Metaheuristics are widely used to address optimization problems, but their efficacy varies considerably across different problem instances. This performance variability stems mainly from poor balance between exploration and exploitation. To address this limitation, this paper introduces the Harp seal (HaS) optimizer, inspired by the natural behaviors of HaSs. HaS consists of three procedures: novel selection-combination, migration, and pupping. The proposed work aims to efficiently solve a wider range of complex optimization problems. To validate the effectiveness of HaS, it was tested on 19 classical benchmarks, 10 CEC-2019 benchmarks, and a real-world application. The achieved results compared to well-known algorithms, including the genetic algorithm, multi-verse optimizer, and learner performance-based behavior. HaS outperformed or equaled other algorithms in 14 out of 19 classical benchmarks and 6 out of 10 CEC-2019 functions. It has shown robust capability across unimodal, multimodal, and composite benchmarks. The results demonstrate the proposed algorithm’s capacity for both exploration and exploitation. Moreover, the good trade-off between exploration and exploitation enhances the ability of the algorithm in optimizing large-scale optimization problems. Furthermore, statistical analysis verified the significance of the observed improvements. Overall, HaS offers robust and superior results that have outperformed existing state-of-the-art algorithms.

This study investigates applying the GM(1,1) model to forecast the infant mortality rate (IMR) in Iraq from 2025 to 2034, utilizing historical data spanning 2015–2024. The findings indicate a consistent decline in IMRs during the analyzed period, reflecting effective public health interventions. The model’s parameters were estimated using the Ordinary Least Squares method, revealing an intercept of 0.0278 and a slope of 26.7693. The forecasting accuracy of the GM(1,1) model was exceptional, demonstrated by a Mean Absolute Percentage Error of only 0.2869% and a precision rate of 99.7131%, categorizing the forecasts as highly accurate. Projected IMRs show a continued decline, decreasing from 20.55 deaths per 1000 live births in 2025 to approximately 16.00 by 2034. These results underscore the utility of the GM(1,1) model in providing reliable forecasts to inform health policy and intervention strategies aimed at improving maternal and child health in Iraq.

Radiologists still face difficulties and mistakes while screening lung computed tomography (CT) images for pulmonary nodules, particularly small and inconspicuous malignant lesions. Frequent radiation exposure, the complexity of radiomic characteristics in low-dose CT scans, and the high cost of imaging therapy are some of the challenges. Our technique proposed a novel automated computer-aided diagnostic technique to address these issues by increasing the accuracy of early lung nodule diagnosis. We suggested a four-step technique that involves (1) a preprocessing step consisting of contrast-limited adaptive histogram equalization to refine the contrast of contribution inputs, followed by extracting and combining texture and shape features in parallel using a gray level co-occurrence matrix for the first features and region of interest (ROI) properties for the second. In addition, we suggested a hybrid U-Net-based deep learning architecture for categorization that successfully blends automatically learned features with manually created features. This integration improves the precision and resilience of pulmonary nodule classification by utilizing the convolutional neural networks (CNN)’s capacity to capture spatial hierarchies. We implemented our proposed technique on the overtly accessible Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, employing the Python programming language for the implementation. Experimental results confirmed that our technique attained segmentation and classification results of 95.35% accuracy, 95.33% sensitivity, 94.23% specificity, and 95.44% AUC rates, outperforming several state-of-the-art methods. This high-performance approach offers a reliable solution for early detection, potentially reducing lung cancer mortality rates through timely diagnosis.

The frequent breaches of healthcare data annually make robust encryption mechanisms crucial, especially those that preserve the usefulness of the data while ensuring privacy. This study addresses specific integer-based homomorphic encryption systems and their critical vulnerabilities. The vulnerability identified in these systems is the possibility of decryption using other values, such as factors or primes, instead of the claimed unique secret key. We propose an enhanced cryptographic formula to address this vulnerability using a double random value technique that ensures decryption depends solely on the designated secret key. We also apply a windowing technique for prime selection to enhance the key properties against pattern detection attacks. Security analysis shows that the enhanced system prevents decryption using values other than the dedicated key while maintaining additive and multiplicative homomorphism. Performance evaluations show that the improved system maintains decryption times and ciphertext expansion ratios similar to the original system, with a reasonable decryption time reduction. Statistical testing results using the National Institute of Standards and Technology tests demonstrate the robustness of the proposed approach compared to the original, with the windowing technique exhibiting superior randomness properties.

Vision impairment is becoming a major health concern, especially in elderly people. While in the medical field, manually detecting ocular pathology has significant difficulty. Therefore, deep learning diagnostic techniques are widely used for identifying eye diseases and play a crucial role in diagnosing vision-related problems. Examination of fundoscopy allows for analyzing eyes for diagnosis of eye diseases, including Diabetic retinopathy (DR), Cataracts, Glaucoma, Age‑related macular degeneration, Pathologic Myopia, and more. In this paper, we propose a concise review of introducing most of the DL, hybrid, and ensemble models utilized for the purpose of identification and classification of eye diseases. Various datasets, feature extraction techniques, and metrics for performance evaluation are discussed. The chosen papers come from conferences and scholarly publications published from 2019 to 2024. We evaluate the performance of chosen researches using different datasets, the most common ones include ocular disease intelligent recognition, Indian DR image dataset, EyePACS, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology-version 2, DIARETDB, Structured analysis of the retina, high-resolution fundus, digital retinal images for vessel extraction, online retinal fundus image dataset for Gl analysis and research, retinal fundus multi-disease image dataset and Kaggle datasets. The detection studies that have been reviewed show that the accuracy of these approaches varies between 73% and 99%, the sensitivity ranges from 69% to 99% and precision is between 89% and 99%. The results show that great accuracy is consistently achieved with DL algorithms compared to traditional Machine learning approaches. However, there are still some challenges and limitations remaining including excessive resource consumption and over-fitting due to dataset size and diversity issues. This review offers useful insight for researchers and healthcare professionals to comprehend AI technologies properly for the detection, classification, and diagnosis of retinal diseases. We succinctly summarize the methodologies of all the chosen studies and focus on the elements that define the aim of the studies.

Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.

Background: The field of nursing remains dominated by women in many countries. Males joining the nursing and midwifery fields are becoming more numerous, and with that comes more problems during training. It is crucial to comprehend the experiences and difficulties faced by male nursing students during their nursing education, as these obstacles differ depending on the culture. Aims: The objectives of the study are to explore the perceptions of male nursing students at Sulaimani Polytechnic University and University of Sulaimani Colleges of Nursing about their clinical maternity hospital. Materials and Methods: A quantitative-descriptive design has been carried out in Sulaimani city. A non-probability, convenience sample size of (100) students was included in this study over a period of 1 month. Through an extensive review of relevant literature, a questionnaire was designed for data collection. Results: The results confirmed that male nursing students generally feel more comfortable performing procedures that do not involve exposure to private parts compared to those that do involve such exposure. The association between age groups, marital status, Residency College, and comfort level is not significant (P > 0.05). While, the P = 0.045, indicating that the association between class and comfort level is significant (P < 0.05). Conclusion: The study indicated that while the participants regarded the maternity clinical practice to be difficult, they did not show any interest in doing the maternity course. It was believed that male nursing students’ experiences with maternity clinical practice would be enhanced by acceptance and more specialized training in the clinical setting.

The study aimed to identify the training needs of the farmers in the field of fig fruit breeding and to determine the relationship between training needs and some variables. The study area included Rania District in Sulaymaniyah Governorate. The research population involved 5205 farmers. The research sample was 104 respondents who were taken using a simple random sampling method, representing 2% of the study population. The research included 20 villages that were included in the research. Data were collected through the questionnaire and personal interviews. The results showed that more than 74% of fig farmers were between (medium and high levels) in need of training in their field of work, and the average training need reached 53.25°. The results showed that there is a statistically significant relationship between the training need and the economic level and the number of years working in the field of fig breeding. The study also showed that there is no significant relationship between the training need and (the area used for raising figs). The study recommended that the responsible authorities increase extension services and activities (such as seminars, training courses, and extension magazines) to increase farmers’ skills and thus increase production.

As an inherent human characteristic, ethnicity plays a fundamental and critical role in biometric identification. On the other hand, the human face is the core of man’s identity, and facts such as age and race are often extrapolated automatically from the face. The objective is to utilize computer technologies to identify and categorize ethnic groups based on facial features. Convolutional neural networks (CNN), which can automatically identify underlying patterns from data, excel at learning image features and have shown state-of-the-art performance in several visual recognition challenges, such as ethnicity detection. Although the automated classification of traits such as age, gender, and ethnicity is a well-researched topic, Iraqi ethnic groupings have not yet been addressed. This study seeks to tackle the challenge of predicting the ethnicity of Iraqi male individuals based on their facial traits for the two largest ethnic groups, the Arabs, and the Kurds. Male Iraqi Kurds and Arabs were each represented by 260 image samples. The dataset underwent a diverse array of preprocessing and data enhancement techniques, including image resizing, isolation, gamma correction, and contrast stretching. Moreover, to augment the dataset and expand its diversity, various techniques such as brightness adjustment, rotation, horizontal flip, and grayscale augmentations were systematically applied, effectively increasing the overall number of images, and enriching the dataset for improved model performance. Face images of Kurds and Arabs were classified using the Faster region-based CNN (RCNN) approach of deep learning. Due to insufficient data in the dataset, we propose employing transfer learning to extract features using several pre-trained models. Specifically, we examined EfficientNetB4, ResNet-50, SqueezeNet, VGG16, and MobileNetV2, resulting in accuracies of 96.73%, 94.91%, 93.39%, 92.48%, and 90.32%, accompanied by corresponding precision values of 0.86, 0.81, 0.80, 0.70, and 0.69, respectively. It is essential to emphasize that the following inference speeds – VGG16 (4.5 ms), ResNet-50 (4.6 ms), SqueezeNet (3.8 ms), MobileNetV2 (3.7 ms), and EfficientNet-B4 (16 ms) – represent the computing times needed for each backbone. Moreover, to achieve a harmonious trade-off between precision and the time required for inference, we chose ResNet-50 as the foundational framework for our model aimed at classifying ethnicity. The study also acknowledges limitations such as the availability and diversity of the dataset. Nevertheless, despite these limitations, it provides valuable perspectives on the automated prediction of Iraqi male ethnicity through facial features, presenting potential applications in various domains. The findings contribute to the broader conversation surrounding biometric identification and ethnic categorization, underscoring the importance of ongoing research and heightened awareness of the inherent limitations associated with such studies.

Vitamin D deficiency is a widespread global health issue, notably prevalent in the Middle East and more severe in women. Vitamin D deficiency increases the incidence of uterine fibroids in black and white women, the most frequent benign gynecologic malignancies. This study examined health-care providers’ understanding of the relationship between vitamin D insufficiency and uterine fibroid at Sulaimani Hospitals in Kurdistan, Iraq. A quantitative design, cross-sectional-descriptive study (non-probability purposive sample) of 113 female nurses and gynecologists. Data were collected using a checklist through Google Forms. The results showed that the majority of the participants were nurses (88.5%), and the remaining (11.5%) were gynecologist. Two-thirds of them work in maternity teaching hospital while others work in smart hospitals and Faroq medical cities. The results revealed that the level of knowledge was significantly associated with the position of participants and level of education (P < 0.05). As well, the study demonstrated that the majority of the participants were not trained regarding uterine fibroid and vitamin D deficiency. The study concluded that nearly a quarter of the health-care professionals had a medium level of knowledge, while nearly a quarter (24.8%) of them had a high level of knowledge. The study recommended to the Ministry of Health giving opportunities to health-care professionals, especially nurses, to participate in training courses, workshops, and conferences regarding the relationship between vitamin D deficiency and uterine fibroid.