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Relevant bibliographies by topics / Optimized detection model

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Journal articles

Academic literature on the topic 'Optimized detection model'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "Optimized detection model"

1

Huang, K. "An Optimized LightGBM Model for Fraud Detection." Journal of Physics: Conference Series 1651 (November 2020): 012111. http://dx.doi.org/10.1088/1742-6596/1651/1/012111.

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2

Hu, JianSheng, JunJie Ma, Bin Xiao, and Rui Zhang. "Improved Lightweight YOLOv3 model for Target Detection Algorithm." Journal of Physics: Conference Series 2370, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2370/1/012029.

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When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created

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3

Boonsim, Noppakun, and Saranya Kanjaruek. "Optimized transfer learning for polyp detection." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, no. 1 (2023): 73–81. http://dx.doi.org/10.37936/ecti-cit.2023171.250910.

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Early diagnosis of colorectal cancer focuses on detecting polyps in the colon as early as possible so that patients can have the best chances for success- ful treatment. This research presents the optimized parameters for polyp detection using a deep learning technique. Polyp and non-polyp images are trained on the InceptionResnetV2 model by the Faster Region Con- volutional Neural Networks (Faster R-CNN) framework to identify polyps within the colon images. The proposed method revealed more remarkable results than previous works, precision: 92.9 %, recall: 82.3%, F1-Measure: 87.3%, and F2-Mea

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4

Behera, Bibhuti Bhusana, Binod Kumar Pattanayak, and Rajani Kanta Mohanty. "Deep Ensemble Model for Detecting Attacks in Industrial IoT." International Journal of Information Security and Privacy 16, no. 1 (2022): 1–29. http://dx.doi.org/10.4018/ijisp.311467.

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In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the

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Al-Sarem, Mohammed, Faisal Saeed, Zeyad Ghaleb Al-Mekhlafi, et al. "An Optimized Stacking Ensemble Model for Phishing Websites Detection." Electronics 10, no. 11 (2021): 1285. http://dx.doi.org/10.3390/electronics10111285.

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Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learn

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6

Ragab, Mahmoud, Khalid Eljaaly, Maha Farouk S. Sabir, Ehab Bahaudien Ashary, S. M. Abo-Dahab, and E. M. Khalil. "Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model." Computers, Materials & Continua 71, no. 3 (2022): 5751–64. http://dx.doi.org/10.32604/cmc.2022.024658.

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7

Vasavi, CH, and N. Divya Sruthi. "Detection of Lung Cancer Using Optimized SVM-CNN Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4608–13. http://dx.doi.org/10.22214/ijraset.2023.54496.

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Abstract: Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is critical for effective treatment. Artificial Intelligence (AI) has shown great promise in improving the accuracy and speed of lung cancer detection. In this study, we present a review of recent research on lung cancer detection using AI, including the use of deep learning, and image analysistechnique. Neural networks have always been several a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human’s computationa

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8

Ghaleb Al-Mekhlafi, Zeyad, Badiea Abdulkarem Mohammed, Mohammed Al-Sarem, et al. "Phishing Websites Detection by Using Optimized Stacking Ensemble Model." Computer Systems Science and Engineering 41, no. 1 (2022): 109–25. http://dx.doi.org/10.32604/csse.2022.020414.

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9

Moukhafi, Mehdi, Khalid El Yassini, and Bri Seddik. "Intrusions detection using optimized support vector machine." International Journal of Advances in Applied Sciences 9, no. 1 (2020): 62. http://dx.doi.org/10.11591/ijaas.v9.i1.pp62-66.

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<p><span>Computer network technologies are evolving fast and the development of internet technology is more quickly, people more aware of the importance of the network security. Network security is main issue of computing because the number attacks are continuously increasing. For these reasons, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, specially in data mining, have produced a wide variety of machine learning methods, which can be integrated into an ID

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10

Feng, Junzhe, Chenhao Yu, Xiaoyi Shi, Zhouzhou Zheng, Liangliang Yang, and Yaohua Hu. "Research on Winter Jujube Object Detection Based on Optimized Yolov5s." Agronomy 13, no. 3 (2023): 810. http://dx.doi.org/10.3390/agronomy13030810.

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Winter jujube is a popular fresh fruit in China for its high vitamin C nutritional value and delicious taste. In terms of winter jujube object detection, in machine learning research, small size jujube fruits could not be detected with a high accuracy. Moreover, in deep learning research, due to the large model size of the network and slow detection speed, deployment in embedded devices is limited. In this study, an improved Yolov5s (You Only Look Once version 5 small model) algorithm was proposed in order to achieve quick and precise detection. In the improved Yolov5s algorithm, we decreased

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