A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems by Ripal Ranpara, Osamah Alsalman, Om Prakash Kumar, Shobhit K. Patel

“…Abstract This paper presents GreenMU, an innovative proposed novel framework designed to address the two main challenges: energy efficiency as one of the main research components and detection performance in intrusion detection systems. …”
Get full text

A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

“…This research paper presents a hybrid deep learning and semantic reasoning framework that enhances threat intelligence and autonomous response. …”
Get full text

Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…For this reason, research to replace widespread lithium batteries with sodium-ion batteries has received more and more attention. In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. …”
Get full text

Machine Learning-Based Shelf Life Estimator for Dates Using a Multichannel Gas Sensor: Enhancing Food Security by Asrar U. Haque, Mohammad Akeef Al Haque, Abdulrahman Alabduladheem, Abubakr Al Mulla, Nasser Almulhim, Ramasamy Srinivasagan

“…Traditional cold storage systems, whilst being capable of monitoring temperature and humidity, lack the intelligence to detect spoilage or predict shelf life in real-time. In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. …”
Get full text

Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. We will present a model that integrates state-of-the-art methodologies in both federated learning and optimization paradigms through the combination of the Federated Residual Convolutional Network (FedRCNet) and the EEL-Levy Fusion Optimization (ELFO) methods. …”
Get full text

Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms by S. Jayanthi, Swathi Sowmya Bavirthi, P. Murali, K. Vijaya Kumar, Hend Khalid Alkahtani, Mohamad Khairi Ishak, Samih M. Mostafa

“…Various methods are presented for attack detection and prevention. However, artificial intelligence (AI)-based Machine learning (ML) and deep learning (DL) methodologies are highly effective for detecting DDoS attacks in cybersecurity. …”
Get full text

Las estrategias de aprendizaje. Radiografías necesarias para su comprensión / The strategies of learning. X-ray necessary for their understanding by Armenio Pérez Martínez

“…Como colofón se plasma la propuesta de una nueva forma de concebir las estrategias de aprendizaje, donde se trascienda los postulados constructivistas y se conciba el desarrollo integral de las mismas desde el enfoque histórico-cultural.   At the present time it is very frequent to listen to speak of learning strategies from different you focus epistemology, without unanimity exists as for the approaches that are good to characterize this concept. …”
Get full text

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. by Nicholas T Gigliotti, Justin Lee, Emily H Mang, Giancarlo R Zambrano, Mitra L Taheri

“…Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. …”
Get full text

IoT-driven smart assistive communication system for the hearing impaired with hybrid deep learning models for sign language recognition by Mashael Maashi, Huda G. Iskandar, Mohammed Rizwanullah

“…This study presents a Smart Assistive Communication System for the Hearing-Impaired using Sign Language Recognition with Hybrid Deep Learning (SACHI-SLRHDL) methodology in IoT. …”
Get full text

A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models by Abdullah M. Alashjaee, Hussah Nasser AlEisa, Abdulbasit A. Darem, Radwa Marzouk

“…The presented HAODVIP-ADL method initially utilizes bilateral filtering (BF) for the image pre-processing stage to reduce noise while preserving edges for clarity. …”
Get full text

Credit card fraud detection in the era of disruptive technologies: A systematic review by Asma Cherif, Arwa Badhib, Heyfa Ammar, Suhair Alshehri, Manal Kalkatawi, Abdessamad Imine

“…Credit card fraud is becoming a serious and growing problem as a result of the emergence of innovative technologies and communication methods, such as contactless payment. In this article, we present an in-depth review of cutting-edge research on detecting and predicting fraudulent credit card transactions conducted from 2015 to 2021 inclusive. …”
Get full text

Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network by Shengwei Qin, Yao Jin, Hailong Hu

“…This module retains important features such as edges and corners by using non-artificial encoding methods and learning mechanisms to avoid the creation of blurred points. …”
Get full text

Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis by Padmapriya K., Ezhumalai Periyathambi

“…For pre-processing system uses an edge detector, which is canny edge detector. The proposed model learns many tasks concurrently, such as categorizing different brain diseases or anomalies, by extracting features from image patches using convolutional neural networks (CNNs). …”
Get full text

Estimating Wheat Traits Using Artificial Neural Network-Based Radiative Transfer Model Inversion by Lukas J. Koppensteiner, Hans-Peter Kaul, Sebastian Raubitzek, Philipp Weihs, Pia Euteneuer, Jaroslav Bernas, Gerhard Moitzi, Thomas Neubauer, Agnieszka Klimek-Kopyra, Norbert Barta, Reinhard W. Neugschwandtner

“…Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. …”
Get full text

A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…To overcome these challenges, this paper presents ORA-DL (Optimized Resource Allocation using Deep Learning) an advanced framework that integrates deep learning, Internet of Things (IoT)-based sensing, and real-time adaptive control to optimize smart grid energy management. …”
Get full text

Ultrafast Laser Beam Profile Characterization in the Front-End of the ELI-NP Laser System Using Image Features and Machine Learning by Tayyab Imran

“…The methodology integrates classical beam diagnostics with image processing and machine learning tools to evaluate anomalies based on high-resolution beam profile images. …”
Get full text

Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index by Rosinus, Manuel

“…These models can fall short in capturing the complex, nonlinear dynamics frequently present in financial markets. This has led to the adoption of machine learning methods like Long Short-Term Memory (LSTM) networks, which are specifically designed to recognize long-term dependencies in sequential data, offering a potential advantage in modeling volatile financial time series. …”
Get full text

Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…These sensor issues, such as noise, missing values, outliers, sensor drift, and faulty readings, can lead to delayed or missed fire predictions, posing significant safety and operational risks in the oil and gas industrial IoT environment. This paper presents an approach for handling faulty sensors in edge servers within an IIoT environment to enhance the reliability and accuracy of fire prediction through multi-sensor fusion preprocessing, machine learning (ML)-driven probabilistic model adjustment, and uncertainty handling. …”
Get full text

GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

Get full text

UAV-StrawFire: A visible and infrared dataset for real-time straw-fire monitoring with deep learning and image fusion by Xikun Hu, Ya Jiang, Xiaoyan Xia, Chen Chen, Wenlin Liu, Pengcheng Wan, Kangcheng Bin, Ping Zhong

“…The dataset is publicly available on IEEE Dataport, offering a valuable resource for researchers in the remote sensing and machine learning communities to advance the development of effective straw-burning monitoring systems.…”
Get full text