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Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in diverse ways. It addresses the need for early ASD detection, offering a more precise and reliable approach compared to traditional evaluations. Challenges in this research include the subjective nature of traditional ASD screening, the complexity of facial analysis, and the need for high precision and recall in distinguishing between autistic and normal faces. To address these challenges, the research employs deep learning (DL) techniques, specifically transfer learning and facial recognition. It aims to automatically extract features from facial images that may not be noticeable through visual inspection alone. The proposed methodology involves the use of a hybrid attention learning model, combining the strengths of fine-tune ResNet101 and EfficientNetB3 architectures to enhance classification accuracy. The research's proposed methodology involves meticulous dataset preprocessing, including augmentation and resizing to standardize image dimensions. It introduces a hybrid attention learning model that leverages transfer learning and deep learning techniques, which are fine-tuned using ResNet101 and EfficientNetB3 architectures. The experimental process begins with comprehensive dataset preprocessing, encompassing augmentation, and image resizing to ensure uniform dimensions. EfficientNetB3 and ResNet101 models, when combined, create a potent synergy that balances computational efficiency with robust feature extraction, enhancing their versatility and effectiveness across diverse machine learning tasks. A thorough performance analysis is conducted to evaluate the accuracy of ASD prediction, aiming for an accuracy rate of 96.50%. Notably, the research introduces self-attention techniques from natural language processing and sequence-to-sequence models to advance ASD prediction, marking a significant innovation in this field. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) is a revolutionary and cutting-edge technology widely used in the medical domain and health informatics in the diagnosis and prognosis of cardiovascular diseases especially. Therefore, we propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI, discharge reasons for the patients admitted in the hospitals, and death types for the affected patients during the hospital stay. We used the Korea Acute Myocardial Infarction Registry (KAMIR-NIH) dataset, which has 13,104 patients' data containing 551 features. After data extraction and preprocessing, we used the 125 useful features and applied the SMOTETomek hybrid sampling technique to oversample the data imbalance of minority classes. Our proposed SVEC applied three ML algorithms, such as random forest, extra tree, and the gradient-boosting machine for predictive modeling of our target variables, and compared with the performances of all base classifiers. The experiments showed that the SVEC outperformed other ML-based predictive models in accuracy (99.0733%), precision (99.0742%), recall (99.0734%), F1-score (99.9719%), and the area under the ROC curve (AUC) (99.9702%). Overall, the performance of the SVEC was better than other applied models, but the AUC was slightly lower than the extra tree classifier for the predictive modeling of ACS outcomes. The proposed predictive model outperformed other ML-based models; hence it can be used practically in hospitals for the diagnosis and prediction of heart problems so that timely detection of proper treatments can be chosen, and the occurrence of disease predicted more accurately. [ABSTRACT FROM AUTHOR]

This research looks at how orientalists see the character of Muhammad's (peace be upon him). The objective of this research is to explain and critique both fair and unfavorable remarks made by orientalists. This study disproves orientalists' accusations in order to damage the credibility of enlightened Islam by twisting the image of Prophet Muhammad (peace be upon him). Despite their disparities in tactics, the study indicates that Islam's opponents have one goal: to ruin the reputation of Islam and its Prophet (peace be upon him). This skewed picture of Master Muhammad (peace be upon him) arose as a result of orientalists' animosity, which was based on weak bogus scriptures written by Muslims. As a result, more research and suggestions are offered. [ABSTRACT FROM AUTHOR]

Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as reducing the product cost. Therefore, this paper proposes an accurate model for the automatic recognition of wafer map failure types using a deep learning-based convolutional neural network (DCNN). For this experiment, we use WM811K, which is an open-source real-time wafer map dataset containing wafer map images of nine failure classes. Our research contents can be briefly summarized as follows. First, we use random sampling to extract 500 images from each class of the original image dataset. Then we propose a deep convolutional neural network model to generate a multi-class classification model. Lastly, we evaluate the performance of the proposed prediction model and compare it with three other popular machine learning-based models—logistic regression, random forest, and gradient boosted decision trees—and several well-known deep learning models—VGGNet, ResNet, and EfficientNet. Consequently, the comprehensive analysis showed that the performance of the proposed DCNN model outperformed those of other popular machine learning and deep learning-based prediction models. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, enterprises were obliged to employ social media and digital tools to complete ordinary work. The pandemic has created a series of complexities and challenges, which have hampered harmonic contact between leaders and followers. The indirect relationship between unethical leadership and extra-role behavior (EXB) via psychological empowerment (PYE) is investigated in this study. We also look into the role of perceived organizational support (POS) as a moderator in the link between unethical leadership and PYE, as well as the indirect link between unethical leadership and EXB. Data were obtained from 258 supervisor–employee dyads from various small- and mid-sized information technology (IT) enterprises using time lag data. Unethical leadership has an impact on employee psychological empowerment as well as EXB. The findings of this study indicated that POS also mitigated the negative consequences of unethical leadership on employee psychological empowerment. Similarly, the role of psychological empowerment as a mediator in the link between unethical leadership and employee EXB is influenced by POS. This study will also benefit researchers and practitioners interested in human resource practices in the IT industry. [ABSTRACT FROM AUTHOR]

Objective: Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms. Methods: We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score. Results: The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models. Conclusions: The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning–based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005–30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning–based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning–based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F -score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients. [ABSTRACT FROM AUTHOR]

In this work, Sr-substituted samples of single-phase spinel monoferrites with chemical formula Ba 1 − x Sr x Fe 2 O 4 (x = 0.00, 0.33, 0.67, 1.00) were synthesized using sol–gel auto-combustion method. In order to confirm the single-phase formation of these samples, a sample (x = 0.00) was chosen for heat treatment at different temperatures (100, 300, 400, 600 and 7 0 0 ∘ C) for 4 h. The heat treated sample was then investigated by X-ray diffraction (XRD) analysis and results showed that a single-phase sample can be successfully synthesized at a temperature of 6 0 0 ∘ C , which is much lower than that reported in earlier literature for synthesis of same structured samples. All the synthesized samples were then sintered at 7 0 0 ∘ C for 4 h to achieve better crystallinity. From XRD patterns, lattice parameters, cell volume and XRD density as a function of Sr-substitution were calculated. Scanning electron microscopy (SEM) results showed that the grain size increased as the temperature was increased. Fourier transform infrared spectroscopy (FTIR) results confirmed the single-phase spinel monoferrites at 7 0 0 ∘ C. From M–H loops (x = 0.0, 0.33, 0.67 and 1.00), different magnetic parameters such as saturation magnetization (M s) , remanance (M r) , coercivity (H c) and magnetic moment (n B) were calculated. Magnetocrystalline anisotropy constant and Y–K angles of Sr-doped Ba monoferrites were also calculated. In addition, the variation of different dielectric parameters (real permittivity, imaginary permittivity, real permeability, imaginary permeability, ac conductivity and loss tangent) as a function of frequency (1–6 GHz) has been discussed in this work. The results suggest that the synthesized materials have many advantages over previously reported single-phase spinel monoferrites. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the knowledge and practice of risk factors, symptoms and screening for breast cancer amongst health care professionals. Material and Methods: It was a descriptive cross sectional study conducted in a Tertiary care Hospital of larkana Sindh-Pakistan from June 2016 to feb 2017.A total 500 questionnaires were distributed among the health care professionals of tertiary care Hospital of larkana, the sample was taken using the sample random technique. From a total of 500 about 485 returned the filled questionnaire leaving the response rate 97%. Results: From the total sample, 54.(63%) were females and 45.(36%) were the males. Among the health care professionals 92% consultants were aware that obesity is one of the risk factor for breast cancer and the 75% nurses were knowing the same. Moreover 80% Pharmacist are knowing that late menopause is a risk factor for breast cancer and 62.7% dispenser were knowing this. Conclusion: Health care professionals have fairly good awareness regarding the risk factors, symptoms and role of mammography. [ABSTRACT FROM AUTHOR]

Dermatofibrosarcoma protuberans is a rare cutaneous malignancy. Usual sites of origin are trunk and extremities. Aetiology of the condition is not well understood but a genetic basis is explained as a reciprocal translocation t (17l;22) (q22; q13). Distant metastasis is quite rare as it is mostly a locally advancing tumour. Histopathology is the most accurate investigation to make a diagnosis. Imaging is still required to rule out distant metastasis. Surgical excision is the treatment option for stage I tumours. Imatinib, a tyrosine kinase inhibitor, has shown promising results in management of locally advanced and metastatic disease. [ABSTRACT FROM AUTHOR]