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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.

Recent advancements in data-driven methodologies have brought significant attention to the computational prediction of material properties. Traditional machine learning (ML) approaches have struggled to achieve high accuracy due to the complex relationships between a material's structure and its properties. To address this challenge, in this work, we present an ML framework for predicting the stability of silicon (Si) and Si-based alkaline metal alloys with reduced error. This emphasizes the model transferability to discover new silicon alloys with diverse electronic configurations and structures. We explore the effectiveness of two atomic structural descriptors including X-ray diffraction (XRD) and sine coulomb matrix (SCM). The dynamic ensemble learning (DEL) model is trained and evaluated using 750 Si alloys from the materials project database (MPD) and optimized via ensemble learning. The results indicate that the XRD descriptor with DEL performs most reliably for formation energy, total energy and packaging fraction prediction, showing the model robustness and transferability for ultimate efficient silicon anode’s material synthesis.

In recent decades, machine learning (ML) algorithms has gained wide popularity in the finance literature. The goal of this research is to exploit machine learning techniques in order to analyze the effect of exchange-traded fund (ETF) illiquidity on tracking errors. We demonstrate the superior performance of the machine learning models – Random Forest and Gradient Boosting Decision Tree, in particular - over traditional linear models in predicting U.S. ETF’s tracking errors. Moreover, our variable importance analysis suggests that the features such as underlying assets based on U.S. assets (Invested in US Asset) and expense ratio (Expense Ratio), are two key factors in the determination of predicting the tracking errors on the ETF illiquidity. Finally, we further conduct SHAP (Shapley Additive exPlanations) technique in order to observe the impact of a particular variable(feature) on the difference between the considered- and average predictions of our machine learning models. Our results indicate that the most relevant variable is Invested in US Asset, which is in align with the previous importance analysis.

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of 22Na, 60Co, and 137Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM >ANN>CNN, while the training time followed the order SVM>ANN>CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

토양수분은 가뭄 발생과 해소를 매개하는 핵심 인자로서, 대기-지표-지하수로 이어지는 수문순환의 연결 고리 역할을 한다. 이처럼 가뭄 연구 및 분석을 위한 토양수분 자료 관측 센서를 설치하여 측정하고 있으나, 기상 및 통신 장애로 결측이 발생하여 자료 활용에 불편함을 겪고 있다. 본 연구에서는 해남·예산 지역에 설치된 토양수분 모니터링 시스템의 토양수분 결측 자료를 보간하기 위하여 먼저 강수 자료를 보간하고, 강수 특징변수로 머신러닝의 학습자료를 5개로 구축하여 학습 및 평가자료 정확도 결과를 비교·분석하였다. 연구 결과, 지연, 누적, 시계열 특징변수로 구성한 D, E 학습자료 기반 머신러닝 모형이 훈련·검증자료 정확도가 우수하였다. 평가자료 정확도는 D, E 학습자료 기반 XGB 모형이 우수하였으나, E 학습자료 기반 XGB 모형은 다른 조합 대비 더 많은 경우에서 우수한 정확도를 보였다. 따라서 E 학습자료 기반 XGB 모형을 활용하여 해남·예산 지역에 설치된 토양수분 모니터링 시스템의 10·20cm 깊이 토양수분 결측 자료를 보간하는게 적절하다고 판단하였다.

Soil moisture is a key variable governing drought onset and recovery and a critical link in the hydrological cycle connecting the atmosphere, land surface, and groundwater. Missing observations frequently occur in soil moisture monitoring systems due to meteorological and communication failures, limiting data usability. In this study, missing soil moisture data from monitoring systems in Haenam and Yesan were gap-filled by first correcting precipitation data and constructing five machine-learning training datasets using precipitation-based features. Model performance was evaluated using training, validation, and evaluation datasets. Results indicate that Training D and E datasets, incorporating lagged, accumulated, and time-series precipitation features, combined with the XGB algorithm, showed superior performance. The D– and E–XGB combinations also achieved high accuracy in the evaluation dataset, with the E–XGB model outperforming others in more cases. Therefore, the E-dataset XGB model is suitable for gap-filling 10- and 20-cm soil moisture data in the Haenam and Yesan regions.

Heart disease is a major cause of mortality in the world that is in dire need of accurate, interpretable predictive measures that could be utilized to manage it proactively. The writer of this paper proposes an Explainable AI (XAI) Ensemble Machine Learning model to predict heart disease using an 1,025 patient record dataset. To achieve methodological rigor and generalization, 5- Fold Stratified Cross-Validation (CV) was used to evaluate all models, such as LightGBM and Random Forest. LightGBM model was stable and better in performance as it had Mean CV Accuracy of ±0.9620 ±0.0178. Integration of XAI (SHAP/LIME) is the means of creating clinical trust; analysis has confirmed maximum heart rate (thalach) and type of chest pain (cp) as medically significant characteristics. This framework supports the sustainable smart city healthcare through a highly transparent decision-support system, which manages the resources in optimizing scalable public health programs.

실제세계에서 데이터 수집의 비용과 한계를 고려할 때, 시뮬레이션 생성 환경은 데이터 생성 과 다양한 시도에 있어 효율적인 대안이다. 이 연구에서는 Unity ML Agent를 로그라이크 장 르에 적합한 강화학습 모델로 구현하였다. 간단한 게임에Agent를 이식하고, 이 Agent가 적을 인식하고 대응하는 과정을 코드로 작성하였다. 초기 모델은 조준사격의 한계를 보였으나 RayPerceptionSensor-Component2D를 통해 Agent의 센서 정보를 직접 제공함으로써, Agent가 적을 감지하고 조준 사격을 하는 능력을 관찰할 수 있었다. 결과적으로, 개선된 모델 은 평균3.81배 향상된 성능을 보여주었으며, 이는 Unity ML Agent가 로그라이크 장르에서 강화학습을 통한 데이터 수집이 가능함을 입증한다.

Collecting large amounts of data in the real world is expensive and has clear limitations. Simulation-generated environments, on the other hand, offer the opportunity to efficiently generate the necessary data and to try different things easily and quickly. In this research, we utilized one of the tools that addresses these challenges, by the Unity Machine Learning tool, to study an efficient automation model that responds to the characteristics of the rogue-like genre. For testing purposes, we implemented a simple game, implanted an agent into the main character of the game, and fed the agent with code to shoot and avoid hostile. The implemented ML Agent successfully recognized the hostile targets and responded by shooting and dodging them. However, instead of learning to prioritize the hostile targets over time by reinforcing itself and shooting the high-risk targets first, it consistently fired in only one of the 360-degree directions given to it at the beginning, which we didn’t expected, so we improved the code. By utilizing the RayPerceptionSensor-Component2D element to directly feed the agent's sensors with information about hostile targets, we found that the agent was able to utilize its ray sensor to detect them and make much more precise aimed shots. In fact, it outperformed the original model by an average of 3.81x, proving that Unity ML Agentcan collect data through reinforcement learning in the roguelike genre.

The integration of technology into agriculture crop recommendation and Prediction has significantly transformed local and global agricultural productivity. Machine learning, has played a crucial role in refining this technology, offering substantial benefits to farmers, especially those operating on a small-scale farming. By using various algorithms, these technological tools have become highly effective, enabling precise predictions with minimal deviation in expected crop growth. This research highlights how different machine learning models, typically used individually, can be integrated to enhance device programming. The study underscores the impact of information technology on agriculture, demonstrating how ensemble algorithms can empower the industry to consistently achieve targeted production levels.

In an effort to raise funds, North Korea now performs hacking assaults against the world's financial institutions. More specifically, the North Korean hackers demand money to decrypt the files they created, and since these transactions are handled anonymously, it is difficult to identify them. Therefore, this research uses the BitcoinHeist dataset to identify cryptocurrency-related ransomware. We construct the experiment with two distinct steps: classification and anomaly detection. The XG boosting technique achieved a 100% accuracy score in the first experiment. Even though anomaly detection methods were used in the second trial for detection, they only managed to get a precision score of 50%, whereas XG boosting produced 92%. These tests indicate that the machine learning method for ransomware detection is effective. This study excels in classification and anomaly detection, which is especially noteworthy given that another paper recently classified ransomware variants except for the "white" designation.

With the increase in cyber data attacks, the manual method of investigating cyber-attacks is more prone to errors and is time consuming. With the increase in advanced cyber threat attacks with the same patterns, timely investigation is not possible. There are many systems proposed which analyse and predict threats using various machine learning methods. In this various models apply machine learning algorithms to analyse and predict cyber-attacks.

Due to the recent increase in the mobile streaming market, mobile traffic is increasing exponentially. IMT-2020, named as the next generation mobile communication standard by ITU, is called the 5th generation mobile communication (5G), and is a technology that satisfies the data traffic capacity, low latency, high energy efficiency, and economic efficiency compared to the existing LTE (Long Term Evolution) system. 5G implements this technology by utilizing a high frequency band, but there is a problem of path loss due to the use of a high frequency band, which is greatly affected by system performance. In this paper, small cell technology was presented as a solution to the high frequency utilization of 5G mobile communication system, and furthermore, the system performance was improved by applying machine learning technology to macro communication and small cell communication method decision. It was found that the system performance was improved due to the technical application and the application of machine learning techniques.

Unlike other games such as chess, draughts and backgammon, computers are currently quite weak at the game of go ( baduk). Brute force is difficult due to the higher branching factor and game length. Human made algorithms become very complex before even approaching human strength on a subproblem of the game. One possible approach to this challenging problem is to use machine learning to let the program learn and improve without increased human effort. Machine learning has been successful in other games (e.g. draughts, backgammon). In this paper we give an overview of existing techniques. We discuss different aspects of learning, and propose some directions of research. In particular we believe that a first order representation language combined with a multistrategy learning system can achieve much more than what currently exists.

This research aims to use machine learning technology in human resource management to predict employees’ work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers’ work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study’s outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

This study investigates the use of machine learning techniques to estimate the value of studio apartments (Officetel), which are increasingly important as combined office and residential spaces for single-person households and freelancers. It aims to identify key variables affecting studio apartment prices and preprocess them for accurate predictions. Transaction data from studio apartments are used to compare the predictive performance of four methods: Multiple Regression Analysis, Random Forest, XG Boosting, and Deep Learning. The study seeks to determine the best-performing models for price estimation and aims to develop predictive models applicable to various real estate types and regions.

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 availability of detailed data on customers’ online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the ‘entropy’ concept from information theory, while ‘random forests’ method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers’ information search behaviors differ significantly across product categories.

본 연구는 뉴스 감성 분석과 주식 가격 데이터를 결합하여 주식 가격 움직임을 예측하는 방법에 관한 연구이다. 본 연구에서는 LDA, 랜덤 포레스트, 로지스틱 회귀, SVM, XGBoost 등 다양한 머신러닝 모델과 소프트 보팅 앙상블 모델을 사용하여 이 들의 주가 예측 성능을 평가한다. 실험 결과 소프트 보팅 앙상블 방법은 독립적으로 동작하는 개별 모델들 보다 우수한 성능을 나타내었으며 0.78의 정확도, 0.73의 정밀도, 0.87의 재현율, 0.88의 AUC를 달성하였다. LDA 모델 역시 0.83의 정확도, 0.84의 정밀도, 0.84의 재현 율, 0.84의 AUC를 보여 의미 있는 결과를 나타내었다. 이는 LDA에서 사용하는 뉴스 주제 추출이 의미 있는 정보를 제공하였음을 의미한다. 본 연구 결과는 뉴스 감성 분석과 머신러닝을 통합한 방법이 주식 가격 예측을 개선할 수 있는 잠재력을 보여 주고 있으 며 , 이는 투자자와 금융 전문가에게 이점을 제공할 수 있다. 향후 연구 방향은 본 연구 결과에 고급 자연어 처리 기술을 추가한 동적 앙상블 모델을 개발하는 것이다.

This study investigates using news sentiment analysis combined with stock price data to predict stock price movements. It explores various machine learning models, including LDA, Random Forest, Logistic Regression, SVM, and XGBoost, as well as a soft voting ensemble model. The results show the soft voting ensemble outperformed individual models, achieving 0.78 accuracy, 0.73 precision, 0.87 recall, and 0.88 AUC. The LDA model also showed promising results, with 0.83 accuracy, 0.84 precision, 0.84 recall, and 0.84 AUC, suggesting news topic extraction can provide valuable insights. The findings highlight the potential of integrating news sentiment analysis and machine learning for improved stock price forecasting, which can benefit investors and financial professionals. Future research directions include exploring advanced NLP techniques, incorporating additional data sources, and developing dynamic ensemble models.

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.