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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Achireddy, Vummadi Sathiya, T. Ravi, Rongala Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Anurag Engineering College Department of Cse Ananthagiri Telangana Kodad India Sona College of Technology Department of Cse Salem India Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

This research begins with the collecting of a varied dataset of annotated facial photos to ensure accurate representation across populations. The photographs will be pre-processed to standardize their size, illumination, and orientation. On this dataset, we will fine-tune a pre-trained CNN, such as ResNet50, to identify various face forms before evaluating accuracy against a separate test set. Furthermore, we will create a dataset of facial photos combined with various haircuts classified by face shape, and use the CNN to extract relevant features and embeddings. A matching algorithm will match user facial features to hairdo embeddings, ranking recommendations according to similarity metrics. The AR component will leverage ARKit or ARCore for real-time face tracking, allowing users to realistically test on different haircuts overlayed on their live camera feeds. The final solution will be available on mobile and online platforms, with a user-friendly design and options for saving preferred haircuts and providing feedback. This project tackles the demand for a more engaging and individualized hairstyle picking experience, utilizing advanced AI and AR technologies to improve customer happiness and interactivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms 4

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Thangam, S. Dixit, Aniket Karn, Alok Kumar Thakur, Arnav Arunbalaji, Cg Jesy Janet Kumari, J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350393354

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion;despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

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computer Systems Science & engineering 2022年第9期42卷 1181-1198页

作者： Mohammad Khalid Imam Rahmani Fahmina Taranum Reshma Nikhat Md.Rashid Farooqi Mohammed Arshad Khan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Muffakham Jah College of Engineering and TechnologyOsmania UniversityHyderabadTelanganaIndia Department of Commerce and Business Management Maulana Azad National Urdu UniversityHyderabadTelanganaIndia Department of Accountancy College of Administrative and Financial SciencesSaudi Electronic UniversityRiyadSaudi Arabia

The COVID-19 pandemic is a virus that has disastrous effects onhuman lives globally;still spreading like wildfire causing huge losses to humanityand economies. There is a need to follow few constraints like social distancingnorms, personal hygiene, and masking up to effectively control the virus *** proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtainedfor mask detection are found to be effective. The system is trained using4500 images to accurately judge and justify its accuracy. The aim is to developan algorithm to automatically detect a mask, but the approach does not facilitatethe percentage of improper usage. Accuracy levels are as low as 50% if the maskis improperly covered and an alert is raised for improper placement. It can be usedat traffic places and social gatherings for the prevention of virus transmission. Itworks by first locating the region of interest by creating a frame boundary, thenfacial points are picked up to detect and concentrate on specific features. Thetraining on the input images is performed using different epochs until the artificialface mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-sourcedatasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier isused to load and pre-process the image dataset for building a fully connectedhead. The objective is to assess the accuracy of the identification, measuringthe efficiency and effectiveness of algorithms for precision, recall, and F1 score.

关键词： COVID-19 pandemic convolution neural network deep learning face detection facemask detection global health machine learning

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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Optimized Image Dehazing using Dark Channel Prior with Minimum Fusion and Improved Atmospheric Light Handling

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IEEE Access 2025年 13卷 100720-100730页

作者： Hussain, Ahmad Iqbal, Mansoor Aslam, Sheraz Alahmari, Saad Alkharashi, Abdulwahab University of Wah Department of Computer Science Cantt Wah Pakistan CTL eurocollege Department of Computer Science Limassol 3077 Cyprus Cyprus University of Technology Department of Electrical Engineering Computer Engineering and Informatics Limassol 3036 Cyprus Northern Border University Applied College Department of Computer Science Arar Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Computer Science Saudi Arabia

Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments. © 2013 IEEE.

关键词： Atmospheric Light Estimation Dark Channel Prior Image Dehazing Minimum Fusion Technique Visual Clarity

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Adaptive Strategies for Scalable and Heterogeneous VANET Network 5

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Thangam, S. Jha, Priyanshu Hawari, Jawed Sah, Nabin Kumar Jain, Nikunj Jesy Janet Kumari, J.J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences It Department Shinas Oman

ISBN: (纸本)9798350385649

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Privacy and security in online teaching during the COVID-19 pandemic: experiences and concerns of teachers in UK higher education 36

Privacy and security in online teaching during the COVID-19 ...

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36th Annual British Human-computer Interaction Conference, HCI 2023

作者： Almekhled, Basmah Petrie, Helen Department of Computer Science University of York Heslington East York United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The coronavirus pandemic led to major changes in higher education around the world. Higher education institutions (HEIs) moved to completely online learning and a range of new technologies including online videoconferencing and chat tools. Research has shown that users have privacy and security concerns about such tools, but little is known about the attitudes of HEI teachers to these issues. An online survey of 37 UK HEI teachers explored their attitudes and concerns about privacy and security in online teaching during the pandemic. Participants' had reasonable awareness of institutional policies and were engaged in relevant training initiatives. Ratings of concern across a range of issues were generally low, however, four situations specifically were rated with moderate levels of concern. These situations involved the unauthorized use or sharing of teaching materials with others, students not turning on their webcams, students making unauthorized recordings, and accidentally sharing a wrong window containing private information. Going forward, it is important of ensuring that appropriate measures are in place to protect the privacy and security of both teachers and learners during online teaching. © Almekhled et al. Published by BCS Learning and Development Ltd.

关键词： Differential privacy

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Optimal Logistic Map with DNA computing Model for Image Encryption 2

Optimal Logistic Map with DNA Computing Model for Image Encr...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Shiny, K.V. Wilfred Blessing, N.R. Jino, R. Palarimath, Suresh Mathumitha, V. Sutherlin Subitha, G. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman Department of Civil Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil nadu Kanyakumari District India

ISBN: (纸本)9798331542573

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times;the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes. © 2024 IEEE.

关键词： Signal to noise ratio

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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

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computer Systems Science & engineering 2023年第4期45卷 753-767页

作者： Mesfer Al Duhayyim Areej A.Malibari Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare *** advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging *** cancer(OC)has commonly occurred in head and neck *** identification of OC enables to improve survival rate and reduce mortality ***,the design of CAD model for OC detection and classification becomes ***,this study introduces a novel computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)*** proposed CADOC-SFOFC model determines the existence of OC on the medical *** accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)***,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification ***,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced *** experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

关键词： Oral cancer computer aided diagnosis deep learning fusion model seagull optimization classification

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