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2024 IEEE International Conference on Big Data, BigData 2024

作者： Qiu, Zhongyi Qiu, Kangyi Lyu, Hanjia Xiong, Wei Luo, Jiebo Georgia Institute of Technology School of Computational Science and Engineering AtlantaGA United States Georgia Institute of Technology School of Computer Science AtlantaGA United States University of Rochester Department of Computer Science RochesterNY United States

ISBN: (纸本)9798350362480

Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text. However, they ignore the essence of users' behavior on social media in that each text can correspond to multiple reasonable emojis. To better assess a model's ability to align with such real-world emoji usage, we propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text. To evaluate how well a model preserves semantics, we assess whether the predicted affective state, demographic profile, and attitudinal stance of the user remain unchanged. If these attributes are preserved, we consider the recommended emojis to have maintained the original semantics. The advanced abilities of Large Language Models (LLMs) in understanding and generating nuanced, contextually relevant output make them well-suited for handling the complexities of semantics preserving emoji recommendation. To this end, we construct a comprehensive benchmark to systematically assess the performance of six proprietary and open-source LLMs using different prompting techniques on our task. Our experiments demonstrate that GPT-4o outperforms other LLMs, achieving a semantics preservation score of 79.23%. Additionally, we conduct case studies to analyze model biases in downstream classification tasks and evaluate the diversity of the recommended emojis (https://***/VIStA-H/SemanticsPreservingEmojiRec). © 2024 IEEE.

关键词： Semantics

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A Novel Enhanced Cuckoo Search-SVM Model for Robust Brain Tumor Segmentation 9

A Novel Enhanced Cuckoo Search-SVM Model for Robust Brain Tu...

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

作者： Alphonsa, J. Kumari, V. Sheeja Roslin Dayana, K. Saveetha School of Engineering Simats University Department of Computational Intelligence Chennai600025 India R.M.D Engineering College Department of Computer Science Kavaraipettai Tiruvallur Chennai601206 India

ISBN: (纸本)9798350377972

The accurate segmentation of brain tumors from medical imaging is a critical task that aids in early diagnosis and treatment planning. Existing techniques often need help with issues like high computational demands, sensitivity to image noise, and suboptimal performance in varied imaging conditions. Enhanced Cuckoo Search-SVM (ECS-SVM) model that integrates an optimization-driven approach with machine learning for robust and efficient segmentation. We utilize the ECS algorithm to fine-tune the hyperparameters of a Support Vector Machine (SVM), which classifies extracted features from MRI images. The proposed approach leverages the search capabilities of the enhanced Cuckoo Search algorithm to identify optimal parameters that enhance the SVM's performance. With the valid application on a comprehensive dataset, the proposed method obtains a high accuracy of 95.3%. In comparative experiments, it is shown that ECS-SVM outperforms traditional and existing hybrid models, making it a great option for automated and reliable brain tumor segmentation. The results indicate significant improvements in segmentation precision, robustness, and computational efficiency, marking a notable advancement in the field. © 2024 IEEE.

关键词： Support vector machines

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A Machine Learning Model: Student Verification and Job Placement with Gradient Boosting Machines 15

A Machine Learning Model: Student Verification and Job Place...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Sakthivelan, R.G. Subha, S. Sree Gopirajan, P.V. Thandalam Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India

ISBN: (纸本)9798331300579

Every year, millions of students graduate, making it difficult to track and process their data. The One Point Student Verification Machine learning Model (OSVMLM) collects rich data on students, including biometric information, secondary education details, list of degrees completed, and detailed information on their last or current degree. In addition, the student's fingerprint is also collected and stored. The government's Aadhar database is used to verify the authenticity of the student. To ease the recruiter, the details collected by OSVMLM were assigned with ranks, information will be sent to the candidates autonomously based on the current job description. This makes it easy for students to identify and apply for available positions. OSVMLM application is built on Ionic, MongoDB, NodeJS and Gradient boosting machines (GBMs). OSVMLM is immensely useful for recruiters, as it makes data collection and processing effortless. This increases the visibility of various jobs open in different domains and suggests suitable job description to the job seekers. The key difference between OSVMLM and other platforms is that it is not limited to institutions and is accessible to all. This saves a lot of time for recruiters during initial screening. This proposed OSVMLM was validated with the real-time students dataset and achieved 91% prediction accuracy. © Grenze Scientific Society, 2024.

关键词： Students

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Explainable Convolutional Neural Network for Phenotype Prediction from Genotype

Explainable Convolutional Neural Network for Phenotype Predi...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Sam, Maxwell Annan, Richard Yang, Pei Anwar, Mohd Rhinhardt, Kristen Qingge, Letu Department of Computer Science North Carolina A&T State University GreensboroNC United States Department of Computer Science Qinghai University Xining China Department of Computational Data Science and Engineering North Carolina A&T State University GreensboroNC United States

ISBN: (纸本)9798350350548

In the realm of precision medicine, the prediction of phenotypic traits from genotype data plays a pivotal role in understanding disease susceptibility, drug response, and overall health outcomes. Machine learning models trained on genotype data offer promising avenues for phenotype prediction. However, the black-box nature of these models often hinders interpretability, raising concerns about their reliability and trustworthiness in clinical decision-making. This paper explores the application of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) on our existing CNN model to identify important features from genotype that contribute to phenotype prediction in yeast data. Both techniques on machine learning models include improving model transparency and interpretability by providing insights into prediction mechanisms. LIME focuses on local interpretability, explaining individual predictions tailored to specific instances swiftly, while SHAP computes feature importance using Shapley values, offering a comprehensive understanding of feature contributions. Our dataset contains 4,390 samples, each with 28,220 features in genotype which are used in the training, testing and validation of our existing CNN model. In our analysis of 20 phenotypes, we identify the number of relevant important features using SHAP, LIME methods, and the intersection which not only improves the Mean Squared Error (MSE) values compared to using all the features in the genotype data, but also the model training time. © 2024 IEEE.

关键词： Convolutional neural networks

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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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Choice of Optimal Voltage for DC Power Distribution Systems on More Electric Aircraft

Choice of Optimal Voltage for DC Power Distribution Systems ...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Wang, Xin Atkin, Jason Yeoh, Seang Shen Bozhko, Serhiy University of Nottingham Faculty of Engineering The Department of Electrical and Electronic Engineering Nottingham United Kingdom University of Nottingham Computational Optimisation and Learning Lab School of Computer Science Nottingham United Kingdom

ISBN: (纸本)9798350364446

The new generation of aircraft will experience increasing electrical power demands because of the more electric trends and advances in recent years. Adopting higher voltage levels is widely discussed for More Electric Aircraft (MEA) Power Distribution Systems (PDSs) to efficiently deliver the increased power ratings and reduce the system weight. However, higher voltage levels also indicate the risk of electrical insulation failures. Hence, it is essential to select optimised voltage levels for onboard PDSs considering the trade-offs. In addition, the relationship between the PDS weight/efficiency and voltage levels is complex rather than linear, resulting in infeasibility issues when optimising voltage levels. This study proposes a voltage optimisation method based on selecting among a set of components, i.e., converters and cables, for onboard DC PDSs. Moreover, to improve feasibility, the Integrated Converter Component (ICC) concept is proposed to combine each converter with its lightest input/output cables as an integrated block. Hence, the optimal bus voltages can be decided by selecting the lightest and most efficient ICCs while meeting the power delivery and voltage conversion requirements. © 2024 IEEE.

关键词： Bus voltage DC power distribution integrated converter component more electric aircraft optimisation

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MediGuard:A Survey on Security Attacks in Blockchain-IoT Ecosystems for e-Healthcare Applications

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computers, Materials & Continua 2025年第6期83卷 3975-4029页

作者： Shrabani Sutradhar Rajesh Bose Sudipta Majumder Arfat Ahmad Khan Sandip Roy Fasee Ullah Deepak Prashar Institute of Engineering and Technology Dibrugarh UniversityDibrugarh786004AssamIndia Departments of Computational Sciences Brainware UniversityKolkata700125West BengalIndia Department of Computer Science&Engineering JIS UniversityKolkata700109West BengalIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610Perak Darul RidzuanMalaysia Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411PunjabIndia Jadara University Research Center Jadara UniversityIrbid21110Jordan

Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare *** digitization has major advantages,mainly enhancing the security barriers of the green tree *** this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system *** addition,we considered several solutions proposed by thousands of researchers *** results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and ***,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power *** propose a multi-layered architecture for the future healthcare ***,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these *** thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional *** conducted in-depth comparative studies on different interoperability parameters in the blockchain *** research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and *** the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.

关键词： Blockchain internet of medical things cloud infrastructure cyber-attacks privacy issues

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Analysis and Classification of Network Security Attacks Using Domain Generation Algorithm

Analysis and Classification of Network Security Attacks Usin...

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

作者： Gomathi, S. Samundeeswari, S. Ponmanirai, S. Nanthini, S. Vandayar Engineering College Department of Computer Science and Engineering Thanjavur India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam India Saveetha School of Engineering Simats University Department of Department of Data Science Thandalam India

ISBN: (纸本)9798350313987

In the realm of cybersecurity, the evolution of network attacks necessitates innovative approaches for threat prediction and mitigation. Traditional methods often fall short in identifying novel attack patterns or adapting swiftly to emerging threats. This study explores the application of Domain Generative Algorithms (DGAs) in the context of network attack prediction. DGAs leverage advanced machine learning techniques to generate synthetic data samples representing various network attack scenarios. By training generative models on diverse and large-scale datasets comprising both historical attack data and benign network traffic, the algorithms learn intricate patterns intrinsic to different types of attacks. Through, systematic analysis of these synthetic attack instances, IDS and IPS algorithms can be fine-tuned to recognize subtle attack patterns that might elude conventional signature-based detection methods. This research underscores the pivotal role of DGAs in revolutionizing the landscape of network attack prediction. By harnessing the power of generative algorithms, organizations can proactively strengthen their cybersecurity posture, effectively mitigating the risks posed by an ever-evolving spectrum of network attacks. © 2023 IEEE.

关键词： Data Traffic Denial of Services Domain Generation Algorithm Malware Network security Pattern Analysis

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Unitary Operator Based Quantum Secure Access Structure for Medical Image Sharing

Unitary Operator Based Quantum Secure Access Structure for M...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Padulkar, Digambar Abraham, Jibi School of Computational Sciences Coep Technological University Department of Computer Science & Engineering Pune India Baramati Savitribai Phule Pune University Vpkbiet Pune India

ISBN: (纸本)9798350350821

Ensuring the secure storage and sharing of medical records in the cloud is increasingly crucial due to vulnerabilities in classical encryption methods, such as key manipulation and cloud collusion. This paper introduces a novel quantum-based approach using Quantum Fourier Transform (QFT) and Unitary operators. We designed three quantum circuits for different image sizes: one for the Unitary operator, one for encryption, and one for decryption. DICOM images are first transformed into quantum images with the NEQR algorithm, then encrypted using a Unitary operator, which serves as the encryption key. This key is securely transmitted via quantum teleportation, enhancing protection against classical and quantum attacks. Extensive simulations show the method's effectiveness and robustness in safeguarding medical data during cloud storage and transmission. This makes it a promising solution for secure medical image sharing in quantum communication networks. © 2024 IEEE.

关键词： Quantum communication

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Augmented Reality and Artificial Intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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