EXPRESSION OF CONCERN FOR: H. A. Hussain, M. A. Subhi, A. S. Al Tmeme, A. D. Radhi, and M. A. Albahar, ``Improving Road Safety Through Multimodal Deep Learning for Driver Drowsiness Detection,'' Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 4, Article 12, 2025. DOI: https://doi.org/10.52866/2788-7421.1330. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss4/12. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Improving Road Safety Through Multimodal Deep Learning for Driver Drowsiness Detection (Manuscript 1330)”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance. Available online Editor's Note: The Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1384) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1330) has been resolved. The publication timeline is as follows: Original Article → Expression of Concern → Resolution.
EXPRESSION OF CONCERN FOR: M. H. Al-Adhaileh, M. I. Ahmed Al-mashhadani, E. M. Alzahrani, and T. H. H. Aldhyani, ``Improving Heart Attack Prediction Accuracy Performance Using Machine Learning and Deep Learning Algorithms,'' Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 2, 2025, Article~3. DOI:~https://doi.org/10.52866/2788-7421.1239. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/3. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Improving Heart Attack Prediction Accuracy Performance Using Machine Learning and Deep Learning Algorithms (Manuscript 1239)”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
NOTICE OF CORRIGENDUM FOR: Almulla, Hussein K.; Mohammed, Hussam J.; Al-Waisy, Alaa S.; Al-Fahdawi, Shumoos; Had, Ahmed Adnan; and AL-Attar, Bourair (2025) ``MotionFusion: A Robust Ensemble Learning Framework for Accurate Sensor-Based Human Activity Recognition,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 14. DOI: https://doi.org/10.52866/2788-7421.1289. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/14. Reason for Corrigendum: In the published version of the above article, a number of typographical and formatting errors were identified. These corrections do not affect the study design, results, or conclusions, and are provided below to ensure accuracy and consistency. 1) Feature subset sizes (text–table inconsistency) Error: The manuscript contains inconsistent feature subset sizes. While the feature-selection setup defines subsets as 420 / 280 / 140 (≈ 75% / 50% / 25% of 561), another sentence incorrectly reports 300 and 100 for 50% and 25%. Correction: The correct subset sizes are: 75%: 420 features 50%: 280 features 25%: 140 features Accordingly, the sentence reporting ``50% (300)'' and ``25% (100)'' should be corrected to ``50% (280)'' and ``25% (140)''. 2) Table 2 formatting merge error (Class weight row) Error: Table 2 contains a layout/line-break merge where page/footer text appears inside a cell (e.g., ``Balanced220 ...''). Correction: Table 2 should display the SVC class weight as ``Balanced'' only, and the footer/page text must be removed from the table content. 3) Incorrect unit/format for F1-score Error: The paper states: ``highest F1-score value of 0.81% ...'', which is a unit-formatting error. Correction: The correct representation is F1-score = 0.81 (or equivalently 81% if the journal uses percent formatting). The ``%'' symbol after 0.81 must be removed (or the value reformatted consistently as 81%). 4) Reference typography issue (invalid symbol) Error: In Reference [1], the title includes an unintended symbol (e.g., ``human° activity''). Correction: The reference title should be corrected to ``human activity recognition dataset ...'' (removing the unintended symbol). 5) Minor table label typo Error: A table label uses ``Matric''. Correction: Replace ``Matric'' with ``Metric'' and standardize ``Features'' capitalization for consistency. The authors apologize for these errors and any inconvenience caused.
The proliferation of unmanned aerial vehicles (UAVs) has necessitated the development of sophisticated object detection algorithms capable of handling the unique challenges posed by aerial imagery. Traditional detection methods often struggle with small object sizes, dense distributions, and complex backgrounds characteristic of UAV-captured scenes. This research presents EYOLOv8-MSAFF (Enhanced YOLOv8 with Multi-Scale Attention and Feature Fusion), a novel deep learning architecture specifically engineered for superior performance in UAV-based object detection tasks. The proposed methodology integrates four innovative components: a Hybrid Spatial-Channel Attention Mechanism (HSCAM) that processes attention information in parallel rather than sequentially, an Adaptive Multi-Scale Feature Fusion Module (AMSFFM) that dynamically weights multi-scale features based on contextual relevance, a Dilated Convolutional Block Attention Module (DCBAM) that captures multi-scale contextual information through varying dilation rates, and a Progressive Feature Refinement Network (PFRN) that iteratively enhances feature representations through multiple refinement stages. Extensive experimental validation conducted on two challenging datasets demonstrates the exceptional performance of the proposed approach. On the VisDrone2019 dataset, EYOLOv8-MSAFF achieves 87.3% mAP@0.5 and 64.2% mAP@0.5:0.95, representing substantial improvements of 6.1% and 7.1% respectively over the baseline YOLOv8l architecture. The method exhibits particularly remarkable performance in small object detection, achieving 52.7% AP_S, which constitutes an 18.2% improvement over the baseline. Similarly, on the DOTA v2.0 dataset, the proposed method attains 85.7% mAP@0.5 and 58.9% mAP@0.5:0.95, surpassing existing state-of-the-art approaches by significant margins. Comprehensive ablation studies validate the effectiveness of each proposed component, while computational analysis demonstrates that the method maintains practical inference speeds of 33.4 FPS despite the incorporation of sophisticated attention mechanisms.
Editor's Note: This Resolution notice concludes the investigation initiated by the Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1385) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1275). The publication timeline is as follows: Original Article → Expression of Concern → Resolution. Resolution of Expression of Concern: The editorial team of the Iraqi Journal for Computer Science and Mathematics has completed a thorough post-publication review of the above-mentioned article, the supporting data, and the authors’ responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research.
\hlThis paper presents the use of StyleGAN2-ADA for fashion image synthesis and editing. We demonstrate that generative adversarial networks (GANs) can generate realistic and diverse fashion images even under moderate data constraints. The model trained on a dataset of 9,000 fashion images achieved a Fréchet Inception Distance (FID) of 10.8, a Garment Structure Accuracy (GSA) of 0.85, a Pattern Continuity Score (PCS) of 0.78, and an Attribute Transfer Accuracy (ATA) averaging 75% across key attributes. These quantitative results confirm the model’s ability to produce high-quality, structurally consistent, and semantically coherent fashion images. Unlike previous studies focusing on either extremely limited or very large datasets, our work explicitly addresses the research gap in practical, moderate-sized scenarios where computational resources are constrained but data availability is not severely scarce. The study also provides practical implementation guidelines for researchers and fashion designers working with limited resources. The source code developed and used in this study is publicly available on GitHub at the following link: GitHub Repository.
An intelligent and explainable, as well as user-friendly, crop recommendation and yield prediction system is developed to facilitate agricultural decision-making under various tillage methods with the help of Explainable AI. The system utilizes Machine Learning algorithms and calculates yield changes as a relative term with respect to various inputs such as location, month of sowing, years of NT adoption, and soil type using real-time environmental data; accordingly, it recommends crops. The prediction framework, which is based on various regression algorithms, including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting, was trained on the dataset of agronomic and climatic variables. Gradient Boosting gives better performance under data constraints, while Random Forest provides comparatively better performance for a larger dataset. For guaranteeing model interpretations and user faith, the system supports explainable AI methods like Shapley Additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for global and local interpretations of feature influence. The web-based interface facilitates smooth interaction and dynamic input handling based on weather and geolocation APIs. All inputs from the user along with the prediction results are stored in the backend database for monitoring and future optimization. The solution provides a systematic, interpretable, and functional approach to increase crop planning and sustainable land management by data-driven insight. The proposed approach helps farmers in achieving crop yields.
NOTICE OF CORRIGENDUM FOR: Mohammed, Mazin Abed; Abd Ghani, Mohd Khanapi; Lakhan, Abdullah; AL-Attar, Bourair; and Khaled, Waleed (2025) ``Federated Learning-Driven IoT and Edge Cloud Networks for Smart Wheelchair Applications,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 9. DOI: https://doi.org/10.52866/2788-7421.1241. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/9. Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items: 1. Table 5 (Task Status entry) — data cell error In Table 5, the entry for Device ID 3, Iteration 5 contains an incorrect/misaligned value in the Task Status field. The row appears as ``3 | 5 | 1 | 1.05 | 74.67 | No'', where the Task Status value is not correctly reported. This has been corrected in the updated version of the article. 2. Algorithm 1 — typo in conditional statement In Algorithm 1, the word ``Trure'' appears in the encryption verification condition and has been corrected to ``True''. 3. Geographic name spelling — consistency correction The term ``Saudia Arabia/Saudia Arbia'' appears in multiple locations and has been corrected to ``Saudi Arabia'' throughout the article for consistency and correctness (including the system description text and Table 2). The authors confirm that these corrections do not affect the overall conclusions of the article.
Editor's Note: This Resolution notice concludes the investigation initiated by the Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1382) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1249). The publication timeline is as follows: Original Article → Expression of Concern → Resolution. Resolution of Expression of Concern: The editorial team of the Iraqi Journal for Computer Science and Mathematics has completed a thorough post-publication review of the above-mentioned article, the supporting data, and the authors’ responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research.
NOTICE OF CORRIGENDUM FOR: Lafta, Mariem Hassan and Hassan, Zahir Abdul Haddi (2025) ``Finding General Mathematical Formulas for Extraction the Minimal Path Sets of Complex Parallel-Series Networks,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 11. DOI: https://doi.org/10.52866/2788-7421.1237. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/11. Reason for Corrigendum: In the published article, the definition of minimal path set is stated incorrectly. The text defines a minimal path set as ``a set of components whose failure leads to failure of the whole CPSN,'' which corresponds to a minimal cut set, not a minimal path set. Correction: A minimal path set is a minimal set of components whose proper functioning (success) guarantees system success. A set is ``minimal'' if removing any component from it means it no longer guarantees system success. This corrigendum corrects the definition only; no other results, tables, or figures are changed.
Editor's Note: This Resolution notice concludes the investigation initiated by the Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1378) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1230). The publication timeline is as follows: Original Article → Expression of Concern → Resolution. Resolution of Expression of Concern: The editorial team of the Iraqi Journal for Computer Science and Mathematics has completed a thorough post-publication review of the above-mentioned article, the supporting data, and the authors’ responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research.
This research proposes and validates a novel Hybrid DenseNet121-InceptionV3 architecture for robust multi-output demographic classification from facial images, with a focus on optimizing both time efficiency and parameter utilization. The model was trained on a curated subset of the UTKFace dataset comprising 4,000 images, balanced across four racial groups with equal gender distribution. Our hybrid approach processes images through parallel feature extractors, strategically combining DenseNet121's parameter-efficient feature reuse with InceptionV3's multi-scale analysis capabilities. This architectural synergy achieves superior performance while maintaining computational efficiency. Through rigorous 3-fold cross-validation, the hybrid model demonstrated significant advantages in accuracy and training time, achieving 98.35% race classification accuracy, 97.60% gender accuracy, and 96.22% combined accuracy—outperforming all standalone baseline models by 2–3%. The model's parameter-optimization strategy enabled faster convergence during training while reducing computational overhead, making it suitable for real-time applications. Notably, the architecture attained this enhanced performance without increasing parameter complexity, demonstrating that intelligent model fusion can simultaneously optimize both temporal efficiency and parameter utilization. These findings establish a new benchmark for developing computationally efficient deep-learning models that balance high accuracy with practical deployment requirements in multi-output classification tasks.
Editor's Note: This Resolution notice concludes the investigation initiated by the Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1388) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1317). The publication timeline is as follows: Original Article → Expression of Concern → Resolution. Resolution of Expression of Concern: The editorial team of the Iraqi Journal for Computer Science and Mathematics has completed a thorough post-publication review of the above-mentioned article, the supporting data, and the authors’ responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research.
Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification accuracy. The suggested study applies the encoding strategy which begins with the transformation of network traffic into genetic codes and then analyses the RNN-based technique. To be more exact, the RNN-based IDS is shown to be more efficient than the previous approaches due to the 98.2% rate of successful detection and 9.8% false alarm rate as well as 97.4 percent rate of correct attack detection. The mathematical modeling of the RNN-based system defines the Detection Rate (DR) and False Alarm Rate (FAR) and accuracy as key performance indicators, which proves the effectiveness of the mathematical model. The RNN identifies high-level genetic code patterns that allow the system to protect against the brute force attacks and Denial-of-Service (DoS) attacks and information-seeking attacks and botnet operations in real time. Learning by RNNs proposed system yields improved detection and allows reliable strategic adaptations that are beyond the traditional methods. Trained RNNs will be connected to CNNs to create hybrid structures and fine-tune data pre-processing modes to maximize the efficiency of the functions. The new method demonstrates that collaboration between various areas of expertise can result in successful means of resolving issues of cybersecurity.
EXPRESSION OF CONCERN FOR: M. A. Tashtoush, Y. Wardat, R. A. Ali, and S. Saleh, ``Artificial Intelligence in Education: Mathematics Teachers' Perspectives, Practices and Challenges,'' Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 1, 2024, Article 20. DOI: https://doi.org/10.52866/ijcsm.2024.05.01.004. Available at: https://ijcsm.researchcommons.org/ijcsm/vol5/iss1/20. Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in ``Artificial Intelligence in Education: Mathematics Teachers' Perspectives''. The journal has initiated an additional editorial assessment of the article's methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal's policies and COPE guidance. Editor's Note: The Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1379) regarding the Original Article (DOI: https://doi.org/10.52866/ijcsm.2024.05.01.004) has been resolved. The publication timeline is as follows: Original Article → Expression of Concern → Resolution.
The rapid advancement of sophisticated generative models has intensified the need for robust fake image detection systems. However, many existing benchmark datasets suffer from limited diversity in content types and generation techniques, constraining the generalization ability of detection models. To address these limitations, we introduce GenPix (Generalized Pixels), a comprehensive dataset encompassing over 80,000 images spanning diverse categories, including faces, objects, and scenes, generated by multiple state-of-the-art models such as Generative Adversarial Networks (GANs) and diffusion-based architectures. The dataset includes samples from different generation methods to ensure broad coverage of fake image characteristics. GenPix provides a realistic evaluation environment that better reflects real-world detection challenges. We establish baseline performance metrics using an Adversarial Autoencoder (AAE) and demonstrate the dataset's utility for developing and evaluating fake image detection systems. The AAE achieves 80.65% F1-score on the full GenPix test set and high inference throughput (488 images/sec).These results show that even relatively simple architectures can achieve promising performance on GenPix, while highlighting areas for improvement in detection methodologies.In contrast, deeper CNNs such as EfficientNet-B3 reach higher F1-score of 98.01% but suffer from low throughput (14 images/sec), suggesting a complementary trade-off between performance and practicality. Overall, GenPix provides a challenging and realistic benchmark for evaluating modern detectors, and the proposed AAE offers an efficient, interpretable baseline for future research on general-purpose fake-image detection.
Object detection in Unmanned Aerial Vehicle (UAV) images presents significant challenges due to the prevalence of small and densely packed objects, as well as variations in scale, orientation, and lighting conditions. This paper introduces a novel object detection algorithm, Hierarchical Multi-Scale Attention YOLO (HMSA-YOLO), which is an improved version of YOLOv8 designed to address these challenges. The proposed method incorporates a novel Hierarchical Multi-Scale Attention (HMSA) module, a Bidirectional Feature Pyramid Network (BiFPN) for enhanced feature fusion, a modified loss function, and an adaptive anchor optimization technique. The HMSA module effectively captures both channel and spatial dependencies at multiple scales, enabling the network to focus on relevant features for small and densely packed objects. The BiFPN enables more efficient and effective feature fusion across different pyramid levels, while the modified loss function addresses class imbalance and improves localization accuracy. The adaptive anchor optimization technique dynamically adjusts anchor boxes based on attention weights, further enhancing the detection of small objects. Comprehensive experiments on the challenging DOTA and VisDrone datasets demonstrate the superior performance of the proposed method. HMSA-YOLO achieves a remarkable mAP@0.5 of 96.2%, representing a significant 19.4% improvement over the baseline YOLOv8. The proposed method also shows substantial improvements in small object detection (91.2% mAP) and dense object detection (93.7% mAP), while maintaining competitive inference speed. The effectiveness of each component is validated through extensive ablation studies, and the qualitative analysis demonstrates the robustness of the proposed method in various UAV scenarios.
Hate speech on social media poses significant societal challenges, necessitating accurate and context-sensitive automated detection. Traditional machine learning (ML) models typically rely on lexical or superficial features, limiting their ability to capture nuanced or contextually ambiguous expressions of hate speech. Recent transformer-based methods (e.g., RoBERTa) provide improved contextual understanding but often lack explicit mechanisms guiding the model’s attention to critical semantic tokens, thereby reducing interpretability and sensitivity to nuanced linguistic contexts. This paper introduces a novel contextual attention-guided transformer model that explicitly incorporates lexicon-guided attention supervision into RoBERTa fine-tuning, significantly enhancing semantic precision in hate speech detection on Twitter. Evaluations on a publicly available Twitter hate speech dataset demonstrate that our proposed model achieves 98.2% accuracy, substantially outperforming classical ML baselines (e.g., Logistic Regression, SVM, Random Forest; best baseline ∼95%) with a notable increase in macro-F1 (from ∼0.88 to ∼0.95), particularly improving precision and recall for the minority hate speech class (F1 increasing from ∼0.74 to ∼0.85). We provide interpretability analyses using attention visualization and LIME explanations, offering transparency into model decisions, an essential feature for real-world deployment.
NOTICE OF RETRACTION FOR: Jassam, Israa Faisal; Mukhlif, Abdulrahman Abbas; Nafea, Ahmed Adil; Tharthar, Mustafa Adnan; and Khudhair, Ahmed Isam (2025) ``A Review of Breast Cancer Histological Image Classification: Challenges and Limitations,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 1. DOI: https://doi.org/10.52866/2788-7421.1232. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/1.
Quantum Learning (QL) has emerged as a promising approach to medical image classification, leveraging the principles of quantum mechanics to improve the performance and efficiency of machine learning algorithms. This systematic review provides a comprehensive critical assessment of the current status of QL techniques developed for medical image classification, with a specific focus on trends, methodologies, and future directions in this rapidly evolving field. A thorough literature search was conducted across five major databases, resulting in a total of 28 relevant studies published between 2018 and 2024. The studies were analyzed and classified based on the type of quantum algorithm, the medical image modality, and the performance metrics used. The analysis revealed a diverse range of QL techniques, including Quantum Support Vector Machines (QSVM), Quantum Convolutional Neural Networks (QCNN), and various hybrid quantum-classical approaches. These techniques have been applied to diverse medical image classification tasks, such as brain tumor classification, skin lesion classification, and COVID-19 detection, demonstrating promising results in terms of accuracy, sensitivity, and specificity. However, several challenges were identified, including the preprocessing and encoding of medical images for quantum processing, the limited scalability of current quantum hardware, and the need for interpretable and explainable QL models. This review underscores the immense potential of QL to revolutionize medical image classification, while also emphasizing the necessity of multidisciplinary collaborations and further research to overcome existing challenges and facilitate the integration of QL techniques into clinical practice. Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in ``Quantum Machine and Deep Learning for Medical Image Classification: A Systematic Review of Trends, Methodologies, and Future Directions (Manuscript 1252)''. The journal has initiated an additional editorial assessment of the article's methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal's policies and COPE guidance." See expression of concern available at: DOI: https://doi.org/10.52866/2788-7421.1387. Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss1/41 We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. See resolution of expression of concern available at: DOI: https://doi.org/10.52866/2788-7421.1408 Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss2/8 The publication timeline is as follows: Original Article → Expression of Concern → Resolution.
