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IntelliHire introduces an advanced automated system to revolutionize candidate evaluation, addressing the limitations of traditional techniques such as biased interviews and manual resume screening. By leveraging cutting-edge technology, IntelliHire provides an impartial assessment of candidates' knowledge, positive mindset, resume content, facial expressions, ethical benchmarks, and language proficiency. This comprehensive method employs diverse components, including contextual resume parsing, facial expressions and personality evaluation, and robust assessments using deep learning models and Machine Learning algorithms. The benefits include reduced bias, enhanced efficiency, cost savings, and refined candidate selection. This research contributes to the evolution of human resources and recruitment strategies, with potential for further development and enhancement of IntelliHire's capabilities. [ABSTRACT FROM AUTHOR]

The globally accepted evaluation method for facial palsy is the House–Brackmann facial grading system; however, it does not reflect minute changes. Several methods have been attempted, but there is no universally accepted evaluation method that is non-time-consuming and quantitative. Recently, Emotrics, a two-dimensional analysis that incorporates machine-learning techniques, has been used in various clinical fields. However, its reliability and validity have not yet been determined. Therefore, this study aimed to examine and establish the reliability and validity of Emotrics. All patients had previously received speech therapy for facial palsy at our hospital between January and November 2022. In speech therapy at our hospital, Emotrics was routinely used to measure the state of the patient's facial palsy. A frame was created to standardize and overcome the limitation of the two-dimensional analysis. Interrater, intrarater, and intrasubject reliability were evaluated with intraclass correlation coefficients (ICC) by measuring the indicators that reflect eye and mouth functions. Validity was evaluated using Spearman's correlation for each Emotrics parameter and the House–Brackmann facial grading system. A total of 23 patients were included in this study. For all parameters, there was significant interrater and intrarater reliability (ICC, 0.61 to 0.99). Intrasubject reliability showed significant reliability in most parameters (ICC, 0.68 to 0.88). Validity showed a significant correlation in two parameters (p-value < 0.001). This single-center study suggests that Emotrics could be a quantitative and efficient facial-palsy evaluation method with good reliability. Therefore, Emotrics is expected to play a key role in assessing facial palsy and in monitoring treatment effects more accurately and precisely. [ABSTRACT FROM AUTHOR]

• We conducted probability of detection and false positive analyses to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. • We compared three image-processing-based segmentation and three sequential-signal-based deep learning algorithms for automatic data analysis. • We demonstrated the potential of using advanced deep-learning techniques to solve complex non-destructive examination data interpretation tasks. Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples. [ABSTRACT FROM AUTHOR]

Automation of descriptive answers evaluation is the need of the hour because of the huge increase in the number of students enrolling each year in educational institutions and the limited staff available to spare their time for evaluations. In this paper, we use a machine learning workbench called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. We attempted to identify the best supervised machine learning algorithm given a limited training set sample size scenario. We evaluated performances of Bayes, SVM, Logistic Regression, Random forests, Decision stump and Decision trees algorithms. We confirmed SVM as best performing algorithm based on quantitative measurements across accuracy, kappa, training speed and prediction accuracy with supplied test set. [ABSTRACT FROM AUTHOR]

• A novel multi-class factorization machine approach with deep neural network is developed. • It performs post-disaster building damage assessment in an automatic and data-driven manner. • A total of 39,352 buildings in 2015 Napa earthquake are taken as a case study for demonstration. • It improves the classification performance over many other popular machine learning methods. • The well-trained model can significantly reduce the burden in earthquake field investigations. This paper develops a hybrid neural network architecture named multi-class factorization machine with deep neural network (multi-FMDNN) to fuse multi-source information for the automatic post-earthquake building damage evaluation. The novel algorithm is a combination of the factorization machine (FM) and the deep neural network (DNN), which adopts the one-vs-all strategy to fuse results from multiple base classifiers. 39,352 buildings affected by the 2015 Nepal earthquake are taken as a case study to validate the effectiveness of the proposed multi-FMDNN. Experimental results confirm that the proposed model outperforms over many other popular machine learning methods due to the powerful feature learning ability, ultimately reaching an overall accuracy, macro F1-score, and weighted F1-score in the value of 0.703, 0.737, and 0.702, respectively. Features associated with building structural characteristics are found to contribute more to classifying damage grades precisely. Besides, data preprocessing for data cleaning, encoding, and transformation is a necessary step to bring additional performance enhancement. For significance in the knowledge aspect, a novel multi-FMDNN algorithm is developed, which is superior in extracting both low- and high-order feature representation automatically from large volumes of destroyed buildings-related data and learning the optimal feature interactions simultaneously to pursue more accurate classification. For significance in the application aspect, the predicted results provide deep insights into a better understanding of the building vulnerability in seismic areas and inform data-driven decisions in disaster relief efforts. A promising future scope is to make full use of the available pre-event data along with some post-event data, which is possible to return fairly promising predictions and reduce the burden in earthquake field investigations for rapid responses. In future work, advanced techniques associated with data augment, hyperparameter optimization, and others will be implemented to constantly improve the overall accuracy and generalizability of the prediction model. [ABSTRACT FROM AUTHOR]