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Electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Munira H. Abdallatif Amany K. Alarbish Abdallah A. Oshah Ahmed Elajeli Rgibi Amal Apojila Oshah Department of Computer Engineering and Information Technology Faculty of Engineering Sabratha University Sabratha Libya

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

In this study, the effectiveness of detecting strokes in their early stages is explored, particularly noting the limitations of relying solely on CT scans. By integrating CT and DW-MRI images, a more comprehensive image is generated, offering enhanced details beyond what either technique could provide independently. DW-MRI is especially noted for its superior performance in identifying acute stroke cases in comparison to CT scans. The research utilizes images from both CT and DW-MRI. The proposed algorithm is structured into four distinct phases. The initial phase involves preprocessing of both sets of images. Subsequently, the second phase aims to identify corresponding cross-sectional areas in the pathological MRI, facilitating the merging of images. The third phase is dedicated to the registration and integration of CT and MRI images. In the final, fourth phase, segmentation of the stroke lesion in the combined image is carried out. The effectiveness of this integration is evaluated based on metrics like the peak signal-to-noise ratio (PSNR) and entropy.

关键词： Visualization PSNR Computed tomography Magnetic resonance imaging Redundancy Wavelet analysis Entropy

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Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

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computers, Materials & Continua 2023年第2期74卷 2625-2640页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Shahab SBand Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathilticsSlovak University of Technology in BratislavaSlovakia

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the *** are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server ***,the attackers have designed even better mechanisms to break these security *** cryptography is the best data protection algorithm that exchanges data between authentic *** this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure ***,the XOR function will be applied to plain text,and then salt technique will be ***,a reversing mechanism will be implemented on that data to provide more data *** decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be *** the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

关键词： Cryptography symmetric algorithm encryption decryption cipher text cloud security asymmetric algorithm

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Robust and High Accuracy Algorithm for Detection of Pupil Images

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computers, Materials & Continua 2022年第10期73卷 33-50页

作者： Waleed El Nahal Hatim G.Zaini Raghad H.Zaini Sherif S.M.Ghoneim Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Faculty of EngineeringMSA UniversityCO12585Egypt Computer Engineering Department College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Electronics and Communications Engineering Department Faculty of EngineeringSinai UniversityArishCO45511Egypt

Recently,many researchers have tried to develop a robust,fast,and accurate *** algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking,gaze-based human-computer interaction,medical applications(such as deaf and diabetes patients),and attention *** real-world conditions challenge the eye appearance,such as illumination,reflections,and *** the other hand,individual differences in eye physiology and other sources of noise,such as contact lenses or *** present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics *** proposed circular hough transform with morphing canny edge detection for Pupillometery(CHMCEP)algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better *** performance of the proposed CHMCEP algorithm was tested against recent pupil detection *** and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11,78.54,58,and 78 according to´Swirski,ExCuSe,Else,and labeled pupils in the wild(LPW)data sets,*** results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.

关键词： Pupil detection eye tracking pupil edge morphing techniques eye images dataset

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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

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Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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Hybrid automation of student activity records in virtual learning environments in semi-dark scenarios

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International Journal of Business Intelligence and Data Mining 2022年第1-2期22卷 16-33页

作者： Hashmani, Manzoor Ahmed Memon, Mehak Maqbool Raza, Kamran Rizvi, Syed Sajjad Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Engineering Science and Technology Iqra University Karachi Pakistan Faculty of Computer Science SZABIST Karachi Pakistan

Online learning is raging due to the offered advantages including the ease of information accessibility and flexible learning experiences remotely. However, it still lacks intelligent authentication and validation of users at the other end. Additional questions are raised for the effectiveness of transfer of knowledge, soft skills, and value of education as the student's engagement. Following the hype and crucial need for full-fledged virtual learning environments (VLEs) in recent times, the literature has witnessed a significant increase in the studies targeting the associated problem areas. The existing VLEs in place fail to provide aggregated functionalities of student authentication and activity tracking in dynamic visual environments. To solve the problem by providing interactive functionality, an automated hybrid information activity system (HIAS) based on facial biometrics, facial emotion recognition using computer vision techniques is proposed. The proposed system solves the problem intelligently by analysing the incoming online video from the tutor's computer screen. © 2023 Inderscience Enterprises Ltd.

关键词： Biometrics

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Utilizing Cooperative Spectrum Sharing for High Speed Wireless Networks 3

Utilizing Cooperative Spectrum Sharing for High Speed Wirele...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Kharbas, Vikash Kumar Sidhu, Amritpal Singh, Shweta Gowrishankar, J. Jaisudha, J. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

Cooperative spectrum sharing is a method to allocate the radio spectrum amongst users for high-velocity wireless networks. It entails multiple unbiased customers that cooperate to percentage their respective spectra to maximize the records fee and the practical spectrum usage. The cooperative spectrum sharing technique lets non-overlapping frequency bands be utilized by multiple customers supplying a high statistics fee with minimum interference. It affords a green way of handling the radio spectrum, permitting multiple customers to share the to-be-had spectrum sources rather than having an unmarried consumer monopolize an upbeat frequency band. Cooperative spectrum sharing is likewise tremendous because of its low implementation cost and occasional power intake. Furthermore, it could be fast deployed to numerous areas to meet the increasing demand for excessive-speed Wi-Fi networks. © 2023 IEEE.

关键词： Cooperative implementation managing monopolize spectrum

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Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation

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IEEE Access 2025年 13卷 86083-86109页

作者： Luo, Keliang Universiti Putra Malaysia Faculty of Computer Science and Information Technology Software Engineering Department Kuala Lumpur43400 Malaysia

This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices. © 2013 IEEE.

关键词： Deep learning

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network 4

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Khan, Imran Khan, Fazlullah Ali, Asad Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Computer Science Department KPK Mardan Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang315104 China University of Engineering and Technology Department of Computer Science KPK Peshawar Pakistan College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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SpamVis: A Visual Interactive System for Spam Review Detection 28th

SpamVis: A Visual Interactive System for Spam Review Detecti...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Lam, Nguyen Thanh Thao Le, Nu Uyen Phuong Islam, Md Rafiqul Sakib, Md KowsarHossain Prome, Shanjita Akter Sanin, Cesar Szczerbicki, Edward Zhou, Jianlong College of Business & Management VinUniversity Hanoi12426 Viet Nam Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD4072 Australia Business Information Systems Australian Institute of Higher Education SydneyNSW Australia School of Computer Science Taylor's University Malaysia Faculty of Management and Economics Gdansk University of Technology Gdansk Poland School of Computer Science University of Technology Sydney Australia

In recent times, the number of spam reviews through various online platforms has emerged as a prime challenge, profoundly impacting businesses and consumers. These fake reviews not only distort clients' perceptions of products and services but also erode trust within the digital ecosystem. Despite the advent of machine learning (ML) techniques for identifying spam reviews, comparing text, and pinpointing groups of spammers, there remain notable gaps in both accuracy and the combination of interactive visualization for real-time decision-making. This paper presents SpamVis, a visual interactive system that leverages deep learning (DL) and ML blended with advanced visualization techniques for spam review detection, enabling analysts to conduct complicated analytical queries. The system allows users to input via click-on or touch to generate interactive charts and plots tailored for spam review analysis. The findings of the baseline test carried out on 67,395 review texts demonstrate that Bidirectional Encoder Representations from Transformers (BERT) carried out the best accuracy (86%) compared to other models. Our outcomes suggest that SpamVis can alleviate the gaps concerning accuracy and visualization needs in contemporary techniques, guiding analysts to make informed decisions for mitigating spam reviews and enhancing consumer trust. Furthermore, SpamVis empowers users to seamlessly discover the online reviews of various social media platforms in real-time, such as Facebook, Youtube, etc., giving them practical insights to navigate the online marketplace effectively. © 2024 The Authors.

关键词： Adversarial machine learning

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