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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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Advancing low-resource dialect identification: A hybrid cross-lingual model leveraging CAMeLBERT and FastText for Algerian Arabic

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Expert systems with Applications 2025年 284卷

作者： Chabane, Mafaza Harrag, Fouzi Shaalan, Khaled Departement of Computer Science University of Ferhat Abbas Setif Algeria Laboratory of Networks and Distributed Systems University of Ferhat Abbas Setif Algeria Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates

This study addresses the issue of dialect identification in low-resource contexts, specifically Algerian Arabic. To mitigate the lack of labeled data for these dialects, we propose an innovative hybrid model that utilizes cross-lingual transfer learning from Modern Standard Arabic (MSA), a high-resource language. Our hybrid model integrates pre-trained CAMeLBERT and FastText embeddings, both trained on MSA, to handle dialectal input via two parallel routes. The CAMeLBERT pathway generates profound contextual embeddings, whereas the FastText pathway utilizes a convolutional layer, a Bidirectional GRU, and an attention mechanism. The combined results are input into a multi-layer perceptron (MLP) for precise dialect classification. The novelty of our work lies in the strategic leveraging of MSA as a bridge language, enabling the transfer of its rich linguistic knowledge to low-resource dialects. The efficacy of our approach is confirmed through meticulous experimentation plus a comprehensive ablation study. The model's versatility is further evidenced by its testing across various datasets that reflect distinct dialects. Significantly, our model surpassed multiple current methodologies in these assessments. These findings highlight the capacity of cross-lingual transfer learning to improve Natural Language Processing (NLP) tasks for under-resourced languages by utilizing the established knowledge of their high-resource equivalents. © 2025 Elsevier Ltd

关键词： Sentiment analysis

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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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Addressing Non-stationarity with Stochastic Trend in the Context of Limited Time Series Data: An Experimental Survey

Research Square

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Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

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Tracking nonregulated micropollutants in sewage sludge: Antimicrobials, OH-PAHs, and microplastics — Environmental risks, fertilizer implications and energy considerations

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Energy Reports 2025年 13卷 4756-4768页

作者： Styszko, Katarzyna Bolesta, Wioleta Worek, Jagoda Prus, Zuzanna Cwynar, Karolina Pyssa, Justyna Uchmanowicz, Dominika Frydel, Laura Daso, Adegbenro Peter Kasprzyk-Hordern, Barbara AGH University of Krakow Department of Fuel Technology Faculty of Energy and Fuels A. Mickiewicza 30 Krakow30-059 Poland AGH University of Krakow Department of Heat Engineering and Environment Protection Faculty of Metals Engineering and Industrial Computer Science A. Mickiewicza 30 Kraków30-059 Poland University of Bath Department of Chemistry BathBA2 7AY United Kingdom University of Bath Research and Impact Services BathBA2 7AY United Kingdom Centre of Excellence in Water-Based Early Warning Systems for Health Protection University of Bath BathBA27AY United Kingdom

This study analysed the content of selected antimicrobials agents (AAs), microplastics (MP), hydroxyl derivatives of polycyclic aromatic hydrocarbons (OH-PAHs) in stabilized sewage sludge and fertilizers produced from them. Eighteen AAs were identified and quantified in both sewage sludge and fertilizer samples using the LC-MS/MS method. The highest concentrations, exceeding 3000 µg kg⁻¹, were found for sulfasalazine, clindamycin, ketoconazole and its deacetylated form, azithromycin, and desmethylated azithromycin. While the fertilizer production process successfully reduced the number of AAs present, 20 compounds persisted, with five exceeding 1000 µg kg⁻¹, posing potential environmental concerns. The FTIR method revealed an average MP content of 2429 ± 758 fractions in stabilized sewage sludge. Both black and colored microplastic fragments were detected, with an average of 1070 and 665 particles, respectively. These findings suggest that microplastic contamination remains an issue even after sewage sludge stabilization. GC-MS/MS analysis identified six OH-PAHs in sewage sludge and fertilizer samples. In stabilized sewage sludge, concentrations ranged from 53 µg kg⁻¹ (2-Hydroxyfluorene) to 587 µg kg⁻¹ (1-Hydroxynaphthalene), while in fertilizers, values ranged from 4.7 µg kg⁻¹ (2-Hydroxynaphthalene) to 31 µg pkg⁻¹ (1-HydroxyPyrene). The fertilizer production process effectively removed 46 % to 88 % of OH-PAHs, with 3-OH-BaP levels falling below detection limits. Despite the effectiveness of the fertilizer production process in reducing several contaminants (e.g., sulfamethoxazole, metronidazole, trimethoprim, pyrazinamide, sulfadiazine, delamanid, and piperacillin), certain pollutants, including clindamycin and ketoconazole, persisted. Additionally, the economic analysis of the annual profitability of processing sewage sludge into a fertilizer product was performed. The estimated costs and profits were taken into account. This analysis indicates that the total annu

关键词： Polycyclic aromatic hydrocarbons

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Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques

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Artificial Intelligence Review 2025年第8期58卷 1-41页

作者： Islam, Saidul Rjoub, Gaith Elmekki, Hanae Bentahar, Jamal Pedrycz, Witold Cohen, Robin Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Department of Computer Science 6 G Research Center Khalifa University Abu Dhabi United Arab Emirates Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Sariyer/Istanbul Turkey David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR), marking a paradigm shift from conventional, manually driven resuscitation practices to intelligent, data-driven interventions. It examines the evolution of CPR through the lens of predictive modeling, AI-enhanced devices, and real-time decision-making tools that collectively aim to improve resuscitation outcomes and survival rates. Unlike prior surveys that either focus solely on traditional CPR methods or offer general insights into ML applications in healthcare, this work provides a novel interdisciplinary synthesis tailored specifically to the domain of CPR. It presents a comprehensive taxonomy that classifies ML techniques into four key CPR-related tasks: rhythm analysis, outcome prediction, non-invasive blood pressure and chest compression modeling, and real-time detection of pulse and Return of Spontaneous Circulation (ROSC). The paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings. Furthermore, it highlights the role of eXplainable AI (XAI) in fostering clinical trust and adoption. By bridging the gap between resuscitation science and advanced ML techniques, this survey establishes a structured foundation for future research and practical innovation in ML-enhanced CPR. It offers clear insights, identifies unexplored opportunities, and sets a forward-looking research agenda identifying emerging trends and practical implementation challenges aiming to improve both the reliability and effectiveness of CPR in real-world emergencies. © The Author(s) 2025.

关键词： Reinforcement learning

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Cyber defense in OCPP for EV charging security risks

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International Journal of Information Security 2025年第3期24卷 1-25页

作者： Hamdare, Safa Brown, David J. Jha, Devki Nandan Aljaidi, Mohammad Cao, Yue Kumar, Sushil Kharel, Rupak Jugran, Manish Kaiwartya, Omprakash Department of Computer Science Nottingham Trent University Nottingham United Kingdom Department of Computing Newcastle University Newcastle United Kingdom Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom JMVL Limited London United Kingdom

The Open Charge Point Protocol (OCPP) is a widely adopted communication standard that enables vendor-independent communication between charging points and Electric Vehicle (EV) charging station management systems. OCPP has significant cyber risks in terms of weak authentication mechanisms and improper session handling, exposing it to potential EV charging-related security threats. The backward incompatibility of the recent version of OCPP also poses challenges in the seamless adoption of the protocol. This paper introduces a comprehensive cyber defense framework to mitigate the security risks associated with OCPP. Through a detailed analysis of its vulnerabilities, the framework proposes targeted enhancements and mitigation strategies to further strengthen its security. The results demonstrate that the proposed OCPP significantly enhances both security and performance, surpassing its predecessor and current state-of-the-art security solutions for EV charging.

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Individual Confidential Computing of Polynomials over Non-Uniform Information

arXiv

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arXiv 2025年

作者： Tarnopolsky, Saar Deng, Zirui Ramkumar, Vinayak Raviv, Netanel Cohen, Alejandro Faculty of ECE Technion Israel Department of Computer Science and Engineering Washington University in St. Louis St. Louis United States Department of Electrical Engineering–Systems Tel Aviv University Israel

In this paper, we address the problem of secure distributed computation in scenarios where user data is not uniformly distributed, extending existing frameworks that assume uniformity, an assumption that is challenging to enforce in data for computation. Motivated by the pervasive reliance on single service providers for data storage and computation, we propose a privacy-preserving scheme that achieves information-theoretic security guarantees for computing polynomials over non-uniform data distributions. Our framework builds upon the concept of perfect subset privacy and employs linear hashing techniques to transform non-uniform data into approximately uniform distributions, enabling robust and secure computation. We derive leakage bounds and demonstrate that information leakage of any subset of user data to untrusted service providers, i.e., not only to colluding workers but also (and more importantly) to the admin, remains negligible under the proposed scheme. © 2025, CC BY.

关键词： Information leakage

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs

Adaptive Quantum and Graph-Attentive Security Framework for ...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Anjum, Asma Rao, P. S. V. Srinivasa Sindhu, M. Bhatt, Pooja Priya, M.R. Maranan, Ramya HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Joginpally B R Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Post Limda Gujarat 391760 India NIE First Grade College Department of Computer Science Karnataka Mysuru570008 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems. © 2025 IEEE.

关键词： Advanced driver assistance systems

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