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27th International Conference on Advanced communications Technology, ICACT 2025

作者： Su, Szu-Lin Huang, Nan-Hsiung Chen, Chao-Lin Lai, Kuei-Chiang Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9791188428137

With higher access capacity, non-orthogonal multiple access (NOMA) is a promising wireless access technology to meet the demands of massive machine-type communications (mMTC) and internet of things (IoT) applications. Focused on NOMAbased uplink multiple access transmission, this paper proposes data detection schemes that integrate sparse code multiple access (SCMA) and successive interference cancellation (SIC) techniques, termed the SCMA-SIC process. Simulation comparisons of various NOMA receiver designs, including hybrid-domain NOMA and iterative detection and decoding (IDD) for coded SCMA, show that the SCMA-SIC process achieves the best system performance. In addition, with power control strategies for uplink access, the SCMA-SIC process still performs the best. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Iterative decoding

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Using artificial neural networks and electromagnetic capacitive NDT sensors for wood engineering application

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HBRC Journal 2025年第1期21卷 211-233页

作者： Al-Mattarneh, Hashem Ismail, Rabah Trrad, Issam Nimer, Hamsa Khodier, Mohanad Jaradat, Yaser Malkawi, Ahmad B. Mohammed, Bashar S. Civil Engineering Department Yarmouk University Irbid Jordan Civil Engineering Department Jadara University Irbid Jordan Computer and Communication Engineering Department Jadara University Irbid Jordan Civil Engineering Department Al-Balqa Applied University Amman Jordan Civil Engineering Department Universiti Teknologi PETRONAS Perak Malaysia

Wood is a sustainable and renewable natural material with a wide variation in its mechanical and physical characteristics. Determining these properties can be challenging due to the complexity involved. Standard methods for measuring wood strength and characteristics are often destructive, expensive, and time-consuming. Therefore, developing a novel nondestructive method for wood property evaluation is crucial. This paper presents the development of an electromagnetic capacitive nondestructive sensor (ECNDTS) to evaluate wood’s physical and mechanical properties and detect defects based on its dielectric characteristics. Wood specimens of 10 mm thickness, representing 20 different wood species, were prepared and tested. Various physical and mechanical properties were investigated, including density, elasticity modulus, compressive and bending strengths, moisture content, and dielectric values. Artificial neural network (ANN) models, including the improved cascade neural network (CFNN) and the feedforward neural network (FFNN), were employed to enhance prediction accuracy. The results demonstrate that the developed ECNDTS can effectively predict wood properties based on measured permittivity values. The models for predicting moisture content, compressive strength, and flexural strength achieved a high coefficient of determination (greater than 90%). The measured permittivity value can also be used to detect wood defects and estimate their size. This study’s findings have the potential to improve wood strength grading methods by enabling automation and reducing human error associated with current manual methods or visual grading standards. The research suggests that combining CFNN and FFNN models with ECNDTS can significantly improve wood strength prediction (accuracy >98.5%) and enhance preliminary strength grading through artificial intelligence. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Permittivity

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Enhanced perceptual wavelet packet features for spontaneous Kannada sentence recognition under uncontrolled conditions

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International Journal of Speech Technology 2025年第1期28卷 153-174页

作者： Shanthamallappa, Mahadevaswamy Pradeep Kumar, B.P. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru Visvesvaraya Technological University Karnataka India Department of Computer Science and Design Atria Institute Of Technology Bengaluru Visvesvaraya Technological University Karnataka India

A Robust Automatic Speech Recognition (ASR) system is proposed through a hybrid combination of Perceptual Wavelet Packet features, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic models, and n-gram language models for recognizing Spontaneously spoken Kannada sentences. Two Experiments are performed on own Kannada speech corpus and the widely recognized TIMIT corpus. First one is the experiment on the clean speech datasets and the second one is on the speech degraded by noise data chosen from Noise-US dataset. The proposed system performance is evaluated through the metric Word Error Rate (WER). A comparative study on the performance analysis is presented. The results of the experimental investigations achieved an average reduction in Word Error Rate (WER) of 1.8% over Mel Frequency Cepstral Coefficients (MFCCs) and 2.1% over Perceptual Linear Prediction (PLP) features. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Hidden Markov models

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GateIDS: A Single Network Architecture for Attack Detection and Multi-Class Classification

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IEEE Access 2025年 13卷 75697-75706页

作者： Youm, Sungkwan Shin, Kwang-Seong Wonkwang University Department of Information and Communication Engineering Iksan54538 Korea Republic of Sunchon National University Department of Computer Engineering 255 Jungang-ro Jeollanam-do Suncheon-si57922 Korea Republic of

Network security faces escalating challenges from sophisticated cyber threats, necessitating advanced Intrusion Detection Systems (IDS) capable of both detecting attacks and classifying their patterns. This paper introduces GateIDS, a pioneering single-network architecture that integrates binary attack detection and multi-class attack pattern classification within a unified framework. Leveraging Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Transformer backbones, GateIDS employs a bifurcated design trained end-to-end to address these dual tasks efficiently. To mitigate class imbalance in the UNSW-NB15 dataset, a class-specific oversampling strategy is applied, enhancing the detection of minority attack types. Experimental results demonstrate that theDNNvariant achieves superior performance, with a binary classification accuracy of 98.30% and a pattern classification accuracy of 86.68%, alongside an inference time of 0.1752 seconds, outperforming LSTM (89.17%, 46.71%) and Transformer (90.63%, 45.79%) variants. This work marks the first unified dual-task IDS, offering computational efficiency and scalability over traditional separate-model approaches. GateIDS establishes a robust baseline for future IDS research, with potential enhancements through clustering and real-world deployment. © 2013 IEEE.

关键词： Network intrusion

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Network Traffic Matrix Estimation Based on Link Loads Sampling

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1524-1539页

作者： Sun, Wenyue Chen, Qian Song, Xuehua Bertino, Elisa Wang, Changda Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Yangzhou Polytechnic College School of Information Engineering Yangzhou225009 China Purdue University Department of Computer Science West LafayetteIN47907 United States

Traffic Matrix (TM) represents traffic between all Origin-Destination (OD) node pairs in a network, playing a crucial role in network management. While the methods for TM acquisition typically require scaling each link load in a network, measurement costs arise drastically as the number of links grows exponentially with the number of nodes. To address this issue while achieving TM with acceptable accuracy aligned with various network management requirements, the paper proposes two novel methods. The first is the ME-TMEIM (TM Estimation with Incomplete Measurement Based on Maximum Entropy) method, an efficient approach with yet acceptable accuracy. The second is the D-TMEIM (Dynamic TMEIM) method. Compared to the ME-TMEIM method, the D-TMEIM method trades the TM's acquisition accuracy for efficiency. It adds temporal constraints on network traffic to improve the precision of the obtained missing link loads, based on which the TM is generated using the CS-OMP (Compressed Sensing-Orthogonal Matching Pursuit) algorithm. Experimental results using publicly available Abilene and GÉANT networks demonstrate that the proposed methods not only enhance TM acquisition efficiency but also maintain nearly the same accuracy as the known methods that acquire TM through exhaustive links measurement. © 2013 IEEE.

关键词： Efficiency

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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks

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IEEE Access 2025年 13卷 74917-74929页

作者： Moaty, Ahmed Hamad, Ehab K. I. Hussein, Aziza I. Mourad Mabrook, M. Donkol, A.A. Nahda University Faculty of Engineering Electrical Engineering Dept Beni-Suef Egypt Aswan University Faculty of Engineering Electrical Engineering Department Aswan81542 Egypt Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia Faculty of Navigation Science & Space Technology Beni-Suef University Space communication Dept Egypt South Valley University Electrical Engineering Faculty of engineering Communication and electronics department Egypt

Millimeter-wave (mmWave) communication systems utilize narrow beamforming to ensure adequate signal power. However, beam alignment requires significant training overhead, especially in high-mobility scenarios. Previous research has utilized synthetic data for position-aided beam prediction, which does not fully capture real-world complexities. In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. In addition, besides showing significant power loss reduction in various scenarios, the proposed E-CNN model has demonstrated robustness regarding real-word conditions and adaptability for various beam setups. The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. This research fills the existing gap between the simulated aid beam alignment and real-world position beam aided alignment, which can be useful in improving beamforming in the upcoming wireless networks. © 2013 IEEE.

关键词： Convolutional neural networks

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VTAF-Ensemble: A Hybrid Vision Transformer and Attention-based Learning Approach for Enhanced Brain Tumor Detection and Classification 1

VTAF-Ensemble: A Hybrid Vision Transformer and Attention-bas...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Fathima, Mohammed Nazneen Singh, Prabhjot Singh, Simrandeep Chandigarh University Department of Electronic and Communication Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331529246

This work introduces a new methodology, Vision Transformer with Attention-based Feature Mapping and Optimized Ensemble Learning (VTAF-Ensemble), which includes three advanced techniques: Vision Transformer-Based Segmentation (VT-Seg), Attention-Based Feature Mapping (AF-Map) and Optimized Ensemble Learning (OE-Ensemble), to boost the brain tumor detection and classification from Magnetic Resonance Imaging (MRI) images. Traditional practice of identifying tumor is error prone, time consuming, hence it requires automation. This thesis proposes an integrated method, which includes VT Seg for accurate segmentation, AF Map for feature enhancement, and OE Ensemble for Ensemble classification using SVM and CNN classifiers. We show that VTAF-Ensemble achieves 92.1% Intersection over Union (IoU), 94.5% Mean Average Precision (mAP), 93.2% Dice Coefficient (DC) and 95% sensitivity in segmentation. Moreover, the model also yields better results on classification metrics compared to others: Area Under the Curve (AUC) of 95.2% and F1 Score of 92.9% and recall of 93%. Overall, this hybrid approach provides substantial accuracy improvements, scalability and utility for medical image applications as they relate to medical imaging applications, in particular, brain tumor specific diagnostic applications. © 2025 IEEE.

关键词： Magnetic resonance imaging

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