Intelligent Analysis Research Articles

Choosing the best artificial intelligence tools for human resource management

The article presents a comprehensive review and comparative analysis of modern artificial intelligence (AI)-based platforms used in human resource (HR) management. Tools such as HireVue, Pymetrics, Eightfold.ai, Workday, and Textio are reviewed. Their functionality, usability, integration with existing systems, data security, cost, and user support are discussed. The article provides practical recommendations for selecting optimal AI solutions, taking into account the specific needs and priorities of different organizations. It also explores the prospective directions of AI development in HR and the ethical aspects of their implementation.

Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis

Background: Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability. Methods: To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provides a holistic view by computing statistical measures related to facial behavior characteristics. In contrast, the fine-grained component uncovers subtle temporal fluctuations by employing a long short-term memory (LSTM) model to capture the temporal dynamics of head pose, facial expression intensity, and expression types. To fully harness the strengths of both analyses, we implemented a feature-level attention mechanism. This not only enhances the model’s interpretability but also provides valuable insights by highlighting the most influential features through attention weights. Results: Upon evaluation using three-fold cross-validation on a self-constructed autism dataset, our integrated approach achieved an average recognition accuracy of 88.74%, surpassing the standalone coarse-grained analysis by 8.49%. Conclusions: This experimental result underscores the improved generalizability of facial behavior features and effectively mitigates the complexities stemming from the pronounced intragroup variability of those with autism, thereby contributing to more accurate and interpretable autism identification.

Evaluation of root canal filling length on periapical radiograph using artificial intelligence.

This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques. 1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long. Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings. Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.

Association of High-Risk Obstructive Sleep Apnea with Artificial Intelligence-Guided, CT-Based Severity Scores in Patients with COVID-19 Pneumonia

Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no or mild TOR), ≥5 and <15 (moderate TOR), and ≥15 (severe TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p < 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27–7.44; p = 0.01). A moderate TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI 1.00–3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI 1.56–5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early intervention and management strategies. The clinical significance of TOR thresholds needs further evaluation in larger samples.

A novel approach to ADHD classification based on severity and emotional impairment: Findings from artificial intelligence analysis

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder characterized by symptoms of inattention and executive dysfunction, although there is not always agreement on the onset, course and long-term stability of the diagnosis. This study aims to detect differences in the cognitive profile according to the subtype of ADHD following a professional diagnosis and to propose an alternative classification. The scores obtained for each cognitive construct were compared using the Student’s t-test. In order to explore different diagnostic categories based on groupings made by Artificial Intelligence (AI) subjects were grouped based on their performance through the K-means clustering technique. The results obtained by Artificial Intelligence (AI) identified groups based on the severity of the cognitive profile and the presence of emotional impairment. Difficulties in perceived planning within family and school environments were highlighted as major risk factors in the severity of ADHD in children. Emotional disturbances perceived by both parents, such as depressive symptoms, anxiety, and somatization, were observed subsequently. In accordance with the results, an alternative way to classify ADHD is possible, involving categorization according to the presence or absence of emotional impairment, along with the severity of impairment in attentional and executive functions.

Application of large language models to intelligently analyze long construction contract texts

The emergence of large language models (LLMs) has provided an opportunity to assist humans in quickly reading, searching, and understanding the contents of construction contracts. However, the limited context length of LLMs restricts their ability to process lengthy contract documents, which hinders their application in the construction industry. This study proposes intelligent analysis methods for long construction contracts, which enables LLMs to handle contracts that exceed their context length through a two-stage text segmentation. We further utilized the segmented text chunks for content compression and intelligent question-answering applications. The FIDIC contract was used for testing. A condensed version of lengthy contracts summarizes the provisions in a shorter format. It maintains a high level of correctness and readability, offering practitioners additional options to read contracts of varying lengths based on their needs. The satisfaction rate of the question-answering outcomes reached 93.3%, allowing practitioners to quickly obtain specific clauses of interest and relevant contract knowledge through personalized queries. The performance of the 8K model using our methods is comparable to that of the 128K long-text models, while reducing computational power. This study expands the potential for applications of LLMs in contract management within the construction industry.

Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.

Evaluating the Performance of Taiwan Airport Renovation Projects: An Application of Multiple Attributes Intelligent Decision Analysis

Performance evaluation is a vital tool for measuring whether construction projects meet their established objectives, particularly in complex tasks. It helps identify key areas for improvement and enhances resource allocation efficiency. Through precise performance evaluation, managers can make optimal decisions regarding resource use, ultimately increasing project productivity and overall performance. The objective of this study is to measure the production efficiency of airport renovation projects in Taiwan using data envelopment analysis (DEA) and to apply artificial neural networks (ANN) for predicting project quality. DEA effectively handles scenarios with multiple inputs and outputs, providing relative efficiency comparisons among projects and quantifying the potential for improvement. ANN, on the other hand, can learn nonlinear patterns from the data, allowing for accurate predictions of project quality. As construction projects become more complex, ensuring efficient operation within limited resources becomes increasingly crucial. Traditional performance evaluation methods are inadequate for addressing scenarios involving multiple inputs and outputs; therefore, using DEA and ANN offers a more accurate framework for analysis and prediction. The results of this study indicate that, through the DEA model, five projects achieved an efficiency score of 1, while twelve inefficient projects need to reduce defect frequency by 54.76% and increase the progress and budget implementation efficiency by an average of 10.33% to improve performance. The ANN model achieved a classification accuracy of 94.1% and a mean squared error (MSE) of 0.11 in regression predictions. By adopting a data-driven approach, ANN facilitates intelligent decision making and forecasting throughout the construction process. This study provides construction managers with concrete guidelines for resource allocation and quality prediction, thus enhancing project management effectiveness.

Revolutionizing Pharmacy Benefit Management: Cost-Reduction through Artificial Intelligence and Data Analytics in Healthcare

Pharmacy Benefit Managers (PBMs) are third party companies that function as intermediaries between insurance providers and pharmaceutical manufacturers. PBMs create formularies, negotiate rebates (discounts paid by a drug manufacturer to a PBM) with manufacturers, process claims, create pharmacy networks, review drug utilization, and occasionally manage mail-order specialty pharmacies. [1] Pharmacy benefit management (PBM) is important for keeping prescription drug costs under control. But the problem is that drug prices keep going up, which makes things tough for the healthcare industry. In this article, we look at how fancy technology like artificial intelligence (AI) and data analysis can help PBM find new ways to save money. By using advanced technology to study a large amount of information, PBMs can find patterns and make educated guesses about how to best use medications. Artificial intelligence makes this even more advanced by helping to quickly find and stop fraud, personalize medication plans, and predict costs more accurately. Remember this: AI and data analytics are being used to make pharmacy benefit programs work better. This helps save money and makes things better for patients. We look at how machine learning models can predict patient adherence, how natural language processing (NLP) can improve medication reviews, and how blockchain technology can keep supply chains transparent and secure. Real-world examples show how these technologies are already making a difference, such as cutting down on administrative work, reducing drug waste, and getting the most out of drug utilization. As more healthcare systems start using these advanced tools, it’s becoming clear that continual innovation and the inclusion of AI and data-driven methods are essential. This article highlights the importance of staying updated with the ever-changing ways of controlling prescription drug expenses by using new technology. For pharmacy benefit managers (PBMs) focused on offering the best value and working effectively despite increasing healthcare costs, adopting these advancements is not just a good choice, it's necessary.

Thematic control and criteria-based assessment of foreign language writing skills using artificial intelligence technologies

Importance. Currently, there is a tendency that when teaching a foreign language, teachers are increasingly resorting to the use of artificial intelligence technologies to plan training sessions, generate educational content, as well as to conduct automated testing of formed communication skills. The monitoring of academic achievements and assessment is one of the key components of the organization of the educational process. Traditional methods of control and assessment require significant time and labor-intensive costs from the teacher, while artificial intelligence technologies make it possible to simplify and automate routine tasks: check tests and written papers, analyze them and identify mistakes, provide feedback. Thanks to the integration of natural language processing (NLP) technologies into chatbots and adaptive intelligent learning systems, it becomes possible on a daily basis to check texts created by students, evaluate them from the point of view of grammatical and lexical correctness, as well as identify stylistic and factual errors in real time. The purpose of this work is to test the applicability of artificial intelligence technologies for thematic control and criteria-based assessment of educational achievements using the example of productive and reproductive writing.Research Methods. In carrying out this study, the following groups of methods were used: theoretical methods aimed at familiarizing with scientific and methodological literature on the topic of research, analysis and classification of theoretical and methodological material for conducting thematic control and criterion assessment in foreign language classes, as well as empirical methods that allowed modeling pedagogical control and assessment processes using artificial intelligence technologies, observation, analysis and description of the results obtained.Definition of Concepts. The main concepts used in the study are “control of educational results” and its variety “thematic control”, “criterion assessment”.Results and Discussion. In the course of the study, various types of monitoring educational results using artificial intelligence technologies were considered: preliminary, current, intermediate, thematic, final. The choice of thematic control is due to the opportunity to trace its applicability within the framework of a single lesson in a foreign language and identify the main difficulties in using this form of control. The following criteria for evaluating writing skills are used to evaluate learning outcomes using artificial intelligence technologies: a) the structure of the written text; b) compliance with the main topic; c) coherence; d) relevance; e) grammatical correctness; f) lexical correctness; g) ethics of writing and stylistic correctness.Conclusion. Artificial intelligence technologies at the present stage have a high degree of adaptability and include a wide range of software and hardware solutions that allow for such important pedagogical procedures as monitoring educational achievements and evaluation in accordance with user-defined evaluation criteria. The obtained results are proposed to be used in research devoted to the study of modern methods of monitoring educational achievements in the methodology of teaching foreign languages using artificial intelligence technologies.

Building the Literary Award System and Marketing of Works in the Era of Intelligent Communication-An Analysis of the Reasons Behind the Bestseller Status of Golden Slumbers

By examining the online systems established by publishers in the era of intelligent communication, and analyzing the award-winning status of Golden Slumbers and its author, as well as the development of relevant literary awards by publishers, this paper finds that both mainstream Japanese literary awards and civilian literary awards have each achieved significant credibility through their respective developmental explorations. Furthermore, the establishment of internal literary award systems by publishers, represented by Shinchosha, not only aids in the marketing of their authors and works but also contributes to the growth of the publishers themselves

Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables

Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables

Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.

Assumptions in intelligence analysis

ABSTRACT This paper critiques the definitions of assumptions in intelligence analysis, highlighting their lack of clarity and precision. It proposes more specific definitions for assumption, presumption, and implicit assumption, emphasizing the overlooked importance of presumptions. The paper aims to enhance the understanding of assumptions among intelligence analysts, improving their ability to identify and work with various types of assumptions. The discussion includes an examination of the circularity and confusion in existing definitions, the importance of recognizing different types of assumptions, and the need for precise terminology in training. Additionally, the paper delves into background assumptions, the role of presumptions in gap closing, and the Toulmin method applied to working with assumptions.

Advanced Personal Budget Analytics: Combining Optical Character Recognition and Natural Language Processing for Automated Budget Categorization and Insight Extraction

A budget tracking system is a tool designed to help users monitor their financial resources, categorize expenses and control their financial resources. An Intelligent Personal Budget Analysis System is designed to redefine personal finance management. IntelliFinance is a user- friendly app designed for seamless and efficient daily expense management removing the necessity for conventional paper documentation. This project inculcates the concepts of Computer Vision, Machine Learning and Data Analytics, aims to surpass traditional budget tracking by incorporating innovative features such as receipt scanning and analysis. The project is implemented using Android Studio and Firebase, incorporating advanced features such as Optical Character Recognition (OCR) to capture receipt images, extract text, and process it for further analysis. Firebase ML Kit is utilized for scanning and text recognition, while Natural Language Processing (NLP) is applied for categorizing the extracted text. The system provides a personal budget analysis that enhances the accuracy of financial tracking by offering users a comprehensive overview of their spending habits, complemented by detailed reports and visualizations. Ultimately, this project serves as a financial companion, empowering users to take control of their finances, make informed decisions, and achieve their financial goals.

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

Coordinated Multi-Agencies Patrols and Border Security along the Kenya-Somalia Border in Wajir County

Securing their boarders has become a concern for all countries globally. This is because unsecure boarders pose a serious threat both to national and international security of states. For instance, the porous Kenya-Somali boarder continues to pose a serious threat to the security and development of Kenya as a country. This study therefore sought to examine how coordinated multi-agencies patrols affects border security along the Kenya-Somalia border. It adopted a descriptive survey research design that used both quantitative and qualitative approaches. Stratified and simple random and purposive sampling techniques were used to collect data for the study. The tools utilized to get data were, questionnaires and interview schedule. Both qualitative and quantitative data were triangulated to provide robust illustrations of the key findings. Quantitative data was analyzed using descriptive statistics and presented as percentages, pie charts, and bar graphs. Qualitative data on the other hand was analyzed thematically and presented as quoted verbatim. The study found that joint coordinated patrols, joint intelligence gathering, sharing and analysis and sharing of resources among the various multi-agency teams played a crucial role in border security management. The study concluded that, the Kenya Police Service, the Kenya Defense Forces (KDF), and local administrative bodies have made significant stride in enhancing boarder security along Kenya-Somali boarder in Wajir County. The study recommended that, as far as the issue of joint multi-agency patrols are concerned, there is need to increase human resource and provide the necessary gears for effective multi-agency patrols. For further research, the study had restricted itself to the multi-agency’s cooperation and border security along the Kenya-Somalia border in Wajir County. However, a number of similarities in the multi-agencies’ cooperation was noticed, mostly through literatures. It would be prudent, to stretch out this research to other regions, to determine the applicability of these findings for the whole of the country Kenya.

Speech based detection of Alzheimer’s disease: a survey of AI techniques, datasets and challenges

Alzheimer’s disease (AD) is a growing global concern, exacerbated by an aging population and the high costs associated with traditional detection methods. Recent research has identified speech data as valuable clinical information for AD detection, given its association with the progressive degeneration of brain cells and subsequent impacts on memory, cognition, and language abilities. The ongoing demographic shift toward an aging global population underscores the critical need for affordable and easily available methods for early AD detection and intervention. To address this major challenge, substantial research has recently focused on investigating speech data, aiming to develop efficient and affordable diagnostic tools that align with the demands of our aging society. This paper presents an in-depth review of studies from 2018–2023 utilizing speech for AD detection. Following the PRISMA protocol and a two-stage selection process, we identified 85 publications for analysis. In contrast to previous literature reviews, this paper places a strong emphasis on conducting a rigorous comparative analysis of various Artificial Intelligence (AI) based techniques, categorizing them meticulously based on underlying algorithms. We perform an exhaustive evaluation of research papers leveraging common benchmark datasets, specifically ADReSS and ADReSSo, to assess their performance. In contrast to previous literature reviews, this work makes a significant contribution by overcoming the limitations posed by the absence of standardized tasks and commonly accepted benchmark datasets for comparing different studies. The analysis reveals the dominance of deep learning models, particularly those leveraging pre-trained models like BERT, in AD detection. The integration of acoustic and linguistic features often achieves accuracies above 85%. Despite these advancements, challenges persist in data scarcity, standardization, privacy, and model interpretability. Future directions include improving multilingual recognition, exploring emerging multimodal approaches, and enhancing ASR systems for AD patients. By identifying these key challenges and suggesting future research directions, our review serves as a valuable resource for advancing AD detection techniques and their practical implementation.

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.