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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology International Journal for Applied Information Management International Journal of Advanced Science and Computer Applications Data Science Insights Journal of Digital Market and Digital Currency International Journal Research on Metaverse
Maidin, Siti Sarah
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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An Effective Investigation of Genetic Disorder Disease Using Deep Learning Methodology Vidhya, B.; Shivakumar, B. L.; Maidin, Siti Sarah; Sun, Jing
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.370

Abstract

This study evaluates the performance of four neural network models—Artificial Neural Network (ANN), ANN optimized with Artificial Bee Colony (ANN-ABC), Multilayer Feedforward Neural Network (MLFNN), and Forest Deep Neural Network (FDNN)—across different iteration levels to assess their effectiveness in predictive tasks. The evaluation metrics include accuracy, precision, Area Under the Curve (AUC) values, and error rates. Results indicate that FDNN consistently outperforms the other models, achieving the highest accuracy of 99%, precision of 98%, and AUC of 99 after 250 iterations, while maintaining the lowest error rate of 2.8%. MLFNN also shows strong performance, particularly at higher iterations, with notable improvements in accuracy and precision, but does not surpass FDNN. ANN-ABC offers some improvements over the standard ANN, yet falls short compared to FDNN and MLFNN. The standard ANN model, though improving with iterations, ranks lowest in all metrics. These findings highlight FDNN's robustness and reliability, making it the most effective model for high-precision predictive tasks, while MLFNN remains a strong alternative. The study underscores the importance of model selection based on performance metrics to achieve optimal predictive accuracy and reliability. 
Optimizing Emergency Logistics Identification: Utilizing A Deep Learning Model in the Big Data Era Sumathi, V.; Shivakumar, B. L.; Maidin, Siti Sarah; Ge, Wu
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.369

Abstract

This study investigates the dynamics of commodity flow across different facilities and settings, evaluating the performance of Simulation and Feed Forward Neural Network (FFNN) methods in optimizing these flows. Analyzing data from various configurations, the research reveals significant variations in commodity distribution patterns. At Facility_1 from the K1 disposer market, the flow of Commodity_1 increased from 770 units to 830 units, while Commodity_2 decreased from 192 units to 166 units. At Facility_2, Commodity_1's flow decreased from 851 units to 793 units, and Commodity_2's flow slightly increased from 139 units to 148 units. Similar trends are observed at facilities from the K2 disposer market, reflecting the complex impact of different settings on commodity flow. The comparative analysis of Simulation and FFNN methods demonstrates their relative strengths. In Setting I, the Simulation method achieved an objective value of 1,800,574.36 Rs with a computation time of 46.78 seconds, while the FFNN method yielded a slightly lower objective value of 1,800,352.24 Rs in 42.01 seconds. In Setting II, the Simulation method provided an objective value of 1,801,025.36 Rs with a computation time of 103.86 seconds, whereas FFNN achieved a comparable objective value of 1,800,847.27 Rs in 63.05 seconds. In Setting III, Simulation resulted in an objective value of 1,801,527.36 Rs with a computation time of 61.12 seconds, while FFNN produced a higher objective value of 1,806,997.32 Rs in 50.03 seconds. The results highlight the trade-offs between solution quality and computational efficiency. The Simulation method consistently delivers higher objective values but requires more time, making it suitable for applications where result accuracy is crucial. Conversely, the FFNN method offers faster computation with competitive or superior objective values, making it advantageous for scenarios where time constraints are significant. This study underscores the importance of selecting appropriate computational methods based on specific operational needs, optimizing both the efficiency and effectiveness of commodity flow management.
Optimized Deep Learning method for Enhanced Medical Diagnostics of Polycystic Ovary Syndrome Detection Praneesh, M.; Nivetha, N.; Maidin, Siti Sarah; Ge, Wu
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.368

Abstract

This paper explores Polycystic Ovary Syndrome (PCOS), a common hormonal disorder caused by elevated androgen levels, which affects women's reproductive health. The primary objective is to enhance early detection and diagnosis of PCOS using advanced machine learning techniques. To achieve this, the study utilizes VGG19 Net, integrated with various machine learning algorithms, to classify ultrasound images of the ovaries. The research involves analyzing ultrasound scans to differentiate between benign and potentially cancerous cysts. The contribution of this study lies in its novel application of VGG19 Net, which achieved an accuracy rate of 96% compared to other techniques: Random Forest (94%), Logistic Regression (91%), Bayesian Classifier (81%), Support Vector Machine (92%), and Artificial Neural Network (90%). The findings indicate that VGG19 Net outperforms traditional methods in precision and accuracy, with a significant improvement in detecting early-stage PCOS. This approach not only provides a clearer diagnostic image but also enables timely intervention, thus addressing the challenge of distinguishing between benign and malignant cysts more effectively. The results underscore the potential of VGG19 Net in revolutionizing PCOS diagnosis through enhanced image classification, offering a valuable tool for medical practitioners.
Ensembling Methods for Data Privacy in Data Science Mahendiran, N; Shivakumar, B L; Maidin, Siti Sarah; Wu, Hao
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.341

Abstract

The rapid advancement of technology has unified systems, data storage, applications, and operations, providing continuous services to organizations. However, this integration also introduces new vulnerabilities, particularly the risk of cyber-attacks. Malware and digital piracy pose significant threats to data security, with the potential to compromise sensitive information, leading to severe financial and reputational damage. This study aims to develop an effective method for detecting malware-infected files on storage devices within the Internet of Things (IoT) environment. The proposed approach utilizes a stacked regression ensemble for data pre-processing and the Sea Lion Optimization Algorithm (sLOA) to extract salient features, enhancing the classification process. Using malware data from an intrusion detection dataset, an ensemble classification technique is applied to identify malicious infections. The experimental results demonstrate that the proposed method achieved an accuracy of 98%, a precision of 99.6%, a recall of 96%, and an F-measure of 95% by the final iteration, significantly outperforming existing techniques in addressing cyber-security challenges within IoT systems.
Current and Future Trends for Sustainable Software Development: Software Security in Agile and Hybrid Agile through Bibliometric Analysis Maidin, Siti Sarah; Yahya, Norzariyah; Fauzi, Muhammad Ashraf bin Fauri; Bakar, Normi Sham Awang Abu
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.473

Abstract

The industrial growth of digitalized era has given rise to a growing concern in software development. The present research investigates the prevailing and projected patterns in sustainable software development, especially those related to process innovation, with a particular emphasis on software security within Agile and Hybrid Agile approaches, employing bibliometric analysis. However, a comprehensive understanding of the security concerns of both agile and hybrid agile is crucial and needs further garnered. However, it is expected that a thorough comprehension of the hybrid agile model landscape would uncover various themes encompassing its implementation. The analysis aims to provide a comprehensive overview of the current, present, and future state of software security for agile and hybrid agile. The study employed a bibliometric approach to gather a total of 1593 journals from the Web of Science (WOS) database. This study utilizes co-citation and co-word analysis techniques to identify the most significant articles, delineate the fundamentals framework, and provide a prognosis for future development. The present investigation has successfully discovered four distinct co-citation and three distinct co-word clusters. This study offers valuable insights regarding the software security in agile and hybrid agile. The increasing evolution of the software ecosystem necessitates the prioritization of bridging the gap between agility and security. This paper provides a detailed roadmap for scholars and practitioners who are navigating this intersection
Empirical Study of the Correlation between Social Media Content and Health Issues among College Students Using Machine Learning Hemalatha, M.; Maidin, Siti Sarah; Sun, Jing
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.365

Abstract

This study analyzes the effect of social media content on college student addiction using data science techniques. It aims to examine the correlation between different types of social media content and addictive behavior in college students. The research methodology used is non-probability sampling with a sample size of 587 college students in Tamil Nadu, India. The study uses statistical tools such as correlation analysis, regression analysis, one-way ANOVA, and Friedman ranking test to analyze the data collected. The findings suggest that the factors influencing social media addiction are positively correlated with the health issues faced by college students. The study indicates that demographic variables such as age, gender, year in college, and place of living may play a role in shaping an individual's perception of social media addiction. The results of the study can inform the development of interventions and prevention strategies to reduce social media addiction among college students. The study recommends a multi-pronged approach to address the root causes of addiction and provide students with the tools and resources they need to manage their social media use and promote their physical and mental health.
Speech Enhancement using Sliding Window Empirical Mode Decomposition with Median Filtering Technique Selvaraj, Poovarasan; Maidin, Siti Sarah; Yang, Qingxue
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.470

Abstract

The Empirical Mode Decomposition is raising significant interest since its first introduction among the nineties. The attention in varied fields such as medical engineering, space analysis, hydrology, synthetic aperture measuring, speech enhancement, watermarking and etc. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by subsequently the least degraded IMF. Hereafter, in this article, SWEMD method is enhanced by using Sliding Window (SW) procedure. This research work has come SDG goals for health and well-being and also this research work concentrated on hearing aid application using noise level adjustment. In this SWEMDH method, the calculation of EMD is performed based on the small and sliding window along with the time axis. For each component, the total of sifting iterations is unwavering by decomposition of many signal windows by standard algorithm and calculating the average amount of sifting steps for each component. The median filter used for removed nonlinear components of this work. SWEMDH technique removed for low frequency Noisy Components. The speech quality was evaluation by the performance matrices of Mean Square Error, Perceptual evaluation of speech quality, signal to noise ratio, peak signal to noise ratio. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.
Cellular Traffic Prediction Models Using Convolutional Long Short-Term Memory Samson, A Sunil; Sumathi, N; Maidin, Siti Sarah; Yang, Qingxue
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.472

Abstract

Precise cellular traffic modeling and prediction is essential to future big data-based cellular network management for providing autonomic control and user-satisfied stable mobile services. However, the traditional methods have difficulty learning the complex hidden patterns of the users’ traffic data from cross-domains because of their shallow learning characteristics. Deep learning (DL)-based methods could somewhat identify these hidden patterns by learning the underlying spatial and temporal features and their dependencies. Yet, they too have constraints in handling the noisy and sparse data, reducing the prediction accuracy with increased computation time and associated storage costs. Therefore, this paper presents an intelligent cellular traffic prediction model (ICTPM) using two improved deep learning algorithms to tackle the negative impacts of noisy and sparse traffic datasets. Firstly, the Enhanced Stacked Denoising Auto-Encoder (ESDAE) is introduced to eliminate the noise in the traffic data by an adaptive Morlet wavelet transform. Secondly, Multi-dimensional Spatiotemporal Sparse-representation Convolutional Long Short-Term Memory (MDSTS-CLSTM) is used to learn the hidden patterns by extracting the spatial-temporal dependencies and predict the cellular usage in the presence of data sparsity problem. This MDSTS-CLSTM is developed by combining the Long Short-Term Memory (LSTM) with the Convolutional Neural Networks (CNN) and improvising the multi-dimensional feature learning, spatial-temporal analysis, and sparse representation properties of the hybrid DL algorithm. Evaluated over real-world cellular traffic cross-domain datasets from Telecom Italia and Open-CellID, the proposed ICTPM outperforms the state-of-the-art methods with 5-10% better performance enhancements.
An Adaptive Cuckoo Search Algorithm with Deep Learning for Addressing Cyber Security Problem Jeyaboopathiraja, J.; Mariajohn, Princess; Maidin, Siti Sarah; Sun, Jing
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.366

Abstract

IoT (Internet of Things) offers continued services to organizations by connecting systems, application and services using the medium of internet. They also leave themselves open to threats including virus attacks and software thefts where the risks of losing crucial information are high. These threats harm both the business’ finances and reputation. This work offers a combined Deep Learning strategy using Artificial Neural Networks that can assist in detecting illegal software and malware tainted files. The proposed cyber security architecture uses data mining techniques to forecast cyber-attacks and prepare Internet of Things for suitable countermeasures. This framework uses two phases namely detections and predictions. This paper proposes Adaptive Cuckoo Search Optimization-based Algorithms for cloud network routes. Adaptive Cuckoo Search Algorithm are a bio-inspired protocol based on cuckoo birds’ characteristics. Artificial Neural Networks classify assaults on cloud environments. The major goal of this work is to separate malicious servers from legitimate servers that are impacted by Denial of Service and Distributed Denial of Service assaults and thus safeguard server data and ensuring they are sent to legitimate servers. The outcome from this research proposed scheme shows better performances for protecting systems from cyber-attacks in terms of values for accuracy, Precision, Recall and F1-Measure when compared to existing algorithms.
Optimizing Survival Prediction in Children Undergoing Hematopoietic Stem Cell Transplantation through Enhanced Chaotic Harris Hawk Deep Clustering Arthi, R.; Priscilla, G Maria; Maidin, Siti Sarah; Yang, Qingxue
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.468

Abstract

Cancer can impact individuals of all ages, including both children and adults. Diagnosing the pediatric cancer can be challenging due to its rarity. Typically, it is not recommended to screen for pediatric cancer as it may lead to potential harm to the children. One of the specialized treatments for pediatric cancer is Hematopoietic Stem Cell Transplant (HSCT). HSCT performs replacement of existing one’s blood cells with the donor’s bone marrow healthy cells. However, forecasting the survival rates following the pediatric HSCT is crucial and poses challenges in early detection. Many machine learning algorithms have been developed to predict the risk of transplant outcomes which depends on the type of disease or patient’s comorbidity. In this work, the enhancement of survival prediction for children who have undergone hematopoietic stem cell transplantation (HSCT) is achieved through the introduction of a deep learning model that is based on behavioral characteristics. The primary aim of this model is to identify and differentiate between the patterns of malignancy, non-malignancy, and hematopoietic conditions within the dataset of bone marrow transplant patients. The existing unsupervised machine learning algorithms, performs clustering of instances with the randomly selected centroids, which often results in local optima and early convergence affects the accuracy rate. Hence, the present approach introduces Chaotic mapping Harris Hawk Optimization (CHHO) in order to enhance the conventional k-means clustering procedure due to its significantly reduced computational complexity. To understand the pattern of the bone marrow transplant dataset, the deep clustering model with its ability of auto encoder and decoder, discriminates the labelled instanced. With the inferred knowledge proposed CHHO with Deep clustering Model (CHHO-DCM) performs the effective clustering of instances with the advantage of both local and global optimization. The simulation outcomes have substantiated the effectiveness of the suggested CHHO-DCM model as it attains the highest level of precision when compared to the prevailing clustering models in predicting the survival of pediatric patients during Hematopoietic Stem Cell Transplantation (HSCT).s enduring HSCT.
Co-Authors Abbas, Elaf Sabah Abbas, Intesar Abdul Radhi, Rafah Hassan Abdul-Kareem, Bushra Jabbar Abdullah Abdullah Ahmed, Mohsen Ali Ahmed, Saif Saad Ajitha, Ajitha Al Hilfi, Thamer Kadum Yousif Al-Dosari, Ibraheem Hatem Mohammed Alfalahi, Saad.T.Y. Ali, Taghreed Alaa Mohammed Arthi, R. Attarbashi, Zainab S. Ayyasy, Yahya Bakar, Normi Sham Awang Abu Binti Abdul Rahim, Yusrina Dhilipan, J. Fallah, Dina Faraj, Lydia Naseer Fauzi, Muhammad Ashraf bin Fauri Ge, Wu Gelar Budiman Govindaraju, S Guangfa, Wu Hadi, Shahd Imad Hafedh, Milad Abdullah Hammad, Qudama Khamis Hamodi Aljanabi, Yaser Issam Hao Wu Haodic, Gao Hemalatha, M. Indirani, M Indrarini Dyah Irawati Ishak, Wan Hussain Wan Ismail, Laith S. Jafar, Qusay Mohammed Jaleel Maktoof, Mohammed Abdul Jamil, Abeer Salim Janan, Ola Jaya, M. Izham Jeyaboopathiraja, J. Jing Sun Khalil, Baker Mohammed Kowthalam, Vijay Rathnam Kumar, B.L. Shiva Lie, Ye Luo Jun Mahdi, Ammar Falih Mahdi, Mohammed Fadhil Mahendiran, N Majeed, Adil Abbas Mariajohn, Princess Mohammed, Doaa Thamer Murad, Nada Mohammed Nayef, Hamdi Abdullah Nazar, Mustafa Nivetha, N. Praneesh, M. Priscilla, G Maria Priscilla, G. Maria Rahmafadilla, Rahmafadilla Rizal, Mochammad Fahru Sajid, Wafaa Adnan Salman, Khdier Samson, A Sunil Selvaraj, Poovarasan Shaker, Alhamza Abdulsatar Shanmugam, D.B. Shing, Wong Ling Shivakumar, B L Shivakumar, B. L. Shnain, Saif Kamil Subramanian, Devibala Sumathi, N Sumathi, V. Taher, Nada Adnan Thavamani, S. Triasari, Biyantika Emili Varun, S. T. Vidhya, B. Vijayalakshmi, N. Wan Ishak, Wan Hussain Wei, Jingchuan Wider, Walton Yahya, Norzariyah Yamin, Fadhilah Yang, Qingxue Yi, Ding Yilin, Li Yousif Al Hilfi, Thamer Kadum YULI SUN HARIYANI Zhang Xing Zhao, Zhong Zhaoji, Fu