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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Waleed Awwad Hamzah Alqudah Mohammed Aladaileh Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba Amman Jordan Department of Computer Science Faculty of Information Technology University of Petra (UoP) Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper proposes a novel hybrid model for cloud intrusion detection, integrating transfer learning with bio-inspired optimization techniques to enhance detection accuracy and efficiency. Cloud environments present unique challenges due to their dynamic and shared-resource nature, making traditional Intrusion Detection Systems (IDS) inadequate. The proposed model leverages Convolutional Neural Networks (CNNs) for feature extraction, utilizing pre-trained models through transfer learning to reduce training time while maintaining high detection performance. In addition, bio-inspired optimization algorithms, specifically Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO), are employed to fine-tune the CNN's hyperparameters, such as learning rate and batch size, ensuring optimal performance. The hybrid approach addresses the limitations of manual tuning and improves the model's ability to generalize across different cloud environments. Using the CICIDS 2017 dataset, the proposed system demonstrates superior performance in detecting both known and unknown threats, including zero-day attacks. The experimental results show significant improvements in accuracy of 99.3%, precision of 98.7%, recall of 98.5%, and an F1-score of 98.6% compared to state-of-the-art models.

关键词： Training Accuracy Biological system modeling Heuristic algorithms Transfer learning Intrusion detection Convolutional neural networks Particle swarm optimization Optimization Tuning

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Design an Ensemble Pretrained Deep Learning Model for Classification of Melanoma Skin Cancer Images

Design an Ensemble Pretrained Deep Learning Model for Classi...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Dheeb Albashish Nedaa Almansour Azizi Abdullah Hossam M. J. Mustafa Manar Rizik AlSayyed Oraib Alrashdan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Center For Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Pathology Department University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The recent global outbreak of melanoma skin cancer has prompted increased attention toward computer-aided diagnosis systems (CAD). The primary application of CAD in melanoma is to discriminate between benign and malignant skin medical images. Additionally, the survival rate improves with early cancer detection. This study presents a CAD diagnosis method for melanoma skin diagnosis, which utilizes deep learning and medical image processing. To elaborate, the proposed model comprises two stages: Firstly, fine-tune three powerful pretrained models, namely VGG16, NASNetMobile, and ResNet50V2, to address the current task. Secondly, we leverage their decisions through ensemble learning and hard voting techniques. The evaluation of the proposed ensemble model on a skin cancer dataset yielded a notable accuracy of 91.5% on a test portion of the dataset. This method reveals the potential for diagnosing melanoma while also illustrating the efficacy of the ensemble model in augmenting diagnostic performance.

关键词： Deep learning Solid modeling Design automation Computational modeling Melanoma Skin Computer aided diagnosis Ensemble learning Medical diagnostic imaging Computational intelligence

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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation Systems with Neural Collaborative Filtering and Sentiment Analysis

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SN Computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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Big data-Enabled Federated Learning for Secure and Collaborative Industrial IoT in Industry 4.0

Big Data-Enabled Federated Learning for Secure and Collabora...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Hamzah Alqudah Hazem Bani Abdoh Fuad Fataftah Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Computer Science Department Faculty of Computer Science and Information Technology Jerash university Jerash Jordan Mutlimedia systems and Virtual Reality School of Computer science Universiti Sains Malaysia (USM) Penang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Integrating the Internet of Things (IoT), Artificial Intelligence and big data analytics in Industry 4.0 has revolutionized industrial processes, enabling enhanced operational efficiency, predictive maintenance, and innovation. However, the increasing volume of sensitive and decentralized data generated by Industrial IoT (IIoT) devices introduces significant challenges, including data fragmentation, privacy concerns, and interoper-ability issues. Traditional centralized data analysis methods often fail to address these challenges effectively. This paper proposes a novel privacy-preserving federated learning framework tailored for IIoT environments to bridge these gaps. The framework enables secure and decentralized distributed big data analysis while ensuring data sovereignty and minimizing communication overhead. The proposed approach enhances predictive maintenance and anomaly detection by integrating advanced deep learning models with edge-fog-cloud architectures, fostering cross-company collaboration and scalability. Experimental evaluations using real-world predictive maintenance datasets demonstrate the framework's effectiveness in achieving high accuracy, optimized resource utilization, and reduced runtime. Additionally, incor-porating clustering techniques improves model personalization, enhancing performance without compromising data privacy. This research establishes FL as a transformative solution for secure, collaborative intelligence in Industry 4.0 ecosystems, paving the way for sustainable and intelligent manufacturing environments.

关键词： data privacy Technological innovation Biological system modeling Scalability Collaboration Big data Fourth Industrial Revolution Optimization Industrial Internet of Things Predictive maintenance

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Siddiqi, Hafied Nur Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Technology PO Box 84428 Riyadh11671 Saudi Arabia Shenzhen University College of Computer Science & Software Engineering Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Prince Sultan University EIAS Data Science and Blockchain Laboratory CCIS Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a security mechanism that exploits the unique, unrepeatable physical characteristics of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The unique identification of IoT devices along the supply chain is implemented by using PUFs as tamper-resistant IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its potential to combat several attack scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing

关键词： Supply chain management

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An Enhanced Evolutionary Algorithm for Optimized Real-Time Scheduling in Cloud Computing

An Enhanced Evolutionary Algorithm for Optimized Real-Time S...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Hamzah Alqudah Mohammad A. Alsharaiah Waleed Awwad Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The exponential growth of cloud computing applications has posed significant challenges in achieving efficient resource management, reduced processing time, high through-put, and minimized energy consumption. Despite advancements, existing evolutionary algorithms often suffer from premature convergence, inefficient exploration-exploitation balance, and high energy overhead, particularly under heavy workloads. This paper proposes an enhanced hybrid Whale Optimization Algorithm (WOA) integrated with the Lévy Flight Mechanism for optimized resource management in cloud computing environments to address these challenges. The primary motivation for this work stems from the limitations observed in state-of-the-art algorithms. The research aims to bridge this gap by designing an evolutionary-based approach that improves resource utilization, reduces task execution delays, and optimizes energy consumption under varying workloads. The proposed model employs the WOA, enhanced with the Lévy Flight Mechanism, to ensure a more effective balance between exploration and exploitation during optimization. Extensive simulations were conducted on workloads ranging from 100 to 700 tasks, comparing the proposed method against leading algorithms. The results demonstrate that the proposed algorithm consistently outperforms the state-of-the-art techniques. Specifically, it achieves higher resource utilization (1.1 vs. 0.9), lower processing time, improved throughput (up to 500), and reduced energy consumption (50 joules compared to 70+ joules) under heavy workloads. These findings validate the enhanced WOA's effectiveness in addressing critical cloud computing optimization problems. The proposed approach provides a robust solution for dynamic resource allocation, ensuring energy efficiency and performance scalability, making it suitable for modern cloud-based environments with increasing computational demands.

关键词： Cloud computing Energy consumption Processor scheduling Scalability Evolutionary computation Throughput Whale optimization algorithms Real-time systems Resource management Optimization

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Understanding the spatio-temporal dynamics of ecosystem services under multiple future scenarios to assess the progress of Sustainable Development Goals implementation

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Journal of Geographical sciences 2025年第4期35卷 745-762页

作者： ZHANG Ze JIANG Weiguo LING Ziyan PENG Kaifeng WU Zhifeng LI Zhuo State Key Laboratory of Remote Sensing and Digital Earth Science Faculty of Geographical SciencesBeijing Normal UniversityBeijing 100875China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520ShandongChina College of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin 300387China School of Geography and Remote Sensing Guangzhou UniversityGuangzhou 510006China National Marine Data and Information Service Tianjin 300171China

Ecosystem services in urban agglomerations are the environmental conditions under which human survival and development are *** assessment of ecosystem services and complex interactions can contribute positively to the achievement of the Sustainable Development Goals(SDGs)for urban ***,studies on the future contribution of multi-scenario ecosystem services to the SDGS are *** pronovel integrated modeling framework that integrates the CLUES,InVEST,SOM,and GWR approaches to address the complex relationship between ecosystem services over a long“past-present-future”time *** construct a novel ecosystem service bundle-based approach for measuring urban agglomerations progress towards achieving ecologically relevant sustainable development goals at multiple *** the future scenario,the water yield(WY),habitat quality(HQ),and soil conservation(SC)show similar spatial patterns,with comparable spatial grids,while carbon stock(CS)remains predominantly unchanged and the ecological protection scenario(EPS)improves more *** high-synergy regions are mainly distributed in bundle 4,and most of the trade-off regions appear in bundles 1 and *** the last 30 years,all but the water-related SDGs are declining in bundle 1 of the two urban agglomerations,which are 15%higher in the Guangxi Beibu Gulf(GBG)than in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA).From 2020 to 2035,the three scenarios demonstrate that the optimization of the SDGs progresses most effectively under the future ecological protection scenario(EPS).In particular,bundles 3 and 4 are significantly *** critical new knowledge can be used in sustainable ecosystem management and decision-making in urban agglomerations.

关键词： Sustainable Development Goals(SDGs) ecosystem service bundle trade-offs and synergies InVEST model self-organizing mapping(SOM)algorithm urban agglomeration in south China

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Robust Adversarial Defense Based on Non-Transferability of Attack Across Foundation Models

Robust Adversarial Defense Based on Non-Transferability of A...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Koshiro Toishi Keisuke Maeda Ren Togo Takahiro Ogawa Miki Haseyama School of Engineering Hokkaido University Japan Research Emergence Department Data-Driven Interdisciplinary Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a novel adversarial defense method that exploits non-transferability of attack across foundation models. Existing adversarial training methods have insufficient robustness to adversarial examples under powerful adversarial attacks in a white-box setting. We clarify that there is no attack transferability between various foundation models through preliminary experiments and propose a defense method that combines the non-transferability with adversarial training. The proposed method constructs a common embedding space of different models and detects adversarial examples by leveraging the different outputs from each model. Based on the detection of adversarial examples, the proposed method can adaptively select a robust model depending on the input images based on non-transferability of attack between models. Experimental results show that the proposed method improves the accuracy of zero-shot class classification for adversarial examples against each model.

关键词： Training Adaptation models Accuracy Foundation models Signal processing Feature extraction Robustness Acoustics Speech processing Glass box

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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM

Design of a Smart Home Door Security System with Face Detect...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Nurhusna Salsabila Apri Siswanto Luhur Bayuaji Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security.

关键词： Software testing Face recognition System performance Smart homes Chatbots Real-time systems Software reliability Security Face detection Solenoids

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