Skill Gap Analysis Using Machine Learning

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

This paper examines the application of Artificial Intelligence (AI) for conducting skill gap analysis in seafood processing and export units, with particular relevance to emerging seafood hubs where traditional workforce assessment methods remain prevalent. The seafood industry faces persistent challenges in maintaining product quality, regulatory compliance, operational efficiency, and sustainability, many of which stem from deficiencies in workforce skills. Conventional approaches are often slow, subjective, and incapable of delivering real-time insights. In contrast, AI enables data-driven, scalable, and predictive assessment of workforce competencies. The study proposes a multi-phase AI-driven framework that integrates machine learning, natural language processing, IoT-based monitoring, and predictive analytics to identify, measure, and address skill gaps across processing, logistics, compliance, and sustainability functions. Data sources include employee profiles, performance records, training histories, and industry benchmarks. AI tools such as computer vision for quality inspection, digital twins for process optimization, blockchain for traceability, and AR/VR platforms for training are analyzed for their role in enhancing workforce capability. Findings from a case study of a mid-sized seafood processing unit reveal significant deficiencies in advanced quality testing and export documentation knowledge. AI-based adaptive training improved compliance rates by 25%, reduced processing errors by 15%, and shortened export cycle time by 10%, demonstrating measurable operational gains. However, implementation challenges persist, including limited data availability, workforce resistance, digital literacy gaps, and high initial investment costs. The paper recommends the development of unified data platforms, change-management initiatives, scalable cloud-based AI solutions, and policy support through subsidies and skill-development programs. Overall, the study concludes that AI-enabled skill gap analysis can significantly enhance productivity, sustainability, and global competitiveness in seafood processing and export industries, providing a strategic pathway for modernization in an increasingly technology-driven global market.

AI (Artificial Intelligence) has been a major factor in changing the financial landscape in recent years.  The term "Smart Finance" describes use of AI tools like RPA (robotic process automation), ML (machine learning), natural language processing (NLP) to improve financial decision-making, automate repetitive tasks, and offer more in-depth understanding of how businesses operate.  Financial institutions can improve customer service, manage risk better, streamline operations, and spur long-term development with this data-driven strategy. This chapter explores how AI is transforming finance industry, analyzing its applications, challenges, and opportunities for business growth. We also give a thorough look at future of AI in financial industry and talk about the approaches for integrating AI into financial systems. Combination of AI and finance in the digital era is changing conventional corporate environments. "Smart Finance" refers to integration of AI technologies—involving machine learning (ML), predictive analytics, and NLP—into financial operations to drive strategic decision-making, improve efficiency, and enhance business growth. This paper explores how AI-powered financial systems are transforming data into actionable insights that enable organizations to operate more intelligently and competitively. AI facilitates real-time financial data analysis, enabling companies to forecast trends, detect anomalies, manage risks, and optimize investments with higher accuracy. From automated bookkeeping and fraud detection to intelligent budgeting and customer behavior analysis, AI tools are becoming critical assets in the financial decision-making process. AI improves operational efficiency and frees up financial experts to concentrate on high-level strategic duties by automating repetitive operations and reducing human error. Additionally, platforms powered by AI are able to spot patterns in large datasets that would be very difficult to find by hand. This capability empowers businesses to personalize financial products, improve credit scoring models, and develop dynamic pricing strategies that respond to market shifts. SMEs, in particular, benefit from AI-driven financial insights that were traditionally accessible only to large corporations with extensive resources. But there are drawbacks to using AI in banking as well, such as algorithmic bias, data privacy issues, as well as requirement for qualified personnel to efficiently operate AI systems.  Notwithstanding these challenges, AI has significant long-term benefits for the financial industry, promoting a more robust, informed, and flexible corporate environment.  The objective of this essay is to present thorough examination of how AI is transforming financial operations and promoting long-term company success.  It illustrates that smart finance is not just a technical development but also a strategic necessity for progressive companies through case studies and new trends.

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and effective management. Predictive analytics, powered by machine learning (ML) and artificial intelligence (AI), offers a transformative potential in cancer care by integrating multi-source health data for early diagnosis, risk assessment, and personalized treatment. This presents a predictive analytics framework that leverages diverse data sources, including electronic health records (EHRs), genomic and biomarker data, medical imaging, wearable sensors, and lifestyle/environmental factors. By combining these heterogeneous data streams, AI models can identify high-risk individuals, detect cancer at its earliest stages, and optimize treatment strategies based on patient-specific insights. The framework employs advanced ML techniques, such as supervised learning models (Random Forests, Support Vector Machines), deep learning architectures (Convolutional Neural Networks, Recurrent Neural Networks), and Natural Language Processing (NLP) for analyzing clinical notes. Federated learning is also explored as a privacy-preserving approach to facilitate secure data sharing across institutions while maintaining compliance with regulatory standards such as HIPAA and GDPR. The integration of predictive analytics with telemedicine platforms and AI-driven clinical decision support systems further enhances real-time monitoring and personalized patient care. Despite its promise, challenges such as data privacy concerns, heterogeneity in multi-source data integration, model interpretability, and computational scalability must be addressed to ensure the effective implementation of AI-driven cancer diagnostics. Future advancements in Explainable AI (XAI), blockchain for secure data exchange, and multi-modal data fusion will further strengthen predictive analytics in oncology. This study underscores the critical role of big data and AI in revolutionizing cancer detection and management, paving the way for improved patient outcomes and more efficient healthcare delivery.

Recruitment systems face increasing challenges due to the diversity of résumé formats, lack of personalized guidance for job seekers, and the persistence of human bias in candidate evaluation. Traditional systems often fail to accurately extract essential information, assess résumé quality, and provide actionable recommendations aligned with labor market requirements. This paper presents an intelligent résumé analysis and job recommendation system based on Natural Language Processing (NLP) and transparent rule-based logic. The system extracts structured information from text-based PDF résumés using PDFMiner3, PyResParser, and spaCy. Résumé quality is evaluated using a rule-based scoring mechanism, while skill gap analysis is performed through explicit comparison between extracted skills and predefined domain requirements. Based on this analysis, the system generates personalized job and training recommendations. The system was evaluated through laboratory testing and real-world field evaluation in collaboration with Human Resources specialists. Experimental results show 90% skill extraction accuracy, 92% job domain classification accuracy, 89% skill gap detection accuracy, and over 91% relevance in job and training recommendations. The findings demonstrate that the proposed system provides an effective, interpretable, and fair solution for intelligent recruitment and career development

Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

In the highly competitive e-commerce landscape, retaining customers and predicting their behaviors are critical for sustainable growth. This review explores the advancement of machine learning (ML) frameworks for customer retention and propensity modeling, emphasizing their transformative potential in e-commerce platforms. Customer retention is vital, as retaining an existing customer is significantly more cost-effective than acquiring a new one. Propensity modeling further complements retention strategies by predicting customer actions, such as purchases or churn, enabling businesses to tailor their marketing efforts effectively. Advanced ML techniques, including deep learning, reinforcement learning, and natural language processing (NLP), are reshaping how businesses approach these challenges. These models leverage diverse data sources, such as transaction history, browsing behavior, and customer feedback, to identify actionable insights. Key advancements in feature engineering, real-time data processing, and hyperparameter optimization have enhanced the accuracy and scalability of these frameworks, making them indispensable for e-commerce platforms handling vast datasets. Despite these advancements, challenges persist, such as ensuring data privacy, addressing the interpretability of complex models, and achieving real-time scalability. Successful implementations, including case studies from leading e-commerce platforms, demonstrate the potential of ML to improve customer engagement, increase lifetime value, and drive business growth. Looking forward, integrating AI and ML for enhanced personalization, leveraging real-time predictive analytics, and addressing ethical considerations like bias and fairness are crucial for advancing these frameworks. This review provides a comprehensive overview of state-of-the-art ML techniques for customer retention and propensity modeling, highlighting their practical applications and future prospects in e-commerce. By adopting these innovations, e-commerce platforms can achieve a competitive edge, fostering long-term customer loyalty and business success.

The concept of "quality of life" (QoL) encompasses all aspects of people's standard of living, including economic, social, or health-related factors, as well as their perceptions of their own lives. Although the growing application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in processing and modeling the diverse datasets associated with these domains, there remains a significant challenge in addressing different issues in QoL area of research and in fully harnessing these technologies to improve QoL research outcomes. Despite the technological advancements, current research endeavors often overlook the complex, multifaceted nature of QoL study. This oversight results in fragmented insights and leaves significant areas underexplored. In this work, we conducted a systematic literature review (SLR) to investigate the contribution of AI to QoL studies. For this, we collected 68 research works published between 2008 and 2022. This review covers a range of research questions about the objectives and methods of studies on QoL, the sources and types of data utilized, and the advancements made through the application of natural language processing (NLP), ML, deep learning (DL), statistical models, and semantic approaches. The goal of this review is to tackle the prevalent ambiguity in QoL dimensions, synthesize the research findings, and highlight the contributions, advancements, and most innovative approaches in the field. Moreover, we identify gaps and limitations in the current literature and suggest potential areas for future research, aiming to inspire more cohesive and comprehensive approaches to studying QoL using AI and ML techniques.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

BackgroundMachine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.ObjectiveThe primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practiceMethodsThis systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.ResultsA total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.ConclusionsMachine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

AI has the potential to transform hospital administration and has recently emerged as a solution for improving operational efficiency, enhancing decision-making processes, and optimizing patient care.This paper discusses a few of the AI applications in a hospital administration such as predictive analytics, machine learning and robotic process automation.The article also looks into some of the challenges and hurdles that hinder the mass use of AI like data privacy, integrating systems, and cost of deployment among others.Through examining case studies and literature, this study emphasises the value that AI has for healthcare organizations with discussion around potentially beneficial applications and evolution.The results highlight the need of overcoming implementing challenges to fully leverage AI's potential in healthcare systems. 1.INTRODUCTIONAs a result of increased patient numbers and resource constraints, the operational costs for maintaining quality healthcare services continue to rise.The modern hospital administrator has to cope with a delicate balance of numerous interrelated activities such as: rostering staff, managing resources, overseeing finances, and coordinating the delivery of services to patients (M.Wang & J. Preininger, 2020).These competing priorities cannot be managed by traditional administrative systems that are still largely paper-based, or at best, digitized to a certain extent, hence, much work still being done manually.The implementation of automation and AI technologies, which include machine learning, predictive analytics, and natural language processing, promise to ease the administrative burden AI algorithms can unlock new facets of work improvement throughout the operational workflows.At a minimum, the available AI technologies can perform a wide array of predictive analytics related to staff patient admission, inventory records, and even clinical decision support systems.By shifting the burden of mundane routine work to the automated systems, healthcare institutions may lower their operational costs of serving patients Varghese, Choi, Tan, & Chao, 2017). 2.LITERATURE REVIEWWith the advancement of technology, its capabilities open new opportunities, particularly in healthcare which heavily relies on accurate information.These complex areas are now able to utilize the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) booming everywhere in an attempt to further improve decision making processes within hospitals, enhance daily operations, and most importantly, provide better care for patients. Machine Learning and Predictive AnalyticsThrough Machine Learning, it is now possible to analyze historical hospital data, patient records e.g.transcripts, and even develop expectations about patients, resource management, and decision making to yield better outcomes (Liu et al., 2018).Analytics is a prospective avenue for patient movement supervision, and resource management (Varghese et al., 2017).

This article explores the integration of artificial intelligence and machine learning techniques into Kubernetes log analytics, with a specific focus on environments. As organizations increasingly adopt container orchestration for mission-critical applications, traditional monitoring approaches have proven inadequate for addressing the complexity, scale, and ephemeral nature of cloud-native architectures. The article shows how AI-driven log analytics can transform observability through automated anomaly detection, predictive analytics, and human-AI collaboration frameworks. By leveraging machine learning algorithms, natural language processing, and real-time data processing architectures, these advanced solutions enable organizations to transition from reactive troubleshooting to proactive management. The article presents implementation frameworks, maturity models, and practical case studies demonstrating how AI-enhanced observability significantly improves operational efficiency, reduces mean time to resolution, and enhances system reliability in complex Kubernetes deployments.

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI’s ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability.Methods: We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (i.e. not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information.Results: We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates.Conclusion: NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.

Diabetes is a major chronic syndrome caused by a series of metabolic abnormalities in which blood glucose levels are abnormally high for an indeterminate amount of time. It influences various organs in the human body, resulting in a variety of complex diseases such as stroke, renal disease, pulmonary embolism, eyesight, and so on. Diabetes Disorders (DD) are presently one of the healthcare top causes of mortality. Predictive analytics in the health care system is a huge obstacle, but if accurate early prediction is achieved, the potential risk and degree of diabetes may be significantly decreased. Machine learning (ML) techniques are now used to analyze medical datasets at an earlier stage of life in keeping people safe. In this research, we utilized several ML approaches notably Logistic Regression, Decision Tree (DT), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) on PIMA Indian Diabetes Dataset in order to monitor and evaluate their performances in diabetes prediction. The performance of the various ML algorithms employed in this research suggests which algorithm is most suitable in diabetes prediction. It is observed that among all the models XGBoost had outperformed the other ML techniques with an accuracy of 80.73% while SVM was the second-best performing model with a classification accuracy of 80.21%. Thus, employing ML techniques, this study aims to assist doctors as well as clinicians in the early detection of diabetes.

Artificial intelligence (AI) and machine learning (ML) have transformed business analytics in today’s data-driven environment, bringing new trends, technologies, and capabilities. Focusing on areas such as machine learning, predictive analytics, natural language processing (NLP), text analytics, computer vision, and robotic process automation (RPA), this research examines the AI-based technologies needed to advance business intelligence (BI). By integrating these solutions, organizations can improve operations, gain valuable insights, and make data-driven decisions. Finding patterns and predicting trends in big data is accomplished through machine learning and predictive analytics. Natural Language Processing (NLP) and text analytics help unlock the potential of redundant data by facilitating user insights, service automation, and better decision making. This expands business intelligence to include visual data processing. Also, use RPA and AI to automate processes to increase productivity and efficiency. Big data and IoT can also help businesses better adapt to changing business conditions. Advanced design techniques enhance human-computer interaction and enable intelligent data interpretation. This article explores the future potential of AI-ML NLP to reinvent analytics and decision making by highlighting the latest developments and impacts of AI-ML NLP on BI.