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Academic literature on the topic 'Local XOR patterns'
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Journal articles on the topic "Local XOR patterns"
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Vipparthi, Santosh Kumar, and S. K. Nagar. "Expert image retrieval system using directional local motif XoR patterns." Expert Systems with Applications 41, no. 17 (2014): 8016–26. http://dx.doi.org/10.1016/j.eswa.2014.07.001.
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V., Lakshmi Surekha* &. M. Pradeep. "A NEW ADVANCED APPROACH FOR CONTENT BASED IMAGE RETRIEVAL USING TEXTON PATTERN." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 4, no. 6 (2017): 129–35. https://doi.org/10.5281/zenodo.820831.
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In this paper, a new advanced approach for content based image retrieval using texon pattern is proposed. The proposed method collects the texton pattern which gives the structure of the query image or database image. First, the color image is converted into HSV (hue, saturation and value) color space. Second, the V color space is divided into overlapping sub blocks of size 2 × 2 and textons are collected based on the shape of the textons. Then, local binary pattern operation is performed on the texton image between the center pixel and its surrounding neighbors. Finally, the feature vector is constructed based on the contourlet transform and local binary pattern. The performance of the proposed method is evaluated by testing on benchmark database, Corel-1K, Corel-5K and Corel-10K in terms of precision, recall, average retrieval precision and average retrieval rate.The results after investigation show a significant improvement as compared to the state-of-the-art features for image retrieval.
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Bala, Anu, and Tajinder Kaur. "Local texton XOR patterns: A new feature descriptor for content-based image retrieval." Engineering Science and Technology, an International Journal 19, no. 1 (2016): 101–12. http://dx.doi.org/10.1016/j.jestch.2015.06.008.
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HARIPRASAD, REDDY A., and SUBHASH CHANDRA N. "LOCAL ORIENTATION GRADIENT XOR PATTERNS: A NEW FEATURE DESCRIPTOR FOR IMAGE INDEXING AND RETRIEVAL." i-manager’s Journal on Pattern Recognition 2, no. 4 (2016): 1. http://dx.doi.org/10.26634/jpr.2.4.5943.
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Janakiraman, Bhavithra, Sathiyapriya Shanmugam, Rocío Pérez de Prado, and Marcin Wozniak. "3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier." Sensors 23, no. 11 (2023): 5358. http://dx.doi.org/10.3390/s23115358.
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The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.
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Muratore, Paolo, Cristiano Capone, and Pier Stanislao Paolucci. "Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks." PLOS ONE 16, no. 2 (2021): e0247014. http://dx.doi.org/10.1371/journal.pone.0247014.
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Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of energy consumption and their training requires very few examples. This motivates the search for biologically inspired learning rules for RSNNs, aiming to improve our understanding of brain computation and the efficiency of artificial intelligence. Several spiking models and learning rules have been proposed, but it remains a challenge to design RSNNs whose learning relies on biologically plausible mechanisms and are capable of solving complex temporal tasks. In this paper, we derive a learning rule, local to the synapse, from a simple mathematical principle, the maximization of the likelihood for the network to solve a specific task. We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spatio-temporal spike pattern that encodes the solution to complex temporal tasks. This method makes the learning extremely rapid and precise, outperforming state of the art algorithms for RSNNs. While error-based approaches, (e.g. e-prop) trial after trial optimize the internal sequence of spikes in order to progressively minimize the MSE we assume that a signal randomly projected from an external origin (e.g. from other brain areas) directly defines the target sequence. This facilitates the learning procedure since the network is trained from the beginning to reproduce the desired internal sequence. We propose two versions of our learning rule: spike-dependent and voltage-dependent. We find that the latter provides remarkable benefits in terms of learning speed and robustness to noise. We demonstrate the capacity of our model to tackle several problems like learning multidimensional trajectories and solving the classical temporal XOR benchmark. Finally, we show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output. Our model can be applied to different types of biological neurons. The analytically derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction for experimental validation.
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Ekelmans, Pierre, Nataliya Kraynyukovas, and Tatjana Tchumatchenko. "Targeting operational regimes of interest in recurrent neural networks." PLOS Computational Biology 19, no. 5 (2023): e1011097. http://dx.doi.org/10.1371/journal.pcbi.1011097.
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Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, for spiking networks, it is challenging to predict which connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We establish a mapping between the stabilized supralinear network (SSN) and spiking activity which allows us to pinpoint the location in parameter space where these activity regimes occur. Notably, we find that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we show that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.
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Meng, Zhaohui, and Xiaodong Gu. "Palm-Dorsal Vein Recognition Method Based on Histogram of Local Gabor Phase XOR Pattern with Second Identification." Journal of Signal Processing Systems 73, no. 1 (2013): 101–7. http://dx.doi.org/10.1007/s11265-013-0734-6.
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Bedina, S., A. Trofimenko, E. Mozgovaya, S. Spitsina, and M. Mamus. "AB0027 XANTHINE OXIDOREDUCTASE: ENZYME PROFILE OF PLASMA AND LYMPHOCYTES IN RHEUMATOID ARTHRITIS." Annals of the Rheumatic Diseases 81, Suppl 1 (2022): 1148.1–1148. http://dx.doi.org/10.1136/annrheumdis-2022-eular.2667.
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BackgroundDisorders of acquired and adaptive immunity play an important role in the rheumatoid arthritis (RA) pathogenesis. Important changes can nevertheless be detected at the metabolic level in the immune cells along with immune disorders. The imbalance between generation and neutralization of the free radicals during oxidative stress may cause damage of both cellular structures and components of extracellular matrix. One of principal free radical generators is xanthine oxidoreductase (XOR), the prooxidant enzyme complex which could be active either in its oxidase or dehydrogenase form. The study of plasma and lymphocytic xanthine oxidase (XO) and xanthine dehydrogenase (XDG) activities is of emerging interest because of their putative role in initiation and chronization of RA.ObjectivesEvaluation the relationship between RA activity and XO and XDG activities using both plasma and lysed lymphocyte samples.MethodsThe research was carried out in agreement with the WMA Declaration of Helsinki principles after the local ethical board approval. RA diagnosis was verified using ACR/EULAR criteria (2010). RA activity was measured using the Disease Activity Score of 28 joints (DAS28). Blood lymphocytes were isolated by means of density gradient centrifugation in lymphosep (JCN Biomedicals), 1.077-1.079 g/ml. XO (ЕС 1.17.3.2) and XDG (ЕС 1.17.1.4) activities in plasma and lysed lymphocytes were measured using spectrophotometric techniques and expressed as nmol/ml/min [1]. Data are presented as median and quartiles (Me (Q25; Q75). Differences were considered significant when p<0.05.Results77 RA patients (57 women and 20 men) were enrolled in the study. Mean age of patients was 45 (37; 49) years, mean RA duration was 8 (6; 10) years. 16 (21%) RA patients had low disease activity, 49 (63%) patients moderate, and 12 (16%) patients had high one. 30 (19 women and 16 men) healthy persons comprised the control group. Reference ranges for plasma XO and XDG activities were 2.60–3.96 nmol/min/ml and 4.49–5.93 nmol/min/ml, respectively. Reference ranges for XO and XDG activities in lysed lymphocytes were 14.11-31.33 nmol/min/ml and 18.62-39.64 nmol/min/ml, respectively. Plasma enzymatic pattern in RA patients was characterized by increase in XO activity (p<0.001) and a somewhat decrease in XDG activity (p=0.059). Lymphocytic enzymatic pattern in RA patients was characterized by decreased activity of both enzymes (p<0.001 for both cases). Plasma XO activity was increased and lymphocytic XO activity was decreased in all the grades of RA activity. XO activity reached its highest changes at maximum disease activity. Plasma XDG activity was increased in low disease activity (р<0.001), while patients with moderate or high disease activities were characterized by decreased activity of this enzyme (р=0.008). Lymphocytic XDG activity was decreased in all the grades of disease activity (р<0.001). XDG activity reached its highest changes at maximum disease activity. Activities of XO and XDG in lymphocytes negatively correlated with RA activity. Activities of XO in plasma positively correlated with RA activity, while XDG activity negatively correlated with RA activity.ConclusionPlasmatic and lymphocytic enzymatic patterns in RA patients were characterized by multidirectional changes of XO and XDG activities. Multiple correlations were found between RA activity and enzyme activities in both studied compartments. These changes in oxidant enzyme activities may point at involvement of free radical oxidation in the RA pathogenesis. Free radical generation triggers metabolic cascades that lead to cell damage, premature death, promote endothelial dysfunction, extracellular neutrophil trap formation, support chronic inflammation and joint destruction, and other organs and systems under oxidative stress.References[1]Mozgovaya E.E., et al. Siberian Journal of Life Sciences and Agriculture 2021; 13(3):288-304.Disclosure of InterestsNone declared
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Aqel, Amin A., Tala M. Al-Matarneh, Tayf K. Al-Tarawneh, Tahrir Alnawayseh, Mohammed Alsbou, and Yasser Gaber. "Monitoring of Antibiotic Resistance Patterns Within Al-Karak Governmental Hospital, Jordan, in 2022." Antibiotics 13, no. 12 (2024): 1172. https://doi.org/10.3390/antibiotics13121172.
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Background/Objectives: Antimicrobial resistance is considered one of the foremost global public health challenges, and its prevalence is increasing. In Jordan, particularly in Al-Karak Governorate, there is a lack of sufficient data on antimicrobial resistance to make accurate assessments. The main aim of the current study was to evaluate antibiotic resistance trends in clinical specimens from 2022 and assess antibiotic resistance patterns. The emphasis on the WHO antibiotic classification as Access, Watch, and Reserved (AWaRe) was adopted in the current study. Results: Among Gram-positive bacteria, Enterococcus faecalis exhibited 100% susceptibility to nitrofurantoin and 96% to vancomycin, Streptococcus viridans exhibited 100% susceptibility to teicoplanin, while CoNS (coagulase-negative Staphylococci) showed moderate resistance to Trimethoprim + Sulfamethoxazole (63%) and clindamycin (47%). Among Gram-negative bacteria, Escherichia coli and Klebsiella pneumoniae displayed high susceptibility to fosfomycin (E. coli: 95%, K. pneumoniae: 80%) and amikacin (E. coli: 93%, K. pneumoniae: 81%). Resistance was notable for trimethoprim + sulfamethoxazole (E. coli: 47%, K. pneumoniae: 53%) and nitrofurantoin (K. pneumoniae: 30%). Pseudomonas aeruginosa exhibited the highest proportion of XDR strains (15%), followed by K. pneumoniae (11%) and E. coli (4%), while PDR strains were found in P. aeruginosa (6%), K. pneumoniae (3%), and E. coli (0.6%). XDR was observed in 4% of CoNS and 3% of S. viridans (α), with Staphylococcus aureus exhibiting both XDR and PDR at 1%. Methods: A cross-sectional retrospective study of bacterial species and their antimicrobial susceptibility was carried out at a hospital in Al Karak, Jordan, from January to December of 2022, the study included 1187 isolates from all locations in Al-Karak Governmental Hospital. Conclusions: The significant prevalence of XDR and PDR strains in key pathogens, particularly P. aeruginosa and K. pneumoniae, underscores the need for a robust Antimicrobial Stewardship Program (ASP) and infection control measures at Al-Karak Governmental Hospital. High susceptibility in several Access group antibiotics (e.g., amikacin and nitrofurantoin) supports their prioritization in empirical therapy, while the emergence of resistance in Watch and Reserved antibiotics highlights the necessity for rational use. These findings are very important for adjusting the local strategies to lower the spread of resistant strains and improve clinical outcomes.
Book chapters on the topic "Local XOR patterns"
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Tiwari, Laxmikant, Rohit Raja, Vineet Awasthi, and Rohit Miri. "Detection of Nodule and Lung Segmentation Using Local Gabor XOR Pattern in CT Images." In Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing. CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526-4.
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