Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database

Dewi, Christine; Chen, Rung-Ching

doi:10.1007/978-3-031-21743-2_7

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…Proposed Method Accuracy [15] AlexNet CNN 92.50% [13] KOA Based on BILSTM Network 84.09% [51] Deep learning-based method 90.60% [52] Hybrid deep learning 93.84% [53] Linear Model and Naïve Bayes 75.13% [54] Ensemble learning method 89.50% [55] SVM and DL 84.60% [56] Neural network 90.00% [22] RetNet-10 method 98.65% [23] DR-CCTNet method 90.17% Proposed Model CNN(Proposed) 95.27%…”

Section: Namementioning

confidence: 99%

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

Ahnaf Alavee,

Hasan,

Hasnayen Zillanee

et al. 2024

IEEE Access

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Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for singlelabel (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We present insights into their respective accuracy and the challenges they face. We anticipate that these insights will prove valuable to researchers and practitioners in the field. Our ambition is that this in-depth study and comparison of models will inform and inspire future research endeavors, ultimately leading to enhanced disease detection in medical imaging and thereby assisting healthcare professionals.

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Section: Namementioning

confidence: 99%

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

Ahnaf Alavee,

Hasan,

Hasnayen Zillanee

et al. 2024

IEEE Access

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“…(6) The systems proffer data-driven insights, which serve as a fulcrum for informed decision-making in pest management strategies [20]. (7) The optimization of pest control measures facilitated by these systems translates into substantial cost efficiencies for agriculturalists and landowners alike [21]. (8) Resistance to pesticides is a growing concern, and targeted pest control strategies made viable through these systems can play a significant role in managing this resistance [22].…”

Section: Pest Recognition Systems and Datasetmentioning

confidence: 99%

“…A myriad of autonomous pest recognition systems leveraging machine learning algorithms have been developed [3,7]. In a notable study, Wu et al [8] compiled an extensive insect dataset, IP102, which encompasses 102 classes and 75,000 images, and their evaluation employed various machine learning and deep learning methodologies.…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet

Dewi,

Christanto,

Dai

2023

ATAIML

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In the realm of agriculture, crop yields of fundamental cereals such as rice, wheat, maize, soybeans, and sugarcane are adversely impacted by insect pest invasions, leading to significant reductions in agricultural output. Traditional manual identification of these pests is labor-intensive and time-consuming, underscoring the necessity for an automated early detection and classification system. Recent advancements in machine learning, particularly deep learning, have provided robust methodologies for the classification and detection of a diverse array of insect infestations in crop fields. However, inaccuracies in pest classification could inadvertently precipitate the use of inappropriate pesticides, further endangering both agricultural yields and the surrounding ecosystems. In light of this, the efficacy of nine distinct pre-trained deep learning algorithms was evaluated to discern their capability in the accurate detection and classification of insect pests. This assessment utilized two prevalent datasets, comprising ten pest classes of varied sizes. Among the transfer learning techniques scrutinized, adaptations of ResNet-50 and ResNet-101 were deployed. It was observed that ResNet-50, when employed in a transfer learning paradigm, achieved an exemplary classification accuracy of 99.40% in the detection of agricultural pests. Such a high level of precision represents a significant advancement in the field of precision agriculture.

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“…Truncated SVD was employed to reduce the dimensionality of the features to 127. A Complement NB class was used to train a Naive Bayes classifier with the obtained features and target variables for fitting [17]. The model parameters were obtained, and predictions were made, resulting in an accuracy output of 1.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Driver-Induced Human Errors in Smart Construction Tower Crane Operations Based on DEMATEL-ISM-MICMAC

Wang,

2024

JAMP

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With the advent of Industry 4.0, smart construction sites have seen significant development in China. However, accidents involving digitized tower cranes continue to be a persistent issue. Among the contributing factors, human unsafe behavior stands out as a primary cause for these incidents. This study aims to assess the human reliability of tower crane operations on smart construction sites. To proactively enhance safety measures, the research employs text mining techniques (TF-IDF-Truncated SVD-Complement NB) to identify patterns of human errors among tower crane operators. Building upon the SHEL model, the study categorizes behavioral factors affecting human reliability in the man-machine interface, leading to the establishment of the Performance Shaping Factors (PSFs) system. Furthermore, the research constructs an error impact indicator system for the intelligent construction site tower crane operator interface. Using the DEMATEL method, it analyzes the significance of various factors influencing human errors in tower crane operations. Additionally, the ISM-MICMAC method is applied to unveil the hierarchical relationships and driving-dependent connections among these influencing factors. The findings indicate that personal state, operating procedures, and physical environment directly impact human errors, while personal capability, technological environment, and one fundamental organizational management factor contribute indirectly.

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Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database

Cited by 8 publications

References 27 publications

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet

Evaluation of Driver-Induced Human Errors in Smart Construction Tower Crane Operations Based on DEMATEL-ISM-MICMAC

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