Al-Iraqia Journal for Scientific Engineering Research

Efficient Hybrid Machine Learning and Feature Selection Approach for IoMT Attack Detection and Healthcare Security Enhancement

Mustafa Hasan Merza — Sun, 01 Mar 2026 00:00:00 +0000

The increasing interconnectivity of healthcare devices through the Internet of Medical Things (IoMT) has improved patient monitoring and treatment, but also exposed these systems to malicious cyberattacks that threaten both patient safety and data integrity. Existing machine learning (ML)-based approaches have attempted to detect such attacks, but most rely on all dataset features, including irrelevant and redundant ones, which increases computational cost and reduces detection accuracy. Feature selection techniques such as Particle Swarm Optimization (PSO) have been used to address this challenge, yet their default fitness functions fail to select the most suitable features for each classifier, often leading to suboptimal results. To overcome these limitations, this study introduces a novel fitness function integrated with PSO and ML classifiers to identify the most relevant features for accurate attack detection in IoMT devices. The proposed framework was evaluated using the NSL-KDD dataset (41 features) with RF, KNN, SVM, and LR classifiers. The number of correctly predicted labels for the optimal feature subsets was 99.35% for RF, 99.02% for KNN, 98.20% for SVM, and 97.61% for LR, whereas the baseline accuracies for the cases with all the features were 95.41%, 94.76%, 92.86%, and 89.55%, respectively. Moreover, the execution times decreased by almost one-third, showing the efficiency of the method. The report indicates validation of the PSO-based fitness function developed for lightweight-accuracy attacks detection in IoMT devices, thus proving to be efficient and cost-conducive, readily deployable in medical organizations as well as smart home environments, thereby safeguarding future-proof healthcare infrastructures against dynamically evolving threats.

Seismic Response Optimization of RC Slabs: Influence of Opening Location, Size, and Geometry on Structural Performance

Ali S. Ali, Yasir W. Abduljaleel, Bilal Al-Obaidi — Sun, 01 Mar 2026 00:00:00 +0000

In this research, the influence of the location, area and shape of the openings on the seismic behavior and performance of the concrete slab roof was evaluated and studied using the finite element numerical method, and recommendations were given for the optimal design. In order to model and analyze slab samples and perform parametric studies, ABAQUS software was used in this research. Studies were carried out based on different parameters such as different positions of the opening in the slab, the placement of the opening in the corner-side-center of the slab, different ratios of the area of the opening to the area of the slab, to be less than and more than 50% and finally different forms of the opening. In order to evaluate the seismic behavior of the slab, the time history dynamic analysis method was used based on the Tabas (Iran) earthquake record. The results of the analysis showed that for the opening located in the corner and on the side of the slab, the conditions were critical, and for the opening located on the edge of the slab (side of the slab), the conditions were more critical. Also, the results showed that the presence of an opening on the side of the slab increased the energy, force and shear caused by the earthquake to at least 40% compared to other positions of the opening in the slab, and as a result, the most critical conditions related to the placement of the opening in the slab It is considered the edge of the slab. Also, the results of the analysis showed that in the case where the opening surface is more than 50% (slab with a large opening), the condition of the slab's vulnerability is lower, but the maximum energy of the slab with a larger opening is always up to 60% more than the slab with a small opening. Of course, this energy level is related to the early times of the earthquake, and finally, the high ductility of the slab with a larger opening can include better seismic behavior and performance of the slab. Finally, the results of the analysis showed that circular and oval openings and, in general, openings with curved and non-sharp corners have better seismic conditions and performance and experience lower energy values. Trapezoidal opening has better conditions in irregular trapezoidal slabs and square opening has the highest amount of energy among different opening shapes, which is not desirable.

Autism Disorder Diagnosis Enhancement Using Adaptive Ranking Features and Machine Learning Classifiers

Heba Ahmed Jassim, Maysam Kadhim, Hafsa Amer Jasim, Zahraa Khduair Taha — Sat, 14 Mar 2026 00:00:00 +0000

Autism is a disorder of brain function that can appear in children younger than two years and affects their communication and learning. The incidence of Autism in children has been increasing, and at the same time, numerous schemes have been proposed for its diagnosis. However, the researchers continue to face challenges in the early and accurate diagnosis of Autism. Early and accurate detection of autism allows doctors to determine the severity of the disease and begin appropriate treatment protocols to develop communication skills in children. This paper proposes feature ranking methods integrated with four classifiers for the accurate diagnosis of autism. Multiple experiments are conducted by integrating ranking features methods (chi-square, Anova-F, and chi2-yates) with Decision Tree (DT), Multi-layer perceptron (MLP), NaiveBayes Bernoulli (NB), and Support Vector Machine –Radial Basis Function (SVM-RBF) classifiers to achieve accurate early detection. The feature ranking is applied with five sets (5, 10, 20, 40, and 46), and each set is individually evaluated to assess its contribution to autism diagnosis. The best diagnosis model (chi2-yates-based SVM-RBF) has achieved 0.971611, 0.94797, and 0.997335 for the accuracy, F1-score, and ROC-AUC, respectively. The proposed framework would be effective in helping therapists in early detection and selecting suitable treatment for autism.

Dynamic Quantization-Aware Neural Architecture Search for Real-Time Encrypted Traffic Classification in 5G Networks

Mon, 23 Mar 2026 00:00:00 +0000

This paper presents a dynamic quantization-aware neural architecture search (NAS) framework for real-time encrypted traffic classification in 5G networks. The framework jointly optimizes model architecture and quantization policies using reinforcement learning with hardware-in-the-loop feedback, addressing both accuracy and efficiency on edge devices. Unlike conventional static methods, the proposed system supports runtime bitwidth switching to adapt precision levels based on fluctuating traffic loads and hardware conditions. Extensive experiments were conducted on ISCX-VPN, USTC-TFC2016, and QUIC-5G datasets. The results show that the proposed approach achieves 94.2% accuracy on ISCX-VPN, 92.7% on USTC, and 89.4% on QUIC-5G, outperforming baseline methods while reducing inference latency by 28–42% and lowering energy consumption by up to 26%. The framework maintains robustness under low-precision constraints, with a mean Quantization Stability Score (QSS) of 0.91, compared to 0.82–0.87 for existing approaches. Hardware-specific optimizations provide additional gains, such as a 2.1 × speedup on Raspberry Pi 4, reduced latency on Jetson Xavier and lower energy consumption on Intel NCS2. The results of Ablation studies affirm that dynamic quantization, hardware feedback, and stability and mechanisms are an absolute necessity, improving accuracy by up to 3.1% and decreasing latency by 31% over sequential optimization. These results prove the efficiency of dynamic quantization-aware NAS to classify encrypted traffic and emphasize its prospects in broader 5G edge AI applications.