Improving Android Malware Detection with Convolutional Neural Networks and Long Short-Term Memory Dr. Rafid Sagban, Dr. Rana Hikmet Tobia Saloom, Dr. Naseer Ali Hussien Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2026 Cybercriminals have become increasingly interested in the spread of critical information, particularly in interpersonal contact and mass distribution of programs and file downloads. This has heightened researchers' awareness of the rampant spread of malware and data breaches. It is anticipated that the number and intensity of malicious software will continue to rise, underscoring the imperative need for strong security architectures. This is especially critical for mobile networks and ubiquitous computing security, given the growing threat posed by hackers. The proposed project involves creating a new dataset using a strictly controlled, shared sample pool to address security threats in wireless mobile networks, leveraging dynamic analysis techniques for malware detection. The dataset aims to enhance the recognition of malicious software by leveraging methods such as encryption and obfuscation. The suggested classification algorithm is CNN-LSTM, a combination of Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) model, which excels at learning complex, sequential features. The CNN and LSTM models were tested on a dataset comprising more than 10,000 malware samples and achieved accuracies of 98% and 97%, respectively. These findings demonstrate how deep learning models can be used to enhance the security of mobile networks and provide effective protection against emerging threats in mobile and ubiquitous computing systems, in a highly beneficial way.
A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks Haydar Abdulameer Marhoon, Rafid Sagban, Atheer Y. Oudah, Saadaldeen Rashid Ahmed Computers Materials and Continua, 2025 : In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set, and this method uses more advanced machine learning models, like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks together, to see coIn our work, we used five different types of machine learning models. These are the forward artificial neural network (ANN), the CNN-LSTM hybrid models, the LR meta-model for linear regression, the Extreme Gradient Boosting (XGB) regression, and the ensemble model. We implemented Random Forest (RF), Gradient Boosting, and XGBoost as baseline models. To train and evaluate the five models, we used four possible features: the size of the circular area, the sensing range, the communication range, and the number of sensors for both Gaussian and uniform sensor distributions. We used Monte Carlo simulations to extract these traits. Based on the comparison, the CNN-LSTM model with Gaussian distribution performs best, with an R-squared value of 99% and Root mean square error (RMSE) of 6.36%, outperforming all the other models.
Enhanced Diagnostic Precision: Deep Learning for Tumors Lesion Classification in Dermatology Rafid Sagban, Haydar Abdulameer Marhoon, Saadaldeen Rashid Ahmed Intelligent Automation and Soft Computing, 2024 Skin cancer is a highly frequent kind of cancer. Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities. Melanoma, basal, and squamous cell carcinomas are well-recognized cut... | Find, read and cite all the research you need on Tech Science Press
Fitness-Dependent Optimizer for IoT Healthcare Using Adapted Parameters: A Case Study Implementation Aso M. Aladdin, Jaza M. Abdullah, Kazhan Othman Mohammed Salih, Tarik A. Rashid, Rafid Sagban, Abeer Alsaddon, Nebojsa Bacanin, Amit Chhabra, S. Vimal, Indradip Banerjee Practical Artificial Intelligence for Internet of Medical Things Emerging Trends Issues and Challenges, 2023 In fitness-dependent optimizer (FDO), the search agent’s position is updated using speed or velocity, but it is done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as genetic algorithm (GA) and particle swarm optimization (PSO) in the original work. The salp-swarm algorithm (SSA), dragonfly algorithm (DA), and whale optimization algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: (1) The implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. (2) It deals with how to tweak the FDO settings to make the metaheuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, the target of this chapter’s enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.
An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping Tao Hai, Biju Theruvil Sayed, Ali Majdi, Jincheng Zhou, Rafid Sagban, Shahab S. Band, Amir Mosavi Geocarto International, 2023 A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard.
A Unified Objective Ant Colony Optimization for Sentiment Oriented Text Summarazation Abeer Raad, Rafid Sagban Ismsit 2022 6th International Symposium on Multidisciplinary Studies and Innovative Technologies Proceedings, 2022 Text summarization is a process of converting a big textual information from single or multi documents into a concise text without change its semantics. Ant Colony Optimization (ACO) is a prominent framework applied successfully for text summarization. However, existing ACO-based text summarization methods did not consider three characteristics in the calculations of the heuristic function of their summarization, they are the main content coverage, the redundancy reduction, and the sentiment reflection. In this paper, the proposed SU-ACO algorithm is based on a new heuristic function that unified the three objectives in ACO-based summarization. Results showed the superiority of the proposed method over other related methods in literature.
Hybrid ant colony optimization and iterated local search for rules-based classification Journal of Theoretical and Applied Information Technology, 2020
Ant colony optimization algorithm for rule-based classification: Issues and potential solutions Journal of Theoretical and Applied Information Technology, 2018
" Evaluating the potential of graphene-like boron nitride as a promising cathode for Mg-ion batteries"[J. Electroanal. Chem. 917 (2022) 116413] R Sivaraman, I Patra, MJC Opulencia, R Sagban, H Sharma, AT Jalil, ... JOURNAL OF ELECTROANALYTICAL CHEMISTRY 977 , 2025 2025
Retraction notice to “Evaluating the potential of graphene-like boron nitride as a promising cathode for Mg-ion batteries”[J. Electroanal. Chem. 917 (2022) 116413] R Sivaraman, I Patra, MJC Opulencia, R Sagban, H Sharma, AT Jalil, ... Journal of Electroanalytical Chemistry 977, 118832 , 2025 2025
A barrier-based machine learning approach for intrusion detection in wireless sensor networks H Marhoon, R Sagban, A Oudah, S Ahmed Computers, Materials, & Continua 82 (3), 4181 , 2025 2025 Citations: 2
Differentiating upper respiratory tract infections from COVID-19 signs and symptoms based on machine learning technique A Al-Talabi, GA Khalid, R Sagban, MM Saeed AIP Conference Proceedings 3232 (1), 040019 , 2024 2024 Citations: 2
Enhanced Diagnostic Precision: Deep Learning for Tumors Lesion Classification in Dermatology. R Sagban, HA Marhoon, SR Ahmed Intelligent Automation & Soft Computing 39 (6) , 2024 2024 Citations: 1
An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping T Hai, B Theruvil Sayed, A Majdi, J Zhou, R Sagban, SS Band, A Mosavi Geocarto International, 2167005 , 2023 2023 Citations: 15
Fitness-dependent optimizer for IoT healthcare using adapted parameters: A case study implementation AM Aladdin, JM Abdullah, KOM Salih, TA Rashid, R Sagban, A Alsaddon, ... Practical Artificial Intelligence for Internet of Medical Things, 45-61 , 2023 2023 Citations: 16
Oil spill segmentation from SAR images using deep neural networks AA Huby, R Alubady, R Sagban 2022 International Symposium on Multidisciplinary Studies and Innovative … , 2022 2022 Citations: 11
A Unified Objective Ant Colony Optimization for Sentiment Oriented Text Summarazation A Raad, R Sagban 2022 International Symposium on Multidisciplinary Studies and Innovative … , 2022 2022 Citations: 1
The effect of hydrophilic and hydrophobic surfaces on the thermal and atomic behavior of ammonia/copper nanofluid using molecular dynamics simulation B Liu, I Khalid, I Patra, OR Kuzichkin, R Sivaraman, AT Jalil, R Sagban, ... Journal of Molecular Liquids 364, 119925 , 2022 2022 Citations: 40
Oil spill detection based on machine learning and deep learning: A review AA Huby, R Sagban, R Alubady 2022 5th International Conference on Engineering Technology and its … , 2022 2022 Citations: 45
Optimization-Based Techniques for Sentiment-Oriented Text Summarization: A Concise Review A Raad, R Sagban NeuroQuantology 20 (8), 2230-2238 , 2022 2022 Citations: 1
Swarm intelligence in anomaly detection systems: an overview S Mishra, R Sagban, A Yakoob, N Gandhi International Journal of Computers and Applications 43 (2), 109-118 , 2021 2021 Citations: 111
Genetic-based pruning technique for ant-miner classification algorithm HNK Al-Behadili, KR Ku-Mahamud, R Sagban International Journal on Advanced Science, Engineering and Information … , 2021 2021 Citations: 5
Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters AM Jabbar, KR Ku-Mahamud, R Sagban International Journal on Advanced Science, Engineering and Information … , 2021 2021 Citations: 4
Comparison of performance among forwarding strategies in CCN: Disaster scenarios R Alubady, R Sagban, HA Marhoon, A Alkhayyat International Journal on Communications Antenna and Propagation 11 (1), 33-41 , 2021 2021
Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care R Sagban, HA Marhoon, R Alubady International Journal of Electrical and Computer Engineering (IJECE) 10 (6 … , 2020 2020 Citations: 24
Hybrid ant colony optimization and genetic algorithm for rule induction HNK Al-Behadili, KR Ku-Mahamud, R Sagban J. Comput. Sci 16 (7), 1019-1028 , 2020 2020 Citations: 22
Adaptive parameter control strategy for ant-miner classification algorithm HNK Al-Behadili, R Sagban, KR Ku-Mahamud Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 8 (1 … , 2020 2020 Citations: 19
Hybrid ant colony optimization and iterated local search for rules-based classification HNK Al-Behadili, KR Ku-Mahamud, R Sagban Journal of Theoretical and Applied Information Technology , 2020 2020 Citations: 25
MOST CITED SCHOLAR PUBLICATIONS
Swarm intelligence in anomaly detection systems: an overview S Mishra, R Sagban, A Yakoob, N Gandhi International Journal of Computers and Applications 43 (2), 109-118 , 2021 2021 Citations: 111
Oil spill detection based on machine learning and deep learning: A review AA Huby, R Sagban, R Alubady 2022 5th International Conference on Engineering Technology and its … , 2022 2022 Citations: 45
Rule Pruning Techniques in the Ant-Miner Classification Algorithm and Its Variants: A Review HNK AL-Behadili, KR Ku-Mahamud, R Sagban 2018 IEEE Symposium on Computer Applications & Industrial Electronics … , 2018 2018 Citations: 41
The effect of hydrophilic and hydrophobic surfaces on the thermal and atomic behavior of ammonia/copper nanofluid using molecular dynamics simulation B Liu, I Khalid, I Patra, OR Kuzichkin, R Sivaraman, AT Jalil, R Sagban, ... Journal of Molecular Liquids 364, 119925 , 2022 2022 Citations: 40
Ant-based sorting and ACO-based clustering approaches: A review AM Jabbar, KR Ku-Mahamud, R Sagban 2018 IEEE Symposium on Computer Applications & Industrial Electronics … , 2018 2018 Citations: 26
Hybrid ant colony optimization and iterated local search for rules-based classification HNK Al-Behadili, KR Ku-Mahamud, R Sagban Journal of Theoretical and Applied Information Technology , 2020 2020 Citations: 25
Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care R Sagban, HA Marhoon, R Alubady International Journal of Electrical and Computer Engineering (IJECE) 10 (6 … , 2020 2020 Citations: 24
Hybrid ant colony optimization and genetic algorithm for rule induction HNK Al-Behadili, KR Ku-Mahamud, R Sagban J. Comput. Sci 16 (7), 1019-1028 , 2020 2020 Citations: 22
An improved ACS algorithm for data clustering AM Jabbar, KR Ku-Mahamud, R Sagban Indonesian Journal of Electrical Engineering and Computer Science , 2020 2020 Citations: 21
Adaptive parameter control strategy for ant-miner classification algorithm HNK Al-Behadili, R Sagban, KR Ku-Mahamud Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 8 (1 … , 2020 2020 Citations: 19
Reactive max-min ant system with recursive local search and its application to TSP and QAP R Sagban, KR Ku-Mahamud, MS Abu Bakar Intelligent Automation & Soft Computing 23 (1), 127-134 , 2017 2017 Citations: 17
Fitness-dependent optimizer for IoT healthcare using adapted parameters: A case study implementation AM Aladdin, JM Abdullah, KOM Salih, TA Rashid, R Sagban, A Alsaddon, ... Practical Artificial Intelligence for Internet of Medical Things, 45-61 , 2023 2023 Citations: 16
Ant Colony Optimization Algorithm for Rule-Based Classification: Issues and Potential Solutions HNK Al-Behadili, KR Ku-Mahamud, R Sagban Journal of Theoretical and Applied Information Technology 96 (21), 7139-7150 , 2018 2018 Citations: 16
An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping T Hai, B Theruvil Sayed, A Majdi, J Zhou, R Sagban, SS Band, A Mosavi Geocarto International, 2167005 , 2023 2023 Citations: 15
Modified ACS centroid memory for data clustering AM Jabbar, KR Ku-Mahamud, R Sagban Journal of Computer Science 15 (10), 1439-1449 , 2019 2019 Citations: 15
ACO ustic: A Nature‐Inspired Exploration Indicator for Ant Colony Optimization R Sagban, KR Ku-Mahamud, MS Abu Bakar The Scientific World Journal 2015 (1), 392345 , 2015 2015 Citations: 15
Reactive memory model for ant colony optimization and its application to TSP R Sagban, KRK Mahamud, MSA Bakar 2014 IEEE International Conference on Control System, Computing and … , 2014 2014 Citations: 14
Nature-inspired parameter controllers for ACO-based reactive search R Sagban, KR Ku-Mahamud, MSA Bakar Research Journal of Applied Sciences, Engineering and Technology 11 (1), 109-117 , 2015 2015 Citations: 13
Oil spill segmentation from SAR images using deep neural networks AA Huby, R Alubady, R Sagban 2022 International Symposium on Multidisciplinary Studies and Innovative … , 2022 2022 Citations: 11
Annealing strategy for an enhance rule pruning technique in ACO-based rule classification HNK Al-Behadili, KR Ku-Mahamud, R Sagban Indones. J. Electr. Eng. Comput. Sci 16 (3), 1499-1507 , 2019 2019 Citations: 11
