• International Journal of Internet, Broadcasting and Communication
• /
• 제16권3호
• /
• pp.290-297
• /
• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• 한국멀티미디어학회논문지
• /
• 제20권8호
• /
• pp.1175-1186
• /
• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.

• 스마트미디어저널
• /
• 제12권11호
• /
• pp.1-9
• /
• 2023

스트레스는 감당하기 어려운 외부 또는 내부 요인으로부터 유발되는 것으로 현대 사회의 주요한 문제 중 하나이다. 높은 스트레스가 장기적으로 지속되면 만성적으로 발전할 수 있으며, 건강 및 생활 전반에 큰 악영향을 초래할 수 있다. 그러나 만성적인 스트레스를 겪는 사람들은 자신이 스트레스를 받고 있는지 알아차리기 어렵기 때문에 사전에 스트레스를 인지하고 관리하는 것이 중요하다. 웨어러블 기기로부터 측정된 생체 신호를 이용하여 스트레스를 탐지한다면, 스트레스를 효율적으로 관리할 수 있을 것이다. 그러나 생체 신호를 이용하는 데에는 두 가지 문제점이 있다. 첫째로 생체 신호에서 수작업 특징을 추출하는 것은 바이어스를 발생시킬 수 있으며, 두 번째는 실험 주체에 따라 분류 모델 성능의 변이가 클 수 있다는 것이다. 본 논문에서는 데이터의 핵심적인 특징을 표현할 수 있는 합성곱 오토인코더를 이용해 바이어스를 줄이고 앙상블 학습 중 하나인 소프트 보팅을 이용해 일반화 능력을 높여 성능의 변이를 줄이는 모델을 제안한다. 모델의 일반화 성능을 확인하기 위하여 LOSO 교차 검증 방법을 이용하여 성능을 평가한다. 본 논문에서 제안한 모델은 WESAD 데이터셋을 이용하여 높은 성능을 보여주었던 기존의 연구들보다 우수한 정확도를 보임을 확인하였다.

• ETRI Journal
• /
• 제33권3호
• /
• pp.310-319
• /
• 2011

In this paper, a new soft-fusion approach for multiple-receiver wireless communication systems is proposed. In the proposed approach, each individual receiver provides the central receiver with a confidence level rather than a binary decision. The confidence levels associated with the local receiver are modeled by means of soft-membership functions. The proposed approach can be applied to wireless digital communication systems, such as amplitude shift keying, frequency shift keying, phase shift keying, multi-carrier code division multiple access, and multiple inputs multiple outputs sensor networks. The performance of the proposed approach is evaluated and compared to the performance of the optimal diversity, majority voting, optimal partial decision, and selection diversity in case of binary noncoherent frequency shift keying on a Rayleigh faded additive white Gaussian noise channel. It is shown that the proposed approach achieves considerable performance improvement over optimal partial decision, majority voting, and selection diversity. It is also shown that the proposed approach achieves a performance comparable to the optimal diversity scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권9호
• /
• pp.2334-2347
• /
• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제14권4호
• /
• pp.240-248
• /
• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권3호
• /
• pp.1264-1286
• /
• 2018

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

• International Journal of Computer Science & Network Security
• /
• 제21권1호
• /
• pp.97-106
• /
• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 한국컴퓨터그래픽스학회논문지
• /
• 제28권1호
• /
• pp.1-9
• /
• 2022

비소세포폐암(NSCLC)은 전체 폐암 중 85%의 높은 비중을 차지하며 사망률(22.7%)이 다른 암에 비해 현저히 높은 암으로 비소세포폐암 환자의 수술 후 예후에 대한 예측은 매우 중요하다. 본 연구에서는 종양을 관심영역으로 갖는 비소세포폐암 환자의 수술 전 흉부 CT 영상 패치의 종류를 종양 관련 정보에 따라 총 다섯 가지로 다양화하고, 이를 입력데이터로 갖는 사전 학습 된 ResNet 과 EfficientNet CNN 네트워크를 사용하여 단일 모델과 간접 투표 방식을 이용한 앙상블 모델, 그리고 3 개의 입력 채널을 활용한 앙상블 모델에서의 실험 결과 및 성능을 오분류의 사례와 Grad-CAM 시각화를 통해 비교 분석한다. 실험 결과, 종양 주변부 패치를 학습한 ResNet152 단일 모델과 EfficientNet-b7 단일 모델은 각각 87.93%와 81.03%의 정확도를 보였다. 또한 ResNet152 에서 총 3 개의 입력 채널에 각각 영상 패치, 종양 주변부 패치, 형상 집중 종양 내부 패치를 넣어 앙상블 모델을 구성한 경우에는 정확도 87.93%를, EfficientNet-b7 에서 간접 투표 방식으로 영상 패치와 종양 주변부 패치 학습 모델을 앙상블 한 경우에는 정확도 84.48%를 도출하며 안정적인 성능을 보였다.

• 지능정보연구
• /
• 제29권2호
• /
• pp.241-265
• /
• 2023

기업의 부실 예측 모델은 기업의 재무 상태를 객관적으로 모니터링하는 데 필수적인 도구 역할을 한다. 적시에 경고하고 대응 조치를 용이하게 하며 파산 위험을 완화하고 성과를 개선하기 위한 효과적인 관리 전략을 수립할 수 있도록 지원한다. 투자자와 금융 기관은 금융 손실을 최소화하기 위해 부실 예측 모델을 이용한다. 기업 부실 예측을 위한 인공지능(AI) 기술 활용에 대한 관심이 높아지면서 이 분야에 대한 광범위한 연구가 진행되고 있다. 해석 가능성과 신뢰성이 강조되며 기업 부실 예측에서 설명 가능한 AI 모델에 대한 수요가 증가하고 있다. 널리 채택된 SHAP(SHapley Additive exPlanations) 기법은 유망한 성능을 보여주었으나 변수 수에 따른 계산 비용, 처리 시간, 확장성 문제 등의 한계가 있다. 이 연구는 전체 데이터 세트를 사용하는 대신 부트스트랩 된 데이터 하위 집합에서 SHAP 값을 평균화하여 변수 수를 줄이는 새로운 변수 선택 접근법을 소개한다. 이 기술은 뛰어난 예측 성능을 유지하면서 계산 효율을 향상시키는 것을 목표로 한다. 해석 가능성이 높은 선택된 변수를 사용하여 랜덤 포레스트, XGBoost 및 C5.0 모델을 훈련하여 분류 결과를 얻고자 한다. 분류 결과는 고성능 모델 설계를 목표로 soft voting을 통해 생성된 앙상블 모델의 분류 정확성과 비교한다. 이 연구는 1,698개 한국 경공업 기업의 데이터를 활용하고 부트스트래핑을 사용하여 고유한 데이터 그룹을 생성한다. 로지스틱 회귀 분석은 각 데이터 그룹의 SHAP 값을 계산하는 데 사용되며, SHAP 값 평균은 최종 SHAP 값을 도출하기 위해 계산된다. 제안된 모델은 해석 가능성을 향상시키고 우수한 예측 성능을 달성하는 것을 목표로 한다.