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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Mudit Arya Priyanshu Upwan Akash Umang Goyal Simran Chawla Dept. of Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era.

关键词： Deepfakes Ethics Reviews Explainable AI Neural networks Privacy breach Trajectory

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Decentralizing the Future: Using Blockchain In Various Sectors

Decentralizing the Future: Using Blockchain In Various Secto...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： China Venkat Ajay Babu Bodapati Varshini Ganta Teresa Kasu Jyoshna Rayalacheruvu Ramaiah Challa Dept. of Computer Science and Engineering KL University Vijayawada India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This study explores the potential of blockchain technology to revolutionize a variety of industries, going beyond bitcoin. Blockchain is a revolutionary invention that is revolutionizing various sectors across the globe. It is a decentralized and impenetrable ledger. The article uses real-world examples to show how blockchain technology, together with cryptocurrencies like Ethereum and Bitcoin, is revolutionizing banking by guaranteeing safe and transparent transactions. It makes traceability easier in supply chain management, which lowers fraud and raises accountability. Electronic health records enabled by blockchain bring about a paradigm shift in the healthcare industry by guaranteeing data privacy and integrity. Automating legal and corporate operations is the idea behind smart contracts, and blockchain-based voting systems improve election transparency. Through the avoidance of traditional banking institutions, cross-border payments are becoming more efficient and speedier. The study looks at how blockchain technology is accelerating the shift to a decentralized future, changing how many industries operate and offering previously unheard-of levels of efficiency, security, and transparency.

关键词： Industries Surveys Supply chain management Smart contracts Force Banking Medical services Blockchains Security Standards

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Vision-Based Approach for Food Weight Estimation from 2D Images

arXiv

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arXiv 2024年

作者： Wimalasiri, Chathura Sahoo, Prasan Kumar Dept. of Computer Engineering University of Peradeniya Peradeniya Sri Lanka Dept. of Computer Science and Information Engineering Chang Gung University Guishan Taiwan

In response to the increasing demand for efficient and non-invasive methods to estimate food weight, this paper presents a vision-based approach utilizing 2D images. The study employs a dataset of 2380 images comprising fourteen different food types in various portions, orientations, and containers. The proposed methodology integrates deep learning and computer vision techniques, specifically employing Faster R-CNN for food detection and MobileNetV3 for weight estimation. The detection model achieved a mean average precision (mAP) of 83.41%, an average Intersection over Union (IoU) of 91.82%, and a classification accuracy of 100%. For weight estimation, the model demonstrated a root mean squared error (RMSE) of 6.3204, a mean absolute percentage error (MAPE) of 0.0640%, and an R-squared value of 98.65%. The study underscores the potential applications of this technology in healthcare for nutrition counseling, fitness and wellness for dietary intake assessment, and smart food storage solutions to reduce waste. The results indicate that the combination of Faster R-CNN and MobileNetV3 provides a robust framework for accurate food weight estimation from 2D images, showcasing the synergy of computer vision and deep learning in practical applications. © 2024, CC BY-NC-ND.

关键词： Mean square error

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Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images

Hybrid Vision Transformer Model for Accurate Prostate Cancer...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Arafath Bin Mohiuddin Al Shahriar Uddin Khondakar Pranta Anamul Sakib Amira Hossain Md. Moniruzzaman Shanto Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Department of Collage of Engineering and Technology Westcliff University CA USA Dept. of Computer Science Wright State University Dayton USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Prostate cancer is a widespread disease in men worldwide, posing challenges in early detection due to its diverse nature and the limitations of current diagnostic techniques. In this study, we have developed a new method that combines CNNs with Vision Transformers (ViTs) to improve the classification of prostate cancer using MRI images. To address data imbalance and enhance model robustness, we used dual synthetic oversampling techniques and data augmentation methods. Our preprocessing pipeline reduced noise, preserved edges, and improved local contrast without amplifying noise. For feature extraction, we utilized the GrayLevel Co-occurrence Matrix (GLCM) and shape-based features to represent complex MRI data better. Among the evaluated architectures, ConvNeXt showed the best performance. Using SMOTE, it achieved an F1-score of 97.21% and a MCC of 95.32%. With ADASYN, performance improved further, reaching an F1-score of 98.82% and an MCC of 97.86%. Additionally, we created a web-based application for realtime analysis of prostate MRI scans, which can aid in clinical decision-making. These results demonstrate the potential of our approach to improve the accuracy of prostate cancer diagnosis and provide an interpretable model for clinical use.

关键词： computer vision Accuracy Magnetic resonance imaging Noise Pipelines Transformers Feature extraction Robustness Prostate cancer Resilience

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The Development of a Digital Forensic Framework for Ease of Forensic Analysis

The Development of a Digital Forensic Framework for Ease of ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Dhruv Suvarna Mahesh KM Mahika Gupta Sonal Gabburi Prasad Honnavalli Sapna VM Dept. of Computer Science & Engineering PES University Bengaluru India

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

With the growing use of digital devices and technologies, the volume of digital forensic evidence in need of analysis also increases. This results in a need for more effective methods to conduct analysis of this evidence in an investigation. This paper discusses the development of a framework for digital forensic processes. The framework will utilize various tools and techniques to enhance the process of collecting, analyzing, and preserving digital evidence. A survey of existing digital forensic tools, including FTK Imager, Autopsy, and The Sleuth Kit was conducted to help identify various pain points faced by investigators throughout the forensic lifecycle. Based on this study, a framework for the analysis of storage data and memory dumps in digital forensics is proposed, incorporating industry best practices and tools such as The Sleuth Kit and Volatility, enhancing ease of use, and thereby promoting more accurate resolutions.

关键词： Surveys Industries Pain Digital forensics Autopsy Personal digital devices Security

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DERM-AI: A Convolutional Neural Network Based Approach For Skin Disease Detection

DERM-AI: A Convolutional Neural Network Based Approach For S...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Sonam Juneja Parth Tiwari Rajni Devi Shikha Atwal Dept. of Computer Science and Engineering Chandigarh University Gharuan India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This paper shows how convolutional neural networks (CNNs) are useful for accurately and quickly diagnosing skin issues, underscoring the need of utilizing state-of-the-art machine learning approaches for accurate and fast diagnosis. A CNN architecture including convolutional and pooling layers, fully linked layers, and a softmax classifier was developed to handle the problem of diagnosing skin diseases. The CNN model was trained using stochastic gradient descent, and its hyperparameters were adjusted using grid search and random search. Future research in this area may benefit from the insights the study offers on CNN model construction and application for skin disease diagnosis.

关键词： Training Accuracy Transfer learning Stochastic processes Skin Convolutional neural networks Task analysis

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2D Echocardiography Image Segmentation via Patch-Based Generative Adversarial Network

2D Echocardiography Image Segmentation via Patch-Based Gener...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Noreen Fatima Sajjad Afrakhteh Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Accurate left ventricle (LV ) segmentation remains challenging due to the complex anatomical variability and imaging artifacts present in echocardiograms. Our ultimate objective is to develop a robust segmentation method for accurate segmentation of the LV , thereby enhancing the estimation of clinical indices. In this study, we introduce a novel hybrid generative adversarial network (GAN) architecture called patch-based GAN for cardiac image segmentation. Our contribution involves using two backbone models: UNET and a patch-based neural network (PBNN). UNET is employed for precise feature extraction and spatial preservation, aiding in constructing the latent space for the generator (G). Meanwhile, PBNN enhances the discriminator’s ability to accurately distinguish between the generated mask and the ground truth mask. The proposed patch-based GAN framework allows the network to learn from both the input data and adversarial feedback, improving its ability to handle variations and artifacts in echocardiograms. The experiment is conducted on the CAMUS dataset, comprising an echocardiographic dataset from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at end-diastolic (ED) and end-systolic (ES) phases. The proposed method achieves high accuracy in LV endocardium segmentation, with mean Dice values of 0.96 and 0.94 for the 2CH view and 0.959 and 0.953 for the 4CH view at ED and ES phases.

关键词： Image segmentation Accuracy Neural networks Imaging Estimation Generative adversarial networks Feature extraction Hybrid power systems Generators Frequency control

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Evaluating Trusted Firmware Remote Attestation on ARM and RISC-V Edge Computing Prototypes

Evaluating Trusted Firmware Remote Attestation on ARM and RI...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Konstantinos Tsampiras Anastasios Liontos Vasileios Tenentes Dept. of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

During remote attestation, a platform must prove to a verifier authority that it is trustworthy and has not been altered in-the-field. There is an increased effort towards centralized and decentralized remote attestation, however there are not many physical platforms benchmarked for their remote attestation capabilities in the literature. In this paper, we present two prototypes that support remote attestation: one with a close architectture, based on an ARM microprocessor implemented on a Raspberry Pi 3 with an Infineon Trusted Platform Module (TPM); and another one with an open RISC-V microprocessor implemented on an Arty A7 FPGA with the RV64GC RISC-V core and the IBM virtual TPM 2.0. Furthermore, we conduct an experimental evaluation of the computational and networking cost of attestation during the execution of the attestation protocol between the implemented prototypes while considering cloud, virtual and physical verifier platforms. We provide insights on the mechanisms and cost of remote attestation for the attester, the verifier and the network, and we identify routines of trusted computing that can be optimized within an open CPU architecture. The identified performance gaps between close and open architectures, highlight challenges that need to be addressed by open architectures to match the performance of close architectures in security workloads.

关键词： Cloud computing Trusted computing Program processors Costs Microprocessors Prototypes computer architecture

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County-Level Associations Between Social and Sleep Deprivation Conditioned by Regional Effects

County-Level Associations Between Social and Sleep Deprivati...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Swapna S. Gokhale Dept. of Computer Science & Engineering University of Connecticut Storrs CT

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Insufficient sleep leads to numerous negative health out-comes, and yet, approximately one-third of the adult U.S. population receives less than the recommended amount of sleep for optimal health. Consequently, sleep deprivation is now recognized as a public health crisis. Strategies to encourage optimal sleep must consider the social, environmental’ and cultural factors linked to insufficient sleep, although studies exploring these links are limited. This paper utilizes machine learning to address a gap in epidemiological modeling by conducting a joint county-level analysis of PLACES data and social deprivation measures across four U.S. regions. The innovation of this research stems from the use of random forest models, which include spatial autocorrelation structures within the modeling framework, commonly applied in ecology and earth science. Our findings reveal that incorporating spatial structures enhances the models' explainability by approximately 20%. Social deprivation measures can account for roughly 70% of the prevalence of sleep deficiency in the South and Midwest, but only about 55% in the Northeast and West, indicating that additional factors, such as the cultural glorification of sleep deprivation and urban lifestyles with lengthy commutes, may influence these regions. An analysis combining all four regions indi-cates that unemployment and educational attainment, rather than poverty as previously suggested, are the primary social deprivation factors affecting sleep duration. These insights could inform intervention strategies to promote optimal sleep, aligning with the objectives of Healthy People 2030.

关键词： Technological innovation Biological system modeling Sociology Length measurement Cultural differences Unemployment Statistics

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PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

arXiv

引用

arXiv 2024年

作者： Ovi, Md Sultanul Islam Rahman, Md. Hasibur Hossain, Mohammad Arif Dept. of Computer Science George Mason University FairfaxVA United States Dept. of Computer Science and Engineering Green University of Bangladesh Bangladesh

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Phishing

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