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Journal articles on the topic 'Genetic Algorithm and Region of Interest'
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1
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.
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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.
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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.
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Liu, Pengyu, and Kebin Jia. "Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding." Scientific World Journal 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/293681.
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A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV) to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI). The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding.
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Tossa, Frantz, Wahabou Abdou, Keivan Ansari, Eugène C. Ezin, and Pierre Gouton. "Area Coverage Maximization under Connectivity Constraint in Wireless Sensor Networks." Sensors 22, no. 5 (2022): 1712. http://dx.doi.org/10.3390/s22051712.
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Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors.
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Salnikov, Nikolay I., Alexey V. Andrianov, and Elena A. Anashkina. "Optimization and Dispersion Tailoring of Chalcogenide M-Type Fibers Using a Modified Genetic Algorithm." Fibers 11, no. 11 (2023): 89. http://dx.doi.org/10.3390/fib11110089.
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M-type optical fibers in which a core is surrounded by a thin ring layer with a higher refractive index have attracted increasing attention in recent years. One of their advantageous features is the ability to operate a non-fundamental LP02 mode possessing unusual dispersion properties, namely, a zero-dispersion wavelength (ZDW) shifted to the short wavelength region relative to the material ZDW. The LP02 mode can be selectively excited since it is predominantly localized near the core, while the fundamental LP01 and other higher modes are localized near the ring (for proper fiber parameters). In this paper, we present a comprehensive theoretical analysis of effective dispersion tailoring for the HE12 mode of highly nonlinear chalcogenide glass fibers (for which the LP mode approximation fails due to large refractive index contrasts). We demonstrate fiber designs for which ZDWs can be shifted to the spectral region < 2 μm, which is of great interest for the development of mid-IR supercontinuum sources and frequency-tunable pulse sources with standard near-IR pumping. We obtained the characteristic equation and solved it numerically to find mode fields and dispersion characteristics. We show the possibility of achieving dispersion characteristics of the HE12 mode with one, two, three, and four ZDWs in the wavelength range of 1.5–5.5 μm. We used a modified genetic algorithm (MGA) to design fibers with desired dispersion parameters. In particular, by applying an MGA, we optimized four fiber parameters and constructed a fiber for which HE12 mode dispersion is anomalous in the 1.735–5.155 μm range.
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Lor, Kuo-Lung, and Chung-Ming Chen. "FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (2021): 2150012. http://dx.doi.org/10.4015/s1016237221500125.
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The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.
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Zhang, Jian, Chi‐Yuen Wang, Yaolin Shi, et al. "Three‐dimensional crustal structure in central Taiwan from gravity inversion with a parallel genetic algorithm." GEOPHYSICS 69, no. 4 (2004): 917–24. http://dx.doi.org/10.1190/1.1778235.
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The genetic algorithm method is combined with the finite‐element method for the first time as an alternative method to invert gravity anomaly data for reconstructing the 3D density structure in the subsurface. The method provides a global search in the model space for all acceptable models. The computational efficiency is significantly improved by storing the coefficient matrix and using it in all forward calculations, then by dividing the region of interest into many subregions and applying parallel processing to the subregions. Central Taiwan, a geologically complex region, is used as an example to demonstrate the utility of the method. A crustal block 120 × 150 km2 in area and 34 km in thickness is represented by a finite‐element model of 76 500 cubic elements, each 2 × 2 × 2 km3 in size. An initial density model is reconstructed from the regional 3D tomographic seismic velocity using an empirical relation between velocity and density. The difference between the calculated and the observed gravity anomaly (i.e., the residual anomaly) shows an elongated minimum of large magnitude that extends along the axis of the Taiwan mountain belt. Among the interpretive models tested, the best model shows a crustal root extending to depths of 50 to 60 km beneath the axis of the Western Central and Eastern Central Ranges with a density contrast of 400 or 500 kg/m3 across the Moho. Both predictions appear to be supported by independent seismological and laboratory evidence.
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Borges, Marco A., Paulo B. Lopes, and Leandro A. da Silva. "Network Optimization of Carbon Monoxide Sensor Nodes in the Metropolitan Region of São Paulo." Electronics 12, no. 22 (2023): 4647. http://dx.doi.org/10.3390/electronics12224647.
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Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population’s wellness.
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Albareda, Ramón, Karl Stephan Olfe, Álvaro Bello, José Javier Fernández, and Victoria Lapuerta. "Comparison of Optimization Methods for the Attitude Control of Satellites." Electronics 13, no. 17 (2024): 3363. http://dx.doi.org/10.3390/electronics13173363.
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The definition of multiple operational modes in a satellite is of vital importance for the adaptation of the satellite to the operational demands of the mission and environmental conditions. In this work, three optimization methods were implemented for the initial calibration of an attitude controller based on fuzzy logic with the purpose of performing an initial exploration of optimal regions of the design space: a multi-objective genetic algorithm (GAMULTIOBJ), a particle swarm optimization (PSO), and a multi-objective particle swarm optimization (MOPSO). The performance of the optimizers was compared in terms of energy cost, accuracy, computational cost, and convergence capabilities of each algorithm. The results show that the PSO algorithm demonstrated superior computational efficiency compared to the others. Concerning the exploration of optimum regions, all algorithms exhibited similar exploratory capabilities. PSO’s low computational cost allowed for thorough scanning of specific interest regions, making it ideal for detailed exploration, whereas MOPSO and GAMULTIOBJ provided more balanced performance with constrained Pareto front elements.
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Zhang, Yingxin, Gaige Wang, and Hongmei Wang. "NSGA-II/SDR-OLS: A Novel Large-Scale Many-Objective Optimization Method Using Opposition-Based Learning and Local Search." Mathematics 11, no. 8 (2023): 1911. http://dx.doi.org/10.3390/math11081911.
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Recently, many-objective optimization problems (MaOPs) have become a hot issue of interest in academia and industry, and many more many-objective evolutionary algorithms (MaOEAs) have been proposed. NSGA-II/SDR (NSGA-II with a strengthened dominance relation) is an improved NSGA-II, created by replacing the traditional Pareto dominance relation with a new dominance relation, termed SDR, which is better than the original algorithm in solving small-scale MaOPs with few decision variables, but performs poorly in large-scale MaOPs. To address these problems, we added the following improvements to the NSGA-II/SDR to obtain NSGA-II/SDR-OLS, which enables it to better achieve a balance between population convergence and diversity when solving large-scale MaOPs: (1) The opposition-based learning (OBL) strategy is introduced in the initial population initialization stage, and the final initial population is formed by the initial population and the opposition-based population, which optimizes the quality and convergence of the population; (2) the local search (LS) strategy is introduced to expand the diversity of populations by finding neighborhood solutions, in order to avoid solutions falling into local optima too early. NSGA-II/SDR-OLS is compared with the original algorithm on nine benchmark problems to verify the effectiveness of its improvement. Then, we compare our algorithm with six existing algorithms, which are promising region-based multi-objective evolutionary algorithms (PREA), a scalable small subpopulation-based covariance matrix adaptation evolution strategy (S3-CMA-ES), a decomposition-based multi-objective evolutionary algorithm guided by growing neural gas (DEA-GNG), a reference vector-guided evolutionary algorithm (RVEA), NSGA-II with conflict-based partitioning strategy (NSGA-II-conflict), and a genetic algorithm using reference-point-based non-dominated sorting (NSGA-III).The proposed algorithm has achieved the best results in the vast majority of test cases, indicating that our algorithm has strong competitiveness.
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Valenzuela, Olga, Xiaoyi Jiang, Antonio Carrillo, and Ignacio Rojas. "Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment." International Journal of Neural Systems 28, no. 09 (2018): 1850022. http://dx.doi.org/10.1142/s0129065718500223.
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Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.
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Wenju Zhou, Wenju Zhou, Xiaofei Han Wenju Zhou, Yuan Xu Xiaofei Han, Rongfei Chen Yuan Xu, and Zhenbo Zhang Rongfei Chen. "Embryo Evaluation Based on ResNet with AdaptiveGA-optimized Hyperparameters." 網際網路技術學刊 23, no. 3 (2022): 527–38. http://dx.doi.org/10.53106/160792642022052303011.
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<p>In vitro fertilization (IVF) embryo evaluation based on morphology is an effective method to improve the success rate of transplantation. Although convolutional neural networks (CNNs) have made great achievements in many image classifications, there are still great challenges in accurately classifying embryos due to the insufficient samples, interference of exfoliated cells, and inappropriate hyperparameter configuration in the classification network. In this paper, a residual neural network optimized by the adaptive genetic algorithm is proposed to evaluate embryos. Firstly, a novel algorithm for extracting the region of interest (ROI) is embedded in the preprocessing part of the model to eliminate exfoliated cells close to the embryo. Secondly, several kinds of specific transformation methods are established to expand the dataset based on the symmetry of embryos. In addition, an adaptive genetic algorithm is adopted to search for optimal hyperparameters. Experiments on the data set provided by Shanghai General Hospital show that the algorithm has an excellent performance in embryo evaluation. The accuracy of our model is 86.4%, the recall is 88.4%, and the AUC is 0.93. Our results indicated that the proposed model can effectively improve the classification performance of ResNet, and thus achieve the clinic requirements of embryo evaluation.</p> <p>&nbsp;</p>
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Deb, S. K., C. M. Kishtawal, P. K. Pal, and P. C. Joshi. "A Modified Tracer Selection and Tracking Procedure to Derive Winds Using Water Vapor Imagers." Journal of Applied Meteorology and Climatology 47, no. 12 (2008): 3252–63. http://dx.doi.org/10.1175/2008jamc1925.1.
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Abstract The remotely sensed upper-tropospheric water vapor wind information has been of increasing interest for operational meteorology. A new tracer selection based on a local image anomaly and tracking procedure, itself based on Nash–Sutcliffe model efficiency, is demonstrated here for the estimation of upper-tropospheric water vapor winds both for cloudy and cloud-free regions from water vapor images. The pressure height of the selected water vapor tracers is calculated empirically using a height assignment technique based on a genetic algorithm. The new technique shows encouraging results when compared with Meteosat-5 water vapor winds over the Indian Ocean region. The water vapor winds produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) from Meteosat-5 and the present algorithm are compared with collocated radiosonde observations according to Coordination Group for Meteorological Satellites guidelines. The proposed algorithm shows better accuracy in terms of mean vector difference, rms vector difference, standard deviation, speed bias, number of collocations, and mean speed and mean direction differences. Also it is found that the sensitivity of the spatial consistency check in the quality indicator is not so significant for the improvement of statistics.
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Prestipino, Salvatore, and Andrea Rapisarda. "A Study of Tennis Tournaments by Means of an Agent-Based Model Calibrated with a Genetic Algorithm." Mathematical and Computational Applications 29, no. 5 (2024): 77. http://dx.doi.org/10.3390/mca29050077.
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In this work, we study the sport of tennis, with the aim of understanding competitions and the associated quantities that determine their outcome. We construct an agent-based model that is able to produce data analogous to real data taken from Association of Tennis Professionals (ATP) tournaments. This model depends on three parameters: the talent weight, the talent distribution width, and the chance distribution width. Unlike other similar works, we do not fix the values of these parameters and we calibrate the model results with the help of a genetic algorithm, thus exploring all possible combinations of parameters in the parameter space that are able to reproduce real system data. We show that the model fits the real data well only for limited regions of the parameter space. Limiting the region of interest in the parameter space allows us to perform further calibrations of the model that give us more information about the competition under study. Finally, we are able to provide useful information about tennis competitions, obtaining quantitative information about all of the important parameters and quantities related to these competitions with very limited a priori constraints. Through our approach, differing from those of other works, we confirm the importance of chance in the studied competitions, which has a weight of around 80% in determining the outcome of tennis competitions.
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Wang, Xiao, and Yan Li. "Facial Recognition System Based on Genetic Algorithm Improved ROI-KNN Convolutional Neural Network." Applied Bionics and Biomechanics 2022 (October 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/7976856.
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The facial recognition system is an application tool that uses artificial intelligence technology and biometrics technology to analyze and recognize the facial feature information of the human face. It is widely used in various fields, such as attendance and access control management in schools and companies, identity monitoring in stations and stores, facial recognition for fugitive criminals, and facial payment on mobile terminals. However, due to the short development time of the facial recognition system, the facial recognition system has the problem of low recognition accuracy when the recognized object is not cooperative. Although some scholars have proposed the region of interest (ROI)-K nearest neighbor algorithm (KNN) convolutional neural network theory by using the ROI and KNN and applied it to face recognition, the facial recognition system based on ROI-KNN convolutional neural network did not solve the problems of insufficient facial recognition accuracy and insufficient security. Under the conditions of insufficient illumination, excessive expression change, occlusion, high similarity of different individuals, and dynamic recognition, the recognition effect of the facial recognition system based on the ROI-KNN convolutional neural network is relatively limited. Therefore, to make the recognition accuracy of the facial recognition system higher and to make the facial recognition system play a greater role in the social and economic fields, this paper used the adaptive quantum genetic algorithm, the improved marker line graph genetic algorithm, and the feature weight value genetic algorithm to study the facial recognition system of the ROI-KNN convolutional neural network. The research results showed that after improving the ROI-KNN convolutional neural network based on the genetic algorithm, the recognition accuracy of the facial recognition system was increased by 4.99%, the recognition speed was increased by 7.46%, and the recognition security was increased by 2.66%.
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Jiang, Huiyan, Baochun He, Di Fang, Zhiyuan Ma, Benqiang Yang, and Libo Zhang. "A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/743870.
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We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure’s center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.
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Alawad, Duaa Mohammad, Avdesh Mishra, and Md Tamjidul Hoque. "AIBH: Accurate Identification of Brain Hemorrhage Using Genetic Algorithm Based Feature Selection and Stacking." Machine Learning and Knowledge Extraction 2, no. 2 (2020): 56–77. http://dx.doi.org/10.3390/make2020005.
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Brain hemorrhage is a type of stroke which is caused by a ruptured artery, resulting in localized bleeding in or around the brain tissues. Among a variety of imaging tests, a computerized tomography (CT) scan of the brain enables the accurate detection and diagnosis of a brain hemorrhage. In this work, we developed a practical approach to detect the existence and type of brain hemorrhage in a CT scan image of the brain, called Accurate Identification of Brain Hemorrhage, abbreviated as AIBH. The steps of the proposed method consist of image preprocessing, image segmentation, feature extraction, feature selection, and design of an advanced classification framework. The image preprocessing and segmentation steps involve removing the skull region from the image and finding out the region of interest (ROI) using Otsu’s method, respectively. Subsequently, feature extraction includes the collection of a comprehensive set of features from the ROI, such as the size of the ROI, centroid of the ROI, perimeter of the ROI, the distance between the ROI and the skull, and more. Furthermore, a genetic algorithm (GA)-based feature selection algorithm is utilized to select relevant features for improved performance. These features are then used to train the stacking-based machine learning framework to predict different types of a brain hemorrhage. Finally, the evaluation results indicate that the proposed predictor achieves a 10-fold cross-validation (CV) accuracy (ACC), precision (PR), Recall, F1-score, and Matthews correlation coefficient (MCC) of 99.5%, 99%, 98.9%, 0.989, and 0.986, respectively, on the benchmark CT scan dataset. While comparing AIBH with the existing state-of-the-art classification method of the brain hemorrhage type, AIBH provides an improvement of 7.03%, 7.27%, and 7.38% based on PR, Recall, and F1-score, respectively. Therefore, the proposed approach considerably outperforms the existing brain hemorrhage classification approach and can be useful for the effective prediction of brain hemorrhage types from CT scan images (The code and data can be found here: http://cs.uno.edu/~tamjid/Software/AIBH/code_data.zip).
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Li, Dahu, Hongyu Zhou, Yuan Chen, Yue Zhou, Yuze Rao, and Wei Yao. "A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage." Energies 16, no. 10 (2023): 4252. http://dx.doi.org/10.3390/en16104252.
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In hybrid energy systems, the intermittent and fluctuating nature of new energy sources poses major challenges for the regulation and control of power systems. To mitigate these challenges, energy storage devices have gained attention for their ability to rapidly charge and discharge. Collaborating with wind power (WP), energy storage (ES) can participate in the frequency control of regional power grids. This approach has garnered extensive interest from scholars worldwide. This paper proposes a two-region load frequency control model that accounts for thermal power, hydropower, ES, and WP. To address complex, nonlinear optimization problems, the dingo optimization algorithm (DOA) is employed to quickly obtain optimal power dispatching commands under different power disturbances. The DOA algorithm’s effectiveness is verified through the simulation of the two-region model. Furthermore, to further validate the proposed method’s optimization effect, the DOA algorithm’s optimization results are compared with those of the genetic algorithm (GA) and proportion method (PROP). Simulation results show that the optimization effect of DOA is more significant than the other methods.
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YASHIN, Sergei N., Egor V. KOSHELEV, and Dmitrii A. SUKHANOV. "Modeling of motivation of key executives of government agencies of regions using a multi-objective genetic algorithm." Finance and Credit 28, no. 5 (2022): 972–99. http://dx.doi.org/10.24891/fc.28.5.972.
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Subject. This article explores the motivation of top managers of government entities to bring into line the interests of the population, the State and its key executives. Objectives. The article aims to create a model of motivation of key executives of government institutions of the regions, which will make it possible to make the intangible motivation of top managers contingent on the achieved strategic potential of the region and their financial incentives. Methods. For the study, we used a multi-objective genetic algorithm and the Pareto Frontier solutions set. Results. The article proposes a procedure for reaching a conclusion about the actual bonus award (incentivization) of key executives of government agencies of the regions. Conclusions and Relevance. The results obtained can be useful to government agencies to develop a rational system of financial and non- financial incentives of their senior leadership.
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Keswani, Kapil, and Dr Anand Bhaskar. "FLOWER POLLINATION AND GENETIC ALGORITHM BASED OPTIMIZATION FOR NODE DEPLOYMENT IN WIRELESS SENSOR NETWORKS." International Journal of Engineering Technologies and Management Research 5, no. 2 (2020): 281–93. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.658.
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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.
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Martí-Carreras, Joan, Olga Mineeva-Sangwo, Dimitrios Topalis, Robert Snoeck, Graciela Andrei, and Piet Maes. "BKTyper: Free Online Tool for Polyoma BK Virus VP1 and NCCR Typing." Viruses 12, no. 8 (2020): 837. http://dx.doi.org/10.3390/v12080837.
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Human BK polyomavirus (BKPyV) prevalence has been increasing due to the introduction of more potent immunosuppressive agents in transplant recipients, and its clinical interest. BKPyV has been linked mostly to polyomavirus-associated hemorrhagic cystitis, in allogenic hematopoietic stem cell transplant, and polyomavirus-associated nephropathy in kidney transplant patients. BKPyV is a circular double-stranded DNA virus that encodes for seven proteins, of which Viral Protein 1 (VP1), the major structural protein, has been extensively used for genotyping. BKPyV also contains the noncoding control region (NCCR), configured by five repeat blocks (OPQRS) known to be highly repetitive and diverse, and linked to viral infectivity and replication. BKPyV genetic diversity has been mainly studied based on the NCCR and VP1, due to the high occurrence of BKPyV-associated diseases in transplant patients and their clinical implications. Here BKTyper is presented, a free online genotyper for BKPyV, based on a VP1 genotyping and a novel algorithm for NCCR block identification. VP1 genotyping is based on a modified implementation of the BK typing and grouping regions (BKTGR) algorithm, providing a maximum-likelihood phylogenetic tree using a custom internal BKPyV database. Novel NCCR block identification relies on a minimum of 12-bp motif recognition and a novel sorting algorithm. A graphical representation of the OPQRS block organization is provided.
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Ramadan, Saleem Z., and Mahmoud El-Banna. "Breast Cancer Diagnosis in Digital Mammography Images Using Automatic Detection for the Region of Interest." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 7 (2020): 902–12. http://dx.doi.org/10.2174/1573405615666190717112820.
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Background: One of the early screening methods of breast cancer that is still used today is mammogram due to its low cost. Unfortunately, this low cost accompanied with low performance rate also. Methods: The low performance rate in mammograms is associated with low capability in determining the best region from which the features are extracted. Therefore, we offer an automatic method to detect the Region of Interest in the mammograms based on maximizing the area under receiver operating characteristic curve utilizing Genetic Algorithms. : The proposed method had been applied to the MIAS mammographic database, which is widely used in literature. Its performance had been evaluated using four different classifiers; Support Vector Machine, Naïve Bayes, K-Nearest Neighbor and Logistic Regression classifiers. Results & Conclusion: The results showed good classification performances for all the classifiers used due to the rich information contained in the features extracted from the automatically selected Region of Interest.
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Dal Poz, A. P., and V. J. M. Fernandes. "BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 339–44. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-339-2017.
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In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects’ polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.
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Dias, Leonardo Pereira, Alex Ferreira Dos Santos, Helder Alves Pereira, et al. "Evolutionary Strategy for Practical Design of Passive Optical Networks." Photonics 9, no. 5 (2022): 278. http://dx.doi.org/10.3390/photonics9050278.
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Passive optical networks (PONs) are an important and interesting technology for broadband access as a result of the growing demand for bandwidth over the past 10 years. An arduous and complex step in the design of such networks involves determining the placement of equipment, optical fiber cables and several other parameters relevant to the proper functioning of the network. In this paper, we propose an evolutionary strategy to optimize the infrastructure design of PONs by using genetic algorithm technique. This meta-heuristic is capable of elaborating fast, automatic and efficient solutions for the design and planning of PONs. Our proposal has been developed using real maps, aiming to minimize deployment costs and time spent to carry out PON projects, achieving pre-defined quality criteria. We considered, in our simulations, two scenarios (non-dense and dense), four possible topologies and two regions of interest. The non-dense consists of a scenario in which subscribers are distributed in a dispersed manner in the region of interest. The dense has a considerably higher number of subscribers distributed in a very close way to each other. Based on the obtained results, the potential of our proposal is quite clear, as well as its relevance from a technical, economic, and commercial point of view.
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Tang, Yuxuan, Yuke Zhang, and Yixi Wu. "Optimizing Water Level Regulation in the Great Lakes: An Integrated Approach Using Genetic Algorithms and Network Flow Models for Sustainable Resource Management." Transactions on Environment, Energy and Earth Sciences 3 (November 26, 2024): 482–89. https://doi.org/10.62051/hqk6e436.
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The Great Lakes, as a vital freshwater resource, are significantly influenced by climate change and anthropogenic activities, leading to acute water level fluctuations with profound impacts on various sectors including agriculture, industry, and ecology. This study addresses the need for scientifically sound water level regulation by developing an integrated approach that employs a genetic algorithm and a network flow model. The model is designed to balance the interests of multiple stakeholders and optimize water levels across different periods, thereby ensuring the sustainable use of water resources and ecological balance. Through comprehensive data collection and processing, the study constructs a robust prediction model that accounts for economic activities and ecological protection. The application of genetic algorithms enhances the efficiency and accuracy of the model, providing a practical tool for decision-makers in water resource management. The findings underscore the model's reliability and its potential to support policy-making for sustainable water resource management in the Great Lakes region.
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Kanadath, Anusree, J. Angel Arul Jothi, and Siddhaling Urolagin. "Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation." Journal of Imaging 9, no. 4 (2023): 78. http://dx.doi.org/10.3390/jimaging9040078.
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Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm intelligence for segmenting histopathology images is less explored. In this study, we introduce a Multilevel Multiobjective Particle Swarm Optimization guided Superpixel algorithm (MMPSO-S) for the effective detection and segmentation of various regions of interest (ROIs) from Hematoxylin and Eosin (H&E)-stained histopathology images. Several experiments are conducted on four different datasets such as TNBC, MoNuSeg, MoNuSAC, and LD to ascertain the performance of the proposed algorithm. For the TNBC dataset, the algorithm achieves a Jaccard coefficient of 0.49, a Dice coefficient of 0.65, and an F-measure of 0.65. For the MoNuSeg dataset, the algorithm achieves a Jaccard coefficient of 0.56, a Dice coefficient of 0.72, and an F-measure of 0.72. Finally, for the LD dataset, the algorithm achieves a precision of 0.96, a recall of 0.99, and an F-measure of 0.98. The comparative results demonstrate the superiority of the proposed method over the simple Particle Swarm Optimization (PSO) algorithm, its variants (Darwinian particle swarm optimization (DPSO), fractional order Darwinian particle swarm optimization (FODPSO)), Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), non-dominated sorting genetic algorithm 2 (NSGA2), and other state-of-the-art traditional image processing methods.
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Breschi, Carlotta, Francesca Ieri, Luca Calamai, et al. "HS-SPME-GC-MS Analysis of the Volatile Composition of Italian Honey for Its Characterization and Authentication Using the Genetic Algorithm." Separations 11, no. 9 (2024): 266. http://dx.doi.org/10.3390/separations11090266.
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Honey’s chemical and sensory characteristics depend on several factors, including its botanical and geographic origins. The consumers’ increasing interest in monofloral honey and honey with a clear indication of geographic origin make these types of honey susceptible to fraud. The aim was to propose an original chemometric approach for honey’s botanical and geographic authentication purposes. The volatile fraction of almost 100 Italian honey samples (4 out of which are from Greece) from different regions and botanical origins was characterized using HS-SPME-GC-MS; the obtained data were combined for the first time with a genetic algorithm to provide a model for the simultaneous authentication of the botanical and geographic origins of the honey samples. A total of 212 volatile compounds were tentatively identified; strawberry tree honeys were those with the greatest total content (i.e., 4829.2 ng/g). A greater variability in the VOCs’ content was pointed out for botanical than for geographic origin. The genetic algorithm obtained a 100% correct classification for acacia and eucalyptus honeys, while worst results were achieved for honeydew (75%) and wildflower (60%) honeys; concerning geographic authentication, the best results were for Tuscany (92.7%). The original combination of HS-SPME-GC-MS analysis and a genetic algorithm is therefore proposed as a promising tool for honey authentication purposes.
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Shaker, Teeba Jamal, and Farqad Ali Hadi. "Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq." Iraqi Geological Journal 55, no. 1C (2022): 112–24. http://dx.doi.org/10.46717/igj.55.1c.9ms-2022-03-28.
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It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy inference system and genetic algorithm. An offset field data was collected from mud logging and wire line log from East Baghdad oil field south region to build the AI models, including datasets of two wells: well 1 for AI modeling and well 2 for validation of the obtained results. The types of interesting formations are sandstone and shale (Nahr Umr and Zubair formations). Nahr Umr and Zubair formations are medium –harder. The prediction results obtained from this study showed that the ANN technique can predict the ROP with high efficiency as well as FIS technique could achieve reliable results in predicting ROP, but GA technique has shown a lower efficiency in predicting ROP. The correlation coefficient and RMSE were two criteria utilized to evaluate and estimate the performance ability of AI techniques in predicting ROP and comparing the obtained results. In the Nahr Umr and Zubair formations, the obtained correlation coefficient values for training processes of ANN, FIS and GA were 0.94, 0.93, and 0.76 respectively. Data sets from another well (well 2) in the same field of interest were utilized to validate of the developed models. Datasets of well 2 were conducted against sandstone and shale formations (Nahr Umr and Zubair formations). The results revealed a good matching between the actual rate of penetration values and the predicted ROP values using two artificial intelligence techniques (neural network, and fuzzy inference technique). In contrast, the genetic algorithm model showed overestimation/ underestimation of the rate of penetration against sandstone and shale formations. This means that the optimum prediction of rate of penetration can be obtained from neural network model rather than using genetic algorithm and genetic algorithm techniques. The developed model can be successfully used to predict the rate of penetration and optimize the drilling parameters, achieving reduce the cost and time of future wells that will be drilled in the East Baghdad Iraqi oil field.
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Dür, Arne, Nicole Huber, and Walther Parson. "Fine-Tuning Phylogenetic Alignment and Haplogrouping of mtDNA Sequences." International Journal of Molecular Sciences 22, no. 11 (2021): 5747. http://dx.doi.org/10.3390/ijms22115747.
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In this paper, we present a new algorithm for alignment and haplogroup estimation of mitochondrial DNA (mtDNA) sequences. Based on 26,011 vetted full mitogenome sequences, we refined the 5435 original haplogroup motifs of Phylotree Build 17 without changing the haplogroup nomenclature. We adapted 430 motifs (about 8%) and added 966 motifs for yet undetermined subclades. In summary, this led to an 18% increase of haplogroup defining motifs for full mitogenomes and a 30% increase for the mtDNA control region that is of interest for a variety of scientific disciplines, such as medical, population and forensic genetics. The new algorithm is implemented in the EMPOP mtDNA database and is freely accessible.
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FRANGU, Blend, Jennie SHEERIN POPP, Michael THOMSEN, and Arben MUSLIU. "Assessing government grants: evidence from greenhouse tomato and pepper farmers in Kosovo." Acta agriculturae Slovenica 111, no. 3 (2018): 691. http://dx.doi.org/10.14720/aas.2018.111.3.17.
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<p class="MDPI13authornames">Genetic matching with an evolutionary algorithm was applied to evaluate the impact of the Ministry of Agriculture, Forestry and Rural Development (MAFRD) grant programs to support greenhouse vegetable production in Kosovo. The primary contribution of the paper is to assess whether grants have an impact on the farmers’ gross seasonal revenue after matching similar grantees to non-grantees. The findings showed that greenhouse tomato grantees make 2,151.80 euros more per growing season in comparison to the non-grantees (95 % confidence interval -324.71 to 4,628.31 euros). Similarly, greenhouse pepper grantees make 2,866.69 euros more per growing season compared to non-grantees (95 % confidence interval 446.42 to 5,286.96 euros). The study identified farmers’ education and region as important matching variables which may be of interest to policy researchers in Kosovo.</p>
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Jameel, Mohammed, and Mohamed Abouhawwash. "A Reference Point-Based Evolutionary Algorithm Solves Multi and Many-Objective Optimization Problems: Method and Validation." Computational Intelligence and Neuroscience 2023 (January 25, 2023): 1–26. http://dx.doi.org/10.1155/2023/4387053.
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The integration of a decision maker’s preferences in evolutionary multi-objective optimization (EMO) has been a common research scope over the last decade. In the published literature, several preference-based evolutionary approaches have been proposed. The reference point-based non-dominated sorting genetic (R-NSGA-II) algorithm represents one of the well-known preference-based evolutionary approaches. This method mainly aims to find a set of the Pareto-optimal solutions in the region of interest (ROI) rather than obtaining the entire Pareto-optimal set. This approach uses Euclidean distance as a metric to calculate the distance between each candidate solution and the reference point. However, this metric may not produce desired solutions because the final minimal Euclidean distance value is unknown. Thus, determining whether the true Pareto-optimal solution is achieved at the end of optimization run becomes difficult. In this study, R-NSGA-II method is modified using the recently proposed simplified Karush–Kuhn–Tucker proximity measure (S-KKTPM) metric instead of the Euclidean distance metric, where S-KKTPM-based distance measure can predict the convergence behavior of a point from the Pareto-optimal front without prior knowledge of the optimum solution. Experimental results show that the algorithm proposed herein is highly competitive compared with several state-of-the-art preference-based EMO methods. Extensive experiments were conducted with 2 to 10 objectives on various standard problems. Results show the effectiveness of our algorithm in obtaining the preferred solutions in the ROI and its ability to control the size of each preferred region separately at the same time.
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Aleksandrov, Vladimir, Tania Kartseva, Ahmad M. Alqudah, et al. "Genetic Diversity, Linkage Disequilibrium and Population Structure of Bulgarian Bread Wheat Assessed by Genome-Wide Distributed SNP Markers: From Old Germplasm to Semi-Dwarf Cultivars." Plants 10, no. 6 (2021): 1116. http://dx.doi.org/10.3390/plants10061116.
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Genetic diversity and population structure are key resources for breeding purposes and genetic studies of important agronomic traits in crops. In this study, we described SNP-based genetic diversity, linkage disequilibrium and population structure in a panel of 179 bread wheat advanced cultivars and old accessions from Bulgaria, using an optimized wheat 25K Infinium iSelect array. Out of 19,019 polymorphic SNPs, 17,968 had a known chromosome position on the A (41%), B (42%) and D (11%) genome, and 6% were not assigned to any chromosome. Homoeologous group 4, in particular chromosome 4D, was the least polymorphic. In the total population, the Nei’s gene diversity was within the range 0.1–0.5, and the polymorphism information content ranged from 0.1 to 0.4. Significant differences between the old and modern collections were revealed with respect to the linkage disequilibrium (LD): the average values for LD (r2), the percentage of the locus pairs in LD and the LD decay were 0.64, 16% and 3.3 for the old germplasm, and 0.43, 30% and 4.1 for the modern releases, respectively. Structure and k-means clustering algorithm divided the panel into three groups. The old accessions formed a distinct subpopulation. The cluster analysis further distinguished the modern releases according to the geographic region and genealogy. Gene exchange was evidenced mainly between the subpopulations of contemporary cultivars. The achieved understanding of the genetic diversity and structure of the Bulgarian wheat population and distinctiveness of the old germplasm could be of interest for breeders developing cultivars with improved characteristics. The obtained knowledge about SNP informativeness and the LD estimation are worthwhile for selecting markers and for considering the composition of a population in association mapping studies of traits of interest.
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Thi My Binh, Nguyen, Abdelhamid Mellouk, Huynh Thi Thanh Binh, Le Vu Loi, Dang Lam San, and Tran Hai Anh. "An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks." Sensors 20, no. 9 (2020): 2586. http://dx.doi.org/10.3390/s20092586.
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Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms.
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Nye, Jessica, Laura Zingaretti, and Miguel Perez-Enciso. "224 Automatic image feature extraction for genetic analysis in cattle." Journal of Animal Science 97, Supplement_3 (2019): 47. http://dx.doi.org/10.1093/jas/skz258.093.
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Abstract Image analysis has increasingly become an important tool for increasing productivity in many industries, yet its application in breeding programs is under utilized. With coat color patterns from dairy bull images, we explore automatic image analysis that extracts features which can be used in genetic analysis. In order to remove the unnecessary background information, the current methods require time consuming human inspection. Here, we present and compare a composite method that creates a mask (i.e., removes the background portion of the image) and calculates the proportion of dark and light coloration in bulls (n = 657) from the breeds Holstein and Ayrshire in dynamic backgrounds (e.g., forest, grass, hay, snow, etc.). This composite method combines the supervised algorithm MASK-RCNN, an unsupervised image segmentation approach, and k-means color clustering. The first step identifies the region of interest removing the majority of the background noise, while the second and third steps optimize the identification of the bull and segments the color patterning. We find a very low discrepancy between the proportion of white and dark between the manual curation and the composite method (+/- 1.40%); with an immense reduction in data collection time. This automatic composite method greatly improves the efficiency of complex image segmentation and analysis without compromising the quality of the data extracted, making analysis computationally feasible for large data sets. The next step is to calculate genetic parameters from these extracted phenotypes with genomic and/or pedigree data.
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Lomotko, Denis, and Denis Kovalov. "The usage of genetic algorithms when planning railway transportation in international connection." Transport technologies 2024, no. 1 (2024): 64–71. http://dx.doi.org/10.23939/tt2024.01.064.
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The railway transport system in Ukraine stands as a pivotal sector within the nation's transportation infrastructure, accounting for a substantial portion of freight and passenger movement compared to domestic alternative modes of transportation. With direct border connections and collaboration with railways in Moldova, Poland, Romania, Slovakia, and Hungary, Ukrzaliznytsia JSC facilitate operations through forty international railway crossings. The political climate in Ukraine, particularly the focus on export to EU countries, has sparked increased interest in transportation towards western border regions [1, 2]. This article delves into the challenges and opportunities surrounding the enhanced cooperation of Ukrainian border terminals with EU countries during wartime and post-war periods. It analyzes the current state of freight transportation to Europe, addressing existing challenges and outlining short- and long-term development prospects for railways. Emphasis is made on the vital role of railway transport in Ukraine's integration into the European transport network, presenting avenues for implementing plans connected with railway reconstruction and development. Container transportation commands a significant market share, with a growing trend towards its adoption. Container transport facilitates a substantial reduction in loading operations, a notable increase in labor productivity, and enables comprehensive mechanization and automation of cross-docking operations. As a transit country—four out of ten existing pan-European transport corridors traverse Ukraine— the nation possesses considerable potential for developing its railway transport system. With the third-largest railway network in Europe (19,787 km, including 9,319 km of electrified tracks), railway transport assumes a leading role in Ukraine's transportation landscape. However, despite its advantages, Ukraine's transportation and logistics system lags behind those of other countries worldwide. Modernization of tracks and rolling stock necessitates significant capital investment and time, underscoring the immediate need to enhance the quality of logistics services [3]. Therefore, this article explores optimization methods for container traffic from Ukraine to EU countries with the use of mathematical methods and algorithms. The genetic algorithm among the discussed methods is recognized as one of the premier mathematical algorithms for the specified task. This approach could play a pivotal role in establishing a robust technical system for railways along Ukraine's western border, optimizing border crossing operations, and enhancing Ukrainian railway transportation capabilities. It not only aids in identifying the fastest or most economical routes but reveals weaknesses in Ukrainian border terminals. Additional strategies can be devised for modernizing and expanding border terminals and stations by leveraging this insight, facilitating the integration of Ukrainian railways into the European transport system.
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Bhuyan, Hemanta Kumar, Vikash Kumar, and Biswajit Brahma. "Detection of Diabetic Retinopathy Using Collaborative Model of CNN with IoMT." ITM Web of Conferences 56 (2023): 05008. http://dx.doi.org/10.1051/itmconf/20235605008.
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The cause of blindness that primarily affects middle-aged adults is diabetic retinopathy (DR), due to excessive blood sugar levels. Internet of Medical Things (IoMT) is capable to collect Diabetic Retinopathy-related information remotely using CAD (Computer-aided diagnostic) systems and provide patients with convincing information. Therefore, the primary goal of this study is to identify and categorize the severity of DR fundus images to prevent a diabetic sufferer from going blind. Thus, this paper developed a unique Diabetic Retinopathy Segmentation (DRS) system by fusing the Deep Learning model with optimization techniques. The preprocessing phase of this system is considered to remove noise from the edges. Next, the usable region from the images is extracted using the increasing region segmentation through K-mean clustering. The characteristics of the Area of Interest (AOI) are then extracted and classified into four severity levels using the suggested Hybrid Genetic and Ant Colony Optimization (HGACO) algorithm with the help of a pertained CNN model, Residual Neural Network (RESnet). Additionally, the test of statistical significance evaluates the DRS system’s Segmentation accuracy. The suggested Diabetic Retinopathy System achieves improved categorization outcomes, with sensitivity, accuracy, and specificity numbers.
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Zifan, Ali, Katelyn Zhao, Madilyn Lee, et al. "Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation." Diagnostics 15, no. 2 (2025): 117. https://doi.org/10.3390/diagnostics15020117.
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Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge. Methods: We introduce a generalizable framework using an adaptive evolutionary genetic algorithm to optimize deep learning models, specifically U-Net, for focused liver segmentation. The algorithm simultaneously adjusts the depth (number of layers) and width (neurons per layer) of the network, dropout, and skip connections. Various architecture configurations are evaluated based on segmentation performance to find the optimal model for liver ultrasound images. Results: The model with a depth of 4 and filter sizes of [16, 64, 128, 256] achieved the highest mean adjusted Dice score of 0.921, outperforming the other configurations, using three-fold cross-validation with early stoppage. Conclusions: Adaptive evolutionary optimization enhances the deep learning architecture for liver ultrasound segmentation. Future work may extend this optimization to other imaging modalities and deep learning architectures.
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Billah, Masum, Matias Bermann, Ching-Yi Chen, et al. "84 Low-cost machine learning algorithms to predict growth and carcass traits in pigs." Journal of Animal Science 102, Supplement_3 (2024): 21–22. http://dx.doi.org/10.1093/jas/skae234.024.
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Abstract Genomic prediction involves analyzing pedigree, phenotype, and genomic data, facilitating the early prediction of the genetic merit of an individual. Phenotyping is an essential component of the genomic selection program. However, depending on the trait, traditional phenotyping can be time-consuming and labor-intensive. On the other hand, with the advancement in digital phenotyping, obtaining regular images of animals has become increasingly accessible. Coupled with computer vision and machine learning algorithms, phenotypes for traits of interest can be extracted from the images and used for different purposes, including genomic predictions. Previous attempts to predict genetic merit using machine learning were ineffective and computationally expensive for routine genetic evaluations. This research aimed to present a low-cost algorithm for predicting traits that are commonly used in pig breeding programs such as weight (WT), loin depth (LD), and backfat thickness (BF) from image data. The dataset included two-dimensional side views of 9,283 pigs captured between 2021 and 2023 and manually recorded phenotypes for WT, LD, and BF. We proposed a pipeline consisting of two major tasks to predict phenotypes: body segmentation and feature extraction using a convolutional neural network (CNN). Initially, we used the YOLOv7 model to leverage existing pre-trained layers to extract the individual region of interest from the source image. Then, low-variance images were removed to ensure a wide range of diversity within the individual. The images are then annotated with prerecorded traits and divided into training and test sets (80% and 20%, respectively), with no overlap. Our study benchmarked VGG16, InceptionV3, and ResNet50 model architectures by extracting features from the outputs of the final convolutional blocks or layers to reduce the impact of the fully connected layer on potential feature loss. The results show that the features extracted from the ResNet50 model had the lowest mean absolute error, with values of 5.22 for weight, 5.28 for loin depth, and 1.31 for backfat thickness. The performance could be improved further by fine-tuning the model parameters. As a future step for this study, the predicted phenotypes will be utilized for genomic evaluations using a multiple-trait model, where the subset of predicted animals will have prerecorded traits to account for the prediction error.
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M, Savitha, and Senthilkumar M. "BIO-INSPIRED BASED SEGMENTATION AND USER AUTHENTICATED KEY MANAGEMENT FOR IOT NETWORKS." ICTACT Journal on Communication Technology 12, no. 1 (2021): 2265–71. http://dx.doi.org/10.21917/ijct.2021.0349.
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Data exchanging and gathering is greatly achieved by several interconnected physical objects or smart devices over the Internet are termed as Internet of Things (IoT). A generic IoT network called Hierarchical IoT Network (HIoTN) inclusive of the organized different nodes in a hierarchy as gateway node, cluster head nodes and sensing nodes. In HIoTN of generic IoT networking environment for a particular application, user direct access in real-time data from the sensing nodes is necessitated. Recent work introduces a User Authenticated Biometric Key Management Protocol (UABKMP) for IoT network. Hence this proposed work exhibits new region of interest based segmentation algorithm with base procedure of Modified Bat optimization (MBO) algorithm hybrid with Active Contour Model. In the MBO algorithm the parameters of the bat is tuned via the use of the Brownian Distribution. Finally an Authenticated Key Management (AKM) is proposed for IoT network. The Real-Or-Random (ROR) model is incorporated in network for proving the scheme formal security and also ensures the informal security being protected from several probable attacks.
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Zhang, Na, Jianping Lin, Bengang Hui, et al. "Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net." Computational and Mathematical Methods in Medicine 2022 (March 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/5112867.
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Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors’ work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors’ diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor’s diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.
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Djojodihardjo, Harijono, Hamed Jamali, Alireza Shokrani, Faizal Mustapha, Rizal Zahari, and Surjatin Wiriadidjaja. "Computational Simulation for Static and Dynamic Load of Rectangular Plate in Elastic Region for Analysis of Impact Resilient Structure." Applied Mechanics and Materials 225 (November 2012): 150–57. http://dx.doi.org/10.4028/www.scientific.net/amm.225.150.
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Impact resilient structures are of great interest in many engineering applications varying from civil, land vehicle, aircraft and space structures, to mention a few examples. To design such structure, one has to resort fundamental principles and take into account progress in analytical and computational approaches as well as in material science and technology. With such perspective, the first objective of this work is to develop a computational algorithm to analyze flat plate as a generic structure subjected to impact loading for numerical simulation and parametric study without considering the surface impact effect. The analysis is carried out from first principles for static and dynamic analysis; the latter is based on dynamic response analysis in the elastic region. The second objective is to utilize the computational algorithm for direct numerical simulation, and as a parallel scheme, commercial off-the shelf numerical code is utilized for parametric study, optimization and synthesis. Through such analysis and numerical simulation, effort is devoted to arrive at optimum configuration in terms of loading, structural dimensions, and material properties, among others. The codes developed are validated for generic cases. Further simulations are carried out using commercial codes for some sample applications to explore impact resilient structural characteristics in the elastic region.
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Raju, Harsha, and Narasimhaiah Veena Kalludi. "Optimized deep learning-based dual segmentation framework for diagnosing health of apple farming with the internet of things." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 876–87. https://doi.org/10.11591/ijai.v13.i1.pp876-887.
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The high disease prevalence in apple farms results in decreased yield and income. This research addresses these issues by integrating internet of things (IoT) applications and deep neural networks to automate disease detection. Existing methods often suffer from high false positives and lack global image similarity. This study proposes a conceptual framework using IoT visual sensors to mitigate apple diseases' severity and presents an intelligent disease detection system. The system employs the augmented Otsu technique for region-aware segmentation and a colour-conversion algorithm for generating feature maps. These maps are input into U-net models, optimized using a genetic algorithm, which results in the generation of suitable masks for all input leaf images. The obtained masks are then used as feature maps to train the convolution neural network (CNN) model for detecting and classifying leaf diseases. Experimental outcomes and comparative assessments demonstrate the proposed scheme's practical utility, yielding high accuracy and low false-positive results in multiclass disease detection tasks.