Results 81 to 90 of about 95,777 (258)

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

Advanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera, Gaurav Duggal, Rolf Müller   +2 more
wiley   +1 more source

Development of Raman Spectroscopy and Machine Learning Methods for Protein Aggregate Quantification: Application to BSA in Chromatographic Processes

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Protein aggregation poses a significant risk to biopharmaceutical product quality, as even minor amounts of oligomeric species can compromise efficacy and safety. Rapid and reliable detection of protein aggregates thus remains a major challenge in biopharmaceutical manufacturing.
Jakob Heyer‐Müller, Robin Schiemer, Lars Robbel, Michael Schmitt, Jürgen Hubbuch   +4 more
wiley   +1 more source

A Taxonomy of Predictive Maintenance as a Basis for Supra‐Regional Sustainability Monitoring—Literature Review

Business Strategy and the Environment, EarlyView.
ABSTRACT The concept of predictive maintenance in advanced manufacturing systems is crucial from the point of view of resource efficiency in the era of high competitiveness forced by energy transformation in the digital economy. Against the backdrop of sustainability and the opportunities a data cooperative offers, the combination of predictive ...
Christian Schachtner, Nadine Baumann, Marek Jabłoński, Adam Jabłoński, Mariusz Bednarek, Sławomir Luściński, Piotr Bilski   +6 more
wiley   +1 more source

Artificial intelligence and machine learning‐assisted digital applications for biopharmaceutical manufacturing

Biotechnology Progress, EarlyView.
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley   +1 more source

Corporate Sustainability Performance and Fraud Risk Management in Nigeria's Extractive Sector: The Moderating Role of Ownership Structure in an Evolving Environmental Policy Landscape

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Using the environmental quality cost management model, this study examines how fraud risk management (FRM) influences corporate sustainability performance (CSP) and how ownership structures moderate it. The study uses artificial neural networks (ANN) and logistic regression models to test two hypotheses. H1 demonstrates that the prevention and
Israel Akinbode Owolabi, Michael Odei Erdiaw‐Kwasie, Emmanuel Tenakwah, Matthew Abunyewah   +3 more
wiley   +1 more source

COMPARATIVE ANALYSIS THE PERFORMANCE OF CLIENT-SIDE AND SERVER-SIDE MACHINE LEARNING TECHNOLOGIES

Електроніка та інформаційні технології
The performance analysis of client-side and server-side machine learning technologies is important for understanding the optimal way to model optimization.
I. Mysiuk, Roman Shuvar
doaj   +1 more source

Sustainable Live Sound Monitoring and Classification System Enabled by a Triboelectric Nanogenerator and Machine Learning Techniques

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A high‐performance Triboelectric Nanogenerator (TENG) acoustic sensor using polyimine/graphite polypropylene (PI/GP) was developed for real‐time, sustainable sound monitoring and classification. The self‐powered device delivers 25.67 μW output power, 92.7% accuracy with MobileNet V1, and powers a wireless transmission circuit, demonstrating dual ...
Majid Haji Bagheri, Araz Rajabi‐Abhari, Owen Gibbs, Pengcheng Xi, Asif Abdullah Khan, Fangzheng Huang, Md Soyaeb Hassan, Ning Yan, Dayan Ban   +8 more
wiley   +1 more source

DETERMINATION OF THE BEST OPTIMIZER FOR A NEURONETWORK IN THE DEVELOPMENT OF AUTOMATIC IMAGE TAGGING SYSTEMS

Електроніка та інформаційні технології
Background. Choosing the best optimizer is an important step in developing efficient automatic image classification systems. In particular, for neural networks based on convolutional neural networks (CNNs), the choice between popular optimization methods
Andrian Kozynets
doaj   +1 more source

Real‐Time Phosphate Monitoring via Plant‐Derived Graphene Ink FET Sensors Integrated with Deep Learning

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Accurate, real‐time nutrient monitoring is challenging in precision agriculture due to cost and technical limitations. Aligned with circular green economics, a deep learning‐integrated remote‐gate field‐effect transistor sensor with plant‐derived graphene electrodes offers a sustainable solution.
Rapti Ghosh, Fengxue Zhang, Hyun‐June Jang, Janan Hui, Kayla Vittore, Haoyang You, Rozyyev Vepa, Wen Zhuang, Xingkang Huang, Haihui Pu, Jeffrey W. Elam, Stuart J. Rowan, DoKyoung Lee, Elizabeth A. Ainsworth, Mark C. Hersam, Yuxin Chen, Junhong Chen   +16 more
wiley   +1 more source

Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm

Paradigma
This study aims to classify vegetable types using the Convolutional Neural Network (CNN) algorithm with a dataset encompassing 15 vegetable classes and a total of 31,000 images.
Wilis Arum Karunia, Safriya Murni Puspita, Dwi Rolliawati, Ahmad Yusuf   +3 more
doaj   +1 more source