study, along with prospects for improving decision-making, streamlining workflow, and overall healthcare delivery effectiveness. The healthcare sector now places a lot of emphasis on providing patients with personalized care that is catered to their unique requirements and features. A significant force behind the development and use of customized healthcare is artificial intelligence (AI). This essay examines the use of AI in personalized healthcare and how it can affect several facets of healthcare provision. The study starts out by talking about how AI is being used in diagnostics, emphasizing how machine learning algorithms and the examination of various datasets can improve diagnostic accuracy. It goes into more detail about how AI can be used to create tailored treatment plans, utilizing patient-specific data and predictive analytics to maximize therapeutic results and reduce side effects. Examined are the ethical issues surrounding AI in customized healthcare, such as data privacy, algorithmic bias, and the value of responsibility and openness. The integration of AI into clinical practice is also covered in the study, along with prospects for improving decision-making, streamlining workflow, and overall healthcare delivery effectiveness.
Detection of Phishing Webpages Using a CNN-BiGRU Hybrid Deep Learning Framework Saima Anwar Lashari; Hadeel Abdulrahman Alsantli; Khan, Abdullah; Dzati Athiar Ramli
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5067

Abstract

Protecting sensitive data, like passwords and financial information in the cyber-world, is becoming a critical challenge day by day. Attackers use various smart ways to exploit the mistakes of internet users. Phishing is one of the most important types of cyber-attack. Researchers have proposed various phishing detection and identification techniques in the last decade against the phishing attacks. However, many state-of-the-art techniques have shortcomings in terms of accuracy and time complexity. But they also have major issues of the high runtime overhead. On the other hand, the simple techniques with low time-complexity have issue of the accuracy because these simple techniques have high false alarm rate. To resolve these issues, this study proposed a novel hybrid-deep-learning algorithm with 3 variants to address these high time-complexity and low accuracy issues. A novel hybrid deep learning model based on Convolutional Neural Network (CNN)-(Bi-GRU) is proposed to classify a web-page phishing or legitimate. To validate the proposed hybrid model with various variants, extensive experiments have been conducted on various benchmark datasets. The experimental results have proved the validity of the proposed model as compared to state-of-the-art techniques in terms of identifying the phishing webpages accurately in comparatively less time.
Unified Deep and Machine Learning Hybrid Models for Alzheimer’s and Mild Cognitive Impairment Detection Khan, Abdullah; Dzati Athiar Ramli; Saima Anwar Lashari
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

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Abstract

Alzheimer’s disease (AD), the most common form of dementia, is characterized by progressive neurodegeneration, leading to memory loss and cognitive decline. Recent studies have reported annual conversion rates from amnestic Mild Cognitive Impairment (MCI) to probable AD. With the advent of Magnetic Resonance Imaging (MRI)-based analysis, advancements in machine learning (ML), particularly deep convolutional neural networks (CNNs), have transformed the diagnostic landscape of AD. However, earlier approaches often struggled to accurately distinguish between different MCI stages. To address this limitation, a deep neural network (DNN) model was developed, employing an enhanced artificial neural network (ANN) architecture to classify individuals into three categories: mild Alzheimer’s dementia, MCI, and normal cognition. The proposed DNN model, trained on a Kaggle dataset, achieved an exceptional accuracy of 0.99. In comparison, conventional classifiers such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) achieved accuracies of 0.97, 0.96, 0.96, and 0.96, respectively. Meanwhile, K-Nearest Neighbors (KNN) attained 0.83, Random Forest (RF) achieved 0.95, and Logistic Regression (LR) reached 0.93. Hybrid models combining DNN with SVM and DT (DNN-SVM and DNN-DT) yielded accuracies of 0.79 and 0.64, respectively. These findings highlight the importance of selecting models that balance interpretability with computational efficiency. Overall, this study provides valuable insights into the strengths and limitations of various classification techniques, enabling informed decisions for different datasets and clinical objectives in Alzheimer’s disease diagnosis.
Co-Authors Ali, Riasat Dzati Athiar Ramli Ghazi, Safdar Rehman Hadeel Abdulrahman Alsantli Khan, Umar Ali Nawaz, Habib Saima Anwar Lashari Shahzada, Gulap