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Batchuluun, Ganbayar, Ja Hyung Koo, Yu Hwan Kim, and Kang Ryoung Park. "Image Region Prediction from Thermal Videos Based on Image Prediction Generative Adversarial Network." Mathematics 9, no. 9 (May 7, 2021): 1053. http://dx.doi.org/10.3390/math9091053.
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Various studies have been conducted on object detection, tracking, and action recognition based on thermal images. However, errors occur during object detection, tracking, and action recognition when a moving object leaves the field of view (FOV) of a camera and part of the object becomes invisible. However, no studies have examined this issue so far. Therefore, this article proposes a method for widening the FOV of the current image by predicting images outside the FOV of the camera using the current image and previous sequential images. In the proposed method, the original one-channel thermal image is converted into a three-channel thermal image to perform image prediction using an image prediction generative adversarial network. When image prediction and object detection experiments were conducted using the marathon sub-dataset of the Boston University-thermal infrared video (BU-TIV) benchmark open dataset, we confirmed that the proposed method showed the higher accuracies of image prediction (structural similarity index measure (SSIM) of 0.9839) and object detection (F1 score (F1) of 0.882, accuracy (ACC) of 0.983, and intersection over union (IoU) of 0.791) than the state-of-the-art methods.
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Batchuluun, Ganbayar, Na Rae Baek, and Kang Ryoung Park. "Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos." Mathematics 9, no. 19 (September 25, 2021): 2379. http://dx.doi.org/10.3390/math9192379.
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Various studies have been conducted for detecting humans in images. However, there are the cases where a part of human body disappears in the input image and leaves the camera field of view (FOV). Moreover, there are the cases where a pedestrian comes into the FOV as a part of the body slowly appears. In these cases, human detection and tracking fail by existing methods. Therefore, we propose the method for predicting a wider region than the FOV of a thermal camera based on the image prediction generative adversarial network version 2 (IPGAN-2). When an experiment was conducted using the marathon subdataset of the Boston University-thermal infrared video benchmark open dataset, the proposed method showed higher image prediction (structural similarity index measure (SSIM) of 0.9437) and object detection (F1 score of 0.866, accuracy of 0.914, and intersection over union (IoU) of 0.730) accuracies than state-of-the-art methods.
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Lei, Ke, Ali B. Syed, Xucheng Zhu, John M. Pauly, and Shreyas V. Vasanawala. "Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network." Bioengineering 10, no. 1 (January 10, 2023): 92. http://dx.doi.org/10.3390/bioengineering10010092.
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Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep learning framework, trained by radiologists’ supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then, the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework’s performance is examined quantitatively with intersection over union (IoU) and pixel error on position and qualitatively with a reader study. The proposed model achieves an average IoU of 0.867 and an average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better than two baseline models and not significantly different from a radiologist. Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.
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Øygard, Sigrid H., Mélanie Audoin, Andreas Austeng, Erik V. Thomsen, Matthias B. Stuart, and Jørgen A. Jensen. "Accurate prediction of transmission through a lensed row-column addressed array." Journal of the Acoustical Society of America 151, no. 5 (May 2022): 3207–18. http://dx.doi.org/10.1121/10.0010528.
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Using a diverging lens on a row-column array (RCA) can increase the size of its volumetric image and thus significantly improve its clinical value. Here, a ray tracing method is presented to predict the position of the transmitted wave so that it can be used to make beamformed images. The usable transmitted field-of-view (FOV) is evaluated for a lensed 128 + 128 element RCA by comparing the theoretic prediction of the emitted wavefront position with three-dimensional (3D) finite element simulation of the emitted field. The FOV of the array is found to be [Formula: see text] in the direction orthogonal to the emitting elements and 28.5°–51.2°, depending on depth and element position, for the direction lying along the element. Moreover, the proposed ray tracing method is compared with a simpler thin lens model, and it is shown that the improved accuracy of the proposed method can increase the usable transmitted FOV up to 25.1°.
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Fang, Yuan, Zhang Xiaoyong, Huang Zhiwu, Wentao Yu, and Yabo Wang. "A Switched Extend Kalman-Filter for Visual Servoing Applied in Nonholonomic Robot with the FOV Constraint." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 185–90. http://dx.doi.org/10.20965/jaciii.2015.p0185.
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In this paper, a switched Kalmanfilter (KF) is used to predict the status of feature points leaving the field of view (FOV), which is one of the most common constraints in FOV. By using the prediction of status to compensate for the real state of feature points, nonholonomic robots conduct visual servoing tasks efficiently. Results of simulation and experiments verify the effectiveness of the proposed approach.
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Li, Jie, Ling Han, Cong Zhang, Qiyue Li, and Weitao Li. "Adaptive Panoramic Video Multicast Streaming with Limited FoV Feedback." Complexity 2020 (December 18, 2020): 1–14. http://dx.doi.org/10.1155/2020/8832715.
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Virtual reality (VR) provides an immersive 360-degree viewing experience and has been widely used in many areas. However, the transmission of panoramic video usually places a large demand on bandwidth; thus, it is difficult to ensure a reliable quality of experience (QoE) under a limited bandwidth. In this paper, we propose a field-of-view (FoV) prediction methodology based on limited FoV feedback that can fuse the heat map and FoV information to generate a user view. The former is obtained through saliency detection, while the latter is extracted from some user perspectives randomly, and it contains the FoV information of all users. Then, we design a QoE-driven panoramic video streaming system with a client/server (C/S) architecture, in which the server performs rate adaptation based on the bandwidth and the predicted FoV. We then formulate it as a nonlinear integer programming (NLP) problem and propose an optimal algorithm that combines the Karush–Kuhn–Tucker (KKT) conditions with the branch-and-bound method to solve this problem. Finally, we evaluate our system in a simulation environment, and the results show that the system performs better than the baseline.
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Huang, Po-Chia, Ho-Hui Hsieh, Ching-Han Hsu, and Ing-Tsung Hsiao. "AN EFFICIENT SENSITIVITY CALCULATION OF TILTED APERTURES FOR PRECLINICAL MULTI-PINHOLE SPECT." Biomedical Engineering: Applications, Basis and Communications 27, no. 01 (February 2015): 1550006. http://dx.doi.org/10.4015/s1016237215500064.
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Sensitivity performance is one of the key issues in multi-pinhole collimator design for small animal pinhole Single photon emission computed tomography (SPECT). Currently, there are two approaches in predicting sensitivity performance: analytical formula and Monte Carlo (MC) simulation. Analytical formula offers fast computation, while MC simulation provides better modeling of various physical effects and generates more accurate sensitivity prediction. Tilted pinhole apertures become popular in modern system design, because they can avoid projection overlapping and increase field-of-view (FOV) compared to traditional multiple pinholes. However, conventional analytical formula for sensitivity prediction cannot be directly applied to tilted apertures. In this research, we present a modified analytical formula to predict the sensitivity performance by considering tilted and translated pinhole apertures in a multi-pinhole collimation design. The modification is based on a construction of a virtual object plane which is parallel to the aperture plane in the tilted pinhole. Since the new formula is derived in the vector domain, it can be readily integrated to computer-aided-design software to greatly simplify the collimator design optimization. The results show that the modified formula generates sensitivity prediction similar to that from the MC simulation for a multi-pinhole system with tilted pinholes. When larger tilted pinholes are used to increase FOV, the formula can also accurately generate sensitivity prediction with a slightly reduced peak value. For a tilted pinhole aperture up to 40°, the simulation results indicate that the conventional analytical formula may overestimate as much as 25% sensitivity.
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Chuang, Shu-Min, Chia-Sheng Chen, and Eric Hsiao-Kuang Wu. "The Implementation of Interactive VR Application and Caching Strategy Design on Mobile Edge Computing (MEC)." Electronics 12, no. 12 (June 16, 2023): 2700. http://dx.doi.org/10.3390/electronics12122700.
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Virtual reality (VR) and augmented reality (AR) have been proposed as revolutionary applications for the next generation, especially in education. Many VR applications have been designed to promote learning via virtual environments and 360° video. However, due to the strict requirements of end-to-end latency and network bandwidth, numerous VR applications using 360° video streaming may not achieve a high-quality experience. To address this issue, we propose relying on tile-based 360° video streaming and the caching capacity in Mobile Edge Computing (MEC) to predict the field of view (FoV) in the head-mounted device, then deliver the required tiles. Prefetching tiles in MEC can save the bandwidth of the backend link and support multiple users. Smart caching decisions may reduce the memory at the edge and compensate for the FoV prediction error. For instance, caching whole tiles at each small cell has a higher storage cost compared to caching one small cell that covers multiple users. In this paper, we define a tile selection, caching, and FoV coverage model as the Tile Selection and Caching Problem and propose a heuristic algorithm to solve it. Using a dataset of real users’ head movements, we compare our algorithm to the Least Recently Used (LRU) and Least Frequently Used (LFU) caching policies. The results show that our proposed approach improves FoV coverage by 30% and reduces caching costs by 25% compared to LFU and LRU.
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Liu, Tailong, Teng Pan, Shuijie Qin, Hui Zhao, and Huikai Xie. "Dynamic Response Analysis of an Immersed Electrothermally Actuated MEMS Mirror." Actuators 12, no. 2 (February 15, 2023): 83. http://dx.doi.org/10.3390/act12020083.
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MEMS mirrors have a wide range of applications, most of which require large field-of-view (FOV). Immersing MEMS mirrors in liquid is an effective way to improve the FOV. However, the increased viscosity, convective heat transfer and thermal conductivity in liquid greatly affect the dynamic behaviors of electrothermally actuated micromirrors. In this paper, the complex interactions among the multiple energy domains, including electrical, thermal, mechanical and fluidic, are studied in an immersed electrothermally actuated MEMS mirror. A damping model of the immersed MEMS mirror is built and dimensional analysis is applied to reduce the number of variables and thus significantly simplify the model. The solution of the fluid damping model is solved by using regression analysis. The dynamic response of the MEMS mirror can be calculated easily by using the damping model. The experimental results verify the effectiveness and accuracy of these models. The difference between the model prediction and the measurement is within 4%. The FOV scanned in a liquid is also increased by a factor of 1.6. The model developed in this work can be applied to study the dynamic behaviors of various immersed MEMS actuators.
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Whang, Allen Jong-Woei, Yi-Yung Chen, Wei-Chieh Tseng, Chih-Hsien Tsai, Yi-Ping Chao, Chieh-Hung Yen, Chun-Hsiu Liu, and Xin Zhang. "Pupil Size Prediction Techniques Based on Convolution Neural Network." Sensors 21, no. 15 (July 21, 2021): 4965. http://dx.doi.org/10.3390/s21154965.
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The size of one’s pupil can indicate one’s physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.
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Yu, Tianlei, Gang Ma, Feng Lu, Xiaohu Zhang, and Peng Zhang. "Quality Scoring of the Fengyun 4A Clear Sky Radiance Product." Remote Sensing 13, no. 18 (September 13, 2021): 3658. http://dx.doi.org/10.3390/rs13183658.
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The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data.
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Tsai, Cheng-Mu, Pin Han, Hsin-Hung Lee, and Chih-Ta Yen. "Lens Design Method Prediction of Local Optimization Algorithm by Using Deep Learning." Crystals 12, no. 9 (August 27, 2022): 1206. http://dx.doi.org/10.3390/cryst12091206.
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A design rule prediction is proposed to assist a lens design in this paper. Deep learning was applied in order to predict a lens design rule that is based on a local optimization algorithm. Three separate lens design rules related to the aperture stop and FOV variation were made for the optimization in the two-lens element optical systems whose structural parameters were created randomly. These random lens structures were optimized by using three separate lens design rules that were developed by Zemax OpticStudio API to create a big optimization dataset. All of the optimization results were collected by means of a further deep learning process to determine which optimization rule would be the better choice for lens optimization when given the lens parameters. The model developed via deep learning shows that the prediction has a 78.89% accuracy in determining an appropriate optimization rule for an assistant lens design.
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Dhou, Salam, and Yuichi Motai. "Scale-invariant optical flow in tracking using a pan-tilt-zoom camera." Robotica 34, no. 9 (December 9, 2014): 1923–47. http://dx.doi.org/10.1017/s0263574714002665.
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SUMMARYAn efficient method for tracking a target using a single Pan-Tilt-Zoom (PTZ) camera is proposed. The proposed Scale-Invariant Optical Flow (SIOF) method estimates the motion of the target and rotates the camera accordingly to keep the target at the center of the image. Also, SIOF estimates the scale of the target and changes the focal length relatively to adjust the Field of View (FoV) and keep the target appear in the same size in all captured frames. SIOF is a feature-based tracking method. Feature points used are extracted and tracked using Optical Flow (OF) and Scale-Invariant Feature Transform (SIFT). They are combined in groups and used to achieve robust tracking. The feature points in these groups are used within a twist model to recover the 3D free motion of the target. The merits of this proposed method are (i) building an efficient scale-invariant tracking method that tracks the target and keep it in the FoV of the camera with the same size, and (ii) using tracking with prediction and correction to speed up the PTZ control and achieve smooth camera control. Experimental results were performed on online video streams and validated the efficiency of the proposed method SIOF, comparing with OF, SIFT, and other tracking methods. The proposed SIOF has around 36% less average tracking error and around 70% less tracking overshoot than OF.
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Babilon, Sebastian, Sebastian Beck, Julian Kunkel, Julian Klabes, Paul Myland, Simon Benkner, and Tran Quoc Khanh. "Measurement of Circadian Effectiveness in Lighting for Office Applications." Applied Sciences 11, no. 15 (July 28, 2021): 6936. http://dx.doi.org/10.3390/app11156936.
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As one factor among others, circadian effectiveness depends on the spatial light distribution of the prevalent lighting conditions. In a typical office context focusing on computer work, the light that is experienced by the office workers is usually composed of a direct component emitted by the room luminaires and the computer monitors as well as by an indirect component reflected from the walls, surfaces, and ceiling. Due to this multi-directional light pattern, spatially resolved light measurements are required for an adequate prediction of non-visual light-induced effects. In this work, we therefore propose a novel methodological framework for spatially resolved light measurements that allows for an estimate of the circadian effectiveness of a lighting situation for variable field of view (FOV) definitions. Results of exemplary in-field office light measurements are reported and compared to those obtained from standard spectral radiometry to validate the accuracy of the proposed approach. The corresponding relative error is found to be of the order of 3–6%, which denotes an acceptable range for most practical applications. In addition, the impact of different FOVs as well as non-zero measurement angles will be investigated.
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Aykut, Tamay, Mojtaba Karimi, Christoph Burgmair, Andreas Finkenzeller, Christoph Bachhuber, and Eckehard Steinbach. "Delay Compensation for a Telepresence System With 3D 360 Degree Vision Based on Deep Head Motion Prediction and Dynamic FoV Adaptation." IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 4343–50. http://dx.doi.org/10.1109/lra.2018.2864359.
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Saha, Arindam, Bibhas Chandra Dhara, Saiyed Umer, Kulakov Yurii, Jazem Mutared Alanazi, and Ahmad Ali AlZubi. "Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications." Sensors 22, no. 17 (August 30, 2022): 6537. http://dx.doi.org/10.3390/s22176537.
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Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.
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Lee, Ho Sang, and Won Ho Jo. "Prediction of interfacial tension of immisciblepolymer pairs using a square gradient theory combined with the FOV equation-of-state free energy expression." Polymer 39, no. 12 (1998): 2489–93. http://dx.doi.org/10.1016/s0032-3861(97)00561-2.
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Brown, Jason R., Donald R. Lannin, Brigid K. Killelea, Michael DiGiovanna, and David Rimm. "Quantitative Ki-67 score as predictive of response to neoadjuvant chemotherapy in breast cancer." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 1085. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.1085.
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1085 Background: Neoadjuvant chemotherapy is administered prior to surgery for locally advanced tumors but does not confer any additional survival benefit. Measurement of Ki-67, a marker of cell proliferation, has been associated with response to therapy, but methods of measurement are controversial. There is no universal cut-point for association due to subjectivity in threshold for positivity and selection of the field of view. Here we propose that quantitative objective measurement for Ki-67 will provide a reproducible assay for likelihood of response to chemotherapy. Methods: A cohort of 115 consecutive (between 2002 and 2010) invasive breast cancer patients that received neoadjuvant therapy were included if pre-surgical biopsies were obtainable. Ki-67 expression was measured using quantitative immunofluorescence (AQUA) technology using the MIB-1 antibody. Images for each specimen were collected in 5 to 100 fields of view (FOV), and summary scores were obtained corresponding to the average and maximum of all the FOVs. Results: AQUA scoring was comparable to automated calculation of percent positive nuclei for prediction of response to chemotherapy (OR: 2.832 vs. 2.712). Both average and maximum AQUA scores showed Ki-67 expression was directly correlated to pathological complete response (pCR) (Ave p = 0.0002; Max p = 0.0011). Although examining the maximum field of view was more predictive of response to therapy (OR: 3.546 vs. 2.832), averaging all fields provided more sensitivity and specificity (AUC 0.769 vs. 0.732). Ki-67 average (p = 0.0025) and maximum (p = 0.0239) AQUA scores were also significant predictors of pCR in multivariable analysis with tumor size, nuclear grade, nodal status, ER status, and HER2 status considered. Conclusions: Measurement of Ki-67 expression by objective quantitative methods shows increased Ki-67 levels are an independent predictor of response to neoadjuvant chemotherapy. This assay is most sensitive and specific when the average Ki-67 expression from all fields of view is used.
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Bareng, Teffany Joy, Jessica E. Gibbs, Natsuko Onishi, David C. Newitt, Barbara LeStage, and Nola M. Hylton. "Abstract P3-03-04: Challenges of achieving high image quality on breast MRI for quantitative measurements in the I-SPY 2 TRIAL." Cancer Research 82, no. 4_Supplement (February 15, 2022): P3–03–04—P3–03–04. http://dx.doi.org/10.1158/1538-7445.sabcs21-p3-03-04.
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Abstract Purpose. To illustrate image quality issues that may impact quantitative measurements used to assess treatment response in a multi-site clinical trial. Background. Quantitative longitudinal measurements on breast MRI are used to assess patient response to neoadjuvant treatment in the multi-site I-SPY 2 TRIAL. A standardized MR protocol is distributed to sites prior to site initiation. Previous work presented image quality issues1, which may affect measurements that rely on automated computerized methods. Functional tumor volume (FTV), a primary imaging biomarker monitoring treatment response, was more predictive of pathological complete response for protocol adherent exams compared to non-adherent exams2. Image quality may also impact background parenchymal enhancement (enhancement level of normal fibroglandular tissue), which is being investigated as a secondary imaging biomarker. This presentation will show example images demonstrating the challenges of quantitative MR analysis in a multi-site clinical trial. Image quality issues include:. Motion. Motion due to patient movement during the MRI may result in skin, fat, and breast tissue being poorly defined and blurry. Mis-registration between pre- and post-contrast can cause errors in measurements. Threshold variation. For image processing, two signal intensity thresholds are applied to pixels within the region of interest (ROI), which may be adjusted when less than 50% of the tumor is segmented. The percent enhancement threshold is lowered when poorly enhancing areas of tumor are not segmented. The background threshold is lowered when a bright pixel within the ROI of the pre-contrast images causes relatively darker areas of the tumor to be poorly segmented. Scan duration variation. Scan duration is the time required to scan each T1-weighted acquisition phase. Scan duration variations occur if scan duration is outside the specified protocol range or differs from the scan duration of the patient’s baseline MRI. Longer scan duration may result in overestimation of FTV. Field of View. Field of view (FOV) is the anatomical area being imaged and is directly related to spatial resolution, which plays a key role in the FTV measurement. Incorrect FTV monitoring can occur if FOV is inconsistent between visits, outside the specified protocol range, or is overlarge for the patient. Discussion. The image quality required for measurements used to assess treatment response in I-SPY 2 differs from the image quality that is acceptable for diagnostic evaluation, including BIRADS category, longest diameter measurements, and visual assessment of washout characteristics. In I-SPY 2, 21 sites use a variety of MRI platforms. Since quantitative measurements are increasingly used to monitor treatment response and guide clinical decision-making, high quality images are essential. Strategies must be implemented to minimize imaging issues and further refine the quantitative measurements. Ongoing studies are examining the impact of image quality factors on accuracy of treatment response prediction. References. 1.Gibbs J et al. Abstract PS11-08: Operational standardization and quality assurance yield high acceptance rate for breast MRI in the I-SPY 2 TRIAL. Poster Session Abstracts. American Association for Cancer Research; 2021. 2.Onishi N et al. Impact of MRI Protocol Adherence on Prediction of Pathological Complete Response in the I-SPY 2 Neoadjuvant Breast Cancer Trial. Tomography. 2020 Jun. Table 1.Image quality issues observed in 1,749 I-SPY 2 MRIs submitted June 2019 to June 2021Image Quality IssueExams (percentage of total)Motion568 (32%)Threshold variation104 (6%)Scan duration variation83 (5%)FOV229 (13%) Citation Format: Teffany Joy Bareng, Jessica E Gibbs, Natsuko Onishi, David C Newitt, Barbara LeStage, I-SPY 2 TRIAL Imaging Working Group, I-SPY 2 TRIAL Coordinators, Nola M Hylton. Challenges of achieving high image quality on breast MRI for quantitative measurements in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-04.
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Wei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang, and Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†." Frontiers of Nursing 8, no. 3 (September 1, 2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
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Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.
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Tolhuisen, Manon L., Jan W. Hoving, Miou S. Koopman, Manon Kappelhof, Henk van Voorst, Agnetha E. Bruggeman, Adam M. Demchuck, et al. "Outcome Prediction Based on Automatically Extracted Infarct Core Image Features in Patients with Acute Ischemic Stroke." Diagnostics 12, no. 8 (July 23, 2022): 1786. http://dx.doi.org/10.3390/diagnostics12081786.
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Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.
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Zhou, Pengyu, Ran Li, Siyun Liu, Jincheng Wang, Lixiang Huang, Bin Song, Xiaoqiang Tang, et al. "Follow-Up Infarct Volume Prediction by CTP-Based Hypoperfusion Index, and the Discrepancy between Small Follow-Up Infarct Volume and Poor Functional Outcome—A Multicenter Study." Diagnostics 13, no. 1 (January 2, 2023): 152. http://dx.doi.org/10.3390/diagnostics13010152.
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(1) Background: Follow-up infarct volume (FIV) may have implications for prognostication in acute ischemic stroke patients. Factors predicting the discrepancy between FIV and 90-day outcomes are poorly understood. We aimed to develop a comprehensive predictive model of FIV and explore factors associated with the discrepancy. (2) Methods: Patients with acute anterior circulation large vessel occlusion were included. Baseline clinical and CT features were extracted and analyzed, including the CTP-based hypoperfusion index (HI) and the NCCT-based e-ASPECT, measured by automated software. FIV was assessed on follow-up NCCT at 3–7 days. Multiple linear regression was used to construct the predictive model. Subgroup analysis was performed to explore factors associated with poor outcomes (90-mRS scores 3–6) in small FIV (<70 mL). (3) Results: There were 170 patients included. Baseline e-ASPECT, infarct core volume, hypoperfusion volume, HI, baseline international normalized ratio, and successful recanalization were associated with FIV and included in constructing the predictive model. Baseline NIHSS, baseline hypertension, stroke history, and current tobacco use were associated with poor outcomes in small FIV. (4) Conclusions: A comprehensive predictive model (including HI) of FIV was constructed. We also emphasized the importance of hypertension and smoking status at baseline for the functional outcomes in patients with a small FIV.
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Lagouarde, J. P., B. K. Bhattacharya, P. Crébassol, P. Gamet, D. Adlakha, C. S. Murthy, S. K. Singh, et al. "INDO-FRENCH HIGH-RESOLUTION THERMAL INFRARED SPACE MISSION FOR EARTH NATURAL RESOURCES ASSESSMENT AND MONITORING – CONCEPT AND DEFINITION OF TRISHNA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 403–7. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-403-2019.
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<p><strong>Abstract.</strong> The Indian and French Space Agencies, ISRO and CNES, have conceptualized a space-borne Thermal Infrared Reflectance (TIR) mission, TRISHNA (Thermal infRared Imaging Satellite for High-resolution Natural Resource Assessment). The primary design drivers of TRISHNA are the monitoring of (i) terrestrial water stress and use, and of (ii) coastal and continental water. A suit of four TIR bands and six optical bands is planned. The TIR bands will be centred at 8.6&thinsp;&mu;m, 9.1&thinsp;&mu;m, 10.3&thinsp;&mu;m and 11.5&thinsp;&mu;m to provide noon-night global observations at 57m nadir resolution over land and coastal regions. The field of view (FOV) is &plusmn;34&deg; and the orbit of 761&thinsp;km altitude was designed to allow 3 sub-cycle acquisitions during the 8-day cycle. The optical bands correspond to blue, green, red, and NIR plus two SWIR bands at 1.38&thinsp;&mu;m and 1.61&thinsp;&mu;m. The green, red, NIR and the 1.61&thinsp;&mu;m SWIR bands will have better radiometry quality than those of AWiFS. ISRO and CNES will develop optical and TIR payloads, respectively. Assessing evapotranspiration and furthermore Gross and Net Primary Productivity (GPP and NPP) will in turn assist in quantifying water use in rainfed and irrigated agriculture, water stress and water use efficiency, with expected applications to agricultural drought and early warning, crop yield prediction, water allocation, implementation of water rights, crop insurance business and agro-advisories to farmers. The other scientific objectives of TRISHNA are also briefly described. TRISHNA instrument will fly aboard a ISRO spacecraft scheduled to be launched from 2024 for a minimum period of 5 years’ mission lifetime.</p>
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Aumann, H. H., and S. G. DeSouza-Machado. "Deep convective clouds at the tropopause." Atmospheric Chemistry and Physics Discussions 10, no. 7 (July 2, 2010): 16475–96. http://dx.doi.org/10.5194/acpd-10-16475-2010.
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Abstract. Data from the Advanced Infrared Sounder (AIRS) on the EOS Aqua spacecraft identify thousands of cloud tops colder than 225 K, loosely referred to as Deep Convective Clouds (DCC). Many of these cloud tops have "inverted" spectra, i.e. areas of strong water vapor, CO2 and ozone opacity, normally seen in absorption, are now seen in emission. We refer to these inverted spectra as DCCi. They are found in about 0.4% of all spectra from the tropical oceans excluding the Western Tropical Pacific (WTP), 1.1% in the WTP. The cold clouds are the anvils capping thunderstorms and consist of optically thick cirrus ice clouds. The precipitation rate associated with DCCi suggests that imbedded in these clouds, protruding above them, and not spatially resolved by the AIRS 15 km FOV, are even colder bubbles, where strong convection pushes clouds to within 5 hPa of the pressure level of the tropopause cold point. Associated with DCCi is a local upward displacement of the tropopause, a cold "bulge", which can be seen directly in the brightness temperatures of AIRS and AMSU channels with weighting function peaking between 40 and 2 hPa, without the need for a formal temperature retrieval. The bulge is not resolved by the analysis in numerical weather prediction models. The locally cold cloud tops relative to the analysis give the appearance (in the sense of an "illusion") of clouds overshooting the tropopause and penetrating into the stratosphere. Based on a simple model of optically thick cirrus clouds, the spectral inversions seen in the AIRS data do not require these clouds to penetrate into the stratosphere. However, the contents of the cold bulge may be left in the lower stratosphere as soon as the strong convection subsides. The heavy precipitation and the distortion of the temperature structure near the tropopause indicate that DCCi are associated with intense storms. Significant long-term trends in the statistical properties of DCCi could be interesting indicators of climate change.
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Meng, Xiangtian, Yilin Bao, Qiang Ye, Huanjun Liu, Xinle Zhang, Haitao Tang, and Xiaohan Zhang. "Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method." Remote Sensing 13, no. 12 (June 10, 2021): 2273. http://dx.doi.org/10.3390/rs13122273.
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In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
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Mi, Han, Wenlong Guo, Lisi Liang, Hongyue Ma, Ziheng Zhang, Yanli Gao, and Linbo Li. "Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm." Materials 15, no. 23 (December 2, 2022): 8608. http://dx.doi.org/10.3390/ma15238608.
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The combination of multilayer aluminum foam can have high sound absorption coefficients (SAC) at low and medium frequencies, and predicting its absorption coefficient can help the optimal structural design. In this study, a hybrid EO-GRNN model was proposed for predicting the sound absorption coefficient of the three-layer composite structure of the aluminum foam. The generalized regression neural network (GRNN) model was used to predict the sound absorption coefficient of three-layer composite structural aluminum foam due to its outstanding nonlinear problem-handling capability. An equilibrium optimization (EO) algorithm was used to determine the parameters in the neuronal network. The prediction results show that this method has good accuracy and high precision. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, the GRNN model optimized by PSO (PSO-GRNN), and the GRNN model optimized by FOA(FOA-GRNN). The prediction results are expressed in terms of root mean square error (RMSE), absolute error, and relative error, and this method performs well with an average RMSE of only 0.011.
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Tian, Anhong, Junsan Zhao, Bohui Tang, Daming Zhu, Chengbiao Fu, and Heigang Xiong. "Hyperspectral Prediction of Soil Total Salt Content by Different Disturbance Degree under a Fractional-Order Differential Model with Differing Spectral Transformations." Remote Sensing 13, no. 21 (October 25, 2021): 4283. http://dx.doi.org/10.3390/rs13214283.
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Soil salinization is an ecological challenge across the world. Particularly in arid and semi-arid regions where evaporation is rapid and rainfall is scarce, both primary soil salinization and secondary salinization due to human activity pose serious concerns. Soil is subject to various human disturbances in Xinjiang in this area. Samples with a depth of 0–10 cm from 90 soils were taken from three areas: a slightly disturbed area (Area A), a moderately disturbed area (Area B), and a severely disturbed area (Area C). In this study, we first calculated the hyperspectral reflectance of five spectra (R, R, 1/R, lgR, 1/lgR, or original, root mean square, reciprocal, logarithm, and reciprocal logarithm, respectively) using different fractional-order differential (FOD) models, then extracted the bands that passed the 0.01 significance level between spectra and total salt content, and finally proposed a partial least squares regression (PLSR) model based on the FOD of the significance level band (SLB). This proposed model (FOD-SLB-PLSR) is compared with the other three PLSR models to predict with precision the total salt content. The other three models are All-PLSR, FOD-All-PLSR, and IOD-SLB-PLSR, which respectively represent PLSR models based on all bands, all fractional-order differential bands, and significance level bands of the integral differential. The simulations show that: (1) The optimal model for predicting total salt content in Area A was the FOD-SLB-PLSR based on a 1.6 order 1/lgR, which provided good predictability of total salt content with a RPD (ratio of the performance to deviation) between 1.8 and 2.0. The optimal model for predicting total salt content in Area B was a FOD-SLB-PLSR based on a 1.7 order 1/R, which showed good predictability for total salt content with RPDs between 2.0 and 2.5. The optimal model for predicting total salt content in Area C was a FOD-SLB-PLSR based on a 1.8 order lgR, which also showed good predictability for total salt content with RPDs between 2.0 and 2.5. (2) Soils subject to various disturbance levels had optimal FOD-SLB-PLSR models located in the higher fractional order between 1.6 and 1.8. This indicates that higher-order FODs have a stronger ability to extract feature data from complex information. (3) The optimal FOD-SLB-PLSR model for each area was superior to the corresponding All-PSLR, FOD-All-PLSR, and IOD-SLB-PLSR models in predicting total salt content. The RPD value for the optimal FOD-SLB-PLSR model in each area compared to the best integral differential model showed an improvement of 9%, 45%, and 22% for Areas A, B, and C, respectively. It further showed that the fractional-order differential model provides superior prediction over the integral differential. (4) The RPD values that provided an optimal FOD-SLB-PLSR model for each area were: Area A (1.9061) < Area B (2.0761) < Area C (2.2892). This indicates that the prediction effect of data processed by fractional-order differential increases with human disturbance increases and results in a higher-precision model.
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Payra, Swagata, and Manju Mohan. "Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool." Advances in Meteorology 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/456065.
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The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.
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Zhu, Linlin, Xiaoyang Han, Zhiwen Liu, Songze Leng, Ningning Shan, Xiao Lv, Kang Lu, Shouyong Hun, Yinhang Wu, and Xin Liu. "Survival prediction model for patients with mycosis fungoides/Sezary syndrome." Future Oncology 16, no. 31 (November 2020): 2487–98. http://dx.doi.org/10.2217/fon-2020-0502.
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Aim: A nomogram was constructed to forecast the overall survival (OS) of patients with mycosis fungoides/Sezary syndrome. Patients & methods: The clinicopathological information of patients was obtained from the Surveillance, Epidemiology and End Results (SEER) database. A model was established based on the independent prognostic factors. Predictive ability of the model was evaluated with the concordance index and calibration curves. Risk stratification was conducted for patients with similar tumor node metastasis (TNM) stages. Results: The model included 1997 eligible patients and seven prognostic factors for OS. The concordance index of the nomogram was 0.84 in the training and external validation cohorts, which indicated good predictive ability of the model and reliability of the results. The high agreement between the model predictions and actual observations was identified by calibration curves, which demonstrated the prediction accuracy of the model. Risk stratification displayed significant differences for patients with similar TNM stages, which suggested that the OS of patients with similar TNM stages could be further distinguished. Conclusion: We established a reliable nomogram to predict the OS of patients with mycosis fungoides/Sezary syndrome, which highlighted the advantages of nomograms over the conventional TNM staging system and promoted the application of individualized therapeutic strategies.
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Zhang, Yongli, and Sanggyun Na. "A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model." Mathematical Problems in Engineering 2018 (November 11, 2018): 1–10. http://dx.doi.org/10.1155/2018/2540681.
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Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.
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Lim, C. H., J. Posom, and P. Sirisomboon. "Prediction of crosslink density of prevulcanised latex using NIR Spectroscopy based on combination of fractional order derivative (FOD) and variable selection methods." IOP Conference Series: Materials Science and Engineering 1234, no. 1 (March 1, 2022): 012003. http://dx.doi.org/10.1088/1757-899x/1234/1/012003.
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Abstract Rapid method in measurement of crosslink density is required in factory. The objective of this study was to develop the prediction model of crosslink densities based on near infrared (NIR) spectroscopy method. The prediction models were developed using partial least squares regression (PLSR) with spectral pre-treatment of fractional order derivatives (FOD) and variable selection methods including successive project algorithm (SPA) and genetic algorithm (GA). The result demonstrated that prevulcanised (PV) latex model had higher accuracy than that of PV50 latex model. Effective model in predicting crosslink densities of PV and PV50 latices could be pre-processed with FOD=1 and 0.75, respectively. The prediction model generated with full wavelength had the standard error of cross validation (SECV) of 3.21% and 3.52%, respectively. The model performance of PV latex could improve with variable selection method of GA which reduced the SECV from 3.21% to 3.17% and number of wavelengths reduced from 1059 to 937. The model performance of PV50 could not reduce by using the variable selection method. However, the GA could reduce the number of wavelengths from 1059 to 216.
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Yimamu, Yiliyasi, Xu Yang, Junxin Chen, Cheng Luo, Wenyang Xiao, Hongyu Guan, and Daohu Wang. "The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer." Journal of Clinical Medicine 11, no. 23 (December 1, 2022): 7164. http://dx.doi.org/10.3390/jcm11237164.
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The recurrence of prostate cancer (PCa) is intrinsically linked to increased mortality. The goal of this study was to develop an efficient and reliable prognosis prediction signature for PCa patients. The training cohort was acquired from The Cancer Genome Atlas (TCGA) dataset, while the validation cohort was obtained from the Gene Expression Omnibus (GEO) dataset (GSE70769). To explore the Gleason score (GS)-based prediction signature, we screened the differentially expressed genes (DEGs) between low- and high-GS groups, and then univariate Cox regression survival analysis and multiple Cox analyses were performed sequentially using the training cohort. The testing cohort was used to evaluate and validate the prognostic model’s effectiveness, accuracy, and clinical practicability. In addition, the correlation analyses between the risk score and clinical features, as well as immune infiltration, were performed. We constructed and optimized a valid and credible model for predicting the prognosis of PCa recurrence using four GS-associated genes (SFRP4, FEV, COL1A1, SULF1). Furthermore, ROC and Kaplan–Meier analysis revealed a higher predictive efficiency for biochemical recurrence (BCR). The results showed that the risk model was an independent prognostic factor. Moreover, the risk score was associated with clinical features and immune infiltration. Finally, the risk model was validated in a testing cohort. Our data support that the GS-based four-gene signature acts as a novel signature for predicting BCR in PCa patients.
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Wang, Zhile, Yijun Wu, Li Wang, Liang Gong, Chang Han, Naixin Liang, and Shanqing Li. "Predicting occult lymph node metastasis by nomogram in patients with lung adenocarcinoma ≤2 cm." Future Oncology 17, no. 16 (June 2021): 2005–13. http://dx.doi.org/10.2217/fon-2020-0905.
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Background: Previous researches had not proposed any prediction models for occult lymph node metastasis (OLNM). Considering the occurrence of OLNM and the importance of OLNM management, we aimed to develop a nomogram to predict OLNM of patients with lung adenocarcinoma ≤2 cm. Methods: Characteristics of patients with lung adenocarcinoma of ≤2 cm diameter at the Peking Union Medical College Hospital were retrospectively reviewed. Univariate and multivariate logistic regressions were performed. A nomogram model was developed. The concordance index (C-index) and calibration and decision curves were used to evaluate the predictive ability. Results: A total of 473 patients were enrolled, with an OLNM incidence of 7.4%. Four factors were selected as risk factors. The model had a C-index of 0.932. Calibration and decision curves were determined. Conclusion: Patients with pure ground-glass opacity (pGGO) or noninvasive adenocarcinoma have significantly lower risk of OLNM. SUVmax, CEA, micropapillary and solid component were identified as independent risk factors. The nomogram model was effective in predicting OLNM preoperatively.
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Zhang, Yongli, Jianguang Niu, and Sanggyun Na. "A Novel Nonlinear Function Fitting Model Based on FOA and GRNN." Mathematical Problems in Engineering 2019 (February 5, 2019): 1–10. http://dx.doi.org/10.1155/2019/2697317.
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The nonlinear function fitting is an essential research issue. At present, the main function fitting methods are statistical methods and artificial neural network, but statistical methods have many inherent strict limits in application, and the back propagation (BP) neural network used widely has too many optimized parameters. For the gaps and lacks of existing researches, the FOA-GRNN was proposed and compared with the GRNN, GA-BP, PSO-BP, and BP through three nonlinear functions from simplicity to complexity for verifying the accuracy and robustness of the FOA-GRNN. The experiment results showed that the FOA-GRNN had the best fitting precision and fastest convergence speed; meanwhile the predictions were stable and reliable in the Mexican Hat function and Rastrgrin function. In the most complex Griewank function, the prediction of FOA-GRNN was becoming unstable and the model did not show better than GRNN model adopting equal step length searching method, but the performance of FOA-GRNN is superior to that of GA-BP, PSO-BP, and BP. The paper presents a new approach to optimize the parameter of GRNN and also provides a new nonlinear function fitting method, which has better fitting precision, faster calculation speed, more few adjusted parameters, and more powerful processing ability for small samples. The processing capacity of FOA for treating high complex nonlinear function needs to be further improved and developed in the future study.
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Liu, Qingqing, Jie Ren, and Haoyu Feng. "Nomograms for predicting long‐term overall survival and cancer‐specific survival in chordoma: a population‐based study." Future Oncology 18, no. 24 (August 2022): 2687–99. http://dx.doi.org/10.2217/fon-2022-0158.
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Background: The aim of this study was to develop two predictive models to predict overall survival (OS) and cancer-specific survival (CSS) in chordoma patients. Methods: We searched for independent prognostic factors by using univariate and multivariate Cox regression analyses. The prediction model of OS and CSS of chordoma patients was constructed by using the screened factors. Results: The study enrolled 362 chordoma patients. Cox regression analysis showed that disease stage, age, surgery, marital status and tumor size are independent influencing factors of OS and CSS in chordoma patients. After testing, the prediction model constructed in this study has good performance. Conclusion: Two predictive models were successfully constructed and validated for chordoma patients’ OS and CSS.
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Shao, Chuange, Dandan Xiang, Hong Wei, Siwen Liu, Ganjun Yi, Shuxia Lyu, Li Guo, and Chunyu Li. "Predicting Virulence of Fusarium oxysporum f. sp. Cubense Based on the Production of Mycotoxin Using a Linear Regression Model." Toxins 12, no. 4 (April 14, 2020): 254. http://dx.doi.org/10.3390/toxins12040254.
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Fusarium wilt caused by Fusarium oxysporum f.sp. cubense (Foc) is one of the most destructive diseases for banana. For their risk assessment and hazard characterization, it is vital to quickly determine the virulence of Foc isolates. However, this usually takes weeks or months using banana plant assays, which demands a better approach to speed up the process with reliable results. Foc produces various mycotoxins, such as fusaric acid (FSA), beauvericin (BEA), and enniatins (ENs) to facilitate their infection. In this study, we developed a linear regression model to predict Foc virulence using the production levels of the three mycotoxins. We collected data of 40 Foc isolates from 20 vegetative compatibility groups (VCGs), including their mycotoxin profiles (LC-MS) and their plant disease index (PDI) values on Pisang Awak plantlets in greenhouse. A linear regression model was trained from the collected data using FSA, BEA and ENs as predictor variables and PDI values as the response variable. Linearity test statistics showed this model meets all linearity assumptions. We used all data to predict PDI with high fitness of the model (coefficient of determination (R2 = 0.906) and adjust coefficient (R2adj = 0.898)) indicating a strong predictive power of the model. In summary, we developed a linear regression model useful for the prediction of Foc virulence on banana plants from the quantification of mycotoxins in Foc strains, which will facilitate quick determination of virulence in newly isolated Foc emerging Fusarium wilt of banana epidemics threatening banana plantations worldwide.
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Han, Duke, Aaron Lim, Laura Mosqueda, Annie Nguyen, Tyler Mason, Gali Weissberger, Laura Fenton, and Peter Lichtenberg. "INTERPERSONAL DYSFUNCTION PREDICTS SUBSEQUENT FINANCIAL EXPLOITATION VULNERABILITY IN OLDER ADULTS." Innovation in Aging 6, Supplement_1 (November 1, 2022): 648. http://dx.doi.org/10.1093/geroni/igac059.2396.
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Abstract The goal of this study was to test whether interpersonal dysfunction, characterized by loneliness and/or dissatisfaction with relationships, is an imminent predictor of financial exploitation vulnerability (FEV) among older adults within a 6-month observation period. This study also tests whether FEV prospectively predicts interpersonal dysfunction. Twenty-six adults aged 50 or older completed a study involving baseline data collection and 13 follow-ups over 6 months. Linear mixed models were used for primary analyses. After adjustment for demographic, psychological, and cognitive covariates, there were between-person effects of FEV and interpersonal dysfunction across follow-ups, suggesting that those with generally higher interpersonal dysfunction compared to other participants also reported greater FEV (B(SE)=1.09(.33), p=.003). There was a within-person effect (B(SE)=.08(.03), p=.007) of elevated interpersonal dysfunction predicting greater FEV two weeks later across all follow-ups. Within-person effect of FEV was not predictive of interpersonal dysfunction (B(SE)=.25(.15), p=.10). Among older adults, individuals with higher interpersonal dysfunction relative to others in the study reported greater FEV throughout the 6-month observation period. Increased loneliness and social dissatisfaction, relative to one’s average level, predicts subsequent increases in FEV, and may be an imminent risk factor for exploitation.
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Wei, Ning, Mengyue Zhou, Siyu Lei, Ling Yang, Zhihong Duan, Youyu Zhang, Zhiheng Zhong, Yang Liu, and Ruihua Shi. "From part to whole, operative link on to endoscopic grading of gastric intestinal metaplasia, pathology to endoscopy: gastric intestinal metaplasia graded by endoscopy." Future Oncology 18, no. 19 (June 2022): 2445–54. http://dx.doi.org/10.2217/fon-2021-1390.
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Objective: To conduct a systematic review and meta-analysis on the prediction of severity of gastric intestinal metaplasia (GIM) in localized and entire gastric mucosa using endoscopy. Methods: The authors searched Web of Science, PubMed, Embase and Cochrane Central Register of Controlled Trials and performed systematic searches on endoscopic grading of GIM of the entire stomach using Meta-DiSc and Stata. Results: Sensitivity and specificity for the stratified prediction of overall GIM were 0.91 (95% CI: 0.85–0.95) and 0.91 (95% CI: 0.88–0.93), respectively. Sensitivity in predicting the different grades of GIM was higher in operative link on GIM assessment grades 0, III and IV but lower in grades I and II. Conclusion: Digital chromoendoscopy is well suited to predicting the severity of localized and overall GIM.
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Yang, Hong, Siliang Wang, Guohui Li, and Tongtong Mao. "A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction." Mathematical Problems in Engineering 2018 (June 6, 2018): 1–8. http://dx.doi.org/10.1155/2018/3136267.
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The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.
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Lu, Wei, Xiaoqiao Zhang, and Xinchen Zhan. "Movie Box Office Prediction Based on IFOA-GRNN." Discrete Dynamics in Nature and Society 2022 (August 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/3690077.
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Predicting movie box office has received extensive attention from academia and industry. At present, the main method of forecasting movie box office is subjective prediction, which is not widely accepted due to its accuracy and applicability. This study improves the fruit fly algorithm to optimize the generalized regression neural network (IFOA-GRNN) model to predict whether a movie can become a high-grossing movie. By using the actual box office data and performing virtual simulation calculations, the root means square error of the IFOA-GRNN model predicting the movie box office is 0.3412, and the classification accuracy is about 90%. By comparing this model with FOA-GRNN, KNN, GRNN, Random Forest, Naive Bayes, Ensembles for Boosting, Discriminant Analysis Classifier, and SVM, it is found that the prediction effect of the IFOA-GRNN model is significantly better than the above eight models. The contribution of this article is to propose a generalized regression neural network model based on an improved fruit fly optimization algorithm, which can greatly improve the accuracy of movie box office prediction.
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Lacidogna, Giordano, Francesca Pitocchi, Alfredo Paolo Mascolo, Federico Marrama, Federica D’Agostino, Alessandro Rocco, Francesco Mori, et al. "CT Perfusion as a Predictor of the Final Infarct Volume in Patients with Tandem Occlusion." Journal of Personalized Medicine 13, no. 2 (February 16, 2023): 342. http://dx.doi.org/10.3390/jpm13020342.
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Background: CT perfusion (CTP) is used in patients with anterior circulation acute ischemic stroke (AIS) for predicting the final infarct volume (FIV). Tandem occlusion (TO), involving both intracranial large vessels and the ipsilateral cervical internal carotid artery could generate hemodynamic changes altering perfusion parameters. Our aim is to evaluate the accuracy of CTP in the prediction of the FIV in TOs. Methods: consecutive patients with AIS due to middle cerebral artery occlusion, referred to a tertiary stroke center between March 2019 and January 2021, with an automated CTP and successful recanalization (mTICI = 2b − 3) after endovascular treatment were retrospectively included in the tandem group (TG) or in the control group (CG). Patients with parenchymal hematoma type 2, according to ECASS II classification of hemorrhagic transformations, were excluded in a secondary analysis. Demographic, clinical, radiological, time intervals, safety, and outcome measures were collected. Results: among 319 patients analyzed, a comparison between the TG (N = 22) and CG (n = 37) revealed similar cerebral blood flow (CBF) > 30% (29.50 ± 32.33 vs. 15.76 ± 20.93 p = 0.18) and FIV (54.67 ± 65.73 vs. 55.14 ± 64.64 p = 0.875). Predicted ischemic core (PIC) and FIV correlated in both TG (tau = 0.761, p < 0.001) and CG (tau = 0.315, p = 0.029). The Bland–Altmann plot showed agreement between PIC and FIV for both groups, mainly in the secondary analysis. Conclusion: automated CTP could represent a good predictor of FIV in patients with AIS due to TO.
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Taylor, John R. "Possessives and topicality." Functions of Language 1, no. 1 (January 1, 1994): 67–94. http://dx.doi.org/10.1075/fol.1.1.05tay.
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This paper deals with certain aspects of prenominal possessives (i.e. expressions of the kind John's wife, the city's destruction) in English. Consideration of the discourse function of the construction leads to the prediction that the possessor nominal will be high in topicality, whilst the possessee nominal will generally denote a highly non-topical entity. A number of text-based studies of the possessive confirm these predictions. It is then argued that the distribution of topicality within the construction offers a unified explanation of a range of grammaticality judgements.
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Hedges, Kerry L., Alys R. Clark, and Merryn H. Tawhai. "Comparison of generic and subject-specific models for simulation of pulmonary perfusion and forced expiration." Interface Focus 5, no. 2 (April 6, 2015): 20140090. http://dx.doi.org/10.1098/rsfs.2014.0090.
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The goal of translating multiscale model analysis of pulmonary function into population studies is challenging because of the need to derive a geometric model for each subject. This could be addressed by using a generic model with appropriate customization to subject-specific data. Here, we present a quantitative comparison of simulating two fundamental behaviours of the lung—its haemodynamic response to vascular occlusion, and the forced expiration in 1 s (FEV 1 ) following bronchoconstriction—in subject-specific and generic models. When the subjects are considered as a group, there is no significant difference between predictions of mean pulmonary artery pressure (mPAP), pulmonary vascular resistance or forced expiration; however, significant differences are apparent in the prediction of arterial oxygen, for both baseline and post-occlusion. Despite the apparent consistency of the generic and subject-specific models, a third of subjects had generic model under-prediction of the increase in mPAP following occlusion, and half had the decrease in arterial oxygen over-predicted; two subjects had considerable differences in the percentage reduction of FEV 1 following bronchoconstriction. The generic model approach can be useful for physiologically directed studies but is not appropriate for simulating pathophysiological function that is strongly dependent on interaction with lung structure.
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Kremenova, Karin, Jiri Lukavsky, Michal Holesta, Tomas Peisker, David Lauer, Jiri Weichet, and Hana Malikova. "CT Brain Perfusion in the Prediction of Final Infarct Volume: A Prospective Study of Different Software Settings for Acute Ischemic Core Calculation." Diagnostics 12, no. 10 (September 22, 2022): 2290. http://dx.doi.org/10.3390/diagnostics12102290.
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CT perfusion (CTP) is used for the evaluation of brain tissue viability in patients with acute ischemic stroke (AIS). We studied the accuracy of three different syngo.via software (SW) settings for acute ischemic core estimation in predicting the final infarct volume (FIV). The ischemic core was defined as follows: Setting A: an area with cerebral blood flow (CBF) <30% compared to the contralateral healthy hemisphere. Setting B: CBF <20% compared to contralateral hemisphere. Setting C: area of cerebral blood volume (CBV) <1.2 mL/100 mL. We studied 47 AIS patients (aged 68 ± 11.2 years) with large vessel occlusion in the anterior circulation, treated in the early time window (up to 6 h), who underwent technically successful endovascular thrombectomy (EVT). FIV was measured on MRI performed 24 ± 2 h after EVT. In general, all three settings correlated with each other; however, the absolute agreement between acute ischemic core volume on CTP and FIV on MRI was poor; intraclass correlation for all three settings was between 0.64 and 0.69, root mean square error of the individual observations was between 58.9 and 66.0. Our results suggest that using CTP syngo.via SW for prediction of FIV in AIS patients in the early time window is not appropriate.
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Dorman, Clive E., Andrey A. Grachev, Ismail Gultepe, and Harindra J. S. Fernando. "Toward Improving Coastal-Fog Prediction (C-FOG)." Boundary-Layer Meteorology 181, no. 2-3 (November 3, 2021): 167–70. http://dx.doi.org/10.1007/s10546-021-00664-8.
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Ma, Guofeng, Yingjie Wang, and Junhong Yang. "Renewable energy company stock dynamics forecast in the period of sustainable development based on Fractal-FOA-LSTM." E3S Web of Conferences 295 (2021): 01065. http://dx.doi.org/10.1051/e3sconf/202129501065.
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In stock trend forecasting system, feature selection and model building are two major factors that affect prediction performance. In order to improve the accuracy of prediction and the stability of the model, a stock trend prediction model of Fractal-FOA-LSTM is proposed. Firstly, the features are selected by using the FOA (fruit fly algorithm) combined with the fractal dimension to reduce the redundancy of the features, and the selected indexes are used as the system input. And proposing a double input LSTM(long-short term memory) network prediction model and optimizing its parameters, it can select the best parameters for different data automatically. This paper test on 4 sets of UCI database and Shanghai Composite Index and proved the feature selection method is effective, through the empirical analysis of the Shanghai Composite Index and S&P500, and compared the results with SVM and PNN, verified the feasibility and superiority of the stock trend forecasting system base on fractal-FOA-LSTM.
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Chen, Ming, Ren Wang, Tingting Zhang, Xiangmin Zhang, Yonglin Wan, and Xiaohong Fu. "Nomogram predicting prostate cancer in patients with negative prebiopsy multiparametric magnetic resonance." Future Oncology 18, no. 12 (April 2022): 1473–83. http://dx.doi.org/10.2217/fon-2021-1538.
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Aim: To build two nomograms for predicting the possibilities of prostate cancer (PCa) and clinically significant PCa (csPCa) in patients with negative prebiopsy multiparametric MRI (mpMRI). Methods: The independent predictors associated with PCa or csPCa in patients with negative mpMRI were determined and served in the construction of the two nomograms. Results: The nomogram predicting PCa consisted of age, positive digital rectal examination, free/total prostate-specific antigen (PSA) ratio and PSA density, while age, positive digital rectal examination and PSA density comprised the nomogram predicting csPCa. The negative predictive value of mpMRI for PCa and csPCa improved from 77.1 and 87.5% to 90.4 and 96.1%, respectively, in the training cohort (n = 376) and from 81.9 and 89.0% to 91.8 and 96.5%, respectively, in the validation cohort (n = 127) when combined with the two nomograms. Conclusion: The negative predictive value of negative mpMRI for the detection of PCa or csPCa was improved with the results of the nomograms.
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Liu, Jun, Jiyan Wang, Junnan Xiong, Weiming Cheng, Huaizhang Sun, Zhiwei Yong, and Nan Wang. "Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets." Remote Sensing 13, no. 23 (December 5, 2021): 4945. http://dx.doi.org/10.3390/rs13234945.
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Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.
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Chen, Yu-Wei, Chui-Yu Chiu, and Mu-Chun Hsiao. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities." Sustainability 13, no. 9 (April 30, 2021): 5050. http://dx.doi.org/10.3390/su13095050.
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Examining the price relationships of Brent Crude with 78 global commodities, our study shows that the spot price of a certain commodity, New York Harbor No. 2 Heating Oil Spot Price FOB, can serve as an auxiliary forecasting index of the rise and fall of the monthly Brent Crude oil price. With an innovative view for evaluating the price relationship and prediction based on simple, practical measurement, our findings provide a helpful auxiliary index tool for investors and analysts by offering a high success rate (82.98%) and predicting the rise and fall of the monthly Brent Crude oil price three weeks in advance.
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Chen, Liang, and Rui Ma. "Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization." Mathematical Problems in Engineering 2022 (May 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/4844856.
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To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction.