28 papers · ranked by Valyu relevance
Farhana Sultana, Abu Sufian, Paramartha Dutta
—Convolutional Neural Network (CNN) is the stateof-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet…
Ido Azuri, Irit Rosenhek-Goldian, Neta Regev-Rudzki, Georg Fantner + 2 more
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to…
Francisco Garibaldi-Márquez, Gerardo Flores, Diego A. Mercado-Ravell, Alfonso Ramírez-Pedraza + 2 more
Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a…
Mauro Tropea, Giuseppe Fedele, Raffaella De Luca, Domenico Miriello + 2 more
'Floriano De Rango' 'Hsiao-Chun Wu'] This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”)…
Jordi-Roger Riba, Rosa Cantero, Pol Riba-Mosoll, Rita Puig + 1 more
The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating…
Rajesh Amerineni, Resh S. Gupta, Lalit Gupta
The brain uses contextual information to uniquely resolve the interpretation of ambiguous stimuli. This paper introduces a deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into the classification process. The model, referred to as the CINET, is…
Michael P. Pound, Alexandra J. Burgess, Michael H. Wilson, Jonathan A. Atkinson + 8 more
Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and…
Zhenhua Wang, Xingxing Wang, Gang Wang
We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more…
Daouda Diouf, D Seck, Mountaga Diop, Abdoulye Ba
Identifying and characterizing the patient's blood samples is indispen-sable in diagnostics of malignance suspicious. A painstaking and sometime sub-jective task are used in laboratories to manually classify white blood cells. Neural mathematical methods as deep learnings can be very useful in the automated recognition…
Na Yao, Fuchuan Ni, Ziyan Wang, Jun Luo + 3 more
Background Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to…
Rajani Raman, Haruo Hosoya
Recent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate…
Serkan Kıranyaz, Onur Avcı, Osama Abdeljaber, Türker İnce + 2 more
'Moncef Gabbouj' 'Daniel Inman'] During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with…
Daniel J. Delbarre, Luis Santos, Habib Ganjgahi, Neil Horner + 5 more
Large scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large-scale clinical trials dataset of Multiple Sclerosis (MS) with over 235,000 magnetic resonance imaging (MRI) scans, we consider the challenge of defacing – anonymisation to remove identifying features on…
Khatereh Davoudi, Parimala Thulasiraman
Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer mortality in women around the world. However, it can be controlled effectively by early diagnosis, followed by effective treatment. Clinical specialists take the advantages of computer-aided diagnosis (CAD) systems to make their…
Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys + 2 more
'José M. Benítez' 'Francisco Herrera'] Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing…
Pouria Parhami, Mansoor Fateh, Mohsen Rezvani, Hamid Alinejad Rokny
It is now well-known that genetic mutations contribute to development of tumors, in which at least 15% of cancer patients experience a causative genetic abnormality including De Novo somatic point mutations. This highlights the importance of identifying responsible mutations and the associated biomarkers (e.g., genes)…
Sumit Kumar, S.Sugantha Priya, Ayush Kumar
The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlying reason is international funding has declined. Malaria, which is spread to people through the bites…
Michelle R. Greene, Bruce C. Hansen
Understanding the computational transformations that enable invariant visual categorization is a fundamental challenge in both systems and cognitive neuroscience. Recently developed deep convolutional neural networks (CNNs) perform visual categorization at accuracies that rival humans, providing neuroscientists with…
Tae Joon Jun, Soo-Jin Kang, June‐Goo Lee, Jihoon Kweon + 5 more
'Daeyoun Kang' 'Do‐Hyeun Kim' 'Daeyoung Kim' 'Young‐Hak Kim'] Abstract Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule.…
Jie Chen, Hengrui Zhang, Carolin Wahl, Wei Liu + 4 more
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected…
Mohannad Elhamod, Kelly M. Diamond, A. Murat Maga, Yasin Bakis + 7 more
Species classification is an important task that is the foundation of industrial, commercial, ecological, and scientific applications involving the study of species distributions, dynamics, and evolution. While conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Sanket Kadulkar, Michael Howard, Thomas Truskett, Venkat Ganesan
We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation…
Yaoda Xu, Maryam Vaziri-Pashkam
Existing single cell neural recording findings predict that, as information ascends the visual processing hierarchy in the primate brain, the relative similarity among the objects would be increasingly preserved across identity-preserving image transformations. Here we confirm this prediction and show that object…
Yanshan Shi
K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Pinaki Saha, Minh Tho Nguyen
Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster including its chemical, optical, magnetic, mechanical and transport properties are mainly governed by the…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…