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

作者： Salam Al-E'mari Yousef Sanjalawe Fuad Fataftah 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 (UJ) Amman Jordan Mutlimedia systems and Virtual Reality School of Computer science Universiti Sains Malaysia (USM) Penang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper provides a comprehensive review of DeepDream neural networks or the Inceptionism technique, a type of deep learning model that has gained popularity due to its ability to generate visually captivating and surrealistic images. The review begins with an introduction to deep learning and its importance in image recognition and generation tasks. It then delves into the details of the DeepDream algorithm, including its architecture, training process, and mathematical foundations. The paper also discusses various applications of DeepDream, such as artistic image generation, style transfer, and image enhancement. Additionally, the review highlights some of the challenges and limitations of DeepDream networks, including issues with overfitting and computational resources. Finally, the paper concludes with a discussion of future directions for research in this area, including the exploration of more advanced architectures and the integration of DeepDream networks with other machine-learning techniques. Overall, this review serves as a valuable resource for researchers and practitioners interested in understanding the capabilities and limitations of DeepDream neural networks.

关键词： Deep learning Training Visualization Art Reviews Shape Computational modeling computer architecture Real-time systems Iterative methods

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Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling

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computers in Biology and Medicine 2025年第Pt B期192卷 110250-110250页

作者： Folorunso, Sakinat O. Abdulwarith, Akinshipo Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Artificial Intelligence Systems Research Group Department of Computer Science Olabisi Onabanjo University Ago-Iwoye Nigeria Department of Oral and Maxillofacial Pathology and Biology Faculty of Dentistry University of Lagos Lagos Nigeria Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MIVA Open University Abuja Nigeria Department of Information Technology Durban University of Technology KZ Durban South Africa Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Nigeria

Background: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection capabilities, imbalanced datasets, and computational inefficiencies. Methods: This study proposes a novel diagnostic framework TR-ROS-BAT-ML that integrates transfer learning, random sampling, and a BAT algorithm-based optimization strategy with ensemble machine learning classifiers. A dataset comprising 1224 hematoxylin and eosin (H&E)-stained histological images (at 100x and 400x magnifications) of normal oral epithelium and Oral Squamous Cell Carcinoma (OSCC) was collected from 230 patients using a Leica ICC50 HD microscopy camera. Pre-trained deep learning models (NANSNetLarge, EfficientNetB7, EfficientNetV2L, EfficientNetV2S, EfficientNetV2M) were employed for feature extraction. To address class imbalance, random oversampling techniques were applied. The BAT algorithm, inspired by bat echolocation behavior, was used for feature selection and hyperparameter tuning. Optimized features were classified using ensemble methods, including XGBoost, AdaBoost, Extra Trees (ET), Histogram-Based Gradient Boosting (HBGC), and MultiLayer Perceptron (MLP). Results: The proposed approach achieved high diagnostic performance across multiple model combinations. The best performance was recorded with the optimized ET model using random oversampling, achieving a recall of 0.992, demonstrating its efficacy in detecting oral lesions. In contrast, the combination of EfficientNetV2S + ROS + MLP yielded the lowest accuracy at 50.8 %. These results confirm the robustness of the TR-ROS-BAT-ML framework in handling imbalanced datasets and optimizing classification performance. Conclusions: This study demonstrates the effectiveness of combining nature-inspired optimization, transfer learning, and ensemble machine learning for enhanced detecti

关键词： Adaptive boosting

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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring

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IEEE Access 2025年 13卷 75100-75118页

作者： Kiouvrekis, Yiannis Panagiotakopoulos, Theodor Nousi, Efthymia Filippopoulos, Ioannis Ploussi, Agapi Spyratou, Ellas Efstathopoulos, Efstathios P. University of Thessaly Mathematics Computer Science and Artificial Intelligence Laboratory Faculty of Public and One Health Karditsa43100 Greece University of Limassol Department of Information Technologies Limassol3020 Cyprus University of Nicosia Business School Nicosia2417 Cyprus University of Patras Department of Management Science and Technology Patras26334 Greece Infralabs Ltd. Nicosia1107 Cyprus University of Limassol Technology And Innovation School Limassol3020 Cyprus National and Kapodistrian University of Athens Medical School Department of Applied Medical Physics Athens11527 Greece

The accurate characterization of signal propagation is critical for optimizing wireless network performance and supporting applications such as electromagnetic field (EMF) exposure assessment and the development of Radio Environmental Maps (REMs). This study proposes a novel, explainable machine learning system to predict electric field strength across diverse urban, semi urban, and rural environments in Cyprus. The system is trained on a rich dataset comprising 6,543 EMF measurements collected in 2023 at mobile phone and digital TV stations, following CEPT/ECC/REC/(02)04 recommendations. The dataset includes geospatial and environmental features such as antenna distance, population density, urbanization level, and detailed built environment characteristics (e.g., volume, surface, and height). We evaluate multiple machine learning models—kNN, neural networks, decision trees, random forests, XGBoost, and LightGBM—using a two-semester split for training and assessment. Best performance was achieved with the Random Forest model, which yielded the lowest RMSE among all models. Gradient boosting models (XGBoost and LightGBM) also performed well, with RMSE values slightly higher than RF while offering flexible and scalable configurations. In contrast, k-NN and neural networks showed higher RMSE values, indicating they were less effective for this specific task. Across all models, confidence intervals were narrow, demonstrating stable and reliable predictions. Explainable AI techniques revealed that antenna distance, building volume, and population density are the most influential predictors of EMF intensity. Our approach outperforms traditional signal models by incorporating urban morphology and demographic context. As part of this system, we also create a Geographic information System (GIS) that displays electromagnetic field strength maps derived from our explainable machine learning models. This contributes a scalable, interpretable framework for EMF exposure mapping t

关键词： Machine learning Predictive models Radio frequency Accuracy Electric fields Random forests Population density Explainable AI Estimation Buildings

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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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AI-IoT based smart agriculture pivot for plant diseases detection and treatment

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Scientific Reports 2025年第1期15卷 1-16页

作者： Ibrahim, Amin S. Mohsen, Saeed Selim, I.M. Alroobaea, Roobaea Alsafyani, Majed Baqasah, Abdullah M. Eassa, Mohamed Electronics and Communication Department Faculty of Engineering Ahram Canidian University (ACU) 6 October City Giza 12591 Egypt Department of Electronics and Communications Engineering Al-Madinah Higher Institute for Engineering and Technology Giza 12947 Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai El-Tor 46511 Egypt Faculty of Computer and Artificial Intelligence Sadat City University Sadat City Egypt Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P. O. Box 11099 Taif 21944 Saudi Arabia Department of Computer Science Faculty of Information Systems and Computer Science October 6 University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases and transforming the agriculture industry into robustness and ecologically conscious. Various IoT smart agriculture techniques are escalated in this field to solve these challenges such as drop irrigation, plant diseases detection, and pest detection. Several agriculture devices were installed to perform these techniques on the agriculture field such as drones and robotics but in expense of their limitations. This paper proposes an AI-IoT smart agriculture pivot as a good candidate for the plant diseases detection and treatment without the limitations of both drones and robotics. Thus, it presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops and controlling their actuators for the plant diseases treatment. For the plant diseases detection, the paper augments a dataset of 25,940 images to classify 11-classes of plant leaves using a pre-trained ResNet50 model, which scores the testing accuracy of 99.8%, compared to other traditional works. Experimentally, the F1-score, Recall, and Precision, for ResNet50 model were 99.91%, 99.92%, and 100%, respectively. © The Author(s) 2025.

关键词： Artificial intelligence (AI) Deep learning (DL) Internet of Things (IoT) Pivot Plant diseases detection Smart agriculture Unmanned aerial vehicle (UAV)

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Enhancing energy consumption forecasting for electric vehicle charging stations with Time Series Dense Encoder (TiDE)

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年 12卷

作者： Nazir, Amril Shaikh, Abdul Khalique Khan, Aftab Ahmed Shah, Abdul Salam Khalique, Nadia Department of Information Systems and Technology Management Zayed University Abu Dhabi 144534 United Arab Emirates Department of Information Systems Sultan Qaboos University Muscat Oman Department of Computer Science Karakoram International University Gilgit Pakistan School of Computer Science Faculty of Innovation and Technology Taylor's University Selangor Subang Jaya 47500 Malaysia

The increasing adoption of electric vehicles has led to the installation of charging stations in various locations in major cities worldwide. This study focuses on energy consumption forecasting for Boulder, Nevada, United States electric vehicle charging stations. Efficient management of energy resources at these charging points is crucial for optimizing resource utilization and reducing charging time. While existing literature has focused on energy consumption prediction in smart homes and grids, the significance of electric charging points in smart cities must be considered. The transformers have handled time series forecasting better with larger datasets like the Temporal Fusion Transformer and the Temporal Convolutional Network. However, they still need help with issues, specifically the higher computation cost and larger dataset for the training process. This research proposes a model that enhances energy forecasting for electric vehicles by employing outlier correction and a time series dense encoder as a forecasting technique. The model's novelty is the low computation cost, consideration of the data lagging, and day series past covariates. The model can be trained on limited data to maintain accuracy and effectiveness. Provides precise predictions of electric vehicle energy consumption based on data lagging, resulting in efficient resource management. This research significantly enhances the energy sector's efficiency and long-term viability by pioneering advanced forecasting models and methodologies. The performance evaluation, conducted using metrics like mean squared error and mean absolute deviation, unequivocally underscores the superior forecasting capabilities of the proposed model compared to the transformer-based models. © 2025 The Authors

关键词： Electric vehicle charging stations Energy consumption forecasting Smart cities Time series dense encoder

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Enhanced Glaucoma Classification Through Advanced Segmentation by Integrating Cup-to-Disc Ratio and Neuro-Retinal Rim Features

SSRN

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SSRN 2025年

作者： Pannu, Rabia Zubair, Muhammad Owais, Muhammad Hassan, Shoaib Umair, Muhammad Usman, Syed Muhammad Al-Bashrawi, Mousa Ahmed Hussain, Irfan Faculty of Information Technology and Computer Science University of Central Punjab Lahore Pakistan Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechanical Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad Pakistan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs such as mild redness in the eyes, slightly blurred vision, and occasional headaches or eye pain may go unnoticed, earning it the nickname "silent thief of sight." Its prevalence is increasing with an aging population, driven by advances in healthcare care and increased life expectancy. Most computer-aided diagnosis (CAD) systems rely on the cup-to-disc ratio (CDR) for the diagnosis of glaucoma. This study introduces a novel approach by integrating CDR with the neuro-retinal rim ratio (NRR), which quantifies rim thickness within the optic disc (OD). NRR improves diagnostic accuracy by recording additional head changes of the optic nerve, such as rim thinning and tissue loss, which were overlooked using CDR alone. We proposed a modified ResUNet architecture for OD and optic cup (OC) segmentation, combining residual learning and U-Net to capture spatial context for semantic segmentation. Its performance was evaluated on the DRISHTI-GS and RIM-ONE datasets. For OC segmentation, the model achieved Dice Coefficient (DC) scores of 0.942 and 0.872 and Intersection over Union (IoU) values of 0.891 and 0.773 for DRISHTI-GS and RIM-ONE, respectively. For OD segmentation, the model achieved DC of 0.972 and 0.950 and IoU values of 0.945 and 0.940 for DRISHTI-GS and RIM-ONE, respectively. CDR and NRR were calculated from segmentation masks and used to train an SVM with a radial basis function, classifying the eyes as healthy or glaucomatous. The model achieved accuracies of 0.969 on DRISHTI-GS and 0.977 on RIM-ONE. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling (G-S-M) approach

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computers in Biology and Medicine 2025年 187卷 109813-109813页

作者： Bakir-Gungor, Burcu Temiz, Mustafa Canakcimaksutoglu, Beyza Yousef, Malik Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Electrical and Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Zefat Academic College Israel

Colorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier Ltd

关键词： Colorectal cancer Human gut microbiome Machine learning Taxonomic biomarkers MicroBiomeGSM

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand

Negative Binomial Regression Model for Predicting the Number...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sthaporn Thepsumritporn Anuchit Jitpattanakul Sakorn Mekruksavanich Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article examines the use of count data models to predict the number of tourists visiting Thailand’s national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models— Poisson (POI), and Negative Binomial (NB)—were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data’s overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model’s utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data.

关键词： Natural resources Measurement Analytical models Accuracy Predictive models Data models Planning Resource management Dispersion

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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.

关键词： Electric vehicles cyber secuirty secuirty risk OCPP

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