Academic literature on the topic 'Genetic Algorithm and Region of Interest'
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Journal articles on the topic "Genetic Algorithm and Region of Interest"
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Fattah, Salmah, Ismail Ahmedy, Mohd Yamani Idna Idris, and Abdullah Gani. "Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 9 (2022): 155013292211235. http://dx.doi.org/10.1177/15501329221123533.
Full textAbstract:
Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.
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Khandhar, Hardev Mukeshbhai, Chintan M. Bhatt, and Simon Fong. "Detection and Segmentation of Medical Images Using Generic Algorithms." International Journal of Extreme Automation and Connectivity in Healthcare 3, no. 1 (2021): 39–46. http://dx.doi.org/10.4018/ijeach.2021010104.
Full textAbstract:
Image processing plays an indispensable and significant role in the development of various fields like medical imaging, astronomy, GIS, disaster management, agriculture monitoring, and so on. Medical images which are recorded in digital forms are processed by high-end computers to extract whatever information we desire. In the fast-developing modern world of medical imaging diagnosis and prognosis, where manual photo interpretation is time-consuming, automatic object detection from devices like CT-Scans and MRIs has limited potential to generate the required results. This article addresses the process of identifying Region of Interests in cancer based medical images based on combination of Otsu’s algorithm and Canny edge detection methods. The primary objective of this paper is to derive meaningful and potential information from medical image in different scenarios by applying the image segementation in combination with genetic algorithms in a robust manner to detect region of interest.
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Kapil, Keswani, and Anand Bhaskar Dr. "FLOWER POLLINATION AND GENETIC ALGORITHM BASED OPTIMIZATION FOR NODE DEPLOYMENT IN WIRELESS SENSOR NETWORKS." International Journal of Engineering Technologies and Management Research 5, no. 2SE (2018): 281–93. https://doi.org/10.5281/zenodo.1244541.
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<strong><em>Wireless sensor network (WSN) most popular area of research where lots of work done in this field. Energy efficiency is one of the most focusing areas because life time of network is most common issue. In the WSN, the node placement is very essential part for the proper communication between the sensor nodes and base station (BS). For better communication nodes should be aware about their own or neighbor node’s location. Better optimization of resources and performance improvement are the main concern for the WSN. Optimal techniques should be utilized to place the nodes at the best possible locations for achieving the desired goal. For node placement, flower pollination optimization and genetic algorithm are useful to generate better result. BS is responsible for the communication of nodes with each other and it should be reachable to nodes. For this Region of Interest (RoI) is helpful to choose the best location. Placement of BS in the middle is suitable place for the static nodes deployment and there should be other strategy for the dynamic environment. Nodes should be connected to each other for the transmission of data from the source to BS properly. From the MATLAB simulation, it has been shown that the proposed methodology improves the network performance in terms of dead nodes, energy remaining and various packets sent to BS</em></strong>
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Castellanos-Alvarez, Alejandro, Laura Cruz-Reyes, Eduardo Fernandez, et al. "A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification." Mathematical and Computational Applications 26, no. 2 (2021): 27. http://dx.doi.org/10.3390/mca26020027.
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Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.
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B. Alnajjar, Aseel, Azhar M. Kadim, Ruaa Abdullah Jaber, et al. "Wireless Sensor Network Optimization Using Genetic Algorithm." Journal of Robotics and Control (JRC) 3, no. 6 (2023): 827–35. http://dx.doi.org/10.18196/jrc.v3i6.16526.
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Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results.
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Liu, Bin Bing, Hai Qing Chen, Chong Huang, and Zhen Gang Yang. "Edge Tracing Based on Improved Genetic Algorithm." Advanced Materials Research 488-489 (March 2012): 904–12. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.904.
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In this paper, we proposed a new edge tracing method with high robustness to noise. Through representing edge with maximal gradient path encoded by chain code, the edge tracing problems can be converted into combinatorial optimization problems, and so they can be solved by genetic algorithm. We optimized the traditional genetic algorithm in order to improve the convergence rate. Our method is effective to edges with any shape because it does not require any prior knowledge about the edges. In this paper we also discussed the problem of edge winding and folding and expatiated how to avoid it by designing proper gene coding method and punishment function. Furthermore, by transforming the region of interests from Cartesian coordinates to polar coordinates before edge tracing, this method can be used for closed edges. The experimental results show this is an effective edge tracing method with high robustness and flexibility.
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Yin, Wei, Tao Yang, GuangYu Wan, and Xiong Zhou. "Identification of image genetic biomarkers of Alzheimer's disease by orthogonal structured sparse canonical correlation analysis based on a diagnostic information fusion." Mathematical Biosciences and Engineering 20, no. 9 (2023): 16648–62. http://dx.doi.org/10.3934/mbe.2023741.
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<abstract> <p>Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and its incidence increases yearly. Because AD patients will have cognitive impairment and personality changes, it has caused a heavy burden on the family and society. Image genetics takes the structure and function of the brain as a phenotype and studies the influence of genetic variation on the structure and function of the brain. Based on the structural magnetic resonance imaging data and transcriptome data of AD and healthy control samples in the Alzheimer's Disease Neuroimaging Disease database, this paper proposed the use of an orthogonal structured sparse canonical correlation analysis for diagnostic information fusion algorithm. The algorithm added structural constraints to the region of interest (ROI) of the brain. Integrating the diagnostic information of samples can improve the correlation performance between samples. The results showed that the algorithm could extract the correlation between the two modal data and discovered the brain regions most affected by multiple risk genes and their biological significance. In addition, we also verified the diagnostic significance of risk ROIs and risk genes for AD. The code of the proposed algorithm is available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/Wanguangyu111/OSSCCA-DIF">https://github.com/Wanguangyu111/OSSCCA-DIF</ext-link>.</p> </abstract>
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Zhu, Bing Kun, Li Hong Xu, and Hai Gen Hu. "A Multi-Objective Robust Preference Genetic Algorithm Based on Decision Variable Perturbation." Advanced Materials Research 211-212 (February 2011): 818–22. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.818.
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Multi-objective optimization is a challenging research topic because it involves the simultaneous optimization of several complex and conflicting objectives. However multi-objectivity is only one aspect of real-world applications and there is a growing interest in the optimization of solutions that are insensitive to parametric variations as well. A new robust preference multi-objective optimization algorithm is proposed in this paper, and the robust measurement of solution is designed based on Latin Hypercube Sampling, which is embedded in the optimization process to guide the optimization direction and help the better robust solution have more chance to survive. In order to obtain different preference of the robust solutions, a new fitness scheme is also presented. Through the adjustment of fitness function parameter preference, robust solutions can be obtained. Results suggest that the proposed algorithm has a bias towards the region where the preference robust solutions lie.
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Bajcsi, A. "Towards a Support System for Digital Mammogram Classification." Studia Universitatis Babeș-Bolyai Informatica 66, no. 2 (2021): 19. http://dx.doi.org/10.24193/subbi.2021.2.02.
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Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively.
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A. Ali, Nizheen, and Ramadhan J. Mstafa. "Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms." Computer Systems Science and Engineering 47, no. 2 (2023): 1451–69. http://dx.doi.org/10.32604/csse.2023.039957.
Full textDissertations / Theses on the topic "Genetic Algorithm and Region of Interest"
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Chen, Ming-Hui, and 陳明輝. "A High-performance Algorithm for Region-of-interest Error Concealment." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/99356689795377907997.
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碩士<br>國立高雄第一科技大學<br>電腦與通訊工程所<br>95<br>This paper presents an high performance error concealment algorithm for recovering ROI (Region of interest) information with hiding techniques. While the channel suffers from cell lost, we need to keep entire ROI data from error decoding. The image performed by DCT transformation, based on the progressive transformation, the low-frequency components of ROI are quantized and then encoded to disperse its information into the high-frequency band or the middle-frequency band of original image. The technique is only embedded fewer coefficients of ROI into original image and not only the changed one coefficient in each intact blocks but also the coefficient sign is not changed after embedded procedure. The capability of protection is carried out with extracting the ROI coefficients form the intact blocks of a damaged image itself without increasing extra information.
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CHEN, HUNG-XIN, and 陳泓欣. "Adaptive Bit-Allocation Algorithm Based on the Region-of-Interest Map." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/p8qyvu.
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碩士<br>國立臺北科技大學<br>資訊工程系<br>107<br>People typically pay attention to the regions of interest (ROI) when watching video. In this thesis, we propose a new ROI-based bit-allocation method to enhance the visual quality of ROI under an overall average bitrate constraint. The latest video coding standard HEVC (High Efficiency Video Coding) adopts a global R-λ rate control (RC) model, which determines the optimal QP (Quantization Parameter) based on video characteristics. The proposed method determines the ROI map according to the average depth of coding units and separates the R-λ RC model for ROI and non-ROI in the frame level. We designed an adaptive update iteration algorithm to determine the adequate K, which represents the bitrate ratio between ROI and non-ROI. Where the number of K is further to determine in term of the size of GOP. Experimental results show that the proposed method produces significantly higher PSNR in ROI with acceptable degradation in bitrate accuracy and overall PSNR. Two groups of test sequences corresponding to high-resolution and low-resolution videos are used for verification. In the high resolution group, the gain of PSNR in ROI goes from 0.44dB to 1.38dB. The average of BD-Rate only increases 3.6% and BD-PSNR is -0.06dB. In contrast to the low resolution group, the gain of PSNR in ROI also can obtain from 0.36dB to 1.43dB. The BD-Rate of overall correspond to -2.39% and the BD-PSNR is 0.10dB.
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Wang, Hao-Yu, and 王?宇. "An Image Super-Resolution Algorithm Based on Multi-Layer Region of Interest." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/53363912392318366330.
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碩士<br>國立臺灣科技大學<br>電機工程系<br>104<br>This thesis proposes an image super-resolution (SR) algorithm, based on multi-layer region of interest (ROI).
Image super-resolution provides a solution to the problem of limited resolution in image or video. However, the algorithms which have high quality of output images need to spend much time in training. The algorithms which have lower time complexity have low quality of output images.
The core idea of the proposed approach in this thesis are: (1) Proposing an image super-resolution algorithm which is getting a balance between time complexity and output image quality; and (2) Combining the concept of region of interest with image super-resolution to improve the output image quality. The algorithm enhances different image details in different layer of region of interest.
The simulation results in this paper has shown the algorithm gets a well balance between the quality of output image and time complexity.
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Lee, Dong-Gang, and 李東岡. "Resolution enhancement of imaging small-scale features of interest using Genetic Algorithm." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/81546507875019473621.
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碩士<br>逢甲大學<br>電機工程所<br>99<br>For high resolution image reconstruction over regions containing small-scale features of interest in a compactly supported object domain, the recent advances of extending the prior discrete Fourier transform (PDFT) make it possible to achieve the desired resolution enhancement, by exploiting differently weighted windows to modulate different spatial-frequency complex exponential bases. In this paper, in particular for the purpose of accommodating the previously-developed technique for more realistic applications the genetic algorithm (GA) is used to optimize the choice of weighted windows for all complex exponential bases. From one- and two-dimensional simulations, our proposed method has been proved a great potential in improving a superior image estimate, as compared to the classical PDFT and the newly-modified PDFT with different bases.
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Wei-Chi, Lai, and 賴偉祺. "DESIGN OF REGION-WISE FUZZY SLIDING MODE CONTROLLER WITH FUZZY TUNER USING GENETIC ALGORITHM." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/50594873131757892027.
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碩士<br>大同工學院<br>電機工程研究所<br>86<br>In this thesis, a fuzzy sliding mode controller with fuzzy tuner is present. We firstly employ the sliding mode technique to design the fuzzy control rules. Secondly, according to the state values of system, the output scaling factoris adjusted by a fuzzy tuner.Then, combine the two input variables as only oneinput variable to decrease the fuzzy control rules. Finally, the genetic algorithm is applied to search the optimal parameters of the controller. The simulation results show that the proposed region-wise fuzzy sliding mode controller with fuzzy tuner using genetic algorithm has the following advantages:(1) The fuzzy rules of the fuzzy controller can be efficiently reduced.(2)The control signal is smooth. (3) The system response is fast and stable,and has the property of robustness.
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Gorur, Pushkar. "Bitrate Reduction Techniques for Low-Complexity Surveillance Video Coding." Thesis, 2016. http://etd.iisc.ac.in/handle/2005/2681.
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High resolution surveillance video cameras are invaluable resources for effective crime prevention and forensic investigations. However, increasing communication bandwidth requirements of high definition surveillance videos are severely limiting the number of cameras that can be deployed. Higher bitrate also increases operating expenses due to higher data communication and storage costs. Hence, it is essential to develop low complexity algorithms which reduce data rate of the compressed video stream without affecting the image fidelity. In this thesis, a computer vision aided H.264 surveillance video encoder and four associated algorithms are proposed to reduce the bitrate. The proposed techniques are (I) Speeded up foreground segmentation, (II) Skip decision, (III) Reference frame selection and (IV) Face Region-of-Interest (ROI) coding.
In the first part of the thesis, a modification to the adaptive Gaussian Mixture Model (GMM) based foreground segmentation algorithm is proposed to reduce computational complexity. This is achieved by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we compute periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal.
In the second part, we propose a skip decision technique that uses a spatial sampler to sample pixels. The sampled pixels are segmented using the speeded up GMM algorithm. The storage pattern of the GMM parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. In the third part, a reference frame selection algorithm is proposed to maximize the number of background Macroblocks (MB’s) (i.e. MB’s that contain background image content) in the Decoded Picture Buffer. This reduces the cost of coding uncovered background regions. Distortion over foreground pixels is measured to quantify the performance of skip decision and reference frame selection techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.
In the final part of the thesis, face and shadow region detection is combined with the skip decision algorithm to perform ROI coding for pedestrian surveillance videos. Since person identification requires high quality face images, MB’s containing face image content are encoded with a low Quantization Parameter setting (i.e. high quality). Other regions of the body in the image are considered as RORI (Regions of reduced interest) and are encoded at low quality. The shadow regions are marked as Skip. Techniques that use only facial features to detect faces (e.g. Viola Jones face detector) are not robust in real world scenarios. Hence, we propose to initially detect pedestrians using deformable part models. The face region is determined using the deformed part locations. Detected pedestrians are tracked using an optical flow based tracker combined with a Kalman filter. The tracker improves the accuracy and also avoids the need to run the object detector on already detected pedestrians. Shadow and skin detector scores are computed over super pixels. Bilattice based logic inference is used to combine multiple likelihood scores and classify the super pixels as ROI, RORI or RONI. The coding mode and QP values of the MB’s are determined using the super pixel labels. The proposed techniques provide a further reduction in bitrate of up to 50.2%.
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Gorur, Pushkar. "Bitrate Reduction Techniques for Low-Complexity Surveillance Video Coding." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2681.
Full textAbstract:
High resolution surveillance video cameras are invaluable resources for effective crime prevention and forensic investigations. However, increasing communication bandwidth requirements of high definition surveillance videos are severely limiting the number of cameras that can be deployed. Higher bitrate also increases operating expenses due to higher data communication and storage costs. Hence, it is essential to develop low complexity algorithms which reduce data rate of the compressed video stream without affecting the image fidelity. In this thesis, a computer vision aided H.264 surveillance video encoder and four associated algorithms are proposed to reduce the bitrate. The proposed techniques are (I) Speeded up foreground segmentation, (II) Skip decision, (III) Reference frame selection and (IV) Face Region-of-Interest (ROI) coding.
In the first part of the thesis, a modification to the adaptive Gaussian Mixture Model (GMM) based foreground segmentation algorithm is proposed to reduce computational complexity. This is achieved by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we compute periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal.
In the second part, we propose a skip decision technique that uses a spatial sampler to sample pixels. The sampled pixels are segmented using the speeded up GMM algorithm. The storage pattern of the GMM parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. In the third part, a reference frame selection algorithm is proposed to maximize the number of background Macroblocks (MB’s) (i.e. MB’s that contain background image content) in the Decoded Picture Buffer. This reduces the cost of coding uncovered background regions. Distortion over foreground pixels is measured to quantify the performance of skip decision and reference frame selection techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.
In the final part of the thesis, face and shadow region detection is combined with the skip decision algorithm to perform ROI coding for pedestrian surveillance videos. Since person identification requires high quality face images, MB’s containing face image content are encoded with a low Quantization Parameter setting (i.e. high quality). Other regions of the body in the image are considered as RORI (Regions of reduced interest) and are encoded at low quality. The shadow regions are marked as Skip. Techniques that use only facial features to detect faces (e.g. Viola Jones face detector) are not robust in real world scenarios. Hence, we propose to initially detect pedestrians using deformable part models. The face region is determined using the deformed part locations. Detected pedestrians are tracked using an optical flow based tracker combined with a Kalman filter. The tracker improves the accuracy and also avoids the need to run the object detector on already detected pedestrians. Shadow and skin detector scores are computed over super pixels. Bilattice based logic inference is used to combine multiple likelihood scores and classify the super pixels as ROI, RORI or RONI. The coding mode and QP values of the MB’s are determined using the super pixel labels. The proposed techniques provide a further reduction in bitrate of up to 50.2%.
Books on the topic "Genetic Algorithm and Region of Interest"
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Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.
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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
Book chapters on the topic "Genetic Algorithm and Region of Interest"
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Agrawal, Pranshu, Gaurav Ojha, and Mahua Bhattacharya. "A Generic Algorithm for Segmenting a Specified Region of Interest Based on Chanvese’s Algorithm and Active Contours." In Advances in Intelligent Systems and Computing. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2656-7_21.
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Cho, Hosang, Kyounghoon Jang, Changhoo Kim, and Bongsoon Kang. "A Region of Interest Labeling Algorithm Using Three Mask Patterns." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6516-0_64.
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Pilania, Urmila, Manoj Kumar, and Gaganjot Kaur. "Region of Interest Using Viola-Jones Algorithm for Video Steganography." In Applied Computational Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2719-5_38.
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Shen, Liquan, Qianqian Hu, Zhi Liu, and Ping An. "A New Rate Control Algorithm Based on Region of Interest for HEVC." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48896-7_56.
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Furong, Wang, Li Yixing, and He Long. "An Image SVD Compression Algorithm for UAV Based on Region of Interest." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_301.
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Yochum, Phatpicha, Liang Chang, Tianlong Gu, Manli Zhu, and Hongliang Chen. "A Genetic Algorithm for Travel Itinerary Recommendation with Mandatory Points-of-Interest." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46931-3_13.
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Gogineni, Ajay K., Raj Kishore, Pranay Raj, Suprava Naik, and Kisor K. Sahu. "Unsupervised Clustering Algorithm as Region of Interest Proposals for Cancer Detection Using CNN." In Computational Vision and Bio-Inspired Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_146.
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Reinaldo Meneghini, Ivan, Frederico Gadelha Guimarães, António Gaspar-Cunha, and Miri Weiss Cohen. "Incorporation of Region of Interest in a Decomposition-Based Multi-objective Evolutionary Algorithm." In Computational Methods in Applied Sciences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57422-2_3.
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Liu, Qiong, and Rui-Min Hu. "Perceptually Motivated Adaptive Quantization Algorithm for Region-of-Interest Coding in H.264." In Advances in Multimedia Information Processing - PCM 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89796-5_14.
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Chen, Zhiqiang, Yun Jiang, and Xudong Chen. "Real-Coded Genetic Algorithm with Oriented Search towards Promising Region for Parameter Optimization." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45049-9_6.
Full textConference papers on the topic "Genetic Algorithm and Region of Interest"
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Roy, Arnab, Joseph L. Pachuau, Nongmeikapam Brajabidhu Singh, and Anish Kumar Saha. "Quantum inspired genetic algorithm and optimization of queuing delay." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902749.
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Ponnambalam, Karthickumar, B. Sivaneasan, A. Sharma, S. S. Lee, and Prasun Chakrabarti. "Battery SOH Estimation Using LSTM with Genetic Algorithm-Based Hyperparameter Optimization." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902858.
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Bhasin, Harsh, Amit Kumar, and Vishal Deshwal. "Diploid Genetic Algorithm-Based Pipeline for the Conversion Prediction of Mild Cognitive Impairment." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752147.
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Manuel, Manu, Benjamin Hien, Simon Conrady, Arne Kreddig, Nguyen Anh Vu Doan, and Walter Stechele. "Region of interest based non-dominated sorting genetic algorithm-II." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3512290.3528872.
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HIWA, Satoru, Yuuki KOHRI, Keisuke HACHISUKA, and Tomoyuki HIROYASU. "Region-of-interest extraction of fMRI data using genetic algorithms." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850135.
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Gonçalves, CB, AC Prado Domingos, B. Yousefi, JR Souza, and H. Fernandes. "Effects of region of interest on breast cancer detection using CNN and infrared imaging." In QIRT. QIRT Council, 2022. http://dx.doi.org/10.21611/qirt.2022.2019.
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Breast cancer is one of the most common type of cancer that affects woman in the World. It affects millions of women every year killing hundreds of thousands yearly. Early detection of this disease is essential for improving chances of cure and recovery of the patients, thus, one of the most important factors in patients’ treatment is early detection. Mammography, the golden standard for detection this disease is not always 100% effective, which means that it is not always recommended.In this sense, infrared imaging is a promising technique that might be used as a complementary examination technique in a computer-aided diagnosis system. In this work we used genetic algorithms and convolutional neural networks to classify images from the DMR-IR public database. We analyzed both the entire image and only the breast region. Best results were F1-score of 0.92 for entire images and 0.90 for breast regions
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Wei Ying, Chang Cunxi, Jia Tong, and Xu Xinhe. "Segmentation of regions of interest in lung CT images based on 2-D OTSU optimized by genetic algorithm." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5195024.
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Shaferman, Vitaly, and Tal Shima. "Co-Evolution Genetic Algorithm for UAV Distributed Tracking in Urban Environments." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59590.
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A distributed approach is proposed for planning a cooperative tracking task for a team of unmanned aerial vehicles (UAVs). In the scenario of interest UAVs are required to autonomously track, using their onboard sensors, a moving target in a known urban environment. The solution methodology involves finding visibility regions, from which a UAV can maintain a line of sight to the target during the scenario; and restricted regions, in which a UAV can not fly, due to the presence of buildings or other airspace limitations. A co-evolution genetic algorithm is derived for searching, in realtime, monotonically improving solutions. In the proposed distributed search method every UAV iteratively manipulates its own population of chromosomes, each encoding its control inputs in the calculated horizon. Team performance is attained by assigning fitness to each solution in the population based on the cooperative performance when using it together with preceding iteration tracking information obtained from teammates. Important attributes of the proposed solution approach are its scalability and robustness; and consequently it can be applied to large sized problems and adapt to the loss of UAV team members. The distributed nature of the algorithm also reduces the computation and communication loads. The performance of the algorithm is studied using a high fidelity simulation test-bed incorporating a visual database of the city of Tel-Aviv, Israel.
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Jovanova, Jovana, Mary Frecker, Reginald F. Hamilton, and Todd A. Palmer. "Target Shape Optimization of Functionally Graded Shape Memory Alloy Compliant Mechanism." In ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/smasis2016-9070.
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Nickel Titanium (NiTi) shape memory alloys (SMAs) exhibit shape memory and/or superelastic properties, enabling them to demonstrate multifunctionality by engineering microstructural and compositional gradients at selected locations. This paper focuses on the design optimization of NiTi compliant mechanisms resulting in single-piece structures with functionally graded properties, based on user-defined target shape matching approach. The compositionally graded zones within the structures will exhibit an on demand superelastic effect (SE) response, exploiting the tailored mechanical behavior of the structure. The functional grading has been approximated by allowing the geometry and the superelastic properties of each zone to vary. The superelastic phenomenon has been taken into consideration using a standard nonlinear SMA material model, focusing only on 2 regions of interest: the linear region of higher Young’s modulus of elasticity and the superelastic region with significantly lower Young’s modulus of elasticity. Due to an outside load, the graded zones reach the critical stress at different stages based on their composition, position and geometry, allowing the structure morphing. This concept has been used to optimize the structures’ geometry and mechanical properties to match a user-defined target shape structure. A multi-objective evolutionary algorithm (NSGA II - Non-dominated Sorting Genetic Algorithm) for constrained optimization of the structure’s mechanical properties and geometry has been developed and implemented.
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Owoyele, Opeoluwa, and Pinaki Pal. "A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning." In ASME 2019 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/icef2019-7237.
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Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (μGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster, with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.
Reports on the topic "Genetic Algorithm and Region of Interest"
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Wisniewski, Michael, Samir Droby, John Norelli, Dov Prusky, and Vera Hershkovitz. Genetic and transcriptomic analysis of postharvest decay resistance in Malus sieversii and the identification of pathogenicity effectors in Penicillium expansum. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597928.bard.
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Use of Lqh2 mutants (produced at TAU) and rNav1.2a mutants (produced at the US side) for identifying receptor site-3: Based on the fact that binding of scorpion alpha-toxins is voltage-dependent, which suggests toxin binding at the mobile voltage-sensing region, we analyzed which of the toxin bioactive domains (Core-domain or NC-domain) interacts with the DIV Gating-module of rNav1.2a. This analysis was based on the assumption that the dissociation of toxin mutants upon depolarization would vary from that of the unmodified toxin should the substitutions affect a site of interaction with the channel Gating-module. Using a series of toxin mutants (mutations at both domains) and two channel mutants that were shown to reduce the sensitivity to scorpion alpha-toxins, and by comparison of depolarization-driven dissociation of Lqh2 derivatives off their binding site at rNav1.2a mutant channels we found that the toxin Core-domain interacts with the Gating-module of DIV. Details of the experiments and results appear in Guret al (2011). Mapping receptor site 3 at Nav1.2a by extensive channel mutagenesis (Seattle): Since previous studies with photoaffinity labeling and antibody mapping implicated domains I and IV in scorpion alpha-toxin binding, Nav1.2 channel mutants containing substitutions at these extracellular regions were expressed and tested for receptor function by whole-cell voltage clamp. Of a large number of channel mutants, T1560A, F1610A, and E1613A in domain IV had ~5.9-, ~10.7-, and ~3.9-fold lower affinities for the scorpion toxin Lqh2, respectively, and mutant E1613R had 73-fold lower affinity. Toxin dissociation was accelerated by depolarization for both wild-type and mutants, and the rates of dissociation were also increased by mutations T1560A, F1610A and E1613A. In contrast, association rates for these three mutant channels at negative membrane potentials were not significantly changed and were not voltage-dependent. These results indicated that Thr1560 in the S1-S2 loop, Phe1610 in the S3 segment, and Glu1613 in the S3-S4 loop in domain IV participate in toxin binding. T393A in the SS2-S6 loop in domain I also showed a ~3.4-fold lower affinity for Lqh2, indicating that this extracellular loop may form a secondary component of the toxin binding site. Analysis with the Rosetta-Membrane algorithm revealed a three-dimensional model of Lqh2 binding to the voltage sensor in a resting state. In this model, amino acid residues in an extracellular cleft formed by the S1-S2 and S3-S4 loops in domain IV that are important for toxin binding interact with amino acid residues on two faces of the wedge-shaped Lqh2 molecule that are important for toxin action. The conserved gating charges in the S4 transmembrane segment are in an inward position and likely form ion pairs with negatively charged amino acid residues in the S2 and S3 segments (Wang et al 2011; Gurevitz 2012; Gurevitzet al 2013).
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Stern, David, and Gadi Schuster. Manipulation of Gene Expression in the Chloroplast. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7575289.bard.
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The steady-state level of a given mRNA is determined by its rates of transcription and degradation. The stabilities of chloroplast mRNAs vary during plant development, in part regulating gene expression. Furthermore, the fitness of the organelle depends on its ability to destroy non-functional transcripts. In addition, there is a resurgent interest by the biotechnology community in chloroplast transformation due to the public concerns over pollen transmission of introduced traits or foreign proteins. Therefore, studies into basic gene expression mechanisms in the chloroplast will open the door to take advantage of these opportunities. This project was aimed at gaining mechanistic insights into mRNA processing and degradation in the chloroplast and to engineer transcripts of varying stability in Chlamydomonas reinhardtii cells. This research uncovered new and important information on chloroplast mRNA stability, processing, degradation and translation. In particular, the processing of the 3' untranslated regions of chloroplast mRNAs was shown to be important determinants in translation. The endonucleolytic site in the 3' untranslated region was characterized by site directed mutagensis. RNA polyadenylation has been characterized in the chloroplast of Chlamydomonas reinhardtii and chloroplast transformants carrying polyadenylated sequences were constructed and analyzed. Data obtained to date suggest that chloroplasts have gene regulatory mechanisms which are uniquely adapted to their post-endosymbiotic environment, including those that regulate RNA stability. An exciting point has been reached, because molecular genetic studies have defined critical RNA-protein interactions that participate in these processes. However, much remains to be learned about these multiple pathways, how they interact with each other, and how many nuclear genes are consecrated to overseeing them. Chlamydomonas is an ideal model system to extend our understanding of these areas, given its ease of manipulation and the existing knowledge base, some of which we have generated.
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Smith, Margaret, Nurit Katzir, Susan McCouch, and Yaakov Tadmor. Discovery and Transfer of Genes from Wild Zea Germplasm to Improve Grain Oil and Protein Composition of Temperate Maize. United States Department of Agriculture, 1998. http://dx.doi.org/10.32747/1998.7580683.bard.
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Project Objectives 1. Develop and amplify two interspecific populations (annual and perennial teosintes x elite maize inbred) as the basis for genetic analysis of grain quality. 2. Identify quantitative trait loci (QTLs) from teosinte that improve oil, protein, and essential amino acid composition of maize grain. 3. Develop near isogenic lines (NILs) to quantify QTL contributions to grain quality and as a resource for future breeding and gene cloning efforts. 4. Analyze the contribution of these QTLs to hybrid performance in both the US and Israel. 5. Measure the yield potential of improved grain quality hybrids. (NOTE: Yield potential could not be evaluated due to environmentally-caused failure of the breeding nursery where seed was produced for this evaluation.) Background: Maize is a significant agricultural commodity worldwide. As an open pollinated crop, variation within the species is large and, in most cases, sufficient to supply the demand for modem varieties and for new environments. In recent years there is a growing demand for maize varieties with special quality attributes. While domesticated sources of genetic variation for high oil and protein content are limited, useful alleles for these traits may remain in maize's wild relative, teosinte. We utilized advanced backcross (AB) analysis to search for QTLs contributing to oil and protein content from two teosinte accessions: Zea mays ssp. mexicana Race Chalco, an annual teosinte (referred to as Chalco), and Z diploperennis Race San Miguel, a perennial teosinte (referred to as Diplo). Major Conclusions and Achievements Two NILs targeting a Diplo introgression in bin 1.04 showed a significant increase in oil content in homozygous sib-pollinated seed when compared to sibbed seed of their counterpart non-introgressed controls. These BC4S2 NILs, referred to as D-RD29 and D-RD30, carry the Diplo allele in bin 1.04 and the introgression extends partially into bins 1.03 and 1.05. These NILs remain heterozygous in bins 4.01 and 8.02, but otherwise are homozygous for the recurrent parent (RD6502) alleles. NILs were developed also for the Chalco introgression in bin 1.04 but these do not show any improvement in oil content, suggesting that the Chalco alleles differ from the Diplo alleles in this region. Testcross Fl seed and sibbed grain from these Fl plants did not show any effect on oil content from this introgression, suggesting that it would need to be present in both parents of a maize hybrid to have an effect on oil content. Implications, both Scientific and Agricultural The Diplo region identified increases oil content by 12.5% (from 4.8% to 5.4% oil in the seed). Although this absolute difference is not large in agronomic terms, this locus could provide additive increases to oil content in combination with other maize-derived loci for high oil. To our knowledge, this is the first confirmed report of a QTL from teosinte for improved grain oil content in maize. It suggests that further research on grain quality alleles from maize wild relatives would be of both scientific and agricultural interest.
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Smith, Margaret, Nurit Katzir, Susan McCouch, and Yaakov Tadmor. Discovery and Transfer of Genes from Wild Zea Germplasm to Improve Grain Oil and Protein Composition of Temperate Maize. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7695846.bard.
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Project Objectives 1. Develop and amplify two interspecific populations (annual and perennial teosintes x elite maize inbred) as the basis for genetic analysis of grain quality. 2. Identify quantitative trait loci (QTLs) from teosinte that improve oil, protein, and essential amino acid composition of maize grain. 3. Develop near isogenic lines (NILs) to quantify QTL contributions to grain quality and as a resource for future breeding and gene cloning efforts. 4. Analyze the contribution of these QTLs to hybrid performance in both the US and Israel. 5. Measure the yield potential of improved grain quality hybrids. (NOTE: Yield potential could not be evaluated due to environmentally-caused failure of the breeding nursery where seed was produced for this evaluation.) Background: Maize is a significant agricultural commodity worldwide. As an open pollinated crop, variation within the species is large and, in most cases, sufficient to supply the demand for modem varieties and for new environments. In recent years there is a growing demand for maize varieties with special quality attributes. While domesticated sources of genetic variation for high oil and protein content are limited, useful alleles for these traits may remain in maize's wild relative, teosinte. We utilized advanced backcross (AB) analysis to search for QTLs contributing to oil and protein content from two teosinte accessions: Zea mays ssp. mexicana Race Chalco, an annual teosinte (referred to as Chalco), and Z diploperennis Race San Miguel, a perennial teosinte (referred to as Diplo). Major Conclusions and Achievements Two NILs targeting a Diplo introgression in bin 1.04 showed a significant increase in oil content in homozygous sib-pollinated seed when compared to sibbed seed of their counterpart non-introgressed controls. These BC4S2 NILs, referred to as D-RD29 and D-RD30, carry the Diplo allele in bin 1.04 and the introgression extends partially into bins 1.03 and 1.05. These NILs remain heterozygous in bins 4.01 and 8.02, but otherwise are homozygous for the recurrent parent (RD6502) alleles. NILs were developed also for the Chalco introgression in bin 1.04 but these do not show any improvement in oil content, suggesting that the Chalco alleles differ from the Diplo alleles in this region. Testcross Fl seed and sibbed grain from these Fl plants did not show any effect on oil content from this introgression, suggesting that it would need to be present in both parents of a maize hybrid to have an effect on oil content. Implications, both Scientific and Agricultural The Diplo region identified increases oil content by 12.5% (from 4.8% to 5.4% oil in the seed). Although this absolute difference is not large in agronomic terms, this locus could provide additive increases to oil content in combination with other maize-derived loci for high oil. To our knowledge, this is the first confirmed report of a QTL from teosinte for improved grain oil content in maize. It suggests that further research on grain quality alleles from maize wild relatives would be of both scientific and agricultural interest.
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Fridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
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Heterosis, the enigmatic phenomenon in which whole genome heterozygous hybrids demonstrate superior fitness compared to their homozygous parents, is the main cornerstone of modern crop plant breeding. One explanation for this non-additive inheritance of hybrids is interaction of alleles within the same locus. This proposal aims at screening, identifying and investigating heterosis trait loci (HTL) for different yield traits by implementing a novel integrated mapping approach in Sorghum bicolor as a model for other crop plants. Originally, the general goal of this research was to perform a genetic dissection of heterosis in a diallel built from a set of Sorghum bicolor inbred lines. This was conducted by implementing a novel computational algorithm which aims at associating between specific heterozygosity found among hybrids with heterotic variation for different agronomic traits. The initial goals of the research are: (i) Perform genotype by sequencing (GBS) of the founder lines (ii) To evaluate the heterotic variation found in the diallel by performing field trails and measurements in the field (iii) To perform QTL analysis for identifying heterotic trait loci (HTL) (iv) to validate candidate HTL by testing the quantitative mode of inheritance in F2 populations, and (v) To identify candidate HTL in NAM founder lines and fine map these loci by test-cross selected RIL derived from these founders. The genetic mapping was initially achieved with app. 100 SSR markers, and later the founder lines were genotyped by sequencing. In addition to the original proposed research we have added two additional populations that were utilized to further develop the HTL mapping approach; (1) A diallel of budding yeast (Saccharomyces cerevisiae) that was tested for heterosis of doubling time, and (2) a recombinant inbred line population of Sorghum bicolor that allowed testing in the field and in more depth the contribution of heterosis to plant height, as well as to achieve novel simulation for predicting dominant and additive effects in tightly linked loci on pseudooverdominance. There are several conclusions relevant to crop plants in general and to sorghum breeding and biology in particular: (i) heterosis for reproductive (1), vegetative (2) and metabolic phenotypes is predominantly achieved via dominance complementation. (ii) most loci that seems to be inherited as overdominant are in fact achieving superior phenotype of the heterozygous due to linkage in repulsion, namely by pseudooverdominant mechanism. Our computer simulations show that such repulsion linkage could influence QTL detection and estimation of effect in segregating populations. (iii) A new height QTL (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 in sorghum, and its genetic dissection in RIL population demonstrated that it affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. (iv) HTL mapping for grain nitrogen content in sorghum grains has identified several candidate genes that regulate this trait, including several putative nitrate transporters and a transcription factor belonging to the no-apical meristem (NAC)-like large gene family. This activity was combined with another BARD-funded project in which several de-novo mutants in this gene were identified for functional analysis.
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Tipton, Kelley, Brian F. Leas, Emilia Flores, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepccer268.
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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-searched reference lists of relevant studies and reviewed suggestions from experts and stakeholders. Review methods. Searches identified 11,500 unique records. Using predefined criteria and dual review, we screened and selected studies to assess one or both Key Questions (KQs): (1) the effect of algorithms on racial and ethnic disparities in health and healthcare outcomes and (2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Outcomes of interest included access to healthcare, quality of care, and health outcomes. We assessed studies’ methodologic risk of bias (ROB) using the ROBINS-I tool and piloted an appraisal supplement to assess racial and ethnic equity-related ROB. We completed a narrative synthesis and cataloged study characteristics and outcome data. We also examined four Contextual Questions (CQs) designed to explore the context and capture insights on practical aspects of potential algorithmic bias. CQ 1 examines the problem’s scope within healthcare. CQ 2 describes recently emerging standards and guidance on how racial and ethnic bias can be prevented or mitigated during algorithm development and deployment. CQ 3 explores stakeholder awareness and perspectives about the interaction of algorithms and racial and ethnic disparities in health and healthcare. We addressed these CQs through supplemental literature reviews and conversations with experts and key stakeholders. For CQ 4, we conducted an in-depth analysis of a sample of six algorithms that have not been widely evaluated before in the published literature to better understand how their design and implementation might contribute to disparities. Results. Fifty-eight studies met inclusion criteria, of which three were included for both KQs. One study was a randomized controlled trial, and all others used cohort, pre-post, or modeling approaches. The studies included numerous types of clinical assessments: need for intensive care or high-risk care management; measurement of kidney or lung function; suitability for kidney or lung transplant; risk of cardiovascular disease, stroke, lung cancer, prostate cancer, postpartum depression, or opioid misuse; and warfarin dosing. We found evidence suggesting that algorithms may: (a) reduce disparities (i.e., revised Kidney Allocation System, prostate cancer screening tools); (b) perpetuate or exacerbate disparities (e.g., estimated glomerular filtration rate [eGFR] for kidney function measurement, cardiovascular disease risk assessments); and/or (c) have no effect on racial or ethnic disparities. Algorithms for which mitigation strategies were identified are included in KQ 2. We identified six types of strategies often used to mitigate the potential of algorithms to contribute to disparities: removing an input variable; replacing a variable; adding one or more variables; changing or diversifying the racial and ethnic composition of the patient population used to train or validate a model; creating separate algorithms or thresholds for different populations; and modifying the statistical or analytic techniques used by an algorithm. Most mitigation efforts improved proximal outcomes (e.g., algorithmic calibration) for targeted populations, but it is more challenging to infer or extrapolate effects on longer term outcomes, such as racial and ethnic disparities. The scope of racial and ethnic bias related to algorithms and their application is difficult to quantify, but it clearly extends across the spectrum of medicine. Regulatory, professional, and corporate stakeholders are undertaking numerous efforts to develop standards for algorithms, often emphasizing the need for transparency, accountability, and representativeness. Conclusions. Algorithms have been shown to potentially perpetuate, exacerbate, and sometimes reduce racial and ethnic disparities. Disparities were reduced when race and ethnicity were incorporated into an algorithm to intentionally tackle known racial and ethnic disparities in resource allocation (e.g., kidney transplant allocation) or disparities in care (e.g., prostate cancer screening that historically led to Black men receiving more low-yield biopsies). It is important to note that in such cases the rationale for using race and ethnicity was clearly delineated and did not conflate race and ethnicity with ancestry and/or genetic predisposition. However, when algorithms include race and ethnicity without clear rationale, they may perpetuate the incorrect notion that race is a biologic construct and contribute to disparities. Finally, some algorithms may reduce or perpetuate disparities without containing race and ethnicity as an input. Several modeling studies showed that applying algorithms out of context of original development (e.g., illness severity scores used for crisis standards of care) could perpetuate or exacerbate disparities. On the other hand, algorithms may also reduce disparities by standardizing care and reducing opportunities for implicit bias (e.g., Lung Allocation Score for lung transplantation). Several mitigation strategies have been shown to potentially reduce the contribution of algorithms to racial and ethnic disparities. Results of mitigation efforts are highly context specific, relating to unique combinations of algorithm, clinical condition, population, setting, and outcomes. Important future steps include increasing transparency in algorithm development and implementation, increasing diversity of research and leadership teams, engaging diverse patient and community groups in the development to implementation lifecycle, promoting stakeholder awareness (including patients) of potential algorithmic risk, and investing in further research to assess the real-world effect of algorithms on racial and ethnic disparities before widespread implementation.