Earticle

본 연구의 목적은 직업상담지원시스템에 탑재하기 위한 직업 추천 모델의 예측 타당도를 분석하고, 예 측 타당성이 높은 항목을 도출하는 데 있다. 이를 위해 기계학습 알고리즘을 활용해 한국직업정보시스템 (KNOW)의 직업특성 설문 문항을 기반으로 직업 추천 항목을 검토하고, 그 성능을 비교 분석하였다. 연 구 목적을 달성하기 위해 1,610명을 대상으로 인적 속성, 흥미, 능력, 일반 업무 관련 변수를 포함한 설문 조사를 실시하였다. 기계학습 알고리즘 분석 결과, 신경망과 그라디언트 부스팅 알고리즘이 가장 높은 성 능을 보였으며, 설문 항목이 직업 추천에 충분한 실용성을 갖추고 있음을 확인하였다. 변수 중요도 분석 을 통해 전공 중분류, 최종 학력, 업무 수행에 필요한 교육 수준, 실외 근무 빈도, 컴퓨터 업무 활동 중요 도 등의 변수가 직업 추천에 중요한 요소로 나타났다. 또한 직업별 학습 모델 성능을 분석하여 분류 정 밀도가 높은 직업과 낮은 직업을 도출하였다. 본 연구는 기계학습 알고리즘을 활용한 직업 추천 시스템 개발에 중요한 기초 자료를 제공하며, 다양한 알고리즘의 비교 분석을 통해 최적의 직업 추천 모델을 도 출하였으며, 이를 통해 구직자의 직업 선택과 경력 개발을 효과적으로 지원할 수 있을 것이다.

The purpose of this study is to analyze the predictive validity of a job recommendation model for use in a career counseling support system and to identify items with high predictive validity. To achieve this, machine learning algorithms were applied to job recommendation items from the Korean Occupational Information System (KNOW), and their performance was compared and analyzed. A survey of 1,610 participants was conducted, including personal attributes, interests, abilities, and general work-related variables. The analysis revealed that the neural network and gradient boosting algorithms performed best, demonstrating that the survey items are practical for job recommendations. Variable importance analysis identified factors such as major classification, education level, job-specific educational requirements, frequency of outdoor work, and the importance of computer-based tasks as key elements in job recommendations. Additionally, model performance for each occupation was analyzed to determine occupations with high and low classification accuracy. This study provides a solid foundation for developing a job recommendation system using machine learning algorithms and identifies the optimal model to support job seekers in their job selection and career development processes.

소셜 네트워크 서비스(이하 SNS)는 회사의 마케팅 채널로 적극 활용되고 있으며, 회사들의 고객층에 적합한 내용과 어조를 활용하여 주기적으로 SNS 메시지를 작성하는 등 활발한 마케팅을 펼치고 있다. 본 논문에서는 이제까지 간과되었던 SNS 메시지에 내포된 심리적 거리에 초점을 맞춰 전통적인 코더를 활용한 내용 분석(content analysis)과 자연어 처리 기법 및 머신 러닝 방법을 혼합하여 심리적 거리를 측정하는 분석 방법을 연구하였다. SNS 메시지의 심리적 거리 분석을 위해 코더들을 활용하여 내용분석을 수행하였으며, 이와 같은 방법으로 레이블링된 데이터를 자연어 처리 방법을 이용하여 워드 임베딩을 수행함으로써 머신 러닝 수행을 위한 입력 데이터를 마련하였다. 머신 러닝 분석법 중 Support Vector Machine(SVM)을 이용하여 SNS 메시지와 심리적 거리 간의 관계를 학습시켰으며, 마지막으로 테스트 데이터를 이용하여 심리적 거리를 예측함으로써 머신 러닝 분석의 성과를 검증하였다. 심리적 거리 측정 방법론 수행 결과, 코더들의 내용분석 결과가 특정 값으로 편향되어 SVM 예측의 민감도와 정밀도가 낮은 결과가 도출되었다. 심리적 거리 응답 비율을 보정하고 코더들의 1차 내용분석 결과 중 답변이 일치한 데이터로 한정지어 머신 러닝을 실행한 결과 심리적 거리 예측의 정확도, 민감도, 특이도, 정밀도 모두 향상되어 심리적 거리가 70% 이상 예측되는 성과를 보였다. 본 연구는 SNS 메시지의 심리적 거리를 측정하는 방법을 제시함으로써 독자와의 심리적 거리를 제어 가능한 전략 요소로 활용 가능하게 할 것이라 기대된다.

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

딥러닝(Deep learning) 기법은 패턴분석, 이미지분류 등 다양한 분야에서 높은 성과를 나타내고 있다. 특히, 주식시장 분석문제는 머신러닝 연구분야에서도 어려운 분야이므로 딥러닝이 많이 활용되는 영역이다. 본 연구에서는 패턴분석과 분류능력이 높은 딥러닝의 일종인 합성곱신경망(Convolutional Neural Network) 모델을 활용하여 주가방향 예측방법을 제안한다. 추가적으로 합성곱신경망 모델을 효율적으로 학습시키기 위한 속성선택(Feature Selection, FS)방법이 적용된다. 합성곱신경망 모델의 성과는 머신러닝 단일 분류기와 앙상블 분류기를 벤치마킹하여 객관적으로 검증된다. 본 연구에서 벤치마킹한 분류기는 로지스틱 회귀분석(Logistic Regression), 의사결정나무(Decision Tree), 인공신경망 (Neural Network), 서포트 벡터머신(Support Vector Machine), 아다부스트(Adaboost), 배깅(Bagging), 랜덤포레스트(Random Forest)이다. 실증분석 결과, 속성선택을 적용한 합성곱신경망이 다른 벤치마킹 분류기보다 분류 성능이 상대적으로 높게 나타났다. 이러한 결과는 합성곱신경망 모델과 속성선택방법을 적용한 예측방법이 기업의 재무자료에 내포된 가치를 보다 정교하게 분석할 수 있는 가능성이 있음을 실증적으로 확인할 수 있었다.

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

Free space optical (FSO) communication systems offer high-bandwidth, secure data transmission over wireless channels. Recent advancements in machine learning (ML) and deep learning (DL) have considerable promise in mitigating these challenges and enhancing the reliability and efficiency of FSO systems. This comprehensive survey examines ML and DL techniques applied to FSO systems, covering advancements in channel modeling and estimation, and demodulation. Additionally, this review highlights the role of ML and DL in hybrid FSO/RF systems, focusing on resource management, dynamic switching, relay selection, underwater FSO, and ATP. Emerging trends, future research directions, standardization efforts, and unresolved challenges are discussed. Our overall conclusion highlights that DL, especially hybrid and attention-based models, demonstrates strong potential in dynamic channel adaptation and tracking under turbulence, while reinforcement learning shows promise for real-time resource allocation and switching.

Sepsis and Neonatal sepsis are major challenges in global healthcare because they cause life-threatening organ dysfunction in intensive care adult and pediatric patients due to downregulated host response to a particular infection. Early clinical identification of sepsis is difficult, and failure to provide prompt treatment can often lead to crucial stages and increase the rates of fatality. Thus an intense study is needed to determine and categorize sepsis in its initial stage. The complexity of varying clinical statistics makes it difficult to attain a precise definition in pediatrics. The advanced Machine Learning (ML) and Deep Learning (DL) technologies in the implementation of protocols show promising real-time models for predicting sepsis at the primary stage and thereby reducing the mortality rate. This review article contemplates the complete list of procedures through which sepsis and neonatal sepsis are speculated by ML and DL and concentrates specifically on data available in the adult emergency care unit as well as the neonatal intensive care unit. The survey process was carried out by searching terms related to ML and DL merged with topics concerning sepsis and neonatal sepsis. The literature analysis was carried out from Scopus, Web of Science, and PubMed databases for the period from 2015 to 2022. The assessment of the risk of bias was carried out for the eleven selected papers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). The eleven papers were selected from different medical care units based on the performance measure AUROC, which ranges from 0.68 to 0.95. Five papers involving ML/DL models reduce the bias and lessen risk occurrence. Five papers generate an increase in bias but can be applied to new data. One paper works with above twenty-five features has high-risk probability but predicts patients within 5?6 h in the future. This survey portrays the role of prediction models that supports the researchers and clinicians for better decision-making and antibiotic administration at an earlier stage.

Cardio-Vascular Diseases (CVD) is found to be rampant in the populace leading to fatal death. The statistics of a recent survey reports that the mortality rate is expanding due to obesity, cholesterol, high blood pressure and usage of tobacco among the people. The severity of the disease is piling up due to the above factors. Studying about the variations of these factors and their impact on CVD is the demand of the hour. This necessitates the usage of modern techniques to identify the disease at its outset and to aid a markdown in the mortality rate. Artificial Intelligence and Data Mining domains have a research scope with their enormous techniques that would aassist in the prediction of the CVD priory and identify their behavioural patterns in the large volume of data. The results of these predictions will help the clinicians in decision making and early diagnosis, which would reduce the risk of patients becoming fatal. This paper compares and reports the various Classification, Data Mining, Machine Learning, Deep Learning models that are used for prediction of the Cardio-Vascular diseases. The survey is organized as threefold: Classification and Data Mining Techniques for CVD, Machine Learning Models for CVD and Deep Learning Models for CVD prediction. The performance metrics used for reporting the accuracy, the dataset used for prediction and classification, and the tools used for each category of these techniques are also compiled and reported in this survey.

홈페이지를 통하여 제공되는 인사말에는 보통 추구하고자 하는 목표 등을 포함한 핵심 정보가 담기며 소 통의 일환으로 이용되고 있다. 개인의 건강증진 및 사회통합의 역할을 담당하는 프로스포츠에서도 홈페이지 인사 말은 그 의미하는 바와 역할이 중요하다. 본 연구에서는 국내 프로스포츠 홈페이지 인사말을 대상으로 machine learning 기법을 이용하여 분석해 보았다. 그 결과 다음을 알 수 있었다, 첫째, 분석 대상으로 삼은 4개 프로리그 모두에서 유사한 결과를 보인바, 차별화를 발견할 수 없었다. 둘째, 워드 클라우드 분석, 네트워크 분석을 통해 보 았을 때, 감사의 인사와 함께 지속적인 성원을 부탁하는 내용이 주를 이루고 있는 것으로 나타났다. 나아가 감성 분석 결과 4개 프로리그 모두 예상했던 대로 긍정적인 단어의 선택이 많았으며, 분산분석 결과 그 차이는 통계적 으로 유의하지 않았다(p>0.05). 이에 향후 홈페이지 인사말 개편 시에는, 각 프로리그의 캐치프레이즈를 충분하게 담아내고, 나아가 각 구단이 프로스포츠 운영을 통하여 이루고자 하는 방향성 등이 잘 포함되도록 개선할 것을 제 안하였다.

Greetings on websites homepages typically contain core information, such as goals and visions, and serve as a means of communication. In professional sports, which play a role in promoting individual health and social integration, website greetings hold significant meaning and importance. This study analyzed greetings on domestic professional sports websites using machine learning techniques. The results are as follows: First, all four professional leagues analyzed showed similar results, revealing no differentiation. Second, word cloud and network analyses indicated that greetings mostly consist of words of gratitude and requests for continued support. Furthermore, sentiment analysis showed that, as expected, positive words were predominantly chosen across all four leagues, and analysis of variance revealed no statistically significant differences (p>0.05). Therefore, it is suggested that in future revisions of websites greetings, they should fully incorporate each professional league’s slogan and direction, as well as the objectives each club aims to achieve through professional sports operations.

The rapid growth of web applications has increased the need for advanced features and strong security. Artificial intelligence (AI) and machine learning (ML) models play a crucial role in meeting these needs by improving efficiency and enhancing security. However, integrating these models into web applications can be challenging due to complex implementation and potential security risks. This paper compares Python and Node.js, two popular technology stacks, to determine their effectiveness in integrating ML models into web applications. It also explores the role of web application firewalls (WAF) and the ML algorithms that support them, analyzing current trends in their use and adoption. The overarching objective is to discern the technology stack that provides superior support for back-end ML integration and to identify the ML algorithms that are most effective in enhancing WAF capabilities against sophisticated security threats. By offering a synthesis of technical and security insights, this research seeks to empower developers and cybersecurity practitioners with the knowledge required to make well-informed decisions regarding technology stack selection and the implementation of ML-driven security mechanisms in web application development.

Purpose: This study analyzed the methodological characteristics of machine learning (ML) applications in nursing research, evaluated their reporting quality against standardized guidelines, and assessed progress toward clinical implementation. Methods: A PRISMA-com pliant systematic review (PROSPERO CRD42024595877) searched nine English- and Ko rean-language databases through September 27, 2024. Included studies applied ML to a nursing question and had at least one nursing-affiliated author. Two reviewers independently extracted data following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Reporting quality was appraised using the TRIPOD+AI checklist. Results: Of 125 included studies, supervised learning predominated (93.6%), with random forest, lo gistic regression, and support vector machines as common algorithms. The most frequent performance metrics were the area under the receiver operating curve and accuracy. Mean TRIPOD+AI compliance was 50.4% (standard deviation=9.37), with reporting quality lowest for data preparation (48.0%) and class imbalance handling (22.4%). Research focused on predicting pressure injuries, falls, and readmissions. Only seven studies described clinical deployment, often citing ethical or workflow barriers. Conclusion: While ML studies in nursing are increasing and show strong discriminatory accuracy, their impact is limited by inconsis tent reporting, limited external validation, and rare clinical deployment. Translating these al gorithms into practice requires adopting comprehensive reporting guidelines like TRIPOD+AI, documenting each CRISP-DM phase, and integrating nurse-centered decision-support path ways.

Background Pressure pain hypersensitivity (PPH) is used to measure pain sensitivity in deep tissues, but factors contributing to PPH remain unclear. Abnormal neck and scapula posture are thought to play a role in shoulder pain. Traditional statistical methods like logistic regression have limitations in capturing complex relationships, while machine learning (ML) can model nonlinear relationships effectively. Purpose The purpose of the present study was to develop, evaluate, and compare the predictive performance of ML models and logistic regression for classifying food service workers (FWs) with and without PPH based on postural analysis data. Study design Cross sectional study. Methods FWs (n=150) meeting specific criteria were assessed for PPH and underwent postural analysis. ML algorithms (logistic regression, neural network, random forest, gradient boosting, decision tree, and support vector machine) were used for classification. Model performance was evaluated using the area under the curve (AUC), accuracy, recall, precision, and F1 score. Feature importance was assessed. Results Gradient boosting exhibited the best performance (AUC: 0.867) in classifying PPH, followed by random forest (AUC: 0.822) in the test dataset. Logistic regression performed less effectively (AUC: 0.613). For feature importance analysis, scapular downward rotation ratio, forward head posture, BMI and rounded shoulder angle were the top four important predictors of PPH in gradient boosting model. Conclusions Gradient boosting, along with identified predictors, offers promise for early intervention and risk assessment tools in addressing musculoskeletal pain in food service workers.

The channel state information (CSI) is essential for the base station (BS) to schedule user equipments (UEs) and efficiently manage the radio resources. Hence, the BS requests UEs to regularly feed back the CSI. However, frequent CSI reporting causes large signaling overhead. To reduce the feedback overhead, we propose two machine learning-based approaches to adjust the CSI feedback interval. We use a deep neural network and reinforcement learning (RL) to decide whether an UE feeds back the CSI. Simulation results show that the RL-based approach achieves the lowest mean squared error while reducing the number of CSI feedback transmissions.

As we enter the digital age, new methods of personality testing-namely, machine learning-based personality assessment scales-are quickly gaining attraction. Because machine learning-based personality assessments are made based on algorithms that analyze digital footprints of people’s online behaviors, they are supposedly less prone to human biases or cognitive fallacies that are often cited as limitations of traditional personality tests. As a result, machine learning-based assessment tools are becoming increasingly popular in operational settings across the globe with the anticipation that they can effectively overcome the limitations of traditional personality testing. However, the provision of scientific evidence regarding the psychometric soundness and the fairness of machine learning-based assessment tools have lagged behind their use in practice. The current paper provides a brief review of empirical studies that have examined the validity of machine learning-based personality assessment, focusing primarily on social media text mining method. Based on this review, we offer some suggestions about future research directions, particularly regarding the important and immediate need to examine the machine learning-based personality assessment tools’ compliance with the practical and legal standards for use in practice (such as inter-algorithm reliability, test-retest reliability, and differential prediction across demographic groups). Additionally, we emphasize that the goal of machine learning-based personality assessment tools should not be to simply maximize the prediction of personality ratings. Rather, we should explore ways to use this new technology to further develop our fundamental understanding of human personality and to contribute to the development of personality theory.

Fault detection of rotating engine components in the aircraft engine is a challenging task that must constantly be monitored to provide aviation safety. In this paper, we propose a novel approach based on multi-layer perceptron (MLP) to detect in real time the degree of faults in a turbine engine disk due to a crack. To further improve detection accuracy while reducing computational complexity, the recursive feature elimination (RFE) is applied as a potent feature selection method. Satisfactorily, simulation results show that the proposed framework is robust to changes in operating conditions and outperforms comparative approaches.

본 연구는 영화 개봉 전 활용 가능한 정적 데이터를 기반으로 AI 및 기계 학습(Machine Learning, ML) 기법을 활용한 영화 흥행 예측 모델을 제안하고, 이를 기존 베이스라인 모델과 비교하여 성능을 평가하였다. 2013년부터 2024년까지 수집된 KOBIS 데이터를 활용하여 배우, 감독, 배급사, 장르별 기술 통계와 상호작용 파생변수를 생성하였으며, RFECV(Recursive Feature Elimination with Cross- Validation)를 통해 주요 변수를 선별하였다. 또한, AutoML 기법을 적용하여 데이터셋에 최적화된 모델을 학습하고, 네이버 검색어 트렌드 데이터를 추가적으로 반영하여 예측 성능을 향상시켰다. 실험 결과, 최종 제안모델은 베이스라인 모델 및 기존 연구모델을 뛰어넘는 성능을 보였으며, F1-Macro 및 AHPR-Bingo를 활용한 평가 척도에서 높은 정확도를 기록하였다. 특히, 상호작용 파생변수는 모델 성능 개선에 기여하는 주요 변수로 나타났으며, 정적 데이터를 활용한 예측 모델이 영화 산업 내 의사결정 과정에서 유의미한 도구로 작용할 수 있음을 입증하였다. 본 연구는 정적 데이터만을 활용한 영화 흥행 예측 모델을 제안함으로써 학술적 및 실무적 기여를 제공한다. 연구 결과는 영화 제작사와 배급사가 개봉 전 전략적 의사결정을 지원하는 데 유용한 프레임워크를 제공하며, 향후 연구는 동적 데이터를 포함한 확장된 모델 개발 및 다양한 데이터셋 적용을 통해 본 연구를 보완할 수 있을 것으로 기대된다.

This study proposes a box office prediction model utilizing AI and machine learning (ML) techniques based on static data available before a film’s release and evaluates its performance by comparing it with existing baseline models. Using KOBIS data collected from 2013 to 2024, we generated descriptive statistics and interaction-derived variables for actors, directors, distributors, and genres. Key features were selected using Recursive Feature Elimination with Cross-Validation (RFECV). Additionally, an AutoML approach was applied to train an optimized model for the dataset, and Naver search trend data was incorporated to enhance predictive performance. Experimental results demonstrated that the proposed model outperformed both baseline and existing research models, achieving high accuracy based on F1-Macro and AHPR-Bingo evaluation metrics. Notably, interaction-derived variables were identified as key contributors to model performance improvement, confirming that prediction models based on static data can serve as valuable tools in the decision-making processes of the film industry. This study makes both academic and practical contributions by presenting a box office prediction model solely based on static data. The findings provide a useful framework for film producers and distributors to support strategic decision-making before release. Future research can extend this study by incorporating dynamic data and applying the model to diverse datasets for further validation and enhancement.

Background Logistics service workers face elevated injury rates, with ankle-related incidents comprising 23% of workplace injuries. Traditional binary foot posture classifications may oversimplify the complex biomechanical relationships affecting dynamic balance performance in occupational settings. Purpose To apply unsupervised machine learning clustering to identify distinct foot posture phenotypes among logistics service workers and compare dynamic balance performance between identified clusters using the Y-Balance Test. Study design Cross-sectional observational study Methods A total of 112 logistics service workers were analyzed using K-means clustering based on age, body mass index, work duration, navicular drop, and resting calcaneal stance position. Silhouette score analysis determined optimal cluster number. Dynamic balance was assessed using the Y-Balance Test, measuring reach distances in anterior, posteromedial, and posterolateral directions. Results Four distinct phenotypes emerged: rearfoot valgus-dominant pronated (n=22), midfoot collapse-dominant pronated (n=32), age-related (n=23), and supinated (n=35) foot types. Significant differences in dynamic balance performance were observed in the posterolateral direction (F=3.900, p=0.011). The supinated phenotype demonstrated superior posterolateral reach performance (77.76±13.63%) compared to midfoot collapse-dominant (66.18±16.37%, p=0.003) and age-related phenotypes (67.78±14.97%, p=0.018). Conclusions Unsupervised machine learning successfully identified naturally occurring foot posture phenotypes with distinct dynamic balance characteristics. Midfoot collapse-dominant pronation demonstrated greater balance impairments than rearfoot valgus patterns, supporting the implementation of phenotype-specific interventions for workplace injury prevention in logistics workers.

Background Frailty is an important condition to detect in its early stages to prevent progression to more severe stages in older adults. Age-related declines in physical performance are strongly associated with frailty. Purpose This study aims to develop a frailty classification model by comparing the performance of machine learning models based on physical performance measures in community-dwelling older adults. Study design A cross-sectional study Methods Physical performance data were collected from older adults aged ≥65 years. Frailty classification models were developed using logistic regression, support vector machine (SVM), K-nearest neighbors (KNN), decision tree, and random forest. Clinical features including short physical performance battery, single-leg stance, SARC-F, body mass index, and mini-mental state examination (MMSE) were used as input variables for model development. The performance of each model was evaluated using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Permutation feature importance was employed to identify key predictors of frailty. Results The KNN model demonstrated the highest classification performance, achieving an accuracy of 0.93, an F1-score of 0.95, and an AUC of 0.86, indicating its suitability for frailty assessment. The logistic regression model achieved an accuracy of 0.86, an F1-score of 0.89, and an AUC of 0.98. The random forest model showed similar results, with an accuracy of 0.86, an F1-score of 0.88, and an AUC of 0.96. The SVM model recorded an accuracy of 0.79, an F1-score of 0.84, and an AUC of 0.80. The decision tree model showed the lowest performance, with an accuracy of 0.71, an F1-score of 0.78, and an AUC of 0.64. Feature importance analysis revealed that MMSE and SARC-F were the most influential predictors in the KNN model. Conclusions This study demonstrates that KNN is well-suited for identifying subtle variations in physical function that contribute to frailty. The results highlight its potential for clinical implementation in automated frailty screening. Feature importance analysis provides insight into key predictors, supporting personalized assessment strategies. However, due to the small sample size, further research is needed to assess the generalizability of frailty classification models in larger populations.

Integrating machine learning (ML) into blockchain consensus mechanisms enhances efficiency, scalability, and resilience. This study introduces the PoA algorithm, an ML-enhanced Proof of Authority mechanism that optimizes signer selection for improved transaction processing. Simulations with models including Random Forest, Logistic Regression, SVM, K-Nearest Neighbors, Decision Tree, and Gradient Boosting showed significant gains. Random Forest reduced latency tenfold, achieving nearly 1000 transactions per second, with 93.33% accuracy, 100% precision, 86.67% recall, and a 92.86% F1-score. These results demonstrate ML’s potential to enhance blockchain performance, making hybrid blockchain-ML solutions a promising research direction.

This review article explores and locates the current state-of-the-art related to application areas from freight transportation, supply chain and logistics that focuses on arrival time, demand forecasting, industrial processes optimization, traffic flow and location prediction, the vehicle routing problem and anomaly detection on transportation data. This review categorizes the related works according to machine learning methodologies so as to present the methods’ evolution through time, their combinations and their connection with the various applications in the specified fields. Thus, a reader would effortlessly get insights about the current state-of-the-art related to machine learning in freight transportation and related application areas.

With the recent advancement in baseband integrated circuits and radio frequency (RF) technology, there is a strong demand to minimize the size of array-based microwave systems. However, array aperture miniaturization often leads to strong mutual coupling in the arrays. To date, mitigating and/or compensating the mutual coupling (nonlinear) effect remains a technical challenge. For instance, realizing microwave orientations in the presence of strong mutual coupling is a challenge. In this paper, we propose a unified electromagnetic machine learning (EMML) technique for overcoming this challenge. Antenna current’s Green function (ACGF) is employed for characterization of the EM response of elements in form of digital signal processing (DSP) module with the inclusion of mutual coupling effect among the elements. An EMML framework is then formulated and combined with the DSP module based on ACGF, to consequently come up with a nonlinear mutual coupling mitigation framework that provides better decoupling capability than the previous linear techniques. EMML is verified by application to a multiple signal classification (MUSIC)-based direction-of-arrival estimation scenario, and compared with the conventional methods. The results obtained show the validity, effectiveness of the EMML method. This method can be further used in coupled array-based beam forming, steering, and nulling applications.

Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication.