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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Nassir, Jana Alasabi, Majeda Qaisar, Saeed Mian Khan, Muzammil Effat University Computer Science Department Jeddah22332 Saudi Arabia Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia Cesi Engineering School Lineact Laboratory Villeurbanne69100 France University of Swat Department of Computer and Software Technology Swat Pakistan

ISBN: (纸本)9798350347050

Epileptic seizures affect millions of people worldwide. Medical treatments exist to help lessen the severity of the damage caused by these seizures. However, people with epilepsy still struggle with unexpected seizures. People who experience epileptic seizures have Electroencephalogram (EEG) signals that show different features in comparison to a healthy brain. In this study, EEG signals are studied to detect the seizures. The incoming signals are denoised by using linear phase filters. In next step these are divided in fix-length segments. Then, each segment is broken down using the Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs). For an automatic identification of EEG signals, features are extracted from the collected IMFs and then processed using machine learning techniques. A dataset on epilepsy that is available to the public is used for evaluation. To determine which is the best predictor for the under-consideration dataset, four different classification methods are performed and the results are examined. The system achieves a classification accuracy of 96.70%. © 2023 IEEE.

关键词： Empirical mode decomposition

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Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： Classification algorithm human action recognition motion sensors machine learning Masi entropy

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Ensemble Learning for Offline Signature Verification using Fused Deep Features

Ensemble Learning for Offline Signature Verification using F...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Sara Tehsin Ali Hassan Farhan Riaz Department of Computer and Software Engineering NUST Islamabad Pakistan Deparment of Computer Engineering HITEC university Taxila

Signature verification plays an important role in document authentication. The efficient and robust system is required for forgery detection in documents in the presence of distortion such as rotation, scaling and noise. This paper proposes a writer-dependent signature verification method, where hand-crafted features are fused with one-dimensional convolutional neural network features to cater different variance in the whole verification process. Proposed feature extraction methodology is validated by bagged ensemble learning technique in which machine learning classifier’s decision is finalized based on majority voting. The effectiveness of the proposed signature verification approach has been evaluated across three distinct publicly accessible datasets. The outcomes highlight the versatility of this method. Moreover, the proposed system surpasses the performance of other pre-existing systems documented in the literature.

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A Two-stage Anomaly Detection System for Fog Computing Services 5

A Two-stage Anomaly Detection System for Fog Computing Servi...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ogundoyin, Sunday Oyinlola Adeniyi Kamil, Ismaila Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Bowen University Electrical and Electronic Engineering Programme Iwo Nigeria University of Ibadan Department of Electrical and Electronic Engineering Ibadan Nigeria Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bando40601 Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai602105 India

ISBN: (纸本)9798331542559

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10-5. The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively. © 2024 IEEE.

关键词： Fog computing

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Weber Law Based Approach for Multi-Class Image Forgery Detection

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computers, Materials & Continua 2024年第1期78卷 145-166页

作者： Arslan Akram Javed Rashid Arfan Jaffar Fahima Hajjej Waseem Iqbal Nadeem Sarwar Department of Computer Science Superior UniversityLahore54000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science Bahria UniversityLahore CampusLahore54600Pakistan

Today’s forensic science introduces a new research area for digital image analysis formultimedia ***,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered *** forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing ***,an image can contain one or more types of *** study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial ***,images are divided into equal blocks to get scale-invariant *** law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing *** proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and ***(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and *** average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

关键词： Copy-Move and splicing non-overlapping block division texture features weber law spatial domain xgboost

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Optimized digital watermarking: Harnessing the synergies of Schur matrix factorization, DCT, and DWT for superior image ownership proofing

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Multimedia Tools and Applications 2025年第18期84卷 19817-19852页

作者： Al-Aiash, Issa Alquran, Rabee AlJamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Al-Fraihat, Dimah Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

The contemporary context of abundant digital dissemination inherently gives rise to the need for media protection and the clear identification of ownership rights. This paper responded to this nagging issue of unauthorized media capture by developing a new digital watermarking approach uniting the Discrete Wavelet Transform DWT, Discrete Cosine Transform DCT, and Schur matrix factorization. The proposed watermark, which was a positive vector uniformly distributed, presented a high degree of robustness to several adversarial operating conditions, including histogram equalization, salt and pepper noise, ripple impact, smoothing attack, and cropping. The obtained results reveal the effectiveness of our approach. The correlation coefficient between watermarked and original images remains exceptionally high at 0.999998, ensuring minimal visual distortion. Mean Squared Error (MSE) graphs for both high-frequency (HH) and low-frequency (LL) domains demonstrate negligible changes after attacks, confirming the watermark's robustness. Notably, the watermark extraction process remains successful even after histogram equalization, as indicated by significantly lower MSE compared to random vectors. Moreover, the average processing times for different attacks are promising;Histogram Equalization and Salt and Pepper attacks each took 0.3 s, Ripple Attack required 0.28 s, Smoothing Attack completed in 0.29 s, and the Cropping Attack took 0.6 s. The robustness of our approach appears against the salt and pepper attack, which historically presented a vulnerability when employing Schur matrix factorization in image watermarking, indicating the novelty of and this contribution to improving media ownership verification. By combining disparate transformation methodologies, this technique offers a promising avenue for reinforcing the integrity and authenticity of digital media, thereby strengthening the foundations of intellectual property rights in the digital domain. © The Author(s), und

关键词： Discrete wavelet transforms

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Assessing Customer Satisfaction and Loyalty in the Energy Sector Through Service Quality: A Modified SERVQUAL Analysis 24th

Assessing Customer Satisfaction and Loyalty in the Energy Se...

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24th International Symposium for Production Research, ISPR 2024

作者： Çiçeklidağ, Paşa Nebati, Emine Elif Gülen, Kemal Güven Zaim, Selim Computer Science and Software Engineer The University of Western Australia Perth Australia Department of Industrial Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Industrial Engineering Tekirdag Namık Kemal University Tekirdag Turkey Department of Management Ibn Haldun University Istanbul Turkey

ISBN: (纸本)9783031836107

This study examines the impact of service quality on customer satisfaction and loyalty in the energy sector using the SERVQUAL model. Key dimensions such as reliability, empathy, and physical features were found to significantly influence customer satisfaction. The research involved an online survey, with data analyzed using Structural Equation Modeling (SEM) to identify both direct and indirect effects of service quality dimensions on customer satisfaction and loyalty. Results show that improving reliability and empathy can notably enhance customer satisfaction, leading to stronger customer loyalty. However, the study acknowledges limitations, such as the generalizability of results due to the online nature of the survey and the study's focus on a specific geographical region. The use of verbal data may also introduce bias, which future research could address by applying more detailed data collection methods and examining sub-factors. The study concludes that energy companies should refine their service strategies to better meet customer expectations, suggesting further research in different regions and sectors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Customer satisfaction

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Comparative Analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer Models in Predicting Stock Prices 5

Comparative Analysis of Long Short-Term Memory (LSTM), Gated...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Umezuruike, Chinecherem Olaniyan, Deborah Olaniyan, Julius Adeniyi, Abidemi Emmanuel Oyebade, Adedoyin Abaneme, David Bowen University Department of Software Engineering Iwo Nigeria Bowen University Department of Computer Science Iwo Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria

ISBN: (纸本)9798331542559

This research explores the application of LSTM, GRU and Transformer models for predicting stock prices, aiming to enhance accuracy in financial forecasting. Stock price prediction is crucial for investment decision-making, yet challenging due to market volatility and complex patterns. The objectives are to evaluate the performance of LSTM, GRU and Transformer models using key metrics such as test loss, MAE, and MSE, and to compare their predictive capabilities. The LSTM model demonstrates robust performance with low test loss and MAE, indicating precise predictions and effective pattern recognition in financial data. In contrast, the Transformer model also shows promising results with relatively low test loss and MAE, albeit with larger errors in MSE and MAE metrics. Both models highlight the potential for accurate stock price prediction, suggesting avenues for future research to optimize model performance and reliability in financial forecasting applications. The experimental results show that GRU Outperformed LSTM and Transformer with an MSE of 0.0008, MAE of 0.0023, and high test accuracy of 0.9833. © 2024 IEEE.

关键词： Analysis Deep learning Financial forecasting Machine learning Stock prices

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