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Journal articles on the topic 'Date kernel extract'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Monajjemi, Ali, Maryam Tabibi, Fateme Kheiri, Alireza Rasouli, Amir Arsalan Asgari, and Gholam Ali Jafari. "Evaluation of Antimicrobial Properties of Bam Date Kernel Extract and Investigation of the Structure of Extract." Archives of Hygiene Sciences 13, no. 1 (2024): 10–15. http://dx.doi.org/10.34172/ahs.13.1.312.2.

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Background & Aims: The prevalence of hospital infections, especially of bacterial origin, is increasing and uncontrollable in many countries. Controlling them, because of their damage to the health and economy, is essential. Therefore, numerous efforts have been made to find new antibiotics as suitable alternatives to current ones. This study aimed to investigate the antibacterial activity of date kernel extract against several hospital pathogens. Materials and Methods: Acetone extract of Mozafati date kernel from Bam city was prepared by immersion method and its antibacterial effects on h

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Kyari, FA, AN Jones, and SG Yadima. "Evaluating the Performance of Desert Date (Balanite aegyptiaca) as a Disinfectant in Raw Water Treatment." Nigerian Research Journal of Engineering and Environmental Sciences 07, no. 02 (2022): 565–70. https://doi.org/10.5281/zenodo.7496729.

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<em>This study evaluated the potential of using desert date (Balanite aegytiaca) seed kernel as disinfectant in domestic water treatment. Four different extracts were prepared from the kernel namely defatted powdered extract (DPE), defatted water extract (DWE), defatted filtered extract (DFE) and crude water extract (CWE). A jar test apparatus was used to determine the effect of different dosages (0.5, 1.0, 1.5, 2.0 and 2.5 g/l) of the extracts in raw water treatment obtained from river Ngadda, Maiduguri, Borno State, Nigeria. The performance of the extracts against total coliform and E. coli

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Karthikeyan, Manivannan, Pai Akshatha, Habeeb Shaik Mohideen, and Balasundaram Usha. "Caesalpinia bonducella Seeds Extracts are Non-toxic to the Gut Bacteria Lactobacillus rhamnosus, as Substantiated by In vitro and In silico Studies." Journal of Pure and Applied Microbiology 18, no. 3 (2024): 2070–84. http://dx.doi.org/10.22207/jpam.18.3.57.

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The seed kernels of Caesalpinia bonducella, a traditional medicinal plant in India, are widely used to treat various disorders, including polycystic ovary syndrome. The seed kernel possesses anti-bacterial properties against many pathogenic bacteria. However, their impact on Lactobacillus spp., a prominent gram-positive gut bacterium, has not been studied till date. The present study employed both in vitro and in silico methods to illustrate the effect of seed extract of C. bonducella against Lactobacillus rhamnosus GG. For this, disc diffusion assay was performed with 100, 500, and 1000 µg/ml

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Terde, Sneha H., V. S. Dandekar, V. N. Patil, et al. "Studies on Sensory Attributes and Cost of Production of Kulfi Blended with Coconut (Cocos nucifera L.) Kernel Extract and Enriched with Whey Protein Powder and Date (Phoenix dactylifera L.) Pulp." Journal of Advances in Biology & Biotechnology 28, no. 6 (2025): 57–70. https://doi.org/10.9734/jabb/2025/v28i62373.

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Aims: The study aims to develop and standardize a process for preparing kulfi enriched with coconut kernel extract, whey protein powder and date pulp. It focuses on evaluating sensory attributes such as colour and appearance, body and texture, flavour and overall acceptability, along with the production cost. The primary objective is to optimize the levels of these ingredients to enhance the nutritional, sensory and economic value of the kulfi while preserving its traditional appeal. Study Design: The study focuses on developing and standardizing a kulfi blended with coconut kernel extract, wh

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Zheng, Xiaolong, Dongdong Guan, Bangjie Li, Zhengsheng Chen, and Lefei Pan. "A Novel Edge Detection Method for Multi-Temporal PolSAR Images Based on the SIRV Model and a SDAN-Based 3D Gaussian-like Kernel." Remote Sensing 15, no. 10 (2023): 2685. http://dx.doi.org/10.3390/rs15102685.

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Edge detection for PolSAR images has demonstrated its importance in various applications such as segmentation and classification. Although there are many edge detectors which have demonstrated an impressive ability to achieve accurate edge detection results, these methods only focus on edge detection in a single-date PolSAR image. However, a single-date PolSAR image cannot fully characterize the changes in scattering mechanisms of land cover in different growth cycles, resulting in some omissions of the true edges. In this paper, we propose a novel edge detection method for multi-temporal PolS

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Kurian, Ashley, Anuj Dubey, Ferhat Yaman, and Aydin Aysu. "TPUXtract: An Exhaustive Hyperparameter Extraction Framework." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 1 (2024): 78–103. https://doi.org/10.46586/tches.v2025.i1.78-103.

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Model stealing attacks on AI/ML devices undermine intellectual property rights, compromise the competitive advantage of the original model developers, and potentially expose sensitive data embedded in the model’s behavior to unauthorized parties. While previous research works have demonstrated successful side-channelbased model recovery in embedded microcontrollers and FPGA-based accelerators, the exploration of attacks on commercial ML accelerators remains largely unexplored. Moreover, prior side-channel attacks fail when they encounter previously unknown models. This paper demonstrates the f

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Embaby, A. A. A., Samar A. El-Masry, A. Galal, and Khaled A. El-Dougdoug. "FEED APPLICATION OF DATE PALM KERNEL EXTRACT TO ENHANCE IMMUNE RESPONSE AND MODIFY HISTOLOGICAL CHANGES IN BROILER CHICKENS CHALLENGED WITH INFECTIOUS BRONCHITIS VIRUS." Egyptian Journal of Nutrition and Feeds 27, no. 2 (2024): 255–74. http://dx.doi.org/10.21608/ejnf.2024.377448.

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Wu, Liwen, Shanshan Huang, Feng Wu, Qian Jiang, Shaowen Yao, and Xin Jin. "Protein Subnuclear Localization Based on Radius-SMOTE and Kernel Linear Discriminant Analysis Combined with Random Forest." Electronics 9, no. 10 (2020): 1566. http://dx.doi.org/10.3390/electronics9101566.

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Protein subnuclear localization plays an important role in proteomics, and can help researchers to understand the biologic functions of nucleus. To date, most protein datasets used by studies are unbalanced, which reduces the prediction accuracy of protein subnuclear localization—especially for the minority classes. In this work, a novel method is therefore proposed to predict the protein subnuclear localization of unbalanced datasets. First, the position-specific score matrix is used to extract the feature vectors of two benchmark datasets and then the useful features are selected by kernel l

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Tan, Seok Shin, Seok Tyug Tan, and Chin Xuan Tan. "The Anti-Hypertensive and Hypoglycemic Potential of Bioactive Compounds Derived from Pulasan Rind." Processes 10, no. 3 (2022): 592. http://dx.doi.org/10.3390/pr10030592.

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Pulasan (Nephelium mutabile Blume) is an underutilized fruit native to tropical countries, including Malaysia, Thailand, and Indonesia. To date, the medicinal potential of pulasan remains unexplored, although this fruit shares the same genus with the well-known rambutan (Nephelium lappaceum). Therefore, the current study aims to examine the antioxidant properties of different parts of pulasan (flesh, rind, and kernel) and investigate the bioactive profile, anti-hypertensive and hypoglycemic properties of pulasan rind. Pulasan were extracted using different solvents, including distilled water,

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Ben Abdessalem Karaa, Wahiba, Eman H. Alkhammash, and Aida Bchir. "Drug Disease Relation Extraction from Biomedical Literature Using NLP and Machine Learning." Mobile Information Systems 2021 (May 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/9958410.

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Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical concepts, including protein and protein, gene and protein, drug and drug, and drug and disease. These relations can be extracted from biomedical literature available on various databases. This study examines the extraction of semantic relations that can occur between diseases and drugs. Findings will help specialists make good decisions when administering a medication to a patient and will allow them to continuously be

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Zhang, Biyao, Huichun Ye, Wei Lu, et al. "A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 11 (2021): 2083. http://dx.doi.org/10.3390/rs13112083.

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Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical sp

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Obaid, R. S., S. H. S. Al-Warshan, and I. A. Abed. "Efficiency of Some Clays and Organic Materials on the Reduction of Aflatoxin B1 Produced from Isolates of the Fungus Aspergillus flavus Contaminating Corn Grains." IOP Conference Series: Earth and Environmental Science 1252, no. 1 (2023): 012003. http://dx.doi.org/10.1088/1755-1315/1252/1/012003.

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Abstract The study was conducted to evaluate the effectiveness of certain types of clay minerals, activated charcoal made from common reed and date palm kernel, in reducing the levels of aflatoxin B1(AFB1) produced by Aspergillus flavus that contaminated corn grains in a liquid medium, Yeast Extract Sucrose (YES). The results showed the presence of nine species of fungal genera that contaminated corn grains, with the Aspergillus spp being the most predominant, accounting for 42%, followed by the Penicillium spp with 27%, and the Fusarium spp with a 21%. The genera Rhizopus spp, Mucor spp, and

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Feng, Duo, and Fuji Ren. "Data-Driven Channel Pruning towards Local Binary Convolution Inverse Bottleneck Network Based on Squeeze-and-Excitation Optimization Weights." Electronics 10, no. 11 (2021): 1329. http://dx.doi.org/10.3390/electronics10111329.

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This paper proposed a model pruning method based on local binary convolution (LBC) and squeeze-and-excitation (SE) optimization weights. We first proposed an efficient deep separation convolution model based on the LBC kernel. By expanding the number of LBC kernels in the model, we have trained a larger model with better results, but more parameters and slower calculation speed. Then, we extract the SE optimization weight value of each SE module according to the data samples and score the LBC kernel accordingly. Based on the score of each LBC kernel corresponding to the convolution channel, we

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Lin, Chuang, Binghui Wang, Xuefeng Zhao, and Meng Pang. "Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/847062.

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Different kernels cause various class discriminations owing to their different geometrical structures of the data in the feature space. In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC) is proposed to solve the problem of automatically choosing kernel functions and their parameters in kernel learning. The proposed method first adopts a so-called data-dependent kernel to generate an efficient kernel optimization algorithm. Then, a constrained optimization function using ge

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Bayomy, Hala M., Eman S. Alamri, and Mahmoud A. Rozan. "Effect of Roasting Hass Avocado Kernels on Nutritional Value and Volatile Compounds." Processes 11, no. 2 (2023): 377. http://dx.doi.org/10.3390/pr11020377.

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Because of the lack of commercial food applications of Hass avocado (Persea americana Mill) kernel, which are a useful agricultural waste and a good source of bioactive compounds, this study investigated the influence of roasting on the chemical composition, antinutritional factors, antioxidant activity, colour, and GC-MS profile in avocado kernels after roasting at 180 °C for 30 min. The nutritional data revealed a significant increase (p < 0.05) in the oil extract, crude fibre, total phenolic compounds, Ca, K, P, Na, Zn, browning index, and redness/greenness after roasting. Conversely, a

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Salim, Aliyu Yusuf, Said Baba Abdurrahman, Idris Abubakar Adamu, and Musa Sayaya Shamsu. "EXTRACTION AND DETERMINATION OF PHYSICOCHEMICAL PROPERTIES OF CASHEW NUT OIL." International Journal of Novel Research in Healthcare and Nursing 10, no. 2 (2023): 77–87. https://doi.org/10.5281/zenodo.8027365.

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<strong>Abstract</strong><strong>:</strong> This study was carried out to extract oil from cashew nuts purchased from SabonGari market in Fagge local government area, Kano state and to characterize the oil; with the view to ascertain itsr suitability for consumption and other uses. Soxhlet apparatus was used for the extraction using n-hexane as solvent. The physicochemical properties of the extracted oil were analyzed. The oil was light yellow in appearance The percentage oil extracted was found to be 29.5%, the boiling point of the oil was 230°C, the acid value was 12.57 mgKOH/g, the |Fre

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Binol, Hamidullah. "Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery." Mathematical Problems in Engineering 2018 (September 6, 2018): 1–14. http://dx.doi.org/10.1155/2018/9632569.

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Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representatio

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KUANG, RUI, EUGENE IE, KE WANG, et al. "PROFILE-BASED STRING KERNELS FOR REMOTE HOMOLOGY DETECTION AND MOTIF EXTRACTION." Journal of Bioinformatics and Computational Biology 03, no. 03 (2005): 527–50. http://dx.doi.org/10.1142/s021972000500120x.

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We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is signif

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Nainika, Kaushik, Kaur Bhatia Manjot, and Rastogi Sonali. "SVM and Cross-Validation using R Studio." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 1 (2020): 46–54. https://doi.org/10.35940/ijeat.A1673.1010120.

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Each passing day data is getting multiplied. It is difficult to extract useful information from such big data. Data Mining is used to extract useful information. Data mining is used in majorly all fields like healthcare, marketing, social media platforms and so on. In this paper, data is loaded and preprocessed by dealing with some missing values. The dataset used is of Airbnb, the platform used for lodging and tourism industry. Analyzing the data by plotting correlation using spearman method. Further, applying PCA and Support Vector Machine classification technique on the dataset. There are v

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Ding, Yijie, Feng Chen, Xiaoyi Guo, Jijun Tang, and Hongjie Wu. "Identification of DNA-Binding Proteins by Multiple Kernel Support Vector Machine and Sequence Information." Current Proteomics 17, no. 4 (2020): 302–10. http://dx.doi.org/10.2174/1570164616666190417100509.

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Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive. Objective: In past several years, various computational methods have been developed to detect DNAbinding proteins. However, most of them do not integrate multiple information. Methods: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, w

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Alansari, Abeer Khalid Abdullah, та Saed Ayidh Al-Thobaiti. "Balanites Aegyptiaca Kernel Extract (Desert Date) Protects against Diabetes-Induced Cardiomyopathy in Rats : A Histological and Biochemical Study = حماية مستخلص بلانيت إيجيبتياكا (بلح الصحراء) من اعتلال عضلة القلب الناجم عن داء السكري في الجرذان : دراسة نسيجية وتحليلية". مجلة الشمال للعلوم الأساسية و التطبيقية 6, № 1 (2021): 55–65. http://dx.doi.org/10.12816/0058338.

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Li, Simin, Shuang-hua Yang, and Yi Cao. "Nonlinear Dynamic Process Monitoring Using Canonical Variate Kernel Analysis." Processes 11, no. 1 (2022): 99. http://dx.doi.org/10.3390/pr11010099.

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Most industrial systems today are nonlinear and dynamic. Traditional fault detection techniques show their limits because they can hardly extract both nonlinear and dynamic features simultaneously. Canonical variate analysis (CVA) shows its excellent monitoring performance in fault detection for dynamic processes but is not applicable to nonlinear processes. Inspired by the CVA method, a novel nonlinear dynamic process monitoring method, namely, the “canonical variate kernel analysis” (CVKA), is proposed in this work. The way to extract nonlinear features is different from a traditional kernel

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Zhang, Jian, Jinshuai Zhang, Kexin Zhou, Yonghui Zhang, Hongda Chen, and Xinyue Yan. "An Improved YOLOv5-Based Underwater Object-Detection Framework." Sensors 23, no. 7 (2023): 3693. http://dx.doi.org/10.3390/s23073693.

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To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a

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Kaanin-Boudraa, Ghania, Fatiha Hamitri-Guerfi, Lydia Harfi, et al. "Physicochemical characterization and antioxidant capacity of the extracted oil from date pits and its effect on storage stability of margarine." North African Journal of Food and Nutrition Research 7, no. 16 (2023): 54–67. http://dx.doi.org/10.51745/najfnr.7.16.54-67.

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Background and aims: The present work deals with the valorization of the date kernel oil of Mech-Degla variety by assessment of its physicochemical and antioxidant properties as well as its use in the formulation of margarine. Methods: Kernels’ oil was extracted using Soxhlet method and its total phenolic (TP), flavonoid and carotenoid contents and DPPH• scavenging activity were estimated using colorimetric assays. After that, this oil was incorporated into margarine. The determined physicochemical parameters were the pH, the salt content, the solid content, the melting point, and the peroxide

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Li, Hongwei, Yining Wang, Michael Vinsky, Tiago Valente, John A. Basarab, and Changxi Li. "114 Accuracy of genomic predictions using single and multiple-trait machine learning methods in Canadian beef cattle population." Journal of Animal Science 102, Supplement_3 (2024): 33–34. http://dx.doi.org/10.1093/jas/skae234.037.

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Abstract The advancement in various machine learning (ML) methods provides tools to extract features from a large data set of complex traits and to predict the outcome of the target trait. In this study, we assessed the genomic prediction accuracy using single trait models based on kernel ridge regression (ST_KRR) and linear support vector regression (STLinearSVR) for female feed intake, feed efficiency and fertility traits of Canadian crossbreed beef cattle (n = 2,834 genotyped with 83,875 single nucleotide polymorphisms) and compared their prediction performances to a single trait genomic be

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Pena-Llamas, Luis R., Ramon O. Guardado-Medina, Arturo Garcia, and Andres Mendez-Vazquez. "Kernel Learning by Spectral Representation and Gaussian Mixtures." Applied Sciences 13, no. 4 (2023): 2473. http://dx.doi.org/10.3390/app13042473.

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One of the main tasks in kernel methods is the selection of adequate mappings into higher dimension in order to improve class classification. However, this tends to be time consuming, and it may not finish with the best separation between classes. Therefore, there is a need for better methods that are able to extract distance and class separation from data. This work presents a novel approach for learning such mappings by using locally stationary kernels, spectral representations and Gaussian mixtures.

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Handayani, Meli, Rika Rosnelly, and Hartono Hartono. "Classification of Basurek Batik Using Pre-Trained VGG-16 and Support Vector Machine." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (2023): 40–44. http://dx.doi.org/10.35842/icostec.v2i1.34.

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By introducing Indonesian batik motifs, we know that the island of Sumatra, especially Bengkulu and Jambi provinces, has a distinctive batik called Basurek batik. This research aims to classify the two batik motifs using the Support Vector Machine (SVM) algorithm. First, we extract the image of the batik motif with a pre trained VGG-16 model and then use them as a dataset for the SVM classification process. The classification process itself uses linear, polynomial, and sigmoid kernels. We divided the data 90:10 and used 10-fold cross-validation to analyze each training and testing data classif

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Husain, Arshi, and Virendra P. Vishvakarma. "Optimized deterministic multikernel extreme learning machine for classification of COVID-19 chest Xray images." Journal of Information and Optimization Sciences 44, no. 4 (2023): 771–93. http://dx.doi.org/10.47974/jios-1319.

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In this paper, a novel technique has been proposed to exploit the capability of residual network (ResNet) deep learning model to extract the features. It is utilized neither in pretrained form nor as a transfer learning model. ResNet uses shortcut connections to create shortcut blocks in order to skip blocks of convolutional layers (residual blocks). These stacked residual blocks significantly increase training effectiveness and address the degradation issue. For the purpose of classification, a multiple kernel learning based deterministic extreme learning machine (MKD-ELM) which uses a linear

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Zu, Yuankun, Shiyu Xia, Xu Yang, Qiufeng Wang, Han Zhang, and Xin Geng. "Inheriting Generalized Learngene for Efficient Knowledge Transfer across Multiple Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 23090–98. https://doi.org/10.1609/aaai.v39i21.34473.

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In practical applications, it is often necessary to transfer knowledge from large pretrained models to small ones with various architectures for tackling different tasks. The Learngene framework, proposed recently, firstly extracts one compact module termed as learngene from a large well-trained model, after which learngene is used to build descendant models for handling diverse tasks. In this paper, we aim to explore extracting and inheriting learngene which can be generalized across different model architectures and tasks, remaining understudied in previous works. Inspired by the existing ob

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Rashid, H. U., A. Khan, G. Hassan, et al. "INTEGRATION OF SOME ALLELOPATHIC SPECIES FOR WEED MANAGEMENT IN SPRING PLANTED HYBRID MAIZE UNDER DIFFERENT TILLAGE REGIMEs." Journal of Animal and Plant Sciences 32, no. 1 (2021): 114–26. https://doi.org/10.36899/japs.2022.1.0408.

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A two-year study was conducted on integrated weed management in maize under different tillage regimes at Agricultural Research Station Swabi Khyber Pakhtunkhwa, Pakistan during Spring 2014 and subsequently repeated in 2015. The experiment was laid out at silt loam soil in Randomized complete block design (RCBD) with a split plot arrangement having three replications. Tillage regimes ((minimum, conventional and deep tillage) were kept in main plots (Factor A) and allelopathic plant residues (sorghum, sunflower and parthenium) as surface mulched in various combinations and their water extracts @

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Davoodi, Fatemeh, and Mohammad Hadi Naji. "Study of the effect of sodium alginate coating containing pomegranate peel extract on chemical, sensory and microbial quality of walnut kernel." Environmental Health Engineering and Management 5, no. 4 (2018): 249–57. http://dx.doi.org/10.15171/ehem.2018.33.

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Background: Due to the adverse effects of artificial preservatives on food and its harmful effects on human health, researchers have been considering replacing these materials with natural substances. In this study, the effect of pomegranate peel extract (PPE) on the stability and antifungal activity of the walnut kernel was studied. Methods: The pomegranate peel was extracted by the solvent and water-solvent method. The extracted sap was evaluated using the antioxidant assay by 2,2-diphenylpicrylhydrazyl (DPPH) assay. The results showed that the extracted sap had 40.11 mg/g dry phenol and 47.

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Morgab, M. A. H., I. F. Kassar, and S. A. Hussien. "Effect of Khistawi Date Kernel Powder on Some Sensory, Chemical and Biological Characteristics of Beef Burger." IOP Conference Series: Earth and Environmental Science 1449, no. 1 (2025): 012170. https://doi.org/10.1088/1755-1315/1449/1/012170.

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Abstract This study was completed with the assistance of the Food Sciences Department Laboratory at the Faculty of Agriculture in Anbar. The kernels of Khistawi were chosen because there are few studies on it, Khistawi dates are widely consumed in Iraq, and a fair proportion of the kernels are not exploited The good quality Khistawi dates were selected and the poor ones were excluded to avoid differences between the same type and to ensure a safe crop with valuable nutritional components, then the kernel were Isolated, cleaned, dried, and finely crushed, The basic components of the seed powder

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Yin, Hao, Chao Li, Haibin Wang, et al. "Adaptive Convolution Kernels Construction Based on Unsupervised Learning for Underwater Acoustic Detection." Journal of Marine Science and Engineering 13, no. 6 (2025): 1136. https://doi.org/10.3390/jmse13061136.

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In the field of Direction of Arrival (DOA) estimation, due to the complexity of ocean noise and the limitations of using array apertures, the bearing time record (BTR) obtained by CBF typically exhibits low signal-to-noise ratio (SNR) and wide mainlobe width. To address this issue, we propose an adaptive 2D convolution kernel construction method that utilizes an improved k-means clustering algorithm to extract adaptive mainlobe visual patterns from historical BTR data as convolution kernels. Experimental results show that our method can effectively reduce noise levels within multiple target en

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Chen, Ying-Nong. "Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification." Remote Sensing 11, no. 24 (2019): 2892. http://dx.doi.org/10.3390/rs11242892.

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In this study, a novel multple kernel FLE (MKFLE) based on general nearest feature line embedding (FLE) transformation is proposed and applied to classify hyperspectral image (HSI) in which the advantage of multple kernel learning is considered. The FLE has successfully shown its discriminative capability in many applications. However, since the conventional linear-based principle component analysis (PCA) pre-processing method in FLE cannot effectively extract the nonlinear information, the multiple kernel PCA (MKPCA) based on the proposed multple kernel method was proposed to alleviate this p

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Hu, Baoquan, Jun Liu, Yue Xu, and Tianlong Huo. "An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2." Shock and Vibration 2024 (February 21, 2024): 1–21. http://dx.doi.org/10.1155/2024/9071328.

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Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network’s adapta

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Parodi, Pietro. "Triangle-free reserving." British Actuarial Journal 19, no. 1 (2013): 168–218. http://dx.doi.org/10.1017/s1357321713000093.

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AbstractThis paper argues that all reserving methods based on claims triangulations (the “triangle trick”), no matter how sophisticated the subsequent processing of the information contained in the triangle is, are inherently inadequate to accurately model the distribution of reserves, although they may be good enough to produce a point estimate of such reserves. The reason is that the triangle representation involves the compression (and ultimately the loss) of crucial information about the individual losses, which comes back to haunt us when we try to extract detailed information on the dist

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Yu, Wenhui, Zixin Zhang, and Zheng Qin. "Low-Pass Graph Convolutional Network for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8954–61. http://dx.doi.org/10.1609/aaai.v36i8.20878.

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Spectral graph convolution is extremely time-consuming for large graphs, thus existing Graph Convolutional Networks (GCNs) reconstruct the kernel by a polynomial, which is (almost) fixed. To extract features from the graph data by learning kernels, Low-pass Collaborative Filter Network (LCFN) was proposed as a new paradigm with trainable kernels. However, there are two demerits of LCFN: (1) The hypergraphs in LCFN are constructed by mining 2-hop connections of the user-item bipartite graph, thus 1-hop connections are not used, resulting in serious information loss. (2) LCFN follows the general

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Muralinath, Rashmi N., Vishwambhar Pathak, and Prabhat K. Mahanti. "Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels." Future Internet 17, no. 3 (2025): 102. https://doi.org/10.3390/fi17030102.

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Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlati

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Xiao, Yongliang, Limin Xia, Shaoping Zhu, Dazu Huang, and Jianquan Xie. "Video Shot Boundary Recognition Based on Adaptive Locality Preserving Projections." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/353261.

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A novel video shot boundary recognition method is proposed, which includes two stages of video feature extraction and shot boundary recognition. Firstly, we use adaptive locality preserving projections (ALPP) to extract video feature. Unlike locality preserving projections, we define the discriminating similarity with mode prior probabilities and adaptive neighborhood selection strategy which make ALPP more suitable to preserve the local structure and label information of the original data. Secondly, we use an optimized multiple kernel support vector machine to classify video frames into bound

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Deng, Feiyue, Yan Bi, Yongqiang Liu, and Shaopu Yang. "Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network." Mathematics 9, no. 23 (2021): 3035. http://dx.doi.org/10.3390/math9233035.

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Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes

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Li, Hao, Pedram Ghamisi, Uwe Soergel, and Xiao Zhu. "Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks." Remote Sensing 10, no. 10 (2018): 1649. http://dx.doi.org/10.3390/rs10101649.

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Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data based on CNN and composite kernels. First, extinction profiles are applied to both data sources in order to extract spatial and elevation features from hyperspectral and LiDAR-derived data, respectively. Second, a three-stream CNN is designed to extract informative spectral, spati

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Reddy, A. Venkat, R. Sunitha Devi, and D. Vishnu Vardhan Reddy. "EVALUATION OF BOTANICAL AND OTHER EXTRACTS AGAINST PLANT HOPPERS IN RICE." Journal of Biopesticides 5, no. 1 (2011): 57–61. http://dx.doi.org/10.57182/jbiopestic.5.1.57-61.

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ABSTRACT A field experiment was conducted to determine the comparative efficacy of ten botanical leaf aqueous extracts, panchagavya, acephate as standard check and untreated control against hoppers in rice during kharif 2009-2010 and 2010-2011.The treatments include: Aqueous leaf extract of Vitex, Pongamia, Custard, Calotropis at 5 and 7.5% concentration, Neem Seed Kernel Extract (NSKE) at 5 and 7.5% concentration, panchagavya at 5 and 7.5% concentration, acephate 75%SP @ 1.5 g/lt and untreated control. The cumulative data reveals that the standard check-Acephate 75 SP @ 1.5 g/lt recorded sign

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Afonso, Sílvia, Ivo Vaz Oliveira, Anne S. Meyer, Alfredo Aires, Maria José Saavedra, and Berta Gonçalves. "Phenolic Profile and Bioactive Potential of Stems and Seed Kernels of Sweet Cherry Fruit." Antioxidants 9, no. 12 (2020): 1295. http://dx.doi.org/10.3390/antiox9121295.

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Every year, large quantities of stems and pits are generated during sweet cherry processing, without any substantial use. Although stems are widely recognized by traditional medicine, detailed and feasible information about their bioactive composition or biological value is still scarce, as well as the characterization of kernels. Therefore, we conducted a study in which bioactivity potential of extracts from stems and kernels of four sweet cherry cultivars (Early Bigi (grown under net cover (C) and without net cover (NC)), Burlat, Lapins, and Van) were examined. The assays included antioxidan

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Samir, Mutmainnah, Purnawansyah, Herdianti Darwis, and Fitriyani Umar. "Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1205–12. http://dx.doi.org/10.25126/jtiik.1067309.

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Daun herbal bermanfaat sebagai obat alternatif karena kandungan alaminya dapat menyembuhkan berbagai penyakit dan menjaga kesehatan tubuh. Klasifikasi citra daun herbal digunakan untuk membedakan jenis tanaman herbal berdasarkan bentuk daun. Penelitian ini Penelitian menggunakan Fourier Descriptor (FD) untuk mengekstraksi fitur pada daun herbal dan mengklasifikasikannya menggunakan metode Support Vector Machine (SVM) dan Naive Bayes (NB). SVM diimplementasikan dengan empat kernel yaitu Linear, polynomial, Radial Basis Function (RBF), dan sigmoid sementara Naive bayes diaplikasikan dengan tiga

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Samir, Mutmainnah, Purnawansyah, Herdianti Darwis, and Fitriyani Umar. "Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1205–12. https://doi.org/10.25126/jtiik.2023107309.

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Daun herbal bermanfaat sebagai obat alternatif karena kandungan alaminya dapat menyembuhkan berbagai penyakit dan menjaga kesehatan tubuh. Klasifikasi citra daun herbal digunakan untuk membedakan jenis tanaman herbal berdasarkan bentuk daun. Penelitian ini Penelitian menggunakan Fourier Descriptor (FD) untuk mengekstraksi fitur pada daun herbal dan mengklasifikasikannya menggunakan metode Support Vector Machine (SVM) dan Naive Bayes (NB). SVM diimplementasikan dengan empat kernel yaitu Linear, polynomial, Radial Basis Function (RBF), dan sigmoid sementara Naive bayes diaplikasikan dengan tiga

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Cao, Jie, Zhidong He, Jinhua Wang, and Ping Yu. "An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN." Shock and Vibration 2020 (December 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/8819313.

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The bearing state signal collected by the vibration sensor contains a large amount of environmental noise in actual processes, which leads to a reduction in the accuracy of the convolutional network in identifying bearing faults. To solve this problem, a one-dimensional convolutional neural network with a multiscale kernel (MSK-1DCNN) is proposed for the classification information enhancement of the input. A two-layer multiscale convolution structure (MSK) is used at the front of the network. MSK has five convolutional kernels with different sizes, and those kernels are used to extract feature

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Riwanti, Pramudita, Burhan Ma'arif, Filicia Regya Primadini, Erika Nur Maulidiah, and Ravy Irsyad Ramadhan. "Standardization of 96% Ethanol Extract of Beluntas Leaves, Kenikir Leaves, and Purple Corn Kernels." Jurnal Jamu Indonesia 10, no. 1 (2025): 11–18. https://doi.org/10.29244/jji.v10i1.312.

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Beluntas (Pluchea indica) leaves, kenikir (Cosmos caudatus) leaves, and purple corn (Zea mays) kernels have the potential to be developed into preparations of traditional medicine, which requires standardization of raw materials. The 96% ethanol extract P. indica leaves from Batu, C. caudatus leaves from Wonosari, and purple corn kernels from Sukabumi have been standardized. Both specific and non-specific requirements are covered. Fragment identifiers of P. indica leaves, such as stomata and trichoma. C. caudatus leaves, such as vascular tissue with stair thickening and multicellular hair cove

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Feng, Yan. "Design and research of music teaching system based on virtual reality system in the context of education informatization." PLOS ONE 18, no. 10 (2023): e0285331. http://dx.doi.org/10.1371/journal.pone.0285331.

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Virtual Reality (VR) technology uses computers to simulate the real world comprehensively. VR has been widely used in college teaching and has a huge application prospect. To better apply computer-aided instruction technology in music teaching, a music teaching system based on VR technology is proposed. First, a virtual piano is developed using the HTC Vive kit and the Leap Motion sensor fixed on the helmet as the hardware platform, and using Unity3D, related SteamVR plug-ins, and Leap Motion plug-ins as software platforms. Then, a gesture recognition algorithm is proposed and implemented. Spe

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Lu, Yu, Han Wu, and Bo Zhou. "Dimensionality reduction method of rotor fault data set based on KPCA-KNPE." Journal of Physics: Conference Series 2232, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2232/1/012010.

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Abstract In order to solve the problem that it is difficult to extract fault information in all directions in the process of dimensionality reduction of high-dimensional and nonlinear fault data sets, this research designs a rotor fault data set that combines Kernel principal component analysis, KPCA and Kernel Neighborhood Preserving Embedding, and KNPE Dimensionality reduction method. This method first uses the KPCA algorithm to effectively reduce the redundant attributes of the data, and retains the global nonlinear information of the original data; then uses the KNPE algorithm to mine the

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Yahaya, M. M., H. M. Bandiya, M. U. Ladan, and H. A. Shindi. "Larvicidal Activity of Solvent Extracts of Nicotiana tabacum L, and Datura metel L, (Solanales: Solanaceae) Against Larvae of Three Mosquito Species." Nigerian Journal of Entomology 37, no. 1 (2021): 11–27. http://dx.doi.org/10.36108/nje/1202/73.0120.

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Mosquitoes are insects that vector many of the life threatening diseases. Control of these insects is one of the major problems of the world today. Leaves and seed kernel extracts of Tobacco; Nicotiana tabacum (L.) and Thorn-Apple; Datura metel (L.) were assayed for larvicidal activity against three mosquito species of Aedes, Anopheles and Culex respectively. Samples of the three species were collected from mosquito breeding sites and reared in the laboratory for continued source of larvae following standard procedures. The plants parts used were extracted with hexane, chloroform and methanol

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