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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.

Customer reviews for wireless earbuds were collected and preprocessed using Playwright and Requests-HTML libraries, ensuring high-quality and relevant data. This paper introduces sentiments associated with these aspects were identified using Recurrent Neural Networks (RNNs) and Bidirectional Encoder Representations from Transformers (BERT) enhanced with attention mechanisms, which helped focus on the most relevant text segments. The models were integrated using ensemble methods, specifically Voting+BERT and Bagging+BERT, to improve accuracy and robustness. The Bagging+BERT model achieved the best performance, with an accuracy of 89.9 %, outperforming traditional machine learning models like Bayesian and logistic regression by 9.6 % and 8.7 %, respectively.

This Leishmaniasis is common skin lesion parasitic disease caused by Leishmania protozoan parasites on exposed body and its polymorphic nature complicates to diagnosis because the lesion may create confusion with other dermatoses likewise fungi, bacteria and non-infectious diseases. The molecular techniques, microscopy, culture, and rapid diagnostic test are conventional methods that are timeconsuming, expensive, susceptible to errors with limited resources in health care services. Early diagnosis with timely identification of multifaceted Leishmaniasis is aided to selection of therapy and provide comfort to patient to combating with it. The promising integration of artificial intelligence (AI) with medical diagnostics has efficacy in numerous fields of identification of diseases as in Dermatology research. The fast, efficient and automatic diagnosing of leishmaniasis with microscopic images of lesion's seamer with VGG-16 deep learning (DL) model is the approach to reach the objective of this designed research to identify the negative and positive results. The exceptional performance of designed VGG-16 is achieved with accuracy of 88.14%, precision 100%, sensitivity 77.42%, specificity 100%, F1-score 0.87%, and ROC curve 97%. The proposed modified VGG-16 model is more precise, swift, reliable, efficient, effectual, economical and user-friendly substitute to address all key factors than human resource to find the leishmaniasis affected that may support medical care services.

The endgame stage of Go presents a unique challenge for scientific research. Contrary to previous stages, in the endgame the key to a successful analysis is board decomposition into smaller, independent local positions. Go players typically analyze these positions separately and prioritize moves based on their value. In this paper, I introduce a novel program that automates this decomposition-based analysis for the endgame stage of Go. AlphaZero has revolutionized Computer Go, by applying a generic move-selection mechanism, based on neural network judgments and the MCTS search algorithm. However, it does not specifically address the complexity of endgame in the aforementioned manner. On the other hand, by leveraging the decomposition-based analysis, my program reaches decisions in the endgame with relatively little computation. Additionally, it offers insights for Go practitioners by providing accurate move value evaluations. Notable prior work on automated endgame analysis was done by Martin Müller (1995). His program Explorer checked all possible variations in every undecided position and aggregated the results based on an algorithm inspired by the Combinatorial Game Theory (CGT). However, due to the exponential growth of the number of variations, Explorer’s application was limited to small, tightly bounded local positions. In contrast, my program leverages a neural network to predict optimal local moves, dramatically reducing the number of variations that need to be explored. Provided that the neural network’s predictions are correct, the program can accurately evaluate move values by considering relatively few variations, just like human Go experts do. Thanks to this approach, it is the first program capable of analyzing large, unbounded local positions, which are commonly encountered in real games. The neural network was fine-tuned from a pre-trained AlphaZero reimplementation on the task of optimal local move prediction. Training data was gathered from KataGo self-play games, utilizing KataGo’s network to perform board decomposition.

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information―both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people’s willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

One of the emerging trends in the marketing field is digital video marketing. Online videos offer rich content typically containing more information than any other type of content (e.g., audible or textual content). Accordingly, previous researchers have examined factors influencing videos’ popularity. However, few studies have examined what causes a video to remain popular. Some videos achieve continuous, ongoing popularity, while others fade out quickly. For practitioners, videos at the recommendation slots may serve as strong communication channels, as many potential consumers are exposed to such videos. So,this study will provide practitioners important advice regarding how to choose videos that will survive as long-lasting favorites, allowing them to advertise in a cost-effective manner. Using deep learning techniques, this study extracts text from videos and measured the videos’ tones, including factual and emotional tones. Additionally, we measure the aesthetic score by analyzing the thumbnail images in the data. We then empirically show that the cognitive features of a video, such as the tone of a message and the aesthetic assessment of a thumbnail image, play an important role in determining videos’ long-term popularity. We believe that this is the first study of its kind to examine new factors that aid in ensuring a video remains popular using both deep learning and econometric methodologies.

This study proposes a surrogate model framework that integrates finite element analysis and deep learning to rapidly estimate equivalent material properties of patterned sheets. Conventional homogenization methods can only be applied after the pattern geometry has been finalized, requiring additional modeling and simulation. In contrast, the proposed approach establishes a surrogate model in advance, enabling the immediate estimation of equivalent material properties once the pattern geometry is defined. A dataset of 5,000 cases was generated using simulations, and Bayesian hyperparameter optimization was applied to improve model performance. The surrogate model achieved R² values above 0.99 for all target properties, confirming high internal consistency. Experimental validation with patterned STS304 specimens yielded meaningful results, with all errors remaining within 15%, which demonstrates the reliability of the proposed surrogate model despite minor deviations caused by fabrication imperfections and limited training data. Despite these limitations, the proposed system enables instant estimation of equivalent properties from pattern geometries, offering significant reduction in computational cost and design time. This approach enhances design reliability and provides a practical tool for the application of patterned materials in industrial engineering.

We developed a deep learning-based algorithm with plant fruit images to predict the quantitative traits, fruit size, and weight. Highbush blueberry was selected as a model plant because of its commercial importance. Mask R-CNN was adopted for a deep learning guidance model to predict fruits' width, length, and weight. The deep learning algorithm had a high performance on object detection and image segmentation with more than 90% accuracy and detection rate.

인터넷 서비스의 확산과 함께 정교해지는 피싱(Phishing) 공격은 개인 정보 탈취 및 금융 피해를 유발하는 심각한 보안 위 협으로 대두되고 있다. 기존의 피싱 탐지 체계는 주로 구글 세이프 브라우징(Google Safe Browsing)이나 피쉬탱크 (PhishTank)와 같은 블랙리스트(Blacklist) 방식에 의존해 왔다. 이 방식은 알려진 위협에 대해서는 신속하고 정확한 차단이 가능하나, 제로데이(Zero-day) 공격을 탐지하지 못하는 치명적인 한계를 가진다. 본 연구에서는 이러한 한계를 극복하기 위해 URL의 어휘적 특징을 기반으로 하는 다양한 인공지능 모델의 탐지 성능을 비교 분석하였다. 실험 대상 모델로는 전통적인 휴 리스틱 알고리즘과 머신러닝 모델인 로지스틱 회귀(Logistic Regression), 서포트 벡터 머신(SVM), 랜덤 포레스트(Random Forest), 그리고 딥러닝 모델인 CNN(1D)과 LSTM을 선정하였다. 실험 결과, 휴리스틱 방식은 44.5%의 저조한 정확도를 보인 반면, SVM(RBF 커널) 모델은 97.0%의 정확도와 0.970의 F1-Score를 기록하며 가장 우수한 성능을 나타냈다. 특히 딥러닝 모 델인 CNN(94.5%)과 LSTM(76.1%) 대비 SVM은 0.165초라는 빠른 추론 속도를 보여 실시간 탐지 환경에서 성능과 효율성의 최적 균형을 갖춘 모델임을 입증하였다.

As internet services proliferate, phishing attacks are becoming increasingly sophisticated and are emerging as a serious security threat that causes the theft of personal information and financial damage. Existing phishing detection systems have primarily relied on blacklist methods such as Google Safe Browsing or PhishTank. While this approach enables the rapid and accurate blocking of known threats, it has a critical limitation in its inability to detect zero-day attacks. To overcome these limitations, this study comparatively analyzed the detection performance of various artificial intelligence models based on the lexical features of URLs. The models selected for the experiment included traditional heuristic algorithms, machine learning models such as Logistic Regression, Support Vector Machine(SVM), and Random Forest, as well as deep learning models like CNN(1D) and LSTM. The experimental results showed that while the heuristic method yielded a poor accuracy of 44.5%, the SVM(RBF kernel) model demonstrated the superior performance, recording an accuracy of 97.0% and an F1-Score of 0.970. In particular, compared to the deep learning models CNN(94.5%) and LSTM(76.4%), SVM demonstrated a fast inference speed of 0.165 seconds, proving it to be the model with the optimal balance between performance and efficiency in a real-time detection environment.

Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia’s ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia’s bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.

This article is two-fold. The ultimate goal of this article is to provide a big data analysis of 330 reviews of the movie Noryang and to evaluate the Naive Bayes model, the Random Forests model, the DNN model, and the LSTM model in machine learning and deep learning. A point to note is that the name Yi, Sun-shin was the most widely used by viewers, followed by the word movie, and the word general, in that order. A major point of this article is that the name Yi, Sun-shin and the word movie showed up twice as the first keyword. This in turn implies that these keywords are the most noteworthy ones. The sentiment analysis argues that about 75% of viewers think of the film as well-made and that they were highly satisfied with it. In this paper, we used the Naive Bayes model, the Random Forests model, the DNN model, and the LSTM model and made them predict whether each review is positive or negative. The Random Forests model works well for our data, whereas the Naive Bayes model does not. When learning took place 25 times, the DNN model worked well for our data (its accuracy rate is 82.76%). When it comes to the LSTM model, its accuracy did not improve even though learning took place 9 times. Yet, the LSTM model is slightly better than the DNN model with respect to the accuracy rate of test data.

Masonry structures account for a large proportion of the building stock worldwide. Presently, the structural conditions of such structures are mostly inspected manually, and which is expensive, laborious and subjective processes. As deep learning technique for computer vision advances, there is an opportunity to automate the visual inspection process using digital images. Several studies are in progress to automatically detect cracks in masonry structures using Deep Learning. However, it is important not only detecting a crack, but also measuring a length of the crack. This is because it is necessary to consider various factors required in the actual environment, such as calculating the cost of reinforcement work. In this paper, we propose the method that detects masonry cracks and measures the length of cracks with digital images. The aim of this study is to implement Deep Learning model for crack detection on masonry structure and to apply the method of crack length measurement additionally.

Background/Objectives Deep learning which main technology in AI has high growth with being applied to field of speech recognition and Image classification. Especially, Deep learning technology in the field of Image classification is being applied as a core technology to Self-driving and crime prevention monitoring system that is recently emerging as the future industry. Methods/Statistical analysis: Various algorithm which is improved and developed CNN being able to do image process is suggested as Deep learning model in image recognition field. In this paper, we introduce various object detection algorithm including CNN. And explore most representative algorithms just R-CNN, Fast R-CNN, Faster R-CNN and difference between versions of YOLO devised to detect and track in real time. Findings: This paper evaluates deep learning algorithm’s performance by comparative analysis about mAP (mean average precision) and FPS (frames per second). In result of performance evaluation, YOLO algorithm is confirmed as that It shows excellent result in speed that detects and recognizes object and accuracy in real time system environment. Finally, we search cases in field of autonomous driving and access control system and home anti-crime system. Improvements/Applications: In this research, we can understand object detection algorithm among speech recognition technologies and proper field in each algorithm, apply security service based on image, recommend proper algorithm in various environment just like autonomous driving and security work, etc.

To train deep learning models faster, distributed training on multiple GPUs is the very popular scheme in recent years. However, the communication bandwidth is still a major bottleneck of training performance. To improve overall training performance, recent works have proposed gradient sparsification methods that reduce the communication traffic significantly. Most of them require gradient sorting to select meaningful gradients such as Top-k gradient sparsification (Top-k SGD). However, Top-k SGD has a limit to increase the speed up overall training performance because gradient sorting is significantly inefficient on GPUs. In this paper, we conduct experiments that show the inefficiency of Top-k SGD and provide the insight of the low performance. Based on observations from our empirical analysis, we plan to yield a high performance gradient sparsification method as a future work.

Globally, chronic diseases have a significant impact on health. The diagnosis of chronic diseases has seen extensive usage of machine learning techniques. Early disease detection and treatment lower the risk of increasing disease severity and, consequently, related mortality. The major goal of this research is to provide a technique that increases classification accuracy while also shortening computing time. This comparative research shows the impact of distinct model architectures and features on disease prediction accuracy in addition to assessing the advantages and disadvantages of each technique. These discoveries have implications for personalized healthcare, allowing medical professionals to select the best models for various chronic conditions. Additionally, this research can direct the creation of better forecasting technologies, as well as influence healthcare legislation and budget allocation. In our study comparative analysis of the state-of-the-art approaches has been presented. Using a hybrid model combination of CNN and RNN could be more beneficial. In conclusion, our comparison research improves our comprehension of the potential of deep machine learning for chronic disease prediction, highlighting the significance of adjusting model selection to certain disease types. To progress the field of chronic disease prediction, future research should concentrate on improving these models, and further explore their applicability across various and larger datasets.

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

본 논문에서는 딥러닝 기술을 국내 광업분야에 효과적으로 도입하기 위하여 광업 분야의 딥러닝 기술 적용연구들을 조사하고 정리하였다. 딥러닝은 ANN에 기원을 둔 머신러닝 알고리즘으로, 지질학적 문제와 같은 고차원비선형 문제의 추상화에 특화되어 있다. 딥러닝 기술이 조사, 탐사, 개발, 광산운영, 재해분석에 이르는 광업주기에 따라 어떻게 적용되었는지 조사되었다. 조사 및 탐사단계에서는 지구화학탐사, 물리탐사, 지질도, 단층선, 지형정보를융합하여 광물 부존가능성 지도화를 수행하는 연구가 다수 제안되었다. 광산 개발 및 관리 단계에서는 센서나 카메라에 의해 측정된 광산 현황 자료로 광산 내 상황 자동감지, 솔루션 자동연산, 자동 장비제어에 대한 연구가 수행되고 있었다. 재해 분석 단계는 상황인식 및 재해 예측이 주된 목적이었다. 딥러닝 기술이 국내광업분야에 효과적으로 도입되기 위해서 관련된 국내 적용연구가 요구된다.

Applications for deep learning technology in the mining industry were investigated to utilize deep learning technology in the domestic mining field. Deep learning is specialized in abstracting high-dimensional nonlinear problems, such as geological problems. The author investigated the applications of deep learning technology for mining cycle that is divided into investigation, exploration, development, mine operation, and disaster analysis. In the survey and exploration phase, a number of studies have been proposed to carry out mineral potential mapping by integrating geochemical and geophysical explorations, geological maps, fault lines, and topographic information. In the mine development and management phase, studies on autonomous situation detection, automatic calculation, and equipment control were carried out using mine status data measured by sensors or cameras. The hazard analysis phase was mainly used for status awareness and hazard prediction. In order for deep learning technology to be effectively utilized in the domestic mining field, relevant domestic research is required.