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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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Design and Development of a Small-Scale Green Hydrogen Vehicle:Hydrogen Consumption Analysis under Varying Loads for Zero-Emission Transport

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Energy engineering 2025年第5期122卷 1788-1803页

作者： Perry Yang Tchie Hunn Hadi Nabipour Afrouzi Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuching93350SarawakMalaysia College of Engineering Faculty of ComputingEngineering and the Built EnvironmentBirmingham City UniversityBirminghamB47XGUK

With growing interest in its potential applications across both stationary and transportation sectors,hydrogen has emerged as a promising alternative for environmentally responsible power *** replacing traditional fuels,hydrogen can significantly reduce greenhouse gas emissions in the transportation *** study focuses on the design and downsizing of a green hydrogen fuel cell car,aiming to scale the concept for larger *** components,including fuel cells,electrolysers,and solar panels,were evaluated through extensive laboratory *** reveal that variations in sunlight impact the solar panel’shydrogenproductionrate,withdifferences of approximately 4.9%attributed to changes in time and *** of consumption rates showed that a 17.4%increase in current consumption leads to a significant reduction in operational *** testing under varying loads demonstrated that higher current demands,such as those from a DC motor,accelerate hydrogen depletion,whereas lower currents extend operational *** results underscore the importance of maximizing solar energy efficiency,reducing reliance on conventional energy sources,and regulating consumption rates to optimize fuel cell *** hydrogen is produced using renewable energy,fuel cell technology is virtually ***,the study highlights the viability of powering vehicles with renewable energy,emphasizing the potential of green hydrogen fuel cell technology as a sustainable transportation solution.

关键词： Environmentally sustainable small scale solar hydrogen fuel cell car

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SAR Analysis in an Anatomical Head Model Using CFL-Optimized Yee Cells and an Accurate Dipole Model at 700-MHz for 5G Mobile Radiation

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IEEE Access 2025年 13卷 82718-82731页

作者： Jariyanorawiss, Terapass Kanjanasit, Komsan Chongburee, Wachira Sornsungnoen, Nattapon Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok10900 Thailand Prince of Songkla University Phuket Campus College of Computing Phuket83120 Thailand

Electromagnetic radiation (EMR) exposure from fifth-generation (5G) mobile technology influences human health. This article proposes a numerical analysis to investigate the specific absorption rate (SAR) using an anatomical human head and accurate dipole model to examine the EMR effect at 700 MHz, a low-band frequency for 5G mobile communication. A three-dimensional (3D) digital model of a realistic human head is reconstructed using a series of magnetic resonance imaging (MRI) scans. This model encompasses various tissue types with their respective bio-material properties. The radiation from a mobile terminal is modeled using a dipole-based structure. In this study, the Unsplit-Field Finite-Difference Time-Domain (UF-FDTD) algorithm is modified for numerical analysis, with a perfectly matched layer (PML) serving as the boundary condition. The electromagnetic (EM) simulation is conducted to study the interaction between the two computational objects in the artificial absorbing environment. The Yee cell is utilized for a structured lattice in the UF-FDTD approach, characterized by its uniformity and consistent application throughout the model. The research problem involves correlating the size of the human head model with the unit cell size, which is associated with the average weight of the human brain for adults. The Yee cell is optimized based on the Courant-Friedrichs-Lewy (CFL) condition. This work enhances simulation accuracy twofold. First, the optimization of the unit cell size relative to the wavelength is refined in comparison to other unit cells without a phase error. This optimization leads to an accurate time-step parameter via the CFL criterion, ensuring the stability of the computer simulation. Second, the optimized grid structure of the UF-FDTD algorithm closely approximates the infinitesimal feeding gap, ensuring precise alignment with the exact dimensions of the feed-gap point in the dipole antenna configuration and accurately reflecting the effects

关键词： Antenna radiation

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kola, Sai Sindhu Kenchi, Harsha Rama, P. Angayarkanni, V. Kalaivani, J. Sivakumar, S. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India Srm Institute of Science and Technology Computing TechnologiesCollege of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Faculty of Engineering and Technology Srmist Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331529833

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based. Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting. © 2025 IEEE.

关键词： Face recognition

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system's capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system's superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management. © 2025 IEEE.

关键词： Environmental monitoring

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Vessel Registration Number Detection and Recognition System

Vessel Registration Number Detection and Recognition System

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Fabijanić, Matej Magdalenić, Maja Obradović, Juraj Kapetanović, Nadir Ferreira, Fausto Mišković, Nikola University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9798331536626

Accurate identification of ships is vital for global trade and maritime security. More specifically, reliable recognition of the vessel registration number helps in orderly navigation and port use for maritime transportation activities. The purpose of this research was to develop and analyze different methods for detecting vessel registration numbers with a monocular camera. We used existing OCR tools available online and modified them to improve their out-of-the-box performance. Three neural network-based text detection methods were developed and tested. All three methods use different text region detection modules for real scenes, while available OCR software is used in all of them for specific character recognition. The methods are compared using standard optical character recognition metrics. The best method that was developed uses a deep neural network model to detect a vessel, then another fine-tuned neural network model to detect a text region, coupled with deterministic image processing methods to improve picture quality. In conclusion, with an accuracy of 72.69% recognized characters of vessel registrations using a limited image dataset, we achieve satisfactory results. Further effort focusing on collecting and annotating diverse maritime environment datasets featuring footage of vessels with visible registration numbers is needed to have a more reliable and robust system. © 2025 IEEE.

关键词： Optical character recognition

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A systematic review on WebRTC for potential applications and challenges beyond audio video streaming

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Multimedia Tools and Applications 2025年第6期84卷 2909-2946页

作者： Mahmoud, Haitham Abozariba, Raouf Faculty of Computing Engineering and Built Environment Steamhouse Belmont Row BirminghamB4 7RQ United Kingdom

Video conferencing and live streaming are being used in various industries, such as healthcare, gaming, telecommunication, manufacturing and others. As technology progresses, the need for real-time data transmission with minimal latency has increased. Web Real-Time Communication (WebRTC) addresses this need effectively. WebRTC is a technology designed to provide real-time communication through web and mobile browsers. Its low latency and P2P communication capabilities make it a convenient technology for secure, efficient communication in real-time applications. This paper reviews the key features of WebRTC, discusses its strengths and weaknesses and investigates a detailed analysis of 83 existing studies. Moreover, It evaluates all use cases that can be adopted by WebRTC by examining their descriptions, problem statements, and research gaps based on literature to date. Finally, It highlights the open research directions for the emerging technologies and enhancements of WebRTC. to identify their potential applications. © The Author(s) 2024.

关键词： Video streaming

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Unveiling the mechanism of selective CO_(2)hydrogenation to CO on amorphous black TiO_(X)in thermal and photo-assisted thermal catalysis:The role of defects in amorphous structure and light irradiation

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Nano Research 2025年第3期18卷 106-122页

作者： Mohammad Fereidooni Mohammad Yazdanpanah Victor Márquez C.V.Paz Martin Salazar Villanueva Supareak Praserthdam Piyasan Praserthdam Center of Excellence on Catalysis and Catalytic Reaction Engineering Department of Chemical EngineeringFaculty of EngineeringChulalongkorn UniversityBangkok 10330Thailand Facultad de Ingeniería Benemerita Universidad Autonoma de PueblaApartado Postal J-39CP 72570 PueblaMexico High-Performance Computing Unit(CECC-HCU) Department of Chemical EngineeringFaculty of EngineeringChulalongkorn UniversityBangkok 10330Thailand

Developing catalysts capable of efficiently utilizing both visible and near-infrared wavelengths of the solar spectrum for CO_(2)hydrogenation has led to growing interest in reduced TiO_(2)*** efficient long-wavelength solar light harvesting requires a high concentration of oxygen vacancies(O_(V)).However,extensive O_(V)formation can lead to atomic rearrangements within TiO_(X),causing a dispersion of O_(V)throughout the material,as opposed to creating localized and distinct O_(V)sites typical of crystalline TiO_(X),which interact directly with *** this study,we synthesized amorphous black TiO_(X)(AM-TiO_(X))catalysts and thoroughly characterized their surface properties,including acidity and the desorption bond strengths of H_(2)and CO_(2).Density functional theory(DFT)simulations were performed to analyze the hydrogen adsorption profile and structural changes in the material due to O_(V)*** found that hydrogen mobility on the surface is restricted due to strong hydrogen *** CO_(2)hydrogenation process was investigated using in situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS),enabling the development of a reaction pathway to elucidate the catalyst’s selectivity towards CO and the effect of light irradiation on product formation ***,m-HCO_(3)^(*)formation was favored,with CO and CH_(4)production proceeding primarily via the formate *** enhance catalyst stability against oxidation during reaction,the surface was decorated with Ru *** findings of this study are relevant to catalysts that leverage extensive O_(V)formation as a strategy to extend light responsiveness,as well as to the design of catalysts for CO_(2)hydrogenation to CO.

关键词： oxygen vacancy amorphous catalytic CO_(2)hydrogenation hydrogen mobility photo-thermal reaction mechanism analysis

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges

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IEEE Access 2025年 13卷 84357-84379页

作者： Dechsupa, Chanon Panboonyuen, Teerapong Vatanawood, Wiwat Padungweang, Praisan So-In, Chakchai Khon Kaen University College of Computing Department of Computer Science Khon Kaen400002 Thailand Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok10300 Thailand

State-space graphs and automata serve as fundamental tools for modeling and analyzing the behavior of computational systems. Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide the foundational architecture that enables language models to process sequential data, capture contextual dependencies, and improve natural language processing tasks. Both RNNs and state-space graphs help evaluate discrete-time systems within this formal framework. However, the basic question of their equivalence remains an open challenge regarding the models governing sentence structure in natural language and the formal model in automata theory. In this paper, we present ENGRU (Enhanced Gated Recurrent Units), a novel deep learning-based approach for formal verification. ENGRU integrates concepts of model checking techniques, Colored Petri Nets (CPNs), and sequential learning to analyze systems at a high level of abstraction. CPN models undergo state-space enumeration via a model-checking tool, generating a state-space graph and an automaton from inherent state transition patterns. These graphs are transformed into sequential representations as sub-paths, enabling ENGRU to learn the execution paths and predict system behaviors. ENGRU effectively predicts goal states within discrete-time models by the competency of gated recurrent mechanisms, encouraging early bug detection and allowing predictive state-space exploration. Experimental results demonstrate that ENGRU’s ability achieves high accuracy and efficiency in goal state predictions. © 2013 IEEE.

关键词： Model checking

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