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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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A Novel Dual-Band Printed SIW Antenna Design Based on Fishnet & CCRR DGS Using Machine Learning for Ku-Band Applications

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Progress In Electromagnetics Research C 2021年 116卷 207-219页

作者： Nakmouche, Mohammed F. Magray, Muhammad I. Allam, Abdemegeed M. Fawzy, Diaa E. Lin, Ding Bing Tarng, Jenn-Hwen Faculty of Engineering Izmir University of Economics Izmir Turkey Hsinchu City Taiwan Department of Communication Engineering German University in Cairo Cairo Egypt Department of Electronics and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

—This paper analyzes and solves the complexity to determine the optimum positions of the Fishnet & Complementary Circular Ring Resonator (CCRR) based Defected Ground Structures (DGS) for Substrate Integrated Waveguide (SIW) based antennas. A new state-of-art technique based on Artificial Neural Network (ANN)-Machine Learning (ML) is proposed for overcoming the lack of solid and standard formulations for the computation of this parameter related to a targeted frequency. As a proof of concept and to test the performance of our approach, the algorithm is applied for the determination of the CCRR and Fishnet-DGS’s optimal positions for a SIW based antenna. The SIW technique provides the advantages of low cost, small size, and convenient integration with planar circuits. The ANN-ML based technique is optimized to attain dual-band resonances with optimal gain and radiation efficiency. The simulation results of the first Fishnet-DGS based antenna show good minimum return losses at two center frequencies, namely, 16.6 GHz (with gain of 6 dB and radiation efficiency of 95%) and 17.7 GHz (with gain and radiation efficiency of 9 dB and 96%, respectively). The second CCRR-DGS based antenna shows about 8 dB gain and a radiation efficiency of 87% at 17.3 GHz, and gain and efficiency of about 8.5 dB and 85% are observed at 17.8 GHz. The proposed CCRR and Fishnet-DGS based antenna are low profiles, low costs, with good gains and radiation efficiencies, making both designs very suitable for Ku-band applications. There is a fair agreement between the measured and simulated results. The achieved dual-band resonances act as a proof of concept that the proposed ANN-ML techniques can be employed for the determination of the optimal positions for CCRR and Fishnet thereby attaining any target dual-bands in the Ku-band with good accuracy of about 98% and a save of 99% in the overall the computational time. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Radiation efficiency

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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Svm-pca based handwritten devanagari digit character recognition

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Recent Advances in computer science and communications 2021年第1期14卷 48-53页

作者： Khamparia, Aditya Singh, Sanjay K. Luhach, Ashish K. Department of Computer Science and Engineering Faculty of Technology and Sciences Lovely Professional Universi-ty Phagwara Punjab India Department of Electrical and Communication Engineering Faculty of Technology and Sciences The PNG University of Technology MorobePapua New Guinea Australia

Objective: The blended fusion of Support Vector Machine (SVM) and Principal Component Analysis (PCA) have been widely used in recognizing handwritten digit characters of Devanagari script. The feature information from the character is extracted using its skeleton structure which optimally reduce data dimensionality using PCA. There is ample information available on handwritten charac-ter recognition on Indian and Non-Indian scripts but very few article emphasized on recognition of Devanagari scripts. Therefore, this paper presents an efficient handwritten Devanagari character recognition system based on block based feature extraction and PCA-SVM classifier. Methods: We have collected samples of handwritten Devanagari characters from different handwritten experts for classification. Results: For experimental work, total of 100 images having Devanagari digit characters been used for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 96.6 % and 96.5% for 5 & 10 fold validations with 70% training and 30% testing data using block based feature and SVM classifier having different kernels. Conclusion: The obtained results achieve maximum accuracy using SVM classifier for digit character recognition. In future deep learning networks will be considered for accuracy enhancement and precision. © 2021 Bentham science Publishers.

关键词： Principal component analysis

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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Coronary Artery Disease Prediction Using Enhanced Multi Layer DCNN 10

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Sharma, Smita Mittal, Ruchi Rao, A. Kakoli Martin, R. John Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Department of Electronics and Communication Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Jazan University Faculty of Computer Science and Information Technology Saudi Arabia Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced Multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process. © 2023 IEEE.

关键词： Coronary Artery Disease deep learning disease prediction Enhanced Multi-Layer Deep Convolutional Neural Network

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm 2

Impact of Image Enhancement for Osteoporosis Detection Based...

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2nd International Conference on computer system, Information Technology, and Electrical engineering, COSITE 2023

作者： Iman, Riza Dianil Roslidar, Roslidar Muchtar, Kahlil Amalia, Amalia Syaryadhi, Mohammad Mufti, Alfatirta Maulina, Novi Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798350343069

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an F1-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance. © 2023 IEEE.

关键词： Image enhancement

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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Recent Advances in Electrical, Electronics, Ubiquitous communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： T Thamaraimanalan C Venkatesan M Ramkumar A Sivaramakrishnan M Marimuthu Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Business System Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra pradesh India Department of Computer Science and Engineering Sona College of Technology Salem India

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection.

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An Efficient Attack Detection Framework in Software-Defined Networking using Intelligent Techniques

An Efficient Attack Detection Framework in Software-Defined ...

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Electronic engineering (ICEEM), International Conference on

作者： Heba A. Hassan Ezz El-Din Hemdan Mona Shokair Fathi E. Abd El-Samie Walid El-Shafai Department of Electronics and Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science Security Engineering Laboratory Prince Sultan University Riyadh Saudi Arabia

Recently, Software-Defined Networking (SDN) architecture has offered great benefits due to the separation between the control and network elements such as routers and switches. Unfortunately, the enormous growth of attacks hinders the wide adoption of SDN. Intrusion Detection systems (IDSs) are used as significant tools to detect and mitigate network attacks of anomalous nature. Several Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been utilized for building IDSs in cyber security in recent years, because they achieve the desired detection results. Nonetheless, little work has been introduced on IDSs in SDN systems. Hence, this paper presents an efficient framework based on intelligent techniques for predicting attacks in SDN systems. This work focuses on the early detection of abnormal attacks existing in an SDN environment. When malicious traffic is identified in an SDN topology, the Artificial Intelligence (AI) module employs Machine Learning (ML) or DL models to identify and stop the attack source. The architecture presented in this research allows for comparing several ML and DL classification techniques that can be used to identify different sorts of network attacks. The proposed framework is tested on the InSDN dataset using several learning models like Random Forest (RF), AdaBoost (AB), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Logistic Regression (LR) classifiers. In addition, DL models such as Deep CNN and Long Short Term Memory (LSTM) are considered. The results demonstrate that the proposed Deep CNN model for multi-class attack data classification achieves the highest accuracy of 99.85% compared to RF, AB, KNN, NB, DT, LR, and LSTM classifiers with accuracy levels of 95%, 88 %, 97%, 93 %, 90 %, 98%, and 88.31 %, respectively.

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A track donation system using blockchain 2

A track donation system using blockchain

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Shaheen, Eisa Hamed, Mohamed Abdl Zaghloul, Walaa Al Mostafa, Eman El Sharkawy, Amr Mahmoud, Asmaa Labeb, Amira Al Enany, Marwa O Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Charities face a difficult funding environment, mainly because charities are not highly transparent. Day over day It became more difficult to know whether donations are reaching the right place, or taking another course, such as financing suspicious campaigns, terrorist projects, etc. As a result, donors lose faith in these organizations, leading to a halt to interactions with charitable organizations. This makes it more difficult for charities to raise money at a great cost. This research proposed a solution to the mentioned problem using Blockchain technology, a decentralized database that provides security, transparency, and lower funding cost by eliminating the presence of third parties between donors and charities. A new track donation model was proposed which introduced several new participants that control the process of donation and remove suspicion of the charity. All donations can be tracked in Blockchain, allowing donors to know where and how their money is being used. The proposed model was implemented using Hyperledger composer then it was tested to obtain a live full track of a donation process. All donations are made using smart contracts, which allow donors to know exactly when and how their donations will be received. The Blockchain track donation system results in time-saving, reducing the cost of donation, and reduces the risk of reaching suspicious campaign donations or terrorist projects. A website was created as a facilitator for users for ease of use of the proposed system. © 2021 IEEE.

关键词： Smart contract

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