Earticle

The most recent language model in NLP, known as BERT, has numerous advantages over other language models. It ‘understands’ human language in a subword unit and can recognize rare words and out-of-vocabulary words. Furthermore, it considers syntax and context during the text-understanding process. We applied the evaluation metric “BERTscore” using this BERT model to evaluate human translation (HT). 120 translated texts were evaluated with five evaluation metrics: BLEU, METEOR, emBLEU, emMETEOR, as well as BERTscore and the result was compared with professional translators’ evaluation. The comparison examines the validity and reliability of these metrics, particularly the BERTscore, for future application for HT evaluation. BERTscore demonstrated a stable performance, taking first place in scores, and third in ranks. The validity of metrics of word2vec models, especially that of emBLEU, was somewhat disappointing, probably owing to the domain difference between the training corpus and test corpus.

This paper begins with the question. Is it possible that a machine can understand and evaluate the meaning of a text? Word2Vec is a neural network method of computing vector representations of words, which means the tool processes a text corpus and produces numeric vectors on the meanings of the words. This paper seeks to examine whether the automatic evaluation metrics, BLEUmodif and METEORmodif, enhanced by word2vec technology, are able to capture the meaning of translated texts better than their standard versions. To this end, first, five literary texts written in English were translated into Korean by 119 students. Then, these translations were evaluated by two professional translators/teachers and by five different automatic evaluation programs. These are standard versions of BLEU and METEOR as well as BLEUmodif and METEORmodif with the word2vec technology and finally Sent2Vec that computes sentence vector representations. For the sake of convenience, the last three systems are referred to as “embedding versions” in this study. The analysis shows that overall, the standard version of METEOR performed the best (grades: 0.838, ranking: 0.727), but at the level of individual texts, the embedding versions of BLEU and METEOR showed a higher correlation with human evaluation (four texts) than their standard versions (one text).

The purpose of this article is to construct an epistemological problematics of the digital writing in the perspective of a non-anthropocenric concept of posthuman writing. The digital media revolution is intimately related to the writing revolution. In particular in the contex of artificial intelligence, it's time to rethink the nature of writing regarding the question of agency of writers. In sum, the author want to pose a fundamental question, who writes in fact in the digital era? In this regard, a non-anthropocentric conception of writing is needed to explain the posthuman literacy which is based upon the assemblages of human and machine writing. This article insists on the fact that the machine behaves in the digital writing. In this context, the performativity of writing and computer code sources might be recognized.

근대성은 미야자키 하야오의 작품에서 중요한 역할을 한다. 미야자키의 작품에서 근대성은 꿈의 실현과 함께 파괴의 재앙이라는 이중적 모습을 보인다. 특히 근대적 풍경의 이중적인 모습은 선, 기계, 속도, 불과 철과 관련해 드러난다. 첫째로 근대는 직선의 시대이다. 직선은 근대가 만들어낸 새로운 발명품으로서, 바둑판 모양으로 구획된 근대 도시는 직선으로 이루어진 효율성의 공간이다. 하지만 미야자키는 곡선을 선호한다. 구불구불한 자연의 풍경, 동글동글한 모양의 비행기가 곡선을 그리면서 비행하는 모습이 자주 등장한다. 둘째로 근대는 속도의 시대이다. 속도는 근대 사회가 이전 사회와 비교해서 새로이 강조된 가치 중의 하나이다. 속도와 효율성의 이데올로기는 근대를 지배하고 있으며, 더 빠르게, 더 많은 물건을 생산하려는 근대사회의 욕구를 반영한다. 하지만 미야자키는 속도의 경제학에 반대해서 느린 속도를 자유와 해방감과 연결시킨다. 셋째로, 근대 사회는 불과 철의 시대이다. 철로 만들어진 기계들과 증기기관과 같은 동력원들은 세상의 풍경을 바꾸어 놓았다. 불과 철은 인간에게 자연으로 벗어날 수 있는 힘을 주었다는 점에서 인간의 힘과 가능성을 확대시켰다. 하지만 불과 철의 힘은 <바람계곡의 나우시카>(1984)에서의 거신병들, <천공의 성, 라퓨타>(1986)의 라퓨타에서 보여지듯이 세상을 불태워 버린다. 또 꿈의 구현물이던 전투기가 세상을 불태우는 차가운 철의 무기가 있음을 미야자키는 동시에 보여주고 있다. 근대성의 네 번째 이미지는 기계들로 이루어진 근대적 풍경이다. 비행기, 탱크, 기차들과 같이 다양한 기계들이 미야자키의 작품에 등장하는데, 이러한 기계들은 인간의 꿈을 실현시키면서, 동시에 인간과 자연을 파괴하는 양면성을 보여주고 있다. 연구자는 이 연구에서 미야자키의 근대성을 네 가지 중심 풍경을 중심으로 살펴보고자 한다.

Modernity plays an important role in Miyazaki's animations. In Miyazaki's animations, two faces of modernity appear- the nice one which frees ourselves from the bondage of nature and the violent one which destroys nature. Miyazaki's modernity is closely related with four images: line, speed, fire and iron, and machines, First, Modern world is an age of straight line. Straight line is an invention of modern world, and modern city is composed of many building and streets of straight lines, which is associated with its efficiency. But Miyazaki prefer circular line, which symbolizes people's harmony with nature. Fast speed is also emphasized with the development of modernity. "Faster and more" is the slogan of modern capitalism. Slow speed is usually related with laziness and inefficiency. But Miyazaki thinks slowness is the essence of freedom and spiritual abundance. Miyazaki' another interest is fire and iron. Modern world is built upon fire and iron, which give us the energy to free ourselves from nature and to control nature. They are used and developed in the age of industrialization and modernization. But our desire for expansion and domination leads to the destruction of nature. Fire and iron can give powerful weapons to modern dictators which have desire for power and domination. Machines, especially weapons, are the important tools to achieve this goal. Miyazaki's works, including Porco Rosso and The Wind Rises, show this double-sidedness. A machine represented by airplane shows harmony with nature. But making an airplanes is also a cursed dream because it also leads to destruction and death.

기계는 미야자키 하야오의 작품에서 매우 중요한역할을 담당한다. 비행기, 탱크, 기차들과 같이 다양한 기계들이 미야자키의 작품에 종종 등장하는데, 획일화된 모습이 아니라 각각 개성이 있는 사물들로 등장한다. 미야자키의 작품에서 기계는 인간의 꿈을 실현시키지만, 동시에 인간과 자연을 파괴하는 도구로서의 양면성을 지니고 있다. 기계는 자연의 한계를벗어나 인간의 능력을 향상시키고, 인간의 꿈을 실현시켜주는 역할을 한다는 점에서 미야자키는 긍정적으로 기계의 모습을 그려내고 있음을 알 수 있다. 특히구식 기계들은 커다란 볼거리를 제공하는데, 그중에프로펠러 비행기는 미야자키에 의해 아름답게 그려지고 있다. 하지만 동시에 기계는 다른 인간을 지배하고, 자연을 파괴하는 도구가 된다. 발전을 거듭하는기계는 일정 단계를 넘어서면, 인간에게 재앙으로 작용한다. <바람이 분다>와 <붉은 돼지>는 이러한 양면성을 잘 보여주는 작품이다. 두 작품은 시대를 배경으로 근대화되어가는 과정에서 자연과 하나되는 아름다운 기계의 모습과 자연을 파괴하는 기계의 모습을동시에 보여주고 있으며, 이러한 이중적 모습을 통해서 미야자키는 기계의 미학을 제시하고 있다.

Machines play important roles in Miyazaki'sanimations. Machines are used and developedwith the age of industrialization andmodernization. Industrialization andmodernization arouse the idea of modernstates and nationalism, which lead to thedomination of nature and other countries. Thedesire for expansion and control is the keyconcept of the desire for domination. Everycountry strives for this aim in the name ofnationalism. Machines are the important toolsto achieve this goal. In Miyazaki's animations, two kinds ofmachines appear, the one which is in harmonywith nature and the one which destroys natureand people. The distinction between aharmonious machine and a destructive onedepends upon the idea of 300km/h. Accordingto Miyazaki, if a plane is flying slower than300km per hour, there is room for inventionsand originality in their designs. This plane canbe made beautiful, elegant and creative in itsform. But if we try to have a plane over300km/h, we will lose the chance of makingbeautiful plane, his plane becomes destructiveweapon. A faster and more powerful machineis invented by men's greed and desire forpower. It controls nature and destroys ourharmonious relationship with nature. Porco Rosso and The Wind Rises show thisdouble-sidedness. Porco Rosso shows anairplane harmonious with nature. All the planesin this animation have individualistic traits andpeculiar beauty. Airplanes, aviation and loveagainst the background of the Adriatic Seaarouse the romantic feeling in our heart. Butthe romantic world comes to an end whenFascist state wants more powerful airplanes. The Wind Rises shows an engineer whomakes an airplane, which is his entire dream. A flying machine is not a thing for him, but itis a dream. But it is a cursed dream becauseit leads to destruction. Jiro's airplanes becomedangerous weapons when he invents afight-plane faster than 300km/h.

Norbert Wiener, in the next days of the Second World War, symbolizes intellectually speaking, the industrial, scientific and military dominion of the U.S.A.. He had elaborated, in 1938, a new theory, the cybernetics, which benefited from an international broadcasting in 1950. In the heart of this doctrine appear the interactions between energy and information as well as the concepts of entropy, redundancy, feedback and regulation. Directly stemming from the engineering, the cybernetics conceives as identical information exchanges uttered and received by machines and/or humans beings. By 1960, this theory took such a scale in the intellectual circles as his designer worried about it publicly. He did not wish that his scientific work guarantees for decision-making by the thinking and communicating machines on the place of the humans beings.

Norbert Wiener, aux lendemains de la Seconde guerre mondiale, symbolise sur le plan intellectuel, la domination industrielle, scientifique et militaire des U.S.A.. Il avait élaboré, en 1938, une nouvelle théorie, la cybernétique, qui bénéficia d’une diffusion internationale en 1950. Au coeur de cette doctrine apparaissent les interactions entre énergie et information ainsi que les concepts d’entropie, de redondance, de rétroaction et de régulation. Directement issue de l’ingénierie, la cybernétique conçoit comme identiques les échanges d’informations émis et reçus par les machines et/ou les hommes. Vers 1960, cette théorie prit une telle ampleur dans les milieux intellectuels que son concepteur s’en inquiéta publiquement. Il ne souhaitait pas que son oeuvre scientifique cautionne les prises de décisions par les machines pensantes et communicantes à la place des hommes.

이 글은 기술의 시대, 문화의 시대라고 불리는 현대에서의 인간의 자기이해를 문제 삼고 있다. 현대인의 자기이해가 가장 잘 드러나는 곳이 대중문화이고, 영화 􌋢매트릭스􌋣가 그러한 대중문화의 산물로서 대단한 상업적인 성공을 거두었기 때문에, 이 글은 특히 이 영화에서 볼 수 있는 현대인의 자기이해에 초점을 맞추었다. 하지만 이 글은 이러한 대중문화적인 자기이해를 규명하는 것을 넘어서서 그러한 자기이해가 가지는 문제를 또한 지적하고자 한다. 􌋢매트릭스􌋣에서 볼 수 있는 현대인의 자기이해들 중에서 특히 주목할 만한 것은 다음 네 가지이다. ‘인간은 기계보다 못한 존재이다’, ‘인간은 기계에 의해 통제되고 있다’, ‘인간은 현재 진실한 자아를 가지고 있지 않다’, 그리고 ‘인간은 사랑이 아니라 힘을 통하여 자아를 실현할 수 있다’. 컴퓨터의 발달속도를 생각할 때 조만간 인간이 컴퓨터보다 못할 가능성이 농후하다. 하지만 이러한 컴퓨터의 우월성은 인간의 능력들 중에서 특정 능력 즉 연산능력에 한정되는 것이지, 전체 능력에서의 우월성이 아니다. 마찬가지로 인간이 기계에 의해서 통제되는 측면이 없는 것은 아니지만 기계는 인간에 의해서 더 많이 통제되고 있다. 영화는 기계의 통제를 의인화하여 과장하고 있으며 관객은 당연한 기계의 통제마저 거부하고 있다. 우리는 때로 자신이 자신이 아니라 는 느낌을 갖기는 하지만, 후기 자본주의에서 이러한 느낌은 기만이나 회의의 산물이라기보다 자본주의적인 삶의 양식의 필연적 결과이다. 영화는 이러한 탈아감을 강조하고 있지만 이는 현실적인 탈아감을 오히려 은폐시키는 결과를 가져올 수 있다는 점에 유의해야 한다. 폭력을 자아실현의 수단으로 보는 태도는 헬레니즘적인 전통으로서 기독교적 전통에 의해서 완전히 극복되지 못했다. 영화는 기독교적인 내용을 헬레니즘적인 전통과 연결시켜 사랑 대신에 폭력을 주창하고 있다. 우리는 폭력이 참된 자아실현의 수단인지 물어야 한다. 􌋢매트릭스􌋣에서 볼 수 있듯이, 오늘날 인간의 올바른 자기이해를 방해하고 있는 것은 과거처럼 종교나 정치가 아니라 대중문화이다. 대중문화는 소비대중을 주체로 구성해냄으로써 기만적 자기이해를 퍼뜨리고 있다. 이러한 기만적 자기이해를 폭로하는 것이 철학적 담론의 한 소명이다.

This article deals with human self-understanding in the contemporary period, the techno-cultural age. Since it is through mass culture that our self-understanding is most clearly revealed, I focus on the film The Matrix, a prominent product of contemporary mass culture. In this article I examine the self-understanding that is found in this film and provide a critique of the self-understanding of mass culture. The self-understanding found in the film The Matrix is reflected in the following motifs: 'Machines exceed humans,' 'Humans are controlled by machines,' 'There is no true self', and 'Humans realize themselves, not through love, but through power.' Considering the development computer technology, the computational speed of computers will surely exceed that of humans in the near future. However, this achievement is limited to to the ability to compute. Humans will still be superior to computers in other areas. While humans are to some extent controlled by machines, machines are still controlled to a greater extent by humans. The Matrix exaggerates the control of machines through anthropomorphism, while the viewers refuse to admit the human control over machines. Sometimes people feel that they are not themselves. This feeling is, in the post-industrial society, not the result of deception and doubt, but an inevitable consequence of a capitalistic society. The Matrix emphasizes this feeling of losing oneself, but conceals the practical loss of self with cinematic self-recovery. We should pay attention to this possibility. Regarding power as the means of self-actualization is a Hellenistic notion, one which was not completely overcome by the Christian tradition. The Matrix supports the idea of power, rather than that of Christian love, by juxtaposing Hellenistic themes with Christian stories. But we must ask whether power is a true means of self-actualization. As the film shows, it is not religion or politics, as in the past, that is disturbing the human endeavor to accomplish a true understanding of oneself; in the contemporary period, it is rather mass culture that frustrates this endeavor. Mass culture spreads a deceptive self-understanding by constituting the consuming mass as the subject. Philosophical discourse must be directed towards the disclosure of this deceptive understanding of oneself,

본 연구는 여러 단어로 구성된 기억 단서에 의해 유발된 뇌파를 이용하여 목격 여부를 우연 수준 이상으로 구별할 수 있는지 알아보기 위해 수행되었다. Ham, Kim과 Jeong(2018)의 연구에서 수집한 사건관련전위 자료(n=69)를 재분석하여 선형 서포트 벡터 머신(support vector machine, SVM) 분류 모델의 정확성을 평가했다. 참가자들마다 절도 범죄를 재연한 동영상에서 목격할 수 있었던 물건과 목격하지 않은 물건을 정확하게 분류한 적중 시행과 정확 기각 시행의 뇌파가 분석에 이용되었다. 선형 SVM 커널을 이용하여 뇌파를 분류한 결과, 기억 단서 제시 후 800∼850ms 구간에서 분류 정확률 평균이 57.43%로 가장 높았다. 이런 결과는 기억 단서가 제시되었을 때 이 단서와 관련된 세부적인 정보 인출이 이뤄지는 것으로 여겨지는 구간의 뇌파를 분석하여 목격 여부를 구별할 수 있음을 시사한다. 마지막으로 수사 현장에서 뇌파를 활용할 때 고려해야 할 점에 대해 논의하였다.

The focus of this study was discriminating the eyewitness memory using single-trial electroencephalogram (EEG) evoked by memory cues composed of multiple words. We reanalyzed the event-related potentials (ERP) data (n=69) from Ham, Kim, & Jeong (2018) studies, and two-class classification was conducted to distinguish correctly witnessed objects vs unwitnessed objects trials using linear support vector machine (SVM) algorithm. The single-trial classification analysis showed the post-stimulus EEG activity between 800 to 850 ms was the most accurate interval for the memory classification (57.43%). This result suggests that the EEG during memory retrieval can be used to determine whether or not it is witnessed. Finally, we discussed some points to consider when using EEG for eyewitness memory classification.

빅데이터 시대에 정형데이터 뿐만 아니라 비정형데이터를 분석하는 것이 중요한 과제로 대두되고 있다. 정부기관이 생산하는 공문서도 텍스트 기반의 대형 비정형데이터로 빅데이터 분석의 대상이 된다. 기관 내부의 업무효율, 지식관리, 기록관리 등의 관점에서 공문서 빅데이터를 분석하여 유용한 시사점을 도출해 나가야 할 것이다. 그러나, 현재 공공기관이 보유중인 공문서의 상당수가 개방포맷이 아니어서 빅데이터 분석을 하려면 비트스트림에서 텍스트를 추출하는 전처리 과정이 요구된다. 또한, 문서파일 내에 맥락 메타데이터가 충분히 저장되어 있지 못하여 품질 높은 분석을 하려면 별도의 메타데이터 확보 노력이 필요하다. 결론적으로 현재의 공문서는 기계가독(machine readable) 수준이 낮아 빅데이터 분석에 비용이 많이 들게 된다. 이 연구에서는 향후 공문서가 기계가독 수준을 높이기 위해서는 공문서의 개방포맷화, 기안문 서식의 표준태그화, 자기 기술(self-descriptive) 메타데이터 확보, 문서 텍스트 태깅 등이 선행될 필요가 있다는 점을 제안한다. 첫째, 문서가 스스로를 설명하기 위해 추가되어야 하는 메타데이터 항목들을 제시하고 이 메타데이터들이 기계가독형이 되도록 문서파일에 저장하는 방법을 제안한다. 둘째, 문서 내용 분석 시 자연어 처리에만 의존하지 않고 행정 맥락에 따라 중요한 키워드를 미리 국제표준 태그로 마킹하여 기계가독형이 되도록 하는 방안을 제안한다.

In the era of big data, analyzing not only structured data but also unstructured data is emerging as an important task. Official documents produced by government agencies are also subject to big data analysis as large text-based unstructured data. From the perspective of internal work efficiency, knowledge management, records management, etc, it is necessary to analyze big data of public documents to derive useful implications. However, since many of the public documents currently held by public institutions are not in open format, a pre-processing process of extracting text from a bitstream is required for big data analysis. In addition, since contextual metadata is not sufficiently stored in the document file, separate efforts to secure metadata are required for high-quality analysis. In conclusion, the current official documents have a low level of machine readability, so big data analysis becomes expensive.

고품위 석회암은 다양한 산업에서 활용되고 있는 고부가가치 자원으로 광체의 안정적인 확보, 채광기술의 효율적인 개선이 요구되고 있다. 본 연구에서는 석회암 광산 현장에서의 신속한 품위 측정 기술 개발을위하여 가시광/근적외선 영역의 스펙트럼을 이용한 CaO 품위 평가 기법을 개발하였다. 개발한 기법은 측정대상 암석을 support vector machine을 이용하여 스펙트럼이 가장 유사한 석회암으로 구분하고, 대상의 CaO 품위를 분류된 석회암의 평균 품위로 평가한다. 제안한 기법을 갑산층 고품위 석회암 광산의 암석에 적용한결과 암석의 재분류 정확도는 59%, 품위의 평균 제곱근 오차는 1.96%로 나타났다. 개발된 기법은 광산에 대한경험이 풍부한 작업자가 없는 경우 석회암의 CaO 품위 추정에 유용하게 활용될 수 있을 것으로 판단된다.

High-calcium limestone is a highly valuable geological material since it has been used as an industrial source material for various applications. Therefore, maintenance of a sustainable ore reserves and development of cost-effective mining techniques are necessary. This study presents a CaO estimation technique using visible/near-infrared reflectance spectrum and support vector machine for rapid in-situ CaO analysis in limestone mining. The proposed method classifies rock samples based on the spectral properties of training datasets using support vector machine and estimates the CaO content as average of CaO of classified results. The developed method was applied to limestone rocks collected from high-calcium limestone mines of Gabsan Formation in Korea. The data analysis results show that the success rate of reclassification is 59% and the root mean square error of estimation is 1.96%, hence it is concluded that the developed method is able to analyze the CaO content in a limestone mine even if experienced mining geologists are unavailable.

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.

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.

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.

4차 산업혁명의 중심에는 인공지능이 있다. 미래 인공지능 기반 사회에 필요한 역량을 기르기 위해 교육은 변 화해야 한다. 본 연구는 초등학교 학생들을 대상으로 Teachable machine을 활용한 인공지능 체험 수업을 개발 및 적용하고, 학생들의 인공지능 흥미도 및 이해도 변화를 분석하였다. 총 10차시의 인공지능 수업 중 4차시는 다양한 인공지능 교육 플랫폼을 이용하였고, 6차시는 Teachable machine 체험을 중심으로 진행하였다. 프로그램 적용 전과 후에 학생들의 인공지능 흥미도와 이해도를 검사하였으며 양적 연구와 질적 연구를 동시에 진행하였 다. 연구 결과, 프로그램 적용 후 학생들의 인공지능 흥미도와 이해도가 모두 향상되었음을 확인할 수 있었다. 또한, 연구 결과를 바탕으로 인공지능 교육 프로그램 개발을 위한 후속 연구를 제언하는 바이다.

Artificial intelligence is at the heart of the Fourth Industrial Revolution. Education must change in order to develop the capabilities necessary for future AI-based societies. This study developed and applied artificial intelligence experience classes using Teachable machine to elementary school students, and analyzed changes in artificial intelligence understanding and interest among students. Among the 10 artificial intelligence classes, 4 classes used various artificial intelligence education platforms, and 6 classes focused on Teachable machines. Before and after the application of the program, students' interest and understanding in artificial intelligence were examined. Quantitative and qualitative studies were conducted together. Studies have shown that both students' interest and understanding of artificial intelligence has improved since the application of the program. Furthermore, based on the findings, we propose a follow-up study for the development of artificial intelligence training programs.