Earticle

본 연구에서는 가격 및 물량 데이터를 포함하는 다중 계층적 이종 네트워크를 사용하여 미국 금융 시장에서 ETF 가격의 예측력을 개선하는 것을 목표로 하였다. 이를 위하여 정보 (1952)가 주창한 정보 엔트로피 개념을 바탕으로 한 상호정보량 (mutual information)과 전이 엔트로피 (transfer entropy)를 주식 수익률 간의 연결을 위한 의존성 지표로 활용하고, 그래프 어텐션 네트워크 기반 방법론을 제안한다. 이 모형은 다양한 연구에서 그 효과가 알려진 네트워크의 연결을 통해 복잡한 시장 관계를 보다 설명 가능성 (explainability)이 높게 묘사함에 따라 가격 예측의 정확성과 신뢰성을 높이고 궁극적으로는 정보에 입각한 투자 선택과 포트폴리오 관리에 대한 의사결정을 돕는 역할을 목표로 한다.

본 연구의 목적은 ADHD와 CD(품행장애) 자동식별과 자동진 단을 위한 생체신호의 인공지능분석방법을 소개하고 리뷰하는 데 있다. ADHD는 흔히 CD와 공존한다. ADHD와 CD는 특히 충 동적 행동장애를 보이며, 동료를 괴롭히며, 이는 성인이 될 때까 지 동반하는 복합적이고 다면적인 뇌장애(brain disorders)이다. 현재 ADHD와 CD를 구별하기 위한 객관적인 실험검증이나 진 단방법은 없으며, ADHD는 흔히 다른 동반질환과 함께 나타나는 일반적인 신경발달장애이기에 진단이 더욱 어렵다. 특히 CD와 관련된 높은 수준의 행동장애에 대한 진단에 어려움이 있다(Tor et al., 2021). 본 연구에서는 ADHD와 CD 자동식별을 위한 분 류모델로 서포트벡터머신(SVM), 다층퍼셉트론(Multilayer perceptron) 등과 같은 기계학습기법(machine learning techniques) 을 제시했다. 향후 본 연구설계대로 ADHD, ADHD+CD, CD 생체신호에 기반한 자동식별과 진단분류를 위한 인공지능 자동진단시스템(Automated Diagnosis System)이 개발된다면, 청소년 재범예방, 재범예측, 재범억제에 대한 지침을 제공할 것이다. 아울러 클라우드시스템과 더불어 웹 기반 응용 프로그램을 실용화하기 위해서는 먼저 청소년과 성인 ADHD와 CD 자동진 단시스템(ADS)을 위한 생체신호의 인공지능분석에서 뇌파(EEG) 와 정량뇌파(QEEG) 사용에 대한 높은 수준의 타당도와 신뢰도 를 검증하기 위해 계속적이고 체계적인 대규모의 연구와 메타분 석이 선행되어야만 할 것이다.

The purpose of this study is to introduce and review “Artificial Intelligence Analysis of Bio-signals for Automated Detection and Automated Diagnosis of ADHD (Attention Deficit/Hyperactivity Disorder) and CD (Conduct Disorder).” ADHD is often comorbid with CD. ADHD and CD especially show impulsive behavioral disorders, harassing colleagues, which are complex and multifaceted brain disorders that accompany adulthood. Currently, there is no objective experimental verification or diagnostic method to differentiate between ADHD and CD, and diagnosis is more difficult because ADHD is a common neurodevelopmental disorder that often occurs together with other comorbidities. In particular, it is difficult to diagnose high-level behavioral disorders related to CD (Tor et al., 2021). In this study, as classification models of automated detection for ADHD and CD, Support Vector Machine (SVM), Multilayer Perceptron, such as machine learning techniques were presented. In the future, if an artificial intelligence automated diagnosis system (ADS) for automated detection and diagnostic classification based on the bio-signals of ADHD, ADHD+CD, and CD is developed according to the design of this study, it will provide guidelines for the prevention, prediction and suppression of adolescent recidivism. In addition, in order to commercialize web-based applications along with cloud systems, continuous and systematic large-scale research and meta-analysis must first be preceded to verify the high level of the validity and reliability of the use of EEG and QEEG in artificial intelligence analysis of bio-signals for adolescents and adults with ADHD and CD.

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.

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

There have been numerous applications of artificial intelligence (AI) technologies to online advertising, especially to optimize the reach of target audiences. Previous studies show that improved computational power significantly advances granular audience targeting capabilities. This study investigates and classifies various machine learning techniques that are used to enhance targeted online advertising. Twenty-three machine learning-based online targeted advertising strategies are identified and classified largely into two categories, user-centric and content-centric approaches. The paper also identifies an underexamined area, algorithm-based detection of click frauds, to illustrate how machine learning approaches can be integrated to preserve the viability of online advertising.

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

Developmental dyslexia is a learning disability that occurs mostly in children during their early childhood. Dyslexic children face difficulties while reading, spelling and writing words despite having average or above-average intelligence. As a consequence, dyslexic children often suffer from negative feelings, such as low self-esteem, frustration, and anger. Therefore, early detection of dyslexia is very important to support dyslexic children right from the start. Researchers have proposed a wide range of techniques to detect developmental dyslexia, which includes game-based techniques, reading and writing tests, facial image capture and analysis, eye tracking, Magnetic reasoning imaging (MRI) and Electroencephalography (EEG) scans. This survey paper critically analyzes recent contributions in detecting dyslexia using machine learning techniques and identify potential opportunities for future research.

Developmental Dyslexia is a learning disorder often discovered in school-aged children who face difficulties while reading or spelling words even though they may have average or above-average levels of intelligence. This ultimately results in anger, frustration, low self-esteem, and other negative feelings. Early detection of Dyslexia can be highly beneficial for dyslexic children as their learning needs can be properly addressed. Researchers have used several testing techniques for early discovery where the data is collected from reading and writing tests, online games, Magnetic reasoning imaging (MRI) and Electroencephalography (EEG) scans, picture and video recording. Several Machine learning techniques have also been used in this regard recently. However, existing works did not focus on the problem of the imbalanced dataset where the percentage of dyslexic participants is much higher compared to non-dyslexic participants, which is expected to be the case for pre-screening among a random population. This paper addresses the imbalanced dataset obtained from dyslexia pre-screening tests and proposes an oversampling and ensemble-based machine learning technique for the detection of Dyslexia. Simulation results show that the proposed approach improves the detection accuracy of the minority class, i.e., dyslexic patients from 80.61% to 83.52%.

Sentiment Analysis of Malayalam Tweets using Machine Learning techniques is done in this paper. The tweets are classified into positive and negative using different machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The different features like Bag of Words (BOW), Term Frequency vs. Inverse Document Frequency (TF IDF), Unigram with Sentiwordnet, and Unigram with Sentiwordnet including negation words are considered for feature vector formation of input dataset. The Random Forest classifier shows higher accuracy while considering Unigram with Sentiwordnet including negation words as a feature.

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

Bandwidth fragmentation is a critical problem for Elastic Optical Networks (EON), and spectrum defragmentation is the most important strategy to mitigate this phenomenon. In this work we propose a Machine Learning (ML) based method for estimating the Blocking Rate, which, when exceeding a threshold, triggers a defragmentation process. This is done in order to achieve better results in terms of the number of blocking demands and the number of re-routed connections. The performance of the proposed method was compared with two other known strategies: fixed-time (FT) defragmentation, and triggering based on one fragmentation metric (BFR). Simulation results were evaluated using two multi-objective metrics. Experimental results show that the proposed method is more efficient than the other two, being the best method in 85.7% of comparisons using the Pareto Coverage metric, and obtaining 47.4% of non-dominated solutions in the Pareto Front.

Automated Search Engine Optimization (SEO) is crucial for streamlining processes, ensuring consistency, and adapting to changes, thereby enhancing a website's overall success and visibility in the competitive online landscape. This research introduces a dataset and a baseline method for classifying website SEO ranks into three categories. Using 26 keywords, data was collected from 780 web pages across various Google rankings, and 36 ranking factors were employed to predict their rank. Key considerations for webpage preparation include anchor text, backlinks, Ref Domain, unique visits, and text length. The Random Forest model exhibited superior performance, achieving an average accuracy of 72% in predicting actual search rankings. The significance of this automated approach lies in identifying web pages requiring SEO improvements, leading to enhanced search engine rankings.

본 논문에서는 인구통계학적, 생리학적 특성을 기반으로 환자에게 가장 적합한 약물을 예측하는 것을 목표로 하는 약물 분류 시스템을 제시한다. 데이터 세트에는 적절한 약물을 결정하기 위한 목적으로 연령, 성별, 혈압(BP), 콜레스테롤 수치, 나트륨 대 칼륨 비율(Na_to_K)과 같은 속성들이 포함된다. 본 연구에 사용된 모델은 KNN(K-Nearest Neighbors), 로지스틱 회귀 분석 및 Random Forest이다. 하이퍼파라미터를 최적화하기 위해 5겹 교차 검증을 갖춘 GridSearchCV를 활용하였으며, 각 모델은 데이터 세트에서 훈련 및 테스트 되었다. 초매개변수 조정 유무에 관계없이 각 모델의 성능은 정확도, 혼동 행렬, 분류 보고서와 같은 지표를 사용하여 평가되었다. GridSearchCV를 적용하지 않은 모델의 정확도는 0.7, 0.875, 0.975인 반면, GridSearchCV를 적용한 모델의 정확도는 0.75, 1.0, 0.975로 나타났다. GridSearchCV는 로지스틱 회귀 분석을 세 가지 모델 중 약물 분류에 가장 효과적인 모델로 식별했으며, K-Nearest Neighbors가 그 뒤를 이었고 Na_to_K 비율은 결과를 예측하는 데 중요한 특징인 것으로 밝혀졌다.

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

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.

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.

Onions are a significant crop in Korea, and cultivation is increasing every year along with high demand. Onions are planted in the fall and mainly harvested in June, the rainy season, therefore, physiological changes in onion bulbs during long-term storage might have happened. Onions are stored in cold room and at adequate relative humidity to avoid quality loss. In this study, bio-yield stress and weight loss were measured as the quality parameters of net packaged onions during 10 weeks of storage, and the storage environmental conditions are monitored using sensor networks systems. Quality estimation of net packaged onion during storage was performed using the storage environmental condition data through machine learning approaches. Among the suggested estimation models, support vector regression method showed the best accuracy for the quality estimation of net packaged onions.

The purpose of this study was to estimate the forest biomass using satellite imagery and machine learning techniques. In this study, Random Forest, XGBoost, SVM, Multiple Linear Regression were used for forest biomass estimation. Research Forest management plan(8th) data and Sentinel-2 imagery information were used to analysis. As the dependent variables, forest biomass was calculated using volume information, and the biomass expansion factor. The 10 bands of Sentinel-2 were used as independent variable. The optimal forest biomass estimation model was selected by comparing the calculated value based on the Research Forest management plan data and the estimate based on the machine learning techniques. MAE, RMSE, and R2 were calculated for comparison of estimated biomass statistics. As a result, the XGBoost model showed the highest RMSE(61.63ton/ha), MAE(44.16ton/ha), and the highest R2(0.48) value, and was evaluated as the optimal biomass estimation model. The average amount of biomass for sub compartments estimated using the XGBoost model was 225.3tons/ha, which was underestimated by 3.4 tons/ha compared to the average amount of biomass calculated using the Research Forest management plan.

Brain tumor is a very terrible disease. Brain tumor is caused by an increased number of cells. The presence of the skull layer around the brain makes it tough in studying the behavior of growth cells. It also raises the complication for the identification of disease. The initial discovery of a brain tumor is necessary to defend the survival of patients. Frequently, the brain cancer segmentation, and classification through the MRI images technique. Though, the radiologists are not providing actual visualization of brain cells in MRI images due to the irregular growth of cells, which forms of cells are growing rapidly and slow at some stage in brain tumors in the brain. So, automatic strategies are required to evaluate thoughts tumors exactly from MRI images in this research automatic, MRI brain tumors are used for classification, segmentation, and Behavior analysis of cell growth. The problem of visualization of cell growth and behavior analysis of brain cells is solved through MRI images which enhance the detection of cancer. To analyze the behavior of cell growth, which forms of cells are growing rapidly and slow at some stage in brain tumors, and analyze the area of images in which type of cells is affected. Single models are less efficient. We will use ensemble models which would also be helpful for better performance and accuracy.