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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Kousar, Shagufta Khan, Areeba Ihsan, Anaum Abbas, Tahir Saeed, Ali Q College of Engineering Department of Computer Engineering Mutah University Jordan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan Lahore Pakistan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

This review paper investigates the advances of artificial intelligence (AI) in the field of email spam detection. The study addresses AI-based techniques used for email spam filtering by classifying threats such as keywords, content-based, sender IDs, and phrases. Email spam remains one of the most pressing issues today, with spammers sending malicious links in junk folders to compromise confidential information. As spam filtering becomes increasingly complex, AI and machine learning techniques like Naïve Bayes, Support Vector Machine (SVM), and Random Forest are commonly used to mitigate these threats. This study proposes a novel framework that combines traditional machine learning models with deep neural networks to develop a robust and flexible spam detection system. The framework is designed to enhance the detection capabilities of the classifier by applying a series of key sequential steps, each intended to improve the system's ability to distinguish between legitimate emails and spam. The paper details the methodology, providing a step-by-step explanation of each phase to demonstrate how the framework advances email spam detection. © 2024 IEEE.

关键词： Deep learning Email spam detection machine learning naive-Bayes SVC

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Emotion identification in human faces through ensemble of deep learning models

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Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 9729-9752页

作者： Naveen Kmuar, M. Godfrey Winster, S. Department of Computer Science and Engineering Saveetha Engineering College Thandalam India Department of Computing Technologies Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. © 2023 - IOS Press. All rights reserved.

关键词： Emotion Recognition

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Enhanced Detection of Eye Coloboma through Global Attention U-Net (GA-UNet) for Accurate Ophthalmic Image Segmentation 4

Enhanced Detection of Eye Coloboma through Global Attention ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dhayanithi, A. Bala, K. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Eye coloboma is classified as a congenital eye disease that results in developmental anomalies of the eye that may result in eye diseases that essentially alter visions. Fractional and timely diagnosis still matter significantly in its effective managing and treating. This work presents a solution with a Transformer-Enhanced U-Net model that was developed with Transfer Learning to help augment the market's ability to identify and delineate eye coloboma from medical images. This work further extend the gains made in U-Net, that has achieved fairly well especially in the biomedical image segmentation problem, with the inclusion of transformer modules for the incorporation of the long range dependencies and contextual information. This enhancement help the model to attend more on such small features as coloboma and overall diagnostic accuracy in addition. By using Transfer Learning with a pretrained backbone such as Efficient Net, feature representations from large pre-existing datasets are fed to the system as a solution to the reduction of training time and while the performance of the system drops no further effect is *** approach involves the use of fine-tuning with Transformer-Enhanced U-Net model utilizing a subset of the eye images, which are labelled according to the existence of coloboma. Next, by adopting the self-attention mechanism, the model becomes able to pay attention to the relevant areas in the image, and hence is sensitive to any form of coloboma. In initial experiments, this scheme outperforms the standard convolutional networks for segmenting complex regions with minimal false negative rates. Moreover, by comparing the features extracted at deep layers with ground truth, the effectiveness of the model to realistic clinical usage is established given that the model is robust to changes in imaging conditions. The purpose of this research therefore is to develop an Auto-Generated diagnostic tool that will be more reliable as it assists the

关键词： Image segmentation

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

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computer Systems science & engineering 2023年第9期46卷 3339-3353页

作者： S.Priya K.Pradeep Mohan Kumar Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennai600089India Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India

An intrusion detection system(IDS)becomes an important tool for ensuring security in the *** recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network *** resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion *** proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the *** attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature ***,DRL model is employed for the proper identification and classification of intrusions that exist in the ***,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection *** ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark *** simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

关键词： Intrusion detection security reinforcement learning machine learning feature selection beetle antenna search

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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An Attention-Locating Algorithm for Eliminating Background Effects in Fine-Grained Visual Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5993-6006页

作者： Huang, Yueting Hechen, Zhenzhe Zhou, Mingliang Li, Zhengguo Kwong, Sam Chongqing University School of Computer Science Chongqing400044 China Institute for Infocomm Research Agency for Science Technology and Research Jurong West 138632 Singapore Lingnan University Department of Computing and Decision Science New Territories Hong Kong

Fine-grained visual classification (FGVC) is a challenging task characterized by interclass similarity and intraclass diversity and has broad application prospects. Recently, several methods have adopted the vision Transformer (ViT) in FGVC tasks since the data specificity of the multihead self-attention (MSA) mechanism in ViT is beneficial for extracting discriminative feature representations. However, these works focus on integrating feature dependencies at a high level, which leads to the model being easily disturbed by low-level background information. To address this issue, we propose a fine-grained attention-locating vision Transformer (FAL-ViT) and an attention selection module (ASM). First, FAL-ViT contains a two-stage framework to identify crucial regions effectively within images and enhance features by strategically reusing parameters. Second, the ASM accurately locates important target regions via the natural scores of the MSA, extracting finer low-level features to offer more comprehensive information through position mapping. Extensive experiments on public datasets demonstrate that FAL-ViT outperforms the other methods in terms of performance, confirming the effectiveness of our proposed methods. © 1991-2012 IEEE.

关键词： Feature extraction Transformers Visualization Data mining computer vision Circuits and systems Training Computational modeling Classification algorithms Background noise

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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