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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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SMS-Shield: A Lightweight Approach for Smishing Detection using Machine Learning 1

SMS-Shield: A Lightweight Approach for Smishing Detection us...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Samad, Saleem Raja Abdul Ganesan, Pradeepa Thangam, S. Balasubramaniyan, Sundaravadivazhagan Rajiakodi, Saranya Al Kaabi, Amna Salim Rashid University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences Information Technology Department Shinas Oman Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India University of Technology and Applied Sciences-Al-Musannah College of Computing and Information Sciences Information Technology Department Al-Musannah Oman Central University of Tamil Nadu Department of Computer Science Tamil Nadu Thiruvarur India

ISBN: (纸本)9798350348637

SMS continues to be an indispensable component of contemporary digital communication. Because it is so widely available, cost-effective, reliable, and delivers information instantly. Its versatility enables its utilization for real-time chats, alerts, and verification purposes. Nevertheless, the ubiquitous nature of SMS renders it an easy prey for smishing, a deceitful form of cyberattack. Cybercriminals exploit the trust associated with SMS messages to deceive recipients. Smishing, an increasingly prevalent deceptive SMS fraud, poses a threat to both businesses and individuals. These messages include websites or phone numbers and purport to be from banks, governmental bodies, or well-known businesses. These misleading text messages lure their users into disclosing private information or clicking on links that could be harmful in many ways. Early detection of such malicious messages prevents users from falling victim to them. Various techniques were presented by researchers to accurately detect smishing attacks. Machine learning models exhibit great potential as a strategy to counter the increasingly dangerous SMS phishing attacks. Machine learning models can discern legitimate from fraudulent SMS messages by analyzing datasets. This paper introduces a novel method (SMS-SHIELD), which combines Machine Learning and Natural Language Processing techniques to enhance smishing detection. This approach yields outstanding results, with random forest and extreme gradient boosting algorithms achieving remarkable accuracy rates of 99.05% and 98.03%, coupled with impressive F1-scores of 98.95% and 97.86%, respectively. © 2024 IEEE.

关键词： Phishing

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Learned multiphysics inversion with differentiable programming and machine learning

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Leading Edge 2023年第7期42卷 474-486页

作者： Louboutin, Mathias Yin, Ziyi Orozco, Rafael Grady, Thomas J. Siahkoohi, Ali Rizzuti, Gabrio Witte, Philipp A. Møyner, Olav Gorman, Gerard J. Herrmann, Felix J. Georgia Institute of Technology School of Earth and Atmospheric Sciences AtlantaGA United States Georgia Insitute of Technology School of Computational Science and Engineering AtlantaGA United States Georgia Institute of Technology College of Computing GA Atlanta United States Utrecht University Utrecht Netherlands Microsoft Corp. RedmondWA United States SINTEF Trondheim Norway Imperial College London Department of Earth Science and Engineering London United Kingdom

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning. © 2023 The Authors. Published by the Society of Exploration Geophysicists. All article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY). See.

关键词： Multiphysics

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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An ideal deeply contextual prediction model for an effective prediction and mitigation of cyberbullying in social networks

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International Journal of Information technology (Singapore) 2025年第3期17卷 1561-1579页

作者： Prathaban, Banu Priya Vaishnavi, T. Ramesh, Addanki Purna Sujaritha, M. Rama, R. Senthil Gracewell, J. Jeffin Department of Networking and Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai 603203 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Electronics and Communication Engineering DNR College of Engineering and Technology West Godavari Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India Department of Electrical and Electronics Engineering DMI College of Engineering Tamil Nadu 600123 Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Social media have evoked a host of problems, especially Twitter and Instagram, considering misconceived notions of free speech. Among the concerns that have been seen as crucial is the impact of cyberbullying;it is a global issue affecting an individual and society in general. Though various methods have been suggested for deterring cyberbullying, most of these depend on victim interaction and are hence ineffective in the task of independently identifying and preventing cyberbullying. Within this context, the research introduces a new, innovative framework that is ideal and deeply contextual in terms of prediction, thus creating an approach to detecting cyberbullying on social networks without directly engaging with any victim. Consequently, the key contribution of this work is developing the Embedded Residual Recurrent Convolution Network, a hybrid deep learning architecture for the detection of cyberbullying data with more precision and accuracy. Further, this is optimized by a Tenacious Projectile Optimizer for minimizing computational time and improving classifier performance. In this regard, the novelty of the proposed I-DCP framework is established in its superior performances on different social media datasets, with an average accuracy of over 99% and an execution time as low as 13 s, hence highly efficient for real-time detection of cyberbullying. These represent important advances compared to previous methodologies and constitute a strong and scalable response in the fight against cyberbullying. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： And classification Artificial intelligence (AI) Cyberbullying detection Deep learning Instagram Optimization Social network Twitter

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A review about Zig Zag and Double Zig Zag connections in Transformer 2

A review about Zig Zag and Double Zig Zag connections in Tra...

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Peter, Geno Stonier, Albert Alexander Sherine, Anli Iderus, Samat University of Technology Sarawak Crisd School of Engineering and Technology Malaysia Department of Electrical and Electronics Engineering Kongu Engineering College India MediaUniversity of Technology School of Computing and Creative Sarawak Malaysia

ISBN: (纸本)9781665437899

Whenever we think of three-phase transformer connections, we think straightaway of either star-connected transformers or delta-connected transformers. A great majority of polyphase transformers are either in series or connected in delta. In either case, the connection is symmetrical or balanced. In this paper, the winding connections, the voltage distribution in the windings, the application of zig-zag connections, and the testing of zig-zag transformers are discussed in detail. © 2022 IEEE.

关键词： Stars

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Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network(CNN)

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Computers, Materials & Continua 2023年第2期74卷 3743-3761页

作者： Arfat Ahmad Khan Malik Muhammad Ali Shahid Rab Nawaz Bashir Salman Iqbal Arshad Shehzad Ahmad Shahid Javeria Maqbool Chitapong Wechtaisong College of Computing Khon Kaen UniverityKhon Kaen40000Thailand Department of Computer Science COMSATS University IslamabadVehari61100Pakistan Department of Petroleum&Gas Engineering University of Engineering&Technology54890Pakistan Electrical Engineering Department SBASSELahore University of Management SciencesLahore54890Pakistan School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand

COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across *** ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the *** is essential to detectCOVID-19 infection caused by different variants to take preventive measures *** existing method of detection of infections caused by COVID-19 and its variants is costly and *** impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the *** is the major symptom of COVID-19 *** radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused *** pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with *** this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology ***,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology *** fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed *** results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.

关键词： COVID-19 pneumonia radiology images omicron convolution neural network(CNN) microscopy

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Observation points classifier ensemble for high-dimensional imbalanced classification

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CAAI Transactions on Intelligence technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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Cross-Device Control and Interaction for Workstations using Mobile Devices

Cross-Device Control and Interaction for Workstations using ...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Nivethitha, V. Rakshika, G.S. Sriram, Suthir Thangavel, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India United Arab Emirates University College of Information and Technology Dubai United Arab Emirates

ISBN: (纸本)9798331522988

The proliferating use of mobile devices has paved the way for new possibilities in enhancing user interactions with workstations. This research paper investigates and demonstrates the practicality of utilizing mobile devices as controllers for workstations. We investigate a wide range of technologies, design considerations, and factors related to user experience. While analyzing the case studies and conducting experiments, we put forth both the positive and adverse effects observed with this novel approach. It is proved by the present study results that the application of mobile devices as controllers of workstations can bring substantial raise in productivity and ease to the end-user. Based on existing studies, this device-free approach addresses the problem of traditional workstation complementing peripherals with mobile technology at the present. This change in how humans interact with computers provokes a variety of efficiency advantages but at the same time provides new challenges such as security and interdevice integration. Focusing on all these complexities will allow the possibility of the full potential of the mobile devices in the capacity of the controller for the workstations. © 2024 IEEE.

关键词： component formatting insert style styling

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