Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model by Jeong-Gi Lee, Deok-Joo Lee

“…On the other hand, MIBM is advantageous in scenarios where computational efficiency and robustness are prioritized. To support method selection, we also analyze the computational complexity of both approaches and derive error bounds. …”
Get full text

Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies by Ayad M. Dalloo, Amjad J. Humaidi

“…In this paper, we propose a framework that uses data-level approximate computing techniques, including by diverse sampling strategies, precision scaling, quantization, and feature selection methods, to evaluate the impact of these techniques on the computational efficiency and accuracy of KNN and SVM models. …”
Get full text

Improving inverter efficiency for electric vehicles: Experimental validation of the neural network-based SHE technique using RT-LAB by Seyf Eddine Bechekir, Mokhtaria Jbilou, Mostefa Brahami, Fatima Zohra Boudjella, Imen Souhila Bousmaha, Mimouna Oukli, Said Nemmich

“… Inverters are essential for converting direct current to alternating current in electric vehicles, relying on pulse width modulation (PWM) for efficiency. This study presents a real-time Selective Harmonic Elimination PWM (SHE-PWM) algorithm using artificial neural networks, validated with the OP5600 RT LAB simulator. …”
Get full text

Sequence-based engineering of pH-sensitive antibodies for tumor targeting or endosomal recycling applications by Wanlei Wei, Traian Sulea

“…This method, called Sequence-based Identification of pH-sensitive Antibody Binding (SIpHAB), was trained on 3D-structure-based calculations of 3,490 antibody-antigen complexes with solved experimental structures. SIpHAB was parametrized to enhance preferential binding either toward or against the acidic pH, for selective targeting of solid tumors or for antigen release in the endosome, respectively. …”
Get full text

HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE by Garrik Brel Jagho Mdemaya, Miguel Landry Foko Sindjoung, Milliam Maxime Zekeng Ndadji, Mthulisi Velempini

Get full text

Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models by Samira Mohammadi, Vahid Rahmanian, Sasan Sattarpanah Karganroudi, Mehdi Adda

“…We focused on metrics such as accuracy, precision, recall, and loss to compare the performance of models VGG19 and DeiT (data-efficient image transformer). RandomSearchCV was used for hyperparameter optimization, and we selectively froze some layers during training to help better tailor the models to our dataset. …”
Get full text

Increasing efficiency and sustainability: A comparative analysis of concrete 3D printing and traditional methods based on case studies by Karamara Merve, Bogdanski Moritz-Ole, Zöller Raphael, Albrecht Sophie Viktoria, Linner Thomas, Bock Thomas, Braml Thomas

“…Concrete 3D printing offers several significant advantages, including the ability to create complex geometries, increased material efficiency, faster build times and cost savings. …”
Get full text

A lightweight Xray-YOLO-Mamba model for prohibited item detection in X-ray images using selective state space models by Kai Zhao, Shufan Peng, Yujin Li, Tianliang Lu

Get full text

Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…The Mayfly Optimization Algorithm (MOA) is then utilized for feature selection, effectively mitigating computational complexity. …”
Get full text

Low‐power fast Fourier transform hardware architecture combining a split‐radix butterfly and efficient adder compressors by Guilherme Ferreira, Guilherme Paim, Leandro M. G. Rocha, Gustavo M. Santana, Renato H. Neuenfeld, Eduardo A. C. Costa, Sergio Bampi

“…Results reveal that the proposed FFT hardware architecture using the split‐radix butterfly is 13.28% more power efficient than the radix‐4 one. The results further show that, by combining 5‐2 AC within the split‐radix butterfly, our proposal saves up to 43.1% of the total power dissipation considering the whole FFT hardware architecture, compared with the state‐of‐the‐art radix‐4 butterfly employing the adder automatically selected by the logic synthesis tool.…”
Get full text

An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters by Hamdy K. Elminir, Mohamed A. El-Brawany, Dina Adel Ibrahim, Hatem M. Elattar, E.A. Ramadan

Get full text

Optimizing linear/non-linear Volterra-type integro-differential equations with Runge–Kutta 2 and 4 for time efficiency by Martin Ndi Azese

“…Additionally, a complex VTIDE is constructed featuring nonlinearities both within and outside the convolutions, as well as a derivative-of-dependent-variable integrant. …”
Get full text

Improved method of targeted user interface updates for enhancing the efficiency of web applications based on reactive streams and virtual DOM by M.V. Havatiuk, I.O. Saiapina

“…While these approaches are widely adopted, they can introduce unnecessary complexity and overhead in managing application state. …”
Get full text

Designing a Stock Recommender System Using the Collaborative Filtering Algorithm for the Tehran Stock Exchange by Marziyeh Nourahmadi, Ali Rahimi, Hojjatollah Sadeqi

“…Stock recommendation systems can assist investors in achieving superior returns by selecting the right stocks. However, traditional stock recommendation systems often lack the necessary accuracy and efficiency. …”
Get full text

Synergistic Removal of Cr(VI) Utilizing Oxalated-Modified Zero-Valent Iron: Enhanced Electron Selectivity and Dynamic Fe(II) Regeneration by Song Hou, Jiangkun Du, Haibo Ling, Sen Quan, Jianguo Bao, Chuan Yi

Get full text

A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning by Junyao Ding, Jianchao Du, Hejie Wang, Song Xiao

“…This paper also adds an adaptive mechanism and evolution strategy to improve the loss of population diversity and degeneration in the later stages of iteration, thereby enhancing search efficiency. The experimental results on eight UCI datasets show that the proposed method significantly improves classification performance and has excellent feature selection capability.…”
Get full text

Talking Resilience: Embedded Natural Language Cyber-Organizations by Design by Andrea Tomassi, Andrea Falegnami, Elpidio Romano

“…By integrating the concepts of simplexity, complixity, and complexity compression, we illustrate how complex cognitive and operational processes can be selectively condensed into efficient outcomes. …”
Get full text

Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression by Raphael Uwamahoro, Raphael Uwamahoro, Kenneth Sundaraj, Farah Shahnaz Feroz

“…The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals.ResultsExperimental results showed that selected features and tuned hyperparameters demonstrated a significant improvement in root mean square error (RMSE), coefficient of determination (R2) and slope with values improving from 0.1330 to 0.1174, 0.7228 to 0.7853 and 0.6946 to 0.7414, respectively for the test dataset. …”
Get full text

A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…This approach effectively captures both structured features and non-linear patterns, making it suitable for datasets with complex dependencies. The second model pairs eXtreme Gradient Boosting (XGBoost), a highly efficient boosting algorithm for tabular data, with an Artificial Neural Network (ANN). …”
Get full text

A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells by Mohammad Aljaidi, Pradeep Jangir, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Samar Hussni Anbarkhan, Laith Abualigah

“…It is challenging to find the best PEMFC parameters because the model is complex and the problem is nonlinear; not all optimization algorithms can solve this problem. …”
Get full text