28 papers · ranked by Valyu relevance
Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine…
Tirtharaj Dash, H. S. Behera
In case of decision making problems, classification of pattern is a complex and crucial task. Pattern classification using multilayer perceptron (MLP) trained with back propagation learning becomes much complex with increase in number of layers, number of nodes and number of epochs and ultimate increases computational…
А. М. Михайлов, Mikhail Karavay
Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome…
Hongzhi Li, Joseph G. Ellis, Lei Zhang, Shih‐Fu Chang
Visual patterns represent the discernible regularity in the visual world. They capture the essential nature of visual objects or scenes. Understanding and modeling visual patterns is a fundamental problem in visual recognition that has wide ranging applications. In this paper, we study the problem of visual pattern…
Alexander O. Komendantov, Siva Venkadesh, Christopher L. Rees, Diek W. Wheeler + 2 more
Systematically organizing the structural, molecular, and physiological properties of hippocampal neurons is important for understanding their computational functions in the cortical circuit. Hippocampome.org identifies 122 neuron types in the rodent hippocampal formation (dentate gyrus, CA3, CA2, CA1, subiculum, and…
Ahmed Bouziane, Ala Eddine Boudemia, Taib Abderaouf Bourega, Mahdjoub Hamdi
Histopathological analysis of whole-slide images is the gold standard technique for diagnosis of lung cancer and classifying it into types and subtypes by specialized pathologists. This labor-based approach is time and effort consuming, which led to development of automatic approaches to assist in reducing the time and…
Juan Li, Cai Dai
The unceasing increase of data quantity severely limits the wide application of mature classification algorithms due to the unacceptable execution time and the insufficient memory. How to fast incrementally obtain high decision reference set and adapt to incremental data environment is urgently needed in incremental…
Shadnaz Asgari, Fabien Scalzo, Magdalena Kasprowicz
Recent advances in data acquisition and various monitoring modalities have resulted in generating and collecting a growing volume of biological and medical data at unprecedented speed and scale [1]. These accumulated data can be utilized for a more effective delivery of care and enhanced clinical decision-making [2].…
Kavya Singh, Anil Kumar Koundal, Navjeet Kaur
The visual descriptor methods like Local Binary Pattern (LBP) capture anatomical structures in captured images along with their disparities, which can be exploited by suitable methods for the diagnosis of medical anomalies. We developed a Local Mean Gradient Pattern (LMGP), based partly on LBP, a feature extraction…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Miraemiliana Murat, Siow-Wee Chang, Arpah Abu, Hwa Jen Yap + 2 more
'Kien-Thai Yong' 'Jun Pang'] Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult…
Madhav Sigdel, Imren Dinc, Madhu S. Sigdel, Semih Dinc + 2 more
Background Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these…
Alicja Miniak-Górecka, Krzysztof Podlaski, Tomasz Gwizdałła, Yilun Shang
'Yilun Shang'] The classification of multi-dimensional patterns is one of the most popular and often most challenging problems of machine learning. That is why some new approaches are being tried, expected to improve existing ones. The article proposes a new technique based on the decision network called…
Matthias Guggenmos, Philipp Sterzer, Radoslaw Martin Cichy
Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to…
Authors not listed
Chemical reactions typically follow mechanistic templates and hence fall into a manageable number of clearly distinguishable classes that usually labeled by names of chemists who discovered or explored them. These ``named reactions'' form the core of reaction ontologies and are associated with specific synthetic…
Hussein Adly, Mohamed A. Moustafa
—Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between…
Abhijit Sen, Giridas Maiti, Bikram Kumar Parida, Mishra + 3 more
—Feature engineering continues to play a critical role in image classification, particularly when interpretability and computational efficiency are prioritized over deep learning models with millions of parameters. In this study, we revisit classical machine learning based image classification through a novel approach…
Mélina Baheux Blin, Vincent Loreau, Frank Schnorrer, Pierre Mangeol
Regular spatial patterns are ubiquitous forms of organization in nature. In animals, regular patterns can be found from the cellular scale to the tissue scale, and from early stages of development to adulthood. To understand the formation of these patterns, how they form and mature, and how they are affected by…
Jianfang Cao, Lichao Chen, Min Wang, Hao Shi + 1 more
Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model.…
Ananya Basu, Suprativ Saha
In post genomic era with the advent of new technologies a huge amount of complex molecular data are generated with high throughput. The management of this biological data is definitely a challenging task due to complexity and heterogeneity of data for discovering new knowledge. Issues like managing noisy and incomplete…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
Ayushi Sharma, Harshit Bhardwaj, Arpit Bhardwaj, Aditi Sakalle + 2 more
'Divya Acharya' 'Wubshet Ibrahim'] Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this…
Fayyaz ul Amir Afsar Minhas
This paper analyzes the efficacy of applying one class classifiers (OCCs) to the problem of abnormal beat detection in ECG. It also proposes a novel OCC called Quadratic Programming Dissimilarity representation based Data Descriptor (QPDDD). A comparison of the proposed classification technique with existing…
Somaieh Amraee, Maryam Chinipardaz, Mohammadali Charoosaei
This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws, nuts, keys, and coins: the histogram of oriented gradients (HOG) and local binary pattern (LBP). The desired features for the labeled images are first extracted and saved in the form of a feature…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…
Denice van Herwerden, Jake O'Brien, Phil Choi, Kevin Thomas + 2 more
Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula…
Trevor Gokey, David L. Mobley
Molecular mechanics force fields require a chemical perception model to assign parameters to molecules. A recent advancement in force fields is the use of the SMARTS substructure query language as the perception model. Although it is straightforward to write SMARTS patterns to define new force field parameters, it is…
Jonathan Fine, Anand Rasjashekar, Gaurav Chopra
We present a deep learning method for identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra without the use of any database, pre-established rules, procedures, or peak-matching methods. We derive patterns and correlations directly from spectral data representing multiple…