Search · four archives
Search · four archives
16 papers · ranked by Valyu relevance
Ahmed M. Elshewey, Mahmoud Y. Shams, Nora El-Rashidy, Abdelghafar M. Elhady + 3 more
'Abdelghafar M. Elhady' 'Samaa M. Shohieb' 'Zahraa Tarek' 'Andrea Facchinetti'] Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people…
Ghada Atteia, Amel A. Alhussan, Nagwan Abdel Samee, Zongyuan Ge + 3 more
'Donghao Zhang' 'Deval Mehta' 'Dwarikanath Mahapatra'] Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial…
Sebastian Blume, Tim Benedens, Dieter Schramm, Soufiene Djahel
Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge is required for the application. At…
Maryam Parsa, John P. Mitchell, Catherine D. Schuman, Robert M. Patton + 2 more
'Robert M. Patton' 'Thomas E. Potok' 'Kaushik Roy'] In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic…
Hassan Ashraf, Asim Waris, Syed Omer Gilani, Uzma Shafiq + 6 more
Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on…
Dawei Zou, Chunhua Ma, Peng Wang, Yanqiu Geng + 1 more
Hyperparameter optimization (HPO), which is also called hyperparameter tuning, is a vital component of developing machine learning models. These parameters, which regulate the behavior of the machine learning algorithm and cannot be directly learned from the given training data, can significantly affect the performance…
Cai Yuanqing, Zhenming Gao, Zhang Jian, Roohallah Alizadehsani + 2 more
The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the…
Amala Mary Vincent, P. Jidesh
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image…
Ankita Sharma, Shalli Rani, Dipak Kumar Sah, Zahid Khan + 2 more
'Wadii Boulila' 'Jose Manuel Molina López'] The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models…
Farkhanda Abbas, Feng Zhang, Muhammad Ismail, Garee Khan + 6 more
'Javed Iqbal' 'Abdulwahed Fahad Alrefaei' 'Mohammed Fahad Albeshr' 'Waheb Abdullah' 'AbdulRahman Alsewari' 'Mario De Oliveira'] Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to…
Robert E. Arbon, Yanchen Zhu, Antonia S. J. S. Mey
To Optimize or Not to Optimize Authors: ['Robert\nE. Arbon' 'Yanchen Zhu' 'Antonia S. J. S. Mey'] Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about…
Sonali Samal, Shyam Sunder, Thippa Reddy Gadekellu, Fatma Hilal Yagin + 2 more
Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional grid or random search methods are often computationally inefficient in…
Masyitah Abu, Nik Adilah Hanin Zahri, Amiza Amir, Muhammad Izham Ismail + 4 more
Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a…
Mikolaj Wojciuk, Zaneta Swiderska-Chadaj, Krzysztof Siwek, Arkadiusz Gertych
The immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task, often requiring repetitive numerical experiments. As a result…
Luming Chen, Sujit K. Ghosh, Donald J. Jacobs
Generative models have gained significant attention in recent years. They are increasingly used to estimate the underlying structure of high-dimensional data and artificially generate various kinds of data similar to those from the real world. The performance of generative models depends critically on a good set of…
Shrooq Alsenan, Isra Al-Turaiki, Mashael Aldayel, Mohamed Tounsi + 1 more
'Yijie Ding'] RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning…