Most recent issue published online in the International Journal of Adaptive and Innovative Systems.
International Journal of Adaptive and Innovative Systems
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International Journal of Adaptive and Innovative Systems
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International Journal of Adaptive and Innovative Systems
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http://www.inderscience.com/browse/index.php?journalID=62&year=2022&vol=3&issue=2
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YOLO-TN: ultrasound diagnosis of thyroid nodules based on transfer learning and convolutional neural networks
http://www.inderscience.com/link.php?id=124344
Thyroid nodules are a common clinical condition. Currently, radiologists commonly use ultrasound as a method of diagnosing them. However, the varying experience of radiologists leads them to describe the features of nodules in the same ultrasound image differently. Incorrect descriptions will lead to misdiagnosis for the patient. To address the problem of difficult diagnosis of benign and malignant thyroid nodules in ultrasound images, we proposed a model for the diagnosis of thyroid nodules, which is named YOLO-TN (thyroid nodules). In this work, we used transfer learning and data augmentation to alleviate the data dependency of the model and designed CSPSE-DarkNet to simulate clinical diagnosis. In addition, we enhanced the detection of small objects and proposed soft ROI selection to enrich the contextual information of thyroid nodules. The model achieved a mAP value of 91.3% on our dataset, providing better performance than some of the popular networks currently available.
YOLO-TN: ultrasound diagnosis of thyroid nodules based on transfer learning and convolutional neural networks
Xun Wang; Ning Zhang; Mao Ding; Nuo Xu
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 87 - 99
Thyroid nodules are a common clinical condition. Currently, radiologists commonly use ultrasound as a method of diagnosing them. However, the varying experience of radiologists leads them to describe the features of nodules in the same ultrasound image differently. Incorrect descriptions will lead to misdiagnosis for the patient. To address the problem of difficult diagnosis of benign and malignant thyroid nodules in ultrasound images, we proposed a model for the diagnosis of thyroid nodules, which is named YOLO-TN (thyroid nodules). In this work, we used transfer learning and data augmentation to alleviate the data dependency of the model and designed CSPSE-DarkNet to simulate clinical diagnosis. In addition, we enhanced the detection of small objects and proposed soft ROI selection to enrich the contextual information of thyroid nodules. The model achieved a mAP value of 91.3% on our dataset, providing better performance than some of the popular networks currently available.]]>
10.1504/IJAIS.2022.124344
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 87 - 99
Xun Wang
Ning Zhang
Mao Ding
Nuo Xu
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China ' College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China ' Department of Neurology Medicine, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China ' Department of Ultrasound, First Medical University Jinan, 250033, Shandong, China
ultrasound images
thyroid nodules
benign and malignant diagnosis
transfer learning
convolutional neural network
2022-07-25T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
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2022-07-25T23:20:50-05:00
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Stock prediction based on LightGBM with feature selection and improved grid search
http://www.inderscience.com/link.php?id=124345
In order to improve the accuracy of stock forecasting, a stock forecasting model based on LightGBM is proposed. Firstly, based on the grid search, an improved grid search is proposed, and the improved grid search is used to search out the best super parameters for LightGBM. At the same time, the normalised function and loss function suitable for the model are also searched out, and the IGS-LightGBM model is constructed. Then, the feature selection in LightGBM is used to further optimise the model and obtained FS-IGS-LightGBM. The model is then applied to the actual stock data. In this paper, the closing prices of Shanghai Composite Index, Shenzhen Composite Index, Shanghai and CSI 300, Growth Enterprise Index, Chuanneng Power and Baiyun Airport are taken as experimental data. The experimental results show that FS-IGS-LightGBM is superior to XGBoost and LightGBM in the evaluation index of MAPE.
Stock prediction based on LightGBM with feature selection and improved grid search
Qihang Zhou; Changjun Zhou; Zhiqiang Liu
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 100 - 118
In order to improve the accuracy of stock forecasting, a stock forecasting model based on LightGBM is proposed. Firstly, based on the grid search, an improved grid search is proposed, and the improved grid search is used to search out the best super parameters for LightGBM. At the same time, the normalised function and loss function suitable for the model are also searched out, and the IGS-LightGBM model is constructed. Then, the feature selection in LightGBM is used to further optimise the model and obtained FS-IGS-LightGBM. The model is then applied to the actual stock data. In this paper, the closing prices of Shanghai Composite Index, Shenzhen Composite Index, Shanghai and CSI 300, Growth Enterprise Index, Chuanneng Power and Baiyun Airport are taken as experimental data. The experimental results show that FS-IGS-LightGBM is superior to XGBoost and LightGBM in the evaluation index of MAPE.]]>
10.1504/IJAIS.2022.124345
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 100 - 118
Qihang Zhou
Changjun Zhou
Zhiqiang Liu
College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321000, China ' College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321000, China ' College of Engineering, Zhejiang Normal University, Jinhua, 321000, China
LightGBM
grid search
stock forecast
optimisation
feature selection
2022-07-25T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
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118
2022-07-25T23:20:50-05:00
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Facial emotion detection using convolutional neural network algorithm
http://www.inderscience.com/link.php?id=124351
Emotions form a part and parcel of our human life. As emotions are a direct reflection of our inner self, it is possible to understand the state of mind of people. Some of the common human emotions are happy, sad, anger, disgust, fear and neutral. Several types of research have been done in the field of facial expression detection out of which the method of using a convolutional neural network proved to be the most reliable and accurate. This paper makes use of CNN architectures like Inception Resnet V2 and Xception to predict the emotion of psychiatric patients from the input video file. The results of the prediction will then be published in the form of graphs in a pdf file.
Facial emotion detection using convolutional neural network algorithm
G.R. Karpagam; B.S. Balasarath; Jeffrey Y. Nicholas; R. Lokesh; Shibi S. Rahul; Souradrita Sarkar
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 119 - 134
Emotions form a part and parcel of our human life. As emotions are a direct reflection of our inner self, it is possible to understand the state of mind of people. Some of the common human emotions are happy, sad, anger, disgust, fear and neutral. Several types of research have been done in the field of facial expression detection out of which the method of using a convolutional neural network proved to be the most reliable and accurate. This paper makes use of CNN architectures like Inception Resnet V2 and Xception to predict the emotion of psychiatric patients from the input video file. The results of the prediction will then be published in the form of graphs in a pdf file.]]>
10.1504/IJAIS.2022.124351
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 119 - 134
G.R. Karpagam
B.S. Balasarath
Jeffrey Y. Nicholas
R. Lokesh
Shibi S. Rahul
Souradrita Sarkar
Computer Science and Engineering Department, PSG College of Technology, Coimbatore †641004, Tamil Nadu, India ' Computer Science and Engineering Department, PSG College of Technology, Coimbatore †641004, Tamil Nadu, India ' Computer Science and Engineering Department, PSG College of Technology, Coimbatore †641004, Tamil Nadu, India ' Computer Science and Engineering Department, PSG College of Technology, Coimbatore †641004, Tamil Nadu, India ' Computer Science and Engineering Department, PSG College of Technology, Coimbatore †641004, Tamil Nadu, India ' Computer Science and Engineering Department, PSG College of Technology, Coimbatore †641004, Tamil Nadu, India
convolutional neural network
CNN
psychiatric patients
facial emotions
deep learning
Inception Resnet v2
Xception
emotion detection
comparison of architectures
Caffe model
2022-07-25T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
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134
2022-07-25T23:20:50-05:00
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MS-PriGAN: prior knowledge-based multi-scale gradient generation adversarial network for ovarian cancer CT image generation
http://www.inderscience.com/link.php?id=124352
Despite the great success of deep neural networks in medical lesion detection tasks, their adaptation to CT images of ovarian cancer is known to be difficult, and the most important reason is the lack of ovarian cancer datasets. A commonly accepted reason for this deficiency is that few people are willing to disclose CT imaging data due to privacy concerns, despite the fact that ovarian cancer is the first most prevalent cancer among women. In this work, we propose a method for generating synthetic high-resolution medical images using generative adversarial networks. The method addresses the instability of previous work in generating work on small-scale datasets by a simple and effective technique to acquire prior knowledge and achieve multi-scale gradient flow. We show that the proposed method still achieves good results even when synthesised on small-scale data, and with FID scores better than the current top-performing networks (GANs).
MS-PriGAN: prior knowledge-based multi-scale gradient generation adversarial network for ovarian cancer CT image generation
Xun Wang; Zhiyong Yu; Lisheng Wang; Mao Ding
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 135 - 143
Despite the great success of deep neural networks in medical lesion detection tasks, their adaptation to CT images of ovarian cancer is known to be difficult, and the most important reason is the lack of ovarian cancer datasets. A commonly accepted reason for this deficiency is that few people are willing to disclose CT imaging data due to privacy concerns, despite the fact that ovarian cancer is the first most prevalent cancer among women. In this work, we propose a method for generating synthetic high-resolution medical images using generative adversarial networks. The method addresses the instability of previous work in generating work on small-scale datasets by a simple and effective technique to acquire prior knowledge and achieve multi-scale gradient flow. We show that the proposed method still achieves good results even when synthesised on small-scale data, and with FID scores better than the current top-performing networks (GANs).]]>
10.1504/IJAIS.2022.124352
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 135 - 143
Xun Wang
Zhiyong Yu
Lisheng Wang
Mao Ding
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China ' College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China ' College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China ' Department of Intensive Care Unit, The Second Hospital of Shandong University, Jinan 250033, China
deep learning
priori knowledge GAN
CT images
ovarian cancer
2022-07-25T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
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143
2022-07-25T23:20:50-05:00
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Agricultural digital twins
http://www.inderscience.com/link.php?id=124364
In recent years, the development of digital twins has advanced by leaps and bounds, and digital twins have gradually begun to combine various fields and apply them to the current digitalisation of the physical world. Digital twins can play an important role in the field of agriculture. Digital twins technology can fully improve the yield and profit of crop products and alleviate food safety issues. Regarding the current common problems in the agricultural field, this article synthesises the existing technologies of digital twins, discusses the development prospects of it in the agricultural field, and puts forward the problems that still exist in the application of digital twins in the agricultural field.
Agricultural digital twins
Yuhang Zhao; Zheyu Jiang; Liang Qiao; Jinkang Guo; Shanchen Pang; Zhihan Lv
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 144 - 156
In recent years, the development of digital twins has advanced by leaps and bounds, and digital twins have gradually begun to combine various fields and apply them to the current digitalisation of the physical world. Digital twins can play an important role in the field of agriculture. Digital twins technology can fully improve the yield and profit of crop products and alleviate food safety issues. Regarding the current common problems in the agricultural field, this article synthesises the existing technologies of digital twins, discusses the development prospects of it in the agricultural field, and puts forward the problems that still exist in the application of digital twins in the agricultural field.]]>
10.1504/IJAIS.2022.124364
International Journal of Adaptive and Innovative Systems, Vol. 3, No. 2 (2022) pp. 144 - 156
Yuhang Zhao
Zheyu Jiang
Liang Qiao
Jinkang Guo
Shanchen Pang
Zhihan Lv
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China ' College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China ' College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China ' College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China ' College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China ' College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China; College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China
digital twins
agricultural field
virtual reality
artificial intelligence
blockchain
2022-07-25T23:20:50-05:00
Copyright © 2022 Inderscience Enterprises Ltd.
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