Relevant bibliographies by topics / Date kernel extract

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Academic literature on the topic 'Date kernel extract'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Date kernel extract"

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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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Book chapters on the topic "Date kernel extract"

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Narmatha, J., C. Ratnarajan, A. John, C. U. Om kumar, and M. Suguna. "Detecting COVID From CT Images using Autoencoders." In Advanced Machine Learning for Complex Medical Data Analysis. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815313383125010010.

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The SARS-CoV-2 virus, popularly referred to as Coronavirus or COVID-19, spread widely and infected a huge number of people since December 2019. There are various tests now detecting Covid 19, however, each of these tests has its own disadvantages. CT (Computed Tomography) scans are one of the most efficient ways of diagnosing covid 19 among these tests. The effectiveness of CT scans is much greater than that of X-rays. However, these scans cannot yield results directly by just looking at them, because there are other common infections such as pneumonia and influenza, which yield similar lung i
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Fu, Xiuju, Lipo Wang, GihGuang Hung, and Liping Goh. "Linguistic Rule Extraction from Support Vector Machine Classifiers." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch072.

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Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from th
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Feng, Hao, Jing Li, Fen Liu, XianFei Zhang, JingJing Wang, and Yixi Wang. "Research on Automatic Identification of Internet of Things Illegal Intrusion Based on Unsupervised Learning." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde231189.

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In order to improve the accuracy and efficiency of Internet of things illegal intrusion identification, an automatic identification method of Internet of things illegal intrusion based on unsupervised learning is proposed. Firstly, in order to accurately and automatically identify the illegal intrusion of the Internet of things, the gradient descent clustering method is used to fully collect the illegal intrusion data of the Internet of things. Secondly, the kernel principal component analysis method is used to extract the characteristics of online intrusion data. Finally, unsupervised learnin
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Fu, Xiuju, Lipo Wang, GihGuang Hung, and Liping Goh. "Linguistic Rule Extraction from Support Vector Machine Classifiers." In Research and Trends in Data Mining Technologies and Applications. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-271-8.ch010.

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Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from th
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Peker, Musa, Serkan Ballı, and Ensar Arif Sağbaş. "Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch017.

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Human activity recognition (HAR) is a growing field that provides valuable information about a person. Sensor-equipped smartwatches stand out in these studies in terms of their portability and cost. HAR systems usually preprocess raw signals, decompose signals, and then extract attributes to be used in the classifier. Attribute selection is an important step to reduce data size and provide appropriate parameters. In this chapter, classification of eight different actions (brushing teeth, walking, running, vacuuming, writing on the board, writing on paper, using the keyboard, and stationary) ha
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Peker, Musa, Serkan Ballı, and Ensar Arif Sağbaş. "Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine." In Advances in Computational Intelligence and Robotics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4766-2.ch017.

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Human activity recognition (HAR) is a growing field that provides valuable information about a person. Sensor-equipped smartwatches stand out in these studies in terms of their portability and cost. HAR systems usually preprocess raw signals, decompose signals, and then extract attributes to be used in the classifier. Attribute selection is an important step to reduce data size and provide appropriate parameters. In this chapter, classification of eight different actions (brushing teeth, walking, running, vacuuming, writing on the board, writing on paper, using the keyboard, and stationary) ha
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V. Sridevi, Dr. "BREAST CANCER PROGNOSIS PREDICTION USING NOVEL SHRUNK KERNEL KNN METHOD WITH MLO AND CC FEATURES." In Futuristic Trends in Artificial Intelligence Volume 3 Book 10. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bgai10p4ch3.

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The most continual malignancy found in women is breast cancer. The good news is that if detected early, it is one of the most treatable forms of cancer. High-dimensional data results in large number of computation redundancy but also advances diagnostic techniques. As a result significant information must be extracted and the feature dimension must be reduced for good prediction and a precise treatment decision. However, past studies for diagnosing breast cancer have relied mostly on labelled data that is difficult to get. To solve this problem, two different sorts of perspectives, such as CC
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Giacco Ferdinando, Esposito Antonietta Maria, Scarpetta Silvia, Giudicepietro Flora, and Marinaro Maria. "Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2009. https://doi.org/10.3233/978-1-60750-072-8-116.

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We applied and compared two supervised pattern recognition techniques, namely the Multilayer Perceptron (MLP) and Support Vector Machine (SVM), to classify seismic signals recorded on Stromboli volcano. The available data are firstly preprocessed in order to obtain a compact representation of the raw seismic signals. We extract from data spectral and temporal information so that each input vector is made up of 71 components, containing both spectral and temporal information extracted from the early signal. We implemented two classification strategies to discriminate three different seismic eve
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Mandal, Bhaswati, Manash Pratim Sarma, and Kandarpa Kumar Sarma. "Design of a Power Aware Systolic Array based Support Vector Machine Classifier." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8493-5.ch005.

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This chapter presents a method for generating binary and multiclass Support Vector Machine (SVM) classifier with multiplierless kernel function. This design provides reduced power, area and reduced cost due to the use of multiplierless kernel operation. Binary SVM classifier classifies two groups of linearly or nonlinearly separable data while the multiclass classification provides classification of three nonlinearly separable data. Here, at first SVM classifier is trained for different classification problems and then the extracted training parameters are used in the testing phase of the same
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Wang, Pingan, Haibin Shi, Shouwu Hou, Liangzhi Fan, and Haiping Zhu. "Fault Diagnosis for Aerostat Model Based on Multiscale Feature Fusion Deep Residual Networks." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220088.

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Deep learning has been widely used in fault diagnosis, especially the convolutional neural network (CNN). However, traditional CNN models usually use single-scale kernels to extract features, which ignores the multiscale features of input data. This article develops a novel residual neural network named multiscale feature fusion deep residual networks for fault diagnosis of aerostat. The designed multiscale feature fusion block (MFF Block) realizes automatic extraction, fusion and compression of multiscale features. The series connection of multiple MFF Blocks makes the proposed model able to
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Conference papers on the topic "Date kernel extract"

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Saida, Taisei, and Mayuko Nishio. "Seismic Fragility Assessment using Explainable Deep Kernel Learning Surrogate Model considering Structural and Seismic Uncertainties." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2581.

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&lt;p&gt;This study proposes a surrogate model using deep kernel learning (DKL) with a convolutional neural network (CNN) and attention mechanism for efficient seismic fragility assessment of infrastructure. The CNN extracts features from seismic response spectrum, enabling efficient Gaussian process (GP) regression in a lower-dimensional space. An automatic relevance determination (ARD) kernel and attention mechanism enhance explainability by evaluating input variable contributions and attention weights for response spectrum. The model achieved high prediction accuracy, outperforming GPs, esp
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Sena, Jessica, and William Robson Schwartz. "Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8310.

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Sensor-based Human Activity Recognition (HAR) provides valuable knowledge to many areas. Recently, wearable devices have gained space as a relevant source of data. However, there are two issues: large number of heterogeneous sensors available and the temporal nature of the sensor data. To handle these issues, we propose a multimodal approach that processes each sensor separately and, through an ensemble of Deep Convolution Neural Networks (DCNN), extracts information from multiple temporal scales of the sensor data. In this ensemble, we use a convolutional kernel with a different height for ea
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Shieh, Meng-Dar, and Hsin-En Fang. "Using Support Vector Regression in the Study of Product Form Images." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-69150.

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In this paper, Support Vector Regression (SVR) training models using three different kernels: polynomial, Radial Basis Function (RBF), and mixed kernels, are constructed to demonstrate the training performance of unarranged data obtained from 32 virtual 3-D computer models. The 32 samples used as input data for training the three SVR models are represented by the coordination value sets of points extracted from 3-D models built by the 3-D software according to the shapes of 32 actual hairdryer products. To train the SVR model, an adjective (streamline) is used to evaluate all the 32 samples by
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He, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, and Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/254.

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Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view la
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Dias Martins, Leonardo, and Fabíola Pantoja Oliveira Araújo. "Mineração de Texto para a Análise do Perfil Emocional de Usuários de Jogo Empático." In Computer on the Beach. Universidade do Vale do Itajaí, 2021. http://dx.doi.org/10.14210/cotb.v12.p370-377.

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Daily, a large amount of data circulates on the Internet, producing a lot of information in the form of images, videos and texts. Then, it is necessary to analyze and extract these information automatically. Therefore, this work presents a case study that applies text mining to extract the emotional and sentimental profiles from the comments of the Last Day of June game users, where the results and the information extracted from the analysis of sentiments were presented. Three classification algorithms were used: Naive Bayes, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to predic
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Dhaouadi, Mouna. "A Data Set of Extracted Rationale from Linux Kernel Commit Messages." In ESEC/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 2023. http://dx.doi.org/10.1145/3611643.3617851.

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Sutter, Erich E., and X. Vaegan. "Lateral interaction component and local luminance nonlinearities of the human pattern ERG." In OSA Annual Meeting. Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.mr32.

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The electroretinogram in response to pattern reversal stimuli (PERG) consists of two distinct components: contribution from the nonlinearities of the local luminance response and effect of lateral interactions in the retina. By subtracting the local luminance nonlinearities from the PERG we extracted the lateral interaction component for checkerboard pattern stimuli of different check sizes. This response component is of special interest as it originates from neural interactions in the retina and cannot be accounted for by nonlinearities of any single active element. This decomposition of the
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Zhang, Boyu, Kai Yang, Lianfang Wang, Yu-an Tan, and Sikang Hu. "Tracing Android Kernel Codes at Early Stage without Extra Hardware Components." In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). IEEE, 2019. http://dx.doi.org/10.1109/dsc.2019.00039.

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Xue, Zhe, Junping Du, Changwei Zheng, Jie Song, Wenqi Ren, and Meiyu Liang. "Clustering-Induced Adaptive Structure Enhancing Network for Incomplete Multi-View Data." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/445.

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Incomplete multi-view clustering aims to cluster samples with missing views, which has drawn more and more research interest. Although several methods have been developed for incomplete multi-view clustering, they fail to extract and exploit the comprehensive global and local structure of multi-view data, so their clustering performance is limited. This paper proposes a Clustering-induced Adaptive Structure Enhancing Network (CASEN) for incomplete multi-view clustering, which is an end-to-end trainable framework that jointly conducts multi-view structure enhancing and data clustering. Our meth
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Luo, Jing, Hang Wang, and Minjun Peng. "Anomaly Detection of Electric Gate Valve Based on Multi-Kernel Support Vector Machine." In 2021 28th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icone28-64766.

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Abstract Valve is an indispensable fluid control component in nuclear power system. Nuclear power station has a large number of gate valve equipment, which works under high temperature, high pressure, high radioactivity and other harsh conditions. In nuclear power plant accidents and economic losses, a considerable part of them are caused by valve failure. Aiming at the fault of electric gate valve, this paper proposes an anomaly detection method based on multi-kernel support vector machine. Firstly, the acoustic emission instrument is used to measure the fault state data and extract the fault
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