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  • Open Access

    ARTICLE

    Detecting Ransomware Using a Hybrid Detection Scheme

    David Conway, Paolina Centonze*

    Journal of Cyber Security, Vol.7, pp. 71-78, 2025, DOI:10.32604/jcs.2025.063711 - 14 May 2025

    Abstract Ransomware is a variant of malicious software that aims to encrypt data or whole systems to lock out the intended users. The attackers then demand a ransom for the decryption key to allow the intended users access to their data or system again. Ransomware attacks have the potential to be used against industries like healthcare and finance, as well as against the public sector, have threatened and forced the operations of key infrastructure used by millions to cease, and extorted millions and millions of dollars from victims. Automated methods have been designed and implemented to More >

  • Open Access

    ARTICLE

    Improving Security-Sensitive Deep Learning Models through Adversarial Training and Hybrid Defense Mechanisms

    Xuezhi Wen1, Eric Danso2,*, Solomon Danso2

    Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606 - 08 May 2025

    Abstract Deep learning models have achieved remarkable success in healthcare, finance, and autonomous systems, yet their security vulnerabilities to adversarial attacks remain a critical challenge. This paper presents a novel dual-phase defense framework that combines progressive adversarial training with dynamic runtime protection to address evolving threats. Our approach introduces three key innovations: multi-stage adversarial training with TRADES (Tradeoff-inspired Adversarial Defense via Surrogate-loss minimization) loss that progressively scales perturbation strength, maintaining 85.10% clean accuracy on CIFAR-10 (Canadian Institute for Advanced Research 10-class dataset) while improving robustness; a hybrid runtime defense integrating feature manipulation, statistical anomaly detection, and… More >

  • Open Access

    ARTICLE

    Evaluating Dying Efficiency and Energy Performance of a Hybrid Solar Dryer with Natural, Forced, and Hybrid Convection Modes for Tomatoes

    Sadaf Gul Unar1, Shoaib Ahmed Khatri1,*, Nayyar Hussain Mirjat1, Muhammad Faraz Arain1, Syed Rafay Ahmed Zaidi1, Laveet Kumar2

    Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 479-505, 2025, DOI:10.32604/fhmt.2025.063937 - 25 April 2025

    Abstract This research focuses on developing innovative hybrid solar dryers that combine solar Photovoltaic (PV) and solar thermal systems for sustainable food preservation in Pakistan, addressing the country’s pressing issues of high post-harvest losses and unreliable energy sources. The proposed active hybrid solar dryer features a drying cabinet, two Direct Current (DC) fans for forced convection, and a resistive heating element powered by a 180 W solar PV panel. An energy-storing battery ensures continuous supply to the auxiliaries during periods of low solar irradiance, poor weather conditions, or nighttime. Tomatoes, a delicate and in-demand crop, were… More >

  • Open Access

    ARTICLE

    Heat Transfer and Flow Dynamics of Ternary Hybrid Nanofluid over a Permeable Disk under Magnetic Field and Joule Heating Effects

    Umi Nadrah Hussein1, Najiyah Safwa Khashi’ie1,*, Norihan Md Arifin2, Ioan Pop3

    Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 383-395, 2025, DOI:10.32604/fhmt.2025.063023 - 25 April 2025

    Abstract This study investigates the heat transfer and flow dynamics of a ternary hybrid nanofluid comprising alumina, copper, and silica/titania nanoparticles dispersed in water. The analysis considers the effects of suction, magnetic field, and Joule heating over a permeable shrinking disk. A mathematical model is developed and converted to a system of differential equations using similarity transformation which then, solved numerically using the bvp4c solver in Matlab software. The study introduces a novel comparative analysis of alumina-copper-silica and alumina-copper-titania nanofluids, revealing distinct thermal conductivity behaviors and identifying critical suction values necessary for flow stabilization. Dual solutions… More >

  • Open Access

    ARTICLE

    Optimization and Intelligent Control in Hybrid Renewable Energy Systems Incorporating Solar and Biomass

    Arpita Johri1,2,*, Varnita Verma3, Mainak Basu1,*

    Energy Engineering, Vol.122, No.5, pp. 1887-1918, 2025, DOI:10.32604/ee.2025.062355 - 25 April 2025

    Abstract The globe faces an urgent need to close the energy demand-supply gap. Addressing this difficulty requires constructing a Hybrid Renewable Energy System (HRES), which has proven to be the most appropriate solution. HRES allows for integrating two or more renewable energy resources, successfully addressing the issue of intermittent availability of non-conventional energy resources. Optimization is critical for improving the HRES’s performance parameters during implementation. This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies. However, energy fluctuations present a problem with the power quality of HRES. To… More > Graphic Abstract

    Optimization and Intelligent Control in Hybrid Renewable Energy Systems Incorporating Solar and Biomass

  • Open Access

    ARTICLE

    Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning

    Steffen Schedler1,*, Michael Bareev-Rudy1, Stefanie Meilinger2, Tanja Clees1,3

    Energy Engineering, Vol.122, No.5, pp. 1755-1770, 2025, DOI:10.32604/ee.2025.061962 - 25 April 2025

    Abstract Germany aims to achieve a national climate-neutral energy system by 2045. The residential sector still accounts for 29% of end energy consumption, with 74% attributed to the direct use of fossil fuels for heating and hot water. In order to reduce fossil energy use in the household sector, great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity and heat. One possibility is to convert parts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of… More >

  • Open Access

    ARTICLE

    UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

    Suhaila Abuowaida1,*, Hamza Abu Owida2, Deema Mohammed Alsekait3,*, Nawaf Alshdaifat4, Diaa Salama AbdElminaam5,6, Mohammad Alshinwan4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3303-3333, 2025, DOI:10.32604/cmc.2025.063470 - 16 April 2025

    Abstract Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and… More >

  • Open Access

    ARTICLE

    HyTiFRec: Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation

    Dawei Qiu1, Peng Wu1,*, Xiaoming Zhang2,*, Renjie Xu3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1753-1769, 2025, DOI:10.32604/cmc.2025.062599 - 16 April 2025

    Abstract Recently, many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences. However, these methods tend to focus more on low-frequency information while neglecting high-frequency information, which makes them ineffective in balancing users’ long- and short-term preferences. At the same time, many methods overlook the potential of frequency domain methods, ignoring their efficiency in processing frequency information. To overcome this limitation, we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation, namely HyTiFRec. Specifically, we design two hybrid filter modules: the learnable… More >

  • Open Access

    ARTICLE

    A Category-Agnostic Hybrid Contrastive Learning Method for Few-Shot Point Cloud Object Detection

    Xuejing Li*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1667-1681, 2025, DOI:10.32604/cmc.2025.062161 - 16 April 2025

    Abstract Few-shot point cloud 3D object detection (FS3D) aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes. Due to imbalanced training data, existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes, which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects. To address these issues, this thesis proposes a… More >

  • Open Access

    ARTICLE

    Real-Time Proportional-Integral-Derivative (PID) Tuning Based on Back Propagation (BP) Neural Network for Intelligent Vehicle Motion Control

    Liang Zhou1, Qiyao Hu1,2,3,*, Xianlin Peng4,5, Qianlong Liu6

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2375-2401, 2025, DOI:10.32604/cmc.2025.061894 - 16 April 2025

    Abstract Over 1.3 million people die annually in traffic accidents, and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems. In modern industrial and technological applications and collaborative edge intelligence, control systems are crucial for ensuring efficiency and safety. However, deficiencies in these systems can lead to significant operational risks. This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control, particularly the limitations of traditional Proportional-Integral-Derivative (PID) controllers in managing nonlinear and time-varying dynamics, such as varying road conditions… More >

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