21 papers · ranked by Valyu relevance
Lidia Cleetus, A Raji Sukumar, N Hemalatha
In this paper, a detection tool has been built for the detection and identification of the diseases and pests found in the crops at its earliest stage. For this, various deep learning architectures were experimented to see which one of those would help in building a more accurate and an efficient detection model. The…
Yixin Liu, Lihang Zhang, Zezhou Hao, Ziyuan Yang + 3 more
'Xiaoguang Zhou' 'Qing Chang'] To explore the application value of convolutional neural network combined with residual attention mechanism and Xception model for automatic classification of benign and malignant gastric ulcer lesions in common digestive endoscopy images under the condition of insufficient data. For the…
Ercument Yilmaz, Cansu Görürgöz, Hatice Cansu Kış, Emin Murat Canger + 1 more
'Bengi Öztaş'] Purpose This study aimed to develop an improved method for forensic age estimation using deep learning models applied to orthopantomography (OPG) images, focusing on distinguishing individuals under 12 years old from those aged 12 and above. Methods A dataset of 1941 pediatric patients aged between five…
WenKai Pan, Dong Zhu, Jutao Wang, Haiyan Zhu
This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents…
Md Humaion Kabir Mehedi, Kh. Fardin Zubair Nafis, Krity Haque Charu, Jia Uddin + 3 more
'Jia Uddin' 'Md Golam Rabiul Alam' 'M.F. Mridha' 'Asadullah Shaikh'] Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh’s rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is…
Hadar Shavit, Filip Jatelnicki, Pol Mor-Puigventós, Wojtek Kowalczyk
In this paper, we present a modified Xception architecture, the NEXcepTion network. Our network has significantly better performance than the original Xception, achieving top-1 accuracy of 81.5% on the ImageNet validation dataset (an improvement of 2.5%) as well as a 28% higher throughput. Another variant of our model…
Sudi Murindanyi, Joyce Nakatumba‐Nabende, Rahman Sanya, Rose Nakibuule + 1 more
Approaches for Crop Classification Authors: ['Sudi Murindanyi' 'Joyce Nakatumba‐Nabende' 'Rahman Sanya' 'Rose Nakibuule' 'Andrew Katumba'] The increasing popularity of Artificial Intelligence (AI) in recent years has led to a surge in interest in image classification, especially in the agricultural sector. With the…
Daniel M. Tompkins, Kshitiz Kumar, Jian Wu
An Xception model reaches state-of-the-art (SOTA) accuracy on the ESC-50 dataset for audio event detection through knowledge transfer from ImageNet weights, pretraining on AudioSet, and an on-the-fly data augmentation pipeline. This paper presents an ablation study that analyzes which components contribute to the boost…
Lifeng Li, Zaimin Yang, Xiongping Yang, Jiaming Li + 1 more
With the increasing global demand for new energy sources, Photovoltaic (PV) is increasingly emphasized as a renewable energy source globally. Consequently, the assessment of PV resources has become crucial. Existing single frameworks and algorithms for PV resource assessment lead to low assessment accuracy. To…
M. A. K. Hasan, Krishno Dey
The recent advancement of edge computing enables researchers to optimize various deep learning architectures to employ them in edge devices. In this study, we aim to optimize Xception architecture which is one of the most popular deep learning algorithms for computer vision applications. The Xception architecture is…
Joshua C.O. Koh, German Spangenberg, Surya Kant
Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high performance end-to-end machine learning pipelines with minimal effort from the user. AutoML with neural architecture search which searches for the best neural network architectures in deep…
Tam The Nguyen, Phong Minh Vu, Tung Thanh Nguyen
—In modern programming languages, exception handling is an effective mechanism to avoid unexpected runtime errors. Thus, failing to catch and handle exceptions could lead to serious issues like system crashing, resource leaking, or negative end-user experiences. However, writing correct exception handling code is often…
Qing Li, Yang Yu, Pathum Kossinna, Theodore Lun + 2 more
Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the “latent variables” in an autoencoder, are difficult to interpret, not…
German Preciat, Agnieszka B. Wegrzyn, Xi Luo, Ines Thiele + 2 more
Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, especially modelling of metabolism. Given a generic model, several methods have been developed to…
German Preciat, Agnieszka B. Wegrzyn, Ines Thiele, Thomas Hankemeier + 1 more
Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Authors not listed
Background: Janus Kinase 2 (JAK2) is a key kinase in cellular signal transduction. Its abnormal activation is closely related to various myeloproliferative neoplasms and inflammatory diseases. Developing selective JAK2 inhibitors is an important direction in drug discovery. Accurate prediction of compound inhibitory…
Lin Zhang, Yuteng Zhang, Dusit Niyato, Lei Ren + 5 more
'Zhen Chen' 'Yuanjun Laili' 'Wentong Cai' 'Agostino Bruzzone'] Abstract—Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into complex product modeling and simulation code generation can significantly enhance the efficiency of the system design phase in Model-Based…
Christina Humer, Henry Heberle, Floriane Montanari, Thomas Wolf + 4 more
The introduction of machine learning to small molecule research – an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate – has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or…
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…
Authors not listed
The extended tight binding (xTB) family of methods opened many new possibilities in the field of computational chemistry. Within just five years, the GFN2-xTB parametrization for all elements up to Z=86 enabled more than a thousand applications, which were previously not feasible with other electronic structure…