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Deep Learning-Augmented Evolutionary Strategies for Intelligent Global Optimization

IEEE Access 2025年 13卷 70138-70178页

作者： Ezugwu, Absalom El-Shamir Oyelade, Olaide Nathaniel Agushaka, Jeffrey Ovre Saha, Apu Kumar Unit for Data Science and Computing North-West University Potchefstroom2520 South Africa School of Electronics Electrical Engineering and Computer Science Queen’s University BelfastBT9 5BN United Kingdom Department of Computer Science Federal University of Lafia Lafia950101 Nigeria Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India

This paper introduces the Susceptible-Infected-Removed Optimizer (SIRO), a novel learned heuristic inspired by biological systems and deep learning techniques. SIRO models its search process after the SIR epidemiological compartmental model, predicting the susceptibility, infection, and recovery dynamics of solutions. SIRO integrates deep learning into its initialization and parameter setting to enhance its efficiency, enabling intelligent and adaptive behavior. This hybridization improves solution quality, accelerates convergence, enhances robustness, and reduces computational costs. The algorithm’s performance was evaluated using CEC 2017 benchmark functions, demonstrating superior results in hybrid functions (C1-C28) despite moderate performance on traditional CEC1-CEC14 functions. Friedman’s test ranked SIRO 4th overall, with SSA as the top-performing algorithm. Additionally, SIRO was tested on real-world optimization problems, including mechanical engineering design, hyperparameter tuning, and feature selection for medical image classification. In the classification task, SIRO-enhanced CNN achieved an accuracy of 0.86 at the 5th epoch, outperforming CNN (0.66), CNN-GA (0.76), and CNN-WOA (0.75). Furthermore, SIRO reported a precision of 0.96, recall of 1.0, and F1-score of 0.98, highlighting its effectiveness. These results validate the benefits of integrating a learning mechanism into SIRO, yielding superior precision, computational efficiency, and performance over conventional optimization approaches. © 2013 IEEE.

关键词： Adversarial machine learning

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Synergistic effect of nitrocellulose coating on structural and reactivity stabilization of ammonium nitrate oxidizer

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Defence technology(防务技术) 2025年第1期43卷 35-43页

作者： Amir Abdelaziz Djalal Trache Ahmed Fouzi Tarchoun Hani Boukeciat Yash Pal Sourbh Thakur Weiqiang Pang Thomas M.Klapötke Energetic Materials Laboratory(EMLab) Teaching and Research Unit of Energetic ProcessesEcole Militaire PolytechniqueBP 17 Bordj El-BahriAlgiers 16046Algeria School of Aeronautical Science Hindustan Institute of Technology and ScienceChennai 603103India Department of Organic Chemistry Bioorganic Chemistry and BiotechnologySilesian University of TechnologyB.Krzywoustego 4Gliwice 44–100Poland Xi'an Modern Chemistry Research Institute 168 Zhangba Rd.EastXi'an 710065China Energetic Materials Research Department of ChemistryLudwig-Maximilian UniversityMunich Butenandtstrasse 5-13(D)Munich D-81377Germany

The present work aims to stabilize the room temperature allotropic transition of ammonium nitrate(AN)particles utilizing a microencapsulation technique,which involves solvent/non-solvent in which nitrocellulose(NC)has been employed as a coating *** SEM micrographs revealed distinct features of both pure AN and NC,contrasting with the irregular granular surface topography of the coated AN particles,demonstrating the adherence of NC on the AN *** analysis via infrared spectroscopy(IR)demonstrated a successful association of AN and NC,with slight shifts observed in IR bands indicating interfacial *** X-ray Diffraction(PXRD)analysis further elucidated the structural changes induced by the coating process,revealing that the NC coating altered the crystallization pattern of its pure *** analysis demonstrates distinct profiles for pure and coated AN,for which the coated sample exhibits a temperature increase and an enthalpy decrease of the room temperature allotropic transition by 6℃,and 36%,***,the presence of NC coating alters the intermolecular forces within the composite system,leading to a reduction in melting enthalpy of coated AN by~39%compared to pure *** thermal decomposition analysis shows a two-step thermolysis process for coated AN,with a significant increase in the released heat by about 78%accompanied by an increase in the activation barrier of NC and AN thermolysis,demonstrating a stabilized reactivity of the AN-NC *** findings highlight the synergistic effect of NC coating on AN particles,which contributed to a structural and reactive stabilization of both AN and NC,proving the potential application of NC-coated AN as a strategically advantageous oxidizer in composite solid propellant formulations.

关键词： Ammonium nitrate Nitrocellulose Stabilization Coating Thermolysis kinetics

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An automated stress detection model based on dual approach of clinical psychologist prediction and machine learning

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International Journal of Information technology (Singapore) 2025年第2期17卷 755-765页

作者： Narzary, Diptimoni Sharma, Uzzal Khanna, Ashish Department of Computer Applications Assam Don Bosco University Guwahati India Department of Computer Science Birangana Sati Sadhani Rajyik Viswavidyalaya Golaghat India Centre for Global Health Research AI & amp Health Unit Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India Maharaja Agrasen Institute of Technology GGSIPU Delhi India

The timely detection of stress has become increasingly difficult because of the growing number of stressed individuals and the lack of availability of diagnostic tools. Clinical psychology mainly relies on manual detection methods, which, although effective, depend on the experience and expertise of the doctor. To address this issue, the proposed research is undertaken where a system is proposed to be developed to assess stress levels using speech samples of the target individual. This study aimed to identify stress levels from recorded audio based on acoustic characteristics. Data from 800 voice recordings of 100 participants at the Lakshmibai National institute of Physical Education (LNIPE), North East Regional Center (NERC) Guwahati, were collected based on responses to eight questions prepared in consultation with the clinical psychiatrist. This is done so that, while answering the questions by any participant, the state of the mind of that individual is expressed. The model employs Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) with a Linear Support Vector Machine (SVM) classifier using the binary kernel, achieving an accuracy of 88%, marking a promising step towards automatic detection of stress. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Clinical psychiatrist LPCC Machine learning Mental state MFCC Stress SVM

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Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications

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Multimedia Tools and Applications 2025年第13期84卷 12299-12327页

作者： Singamaneni, Kranthi Kumar Yadav, Kusum Aledaily, Arwa N. Viriyasitavat, Wattana Dhiman, Gaurav Kaur, Amandeep Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology T.S Gandipet Hyderabad500075 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 173212 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India MEU Research Unit Middle East University Amman Jordan

Data security poses a significant challenge in cloud computing, the Internet of Things (IoT), and blockchain applications. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic solution for secure data sharing and access management in these contexts. This research article focuses on enhancing security in cloud, IoT, and blockchain applications through a comprehensive examination and comparison of various ABE standards. The study begins with an in-depth literature review on ABE requirements, offering insights into the evolving landscape, challenges, and trends. This review serves as the foundation for the research, which aims to compare ABE standards based on cryptographic techniques, encryption/decryption processes, and access control rules, considering criteria like flexibility, efficiency, scalability, and security. Statistical analyses complement the comparative study, generating empirical data on ABE standard performance. Key metrics, including key size, storage overhead, encryption/decryption time, and computational complexity, are considered to identify trends and draw meaningful conclusions. The research also explores use cases for different ABE models within blockchain, IoT, and cloud computing applications, pinpointing scenarios where specific ABE models excel. The study contributes to advancing access control and secure data-sharing technologies in specified domains, aiding researchers, practitioners, and developers in selecting the most appropriate encryption method. By systematically outlining advantages and disadvantages of various Cipher Text ABE standards, the research facilitates informed decision-making. Additionally, practical use cases ensure alignment between ABE models and the distinctive traits and requirements of each domain, expanding the toolset for enhancing data security in blockchain, IoT, and cloud computing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springe

关键词： Internet of things

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran Indian Institute of Information Technology Allahabad Computer Vision and Biometrics Lab Department of Information Technology Uttar Pradesh Prayagraj211015 India University of South Dakota AI Research Lab Department of Computer Science VermillionSD57069 United States Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly to image perturbations that humans can easily recognize. This weakness, often termed as 'attacks,' underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, CalTech256 and NWPU-RESISC45 focusing on image classification with 12 different NUI masks. The resilience of VGG, ResNet, MobilenetV3-small, InceptionV3 and efficientNet b0 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models' classification accuracy when subjected to NUI attacks, due to changes in the image pixel value distribution, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models' resilience against NUI attacks. A comparative study with other attack techniques shows the effectiveness of the NUI attack and defense technique. © 2020 IEEE.

关键词： Convolutional neural networks

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The Sustainability Component of the Brand and the Purchase Intention of Gen Z: State of the Art

The Sustainability Component of the Brand and the Purchase I...

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International Conference on Marketing and Technologies, ICMarkTech 2023

作者： Coutinho, Evelina Maria Oliveira Au-Yong-Oliveira, Manuel Department of Economics Management Industrial Engineering and Tourism Campus Universitário de Santiago University of Aveiro Aveiro3810-193 Portugal Research Unit On Governance Competitiveness and Public Policies Campus Universitário de Santiago University of Aveiro Aveiro3810-193 Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (纸本)9789819736973

In recent decades, the concept of sustainability has taken on a key role in the agendas of political decision-makers, businesses, and society. Brands have incorporated attributes associated with the concept of sustainability, making it essential to understand how these attributes are perceived and how they influence purchasing decisions. Young consumers tend to be well informed about social and environmental aspects and are more sensitive than previous generations to environmental issues. Although Gen Z’s purchasing intentions are influenced by the sustainable practices of organizations, their preferences are not fully understood. Recent studies have shown that the sustainability component associated with the brand influences Gen Z’s purchase intentions, and that social and economic aspects are at the root of this, emphasizing the importance of corporate social responsibility and transparent brand communication in terms of sustainability. Understanding how the sustainability component of brands influences the purchasing decisions of young consumers is fundamental for defining future branding strategies. The aim of this paper is to describe the state of the art of the relationship between the sustainability component of the brand and the purchase intention of Gen Z, based on the literature. The topic has not yet been widely studied, so it leaves clues for future development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sustainable development goals

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CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos

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IEEE Transactions on Image Processing 2025年 34卷 2351-2366页

作者： Liu, Yang Wang, Hongjin Wang, Zepu Zhu, Xiaoguang Liu, Jing Sun, Peng Tang, Rui Du, Jianwei Leung, Victor C. M. Song, Liang Fudan University Academy for Engineering and Technology Shanghai200433 China University of Toronto Department of Computer Science TorontoONM5S 1A1 Canada Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Duke Kunshan University Division of Natural and Applied Sciences Jiangsu Suzhou215316 China University of California at Davis DataLab: Data Science and Informatics DavisCA95616 United States The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Duke Kunshan University Division of Natural and Applied Sciences Suzhou215316 China Fudan University School of Information Science and Technology Shanghai200433 China China Mobile Chengdu Institute of Research and Development Chengdu610065 China

Video Anomaly Detection (VAD) remains a fundamental yet formidable task in the video understanding community, with promising applications in areas such as information forensics and public safety protection. Due to the rarity and diversity of anomalies, existing methods only use easily collected regular events to model the inherent normality of normal spatial-temporal patterns in an unsupervised manner. Although such methods have made significant progress benefiting from the development of deep learning, they attempt to model the statistical dependency between observable videos and semantic labels, which is a crude description of normality and lacks a systematic exploration of its underlying causal relationships. Previous studies have shown that existing unsupervised VAD models are incapable of label-independent data offsets (e.g., scene changes) in real-world scenarios and may fail to respond to light anomalies due to the overgeneralization of deep neural networks. Inspired by causality learning, we argue that there exist causal factors that can adequately generalize the prototypical patterns of regular events and present significant deviations when anomalous instances occur. In this regard, we propose Causal Representation Consistency Learning (CRCL) to implicitly mine potential scene-robust causal variable in unsupervised video normality learning. Specifically, building on the structural causal models, we propose scene-debiasing learning and causality-inspired normality learning to strip away entangled scene bias in deep representations and learn causal video normality, respectively. Extensive experiments on benchmarks validate the superiority of our method over conventional deep representation learning. Moreover, ablation studies and extension validation show that the CRCL can cope with label-independent biases in multi-scene settings and maintain stable performance with only limited training data available. © 1992-2012 IEEE.

关键词： Adversarial machine learning

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A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Aruna, Etikala Sahayadhas, Arun Kalpana, Ponugoti Khan, Surbhi B. Quasim, Tabrez Almusharrf, Ahlam VELS Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Pallavaram Chennai India AVN Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad501510 India University of Salford School of Science Engineering and Environment SalfordM5 4WT United Kingdom Chandigarh University University Centre for Research and Development Mohali India Chitkara University Centre for Research Impact and Outcome India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia King Khalid Univeersity Saudi Arabia

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0-based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models. © 2025 IEEE. All rights reserved.

关键词： Recurrent neural networks

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