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An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs

computer Modeling in engineering & sciences 2025年第4期143卷 1129-1155页

作者： Majdi Rawashdeh Muath A.Obaidat Meryem Abouali Dhia Eddine Salhi Kutub Thakur Department of Business Information Technology Princess Sumaya University for TechnologyAmman11941Jordan Department of Computer Engineering Al Yamamah UniversityRiyadh11512Saudi Arabia Department of Computer Science John Jay College and the Graduate CenterThe City University of New YorkNew YorkNY 10019USA Department of Computer Science John Jay College at the City University of NewYorkNewYorkNY 10019USA LIMOSE Laboratory Department of Computer ScienceM’hamed Bougara UniversityBoumerdes35000Algeria Department of Professional Security Studies New Jersey City UniversityJersey CityNJ 07305USA

Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic *** 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death *** malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival *** study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological *** uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the *** addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and *** proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological *** study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.

关键词： Lung cancer machine learning computer aided diagnosis CNN medical imaging transfer learning

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Semi-Blind Channel Estimation and Achievable Rate Analysis for Uplink RIS-Enhanced Multi-User Networks

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular technology 2025年 6卷 1108-1139页

作者： Farghaly, Samar I. Ismail, Mohamed I. Fouda, Mostafa M. Alwakeel, Ahmed S. Tanta University Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta31111 Egypt Idaho State University Department of Electrical and Computer Engineering College of Science and Engineering PocatelloID83209 United States Modern Academy for Engineering Department of Electronic Engineering and Communication Technology Cairo Governorate 4411602 Egypt Center for Advanced Energy Studies Idaho FallsID83401 United States Suez University Department of Electrical Engineering Faculty of Engineering Suez43221 Egypt

Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35–40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks. © 2020 IEEE.

关键词： Channel state information

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

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computers, Materials & Continua 2025年第3期82卷 4181-4218页

作者： Haydar Abdulameer Marhoon Rafid Sagban Atheer Y.Oudah Saadaldeen Rashid Ahmed Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-Qar64011Iraq College of Computer Sciences and Information Technology University of KerbalaKarbala56001Iraq Enginerring Technical College Al-Ayen UniversityThi-Qar64011Iraq Information Technology College University of BabylonHilla51002Iraq Department of Computer Science College of Education for Pure ScienceUniversity ofThi-QarNasiriyah64001Iraq Artificial Intelligence Engineering Department College of EngineeringAl-Ayen UniversityThi-Qar64001Iraq Department of Computer Science Bayan UniversityErbilKurdistan44001Iraq

In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.

关键词： Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model

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High-Resolution FMCW Radar for Small UAV Detection Using GNU Software-Defined Radio

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IEEE Access 2025年 13卷 86396-86412页

作者： Janpangngern, P. Changnam, P. Charoensiri, W. Samroeng, S. Pakprom, J. Thongsopa, C. Intarapanich, A. Thosdeekoraphat, T. Santalunai, N. Santalunai, S. Suranaree University of Technology School of Electronic Engineering Institute of Engineering Nakhon Ratchasima 30000 Thailand National Science and Technology Development Agency (NSTDA) National Electronics and Computer Technology Center (NECTEC) Pathum Thani 12120 Thailand Rajamangala University of Technology Isan Faculty of Engineering and Technology Department of Telecommunication Engineering Nakhon Ratchasima 30000 Thailand

This article presents a threat detection system for Unmanned Aerial Vehicles (UAVs) designed to enhance operational security in high-risk environments. The system employs Frequency Modulated Continuous Wave (FMCW) radar, optimized to detect UAVs with a radar cross section (RCS) as small as 0.01 m², exemplified by models such as the DJI Phantom 3 and Phantom 4. Operating at 2.45 GHz, the radar achieves a maximum detection range of 300 meters and is implemented on the USRP B210 Software Defined Radio (SDR) platform, with real-time signal processing enabled through GNU Radio and integrated GPU resources. The transmission module includes a 100-watt Power Amplifier (PA) and dual-parabolic unidirectional antennas, while the receiver employs a Band-Pass Filter (BPF) and a Low Noise Amplifier (LNA) with a gain of 20 dB to improve sensitivity to reflected signals. The USRP B210 handles signal acquisition and processing, applying two-dimensional Fast Fourier Transform (FFT) techniques to extract doppler shift information. The processed data is visualized as a range-doppler map, facilitating effective target tracking. The system's modular architecture and parallel processing capability enable precise and efficient detection of low-RCS UAVs, even in cluttered environments. This work demonstrates a practical, cost-effective radar solution for security-sensitive applications, offering advantages in performance, adaptability, and real-time capability over traditional radar systems. © 2013 IEEE.

关键词： Frequency modulated continuous wave (FMCW) radar range-doppler map signal processing threat detection unmanned aerial vehicle (UAV)

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Agricultural consumer Internet of Things devices: Methods for optimizing data aggregation

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Alexandria engineering Journal 2025年 125卷 692-699页

作者： Mengash, Hanan Abdullah Mahgoub, Hany Alshuhail, Asma Darem, Abdulbasit A. Majdoubi, Jihen Yafoz, Ayman Alsini, Raed Alghushairy, Omar Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Engineering and Technology Unit Applied College Majmaah University Al Majmaah11952 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia

With the advent of state-of-the-art computer and digital technology, modern civilisation has been immensely facilitated and optimised. The Internet of Things (IoT) has grown in importance in recent years, allowing us to monitor our physical environments and broadening our horizons. The "practice, science, or art" of farming is defined as tending to land, growing crops with the use of different tools and techniques, and then selling the harvested food. If farmers optimise their operations with the help of a Wireless Sensor Network (WSN), they will be able to work much more efficiently and effectively. Data aggregation involves collecting information from multiple sensors. The data aggregation process is optimised by applying metaheuristic techniques. A Genetic Algorithm (GA) is a method for modelling evolution that uses mutation, crossover, and natural selection as its building blocks. The key benefit of the Artificial Bee Colony (ABC) approach is that it simultaneously considers local and global search, and it doesn't get trapped calculating its local minima. Naturalistic algorithms like ALO model their hunting behaviour after that of ant-lions and doodlebugs. It manages to find a happy medium between exploration and exploitation with just one operator. Experimental evidences show that the proposed metaheuristic technique, ABC-ALO, which combines the best elements of Artificial Bee Colony and Ant Lion Optimisation, is superior to existing metaheuristic approaches in terms of lifetime computation, or the number of alive nodes at different round counts. © 2025

关键词： Genetic algorithms

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Explainable hybrid forecasting model for a 4-node smart grid stability

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Energy Reports 2025年 13卷 4948-4961页

作者： Ghazal, Taher M. Hasan, Mohammad Kamrul Hassan, Rosilah Abdullah, Mustafa Islam, Shayla Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Electric vehicles engineering department Hourani Center for Applied Scientific Research Al-Ahliyyaesearch Al-Ahliyya Amman University Amman Jordan Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia University College Korea University Seoul02841 Korea Republic of

The 4-Node architecture for smart grids (SGs) distinguishes the operational features to enhance its stability and interruption less power distribution. This architecture contains 1 generator node and 3 consumption nodes to retain the stability of SGs. However, the chances of grid component failure, deficient power distribution, or peak demands result in the 4-node architecture imbalance. To forecast such imbalance towards SG stability, this paper exploits the explainable artificial intelligence (AI) and deep learning models to verify the existence of bias in the 4-node architecture due to the above flaws. For this purpose, in this paper, the component functioning rate and demand-to-distribution ratio are computed and verified to compute the bias. The deep learning model trains the explainable AI model for the potential flaws from the distribution intervals. Using the training using the deep learning model, the 4-node architecture's performance is evaluated to forecast the grid stability for future distributions. The 4-node architecture stability is verified under different loads, peak demands, and device performances using the integrated hybrid model with EAI. The proposed model improved the forecast accuracy by 14.53 % and reduced the loss in flaw detection by 13.41 % for the varying dissemination hours. © 2025 The Authors

关键词： Power grids

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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THE INCLUSION-EXCLUSION PRINCIPLE AND RECURRENCES FOR PARTITION NUMBERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 97-106页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania

The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted partition numbers as well as for overpartition numbers. In the case of overpartitions, POD partitions and 3-color partitions, we give combinatorial proofs for the Legendre type theorems and arrive to combinatorial proofs for the recurrence relations. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Combinatorial mathematics

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