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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Joseph Rajan, D. Antony Sivanesan, T.M. Mohankumar, N. Srinivasan, M. Sivaraman, Audithan Velmurugan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Pune India P. S. v College of Engineering and Technology Department of Information Technology Tamilnadu Krishnagiri India Department of Information Technology Jafferpur New Delhi110073 India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331529833

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers. © 2025 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Skin Cancer Detection - A Review

Deep Learning for Skin Cancer Detection - A Review

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： N Raghava Rao D Vasumathi Department of Computer Science and Engineering University College of Engineering Science & Technology Hyderabad JNTU Hyderabad Telangana India Department of Computer Science and Engineering University College of Engineering Science & Technology Hyderabad JNTU Hyderabad Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Skin cancer is also one of the most fatal diseases among all other types of cancer. Diagnosing it in the early stage can reduce the risk factors. Visual assessment and skin biopsy are the traditional ways of diagnosing skin cancer. The results provided by this method are inaccurate and sometimes misdiagnoses occur due to the incapability of human experts. Due to the advancement of technologies, Deep Learning (DL) and Machine Learning (ML) are highly utilized in medical diagnosis, which also produces more accurate results. Many researchers have utilized DL methodologies for skin cancer detection. In this paper, an attempt is made to provide a detailed survey on skin cancer detection methods. For this survey, 25 standard research journals are accessed from sites like Springer, IEEE, and so on. The accessed papers on skin cancer detection are classified based on DL methodologies. Moreover, the DL techniques are also categorized into four categories based on the methods employed for the detection process. This analysis shows that DL-based techniques are the most employed techniques for the detection process in research journals. The majority detection process has been implemented using the Python tool. Moreover, accuracy is the most commonly utilized evaluation metric.

关键词： Measurement Deep learning Surveys Standards publications Accuracy Visual analytics Skin Convolutional neural networks Skin cancer Systematic literature review

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OHMiner: An Overlap-centric System for Efficient Hypergraph Pattern Mining 25

OHMiner: An Overlap-centric System for Efficient Hypergraph ...

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20th European Conference on computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Qi, Hao Luo, Kang He, Ligang Zhang, Yu Cai, Minzhi Dai, Jingxin He, Bingsheng Jin, Hai Zhang, Zhan Zhao, Jin Yue, Hengshan Yu, Hui Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore Zhejiang Lab China Jilin University China

ISBN: (纸本)9798400711961

Hypergraph Pattern Mining (HPM) aims to identify all the instances of user-interested subhypergraphs (patterns) in hypergraphs, which has been widely used in various applications. However, existing solutions either need significant enumeration overhead because they extend subhypergraphs at the granularity of vertices, or suffer from massive redundant computations because they often need to repeatedly fetch and process the same incident hyperedges for different vertices. This paper presents an overlap-centric system named OHMiner to efficiently support HPM. OHMiner proposes an overlap-centric execution model to determine the subhypergraphs isomorphism through computing and comparing overlaps among hyperedges using set operations. This model aims to efficiently handle the vertices that collectively share the same incident hyperedges. To automatically and precisely retrieve an arbitrary pattern’s overlapping semantics without performing redundant set computations, OHMiner further proposes a redundancy-free compiler, which constructs an Overlap Intersection Graph (OIG) for the pattern, optimizes the OIG, and generates an overlap-centric execution plan to guide the procedure of HPM. Moreover, OHMiner designs an overlap-centric parallel execution engine, which adopts an incremental overlap-pruned approach to fast validate candidates for HPM. Additionally, it proposes a degree-aware data store to support efficient generation of candidates. Through evaluating OHMiner on a broad range of real-world hypergraphs with various patterns, our experimental results show that OHMiner outperforms the state-of-the-art HPM system by 5.4×-22.2×. © 2025 Copyright held by the owner/author(s).

关键词： Compiler

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IGG: Image Generation Informed by Geodesic Dynamics in Deformation Spaces

arXiv

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arXiv 2025年

作者： Wu, Nian Jayakumar, Nivetha Xing, Jiarui Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States

Generative models have recently gained increasing attention in image generation and editing tasks. However, they often lack a direct connection to object geometry, which is crucial in sensitive domains such as computational anatomy, biology, and robotics. This paper presents a novel framework for Image Generation informed by Geodesic dynamics (IGG) in deformation spaces. Our IGG model comprises two key components: (i) an efficient autoencoder that explicitly learns the geodesic path of image transformations in the latent space;and (ii) a latent geodesic diffusion model that captures the distribution of latent representations of geodesic deformations conditioned on text instructions. By leveraging geodesic paths, our method ensures smooth, topology-preserving, and interpretable deformations, capturing complex variations in image structures while maintaining geometric consistency. We validate the proposed IGG on plant growth data and brain magnetic resonance imaging (MRI). Experimental results show that IGG outperforms the state-of-the-art image generation/editing models with superior performance in generating realistic, high-quality images with preserved object topology and reduced artifacts. Our code is publicly available at https://***/nellie689/IGG. © 2025, CC BY.

关键词： Computational geometry

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Features of Extended Reality Environments in Semiconductor Fabrication Process Education: A Scoping Review

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IEEE Access 2025年 13卷 80576-80586页

作者： Parnell, Dennis R. Irving, Atayliya N. Smith, Connor S. Buhler, Amy G. Magruder Waisome, Jeremy A. University of Florida Department of Engineering Education GainesvilleFL32611 United States United States Naval Academy Electrical and Computer Engineering Department AnnapolisMD21402 United States University of Florida Marston Science Library GainesvilleFL32611 United States

The passing of the CHIPS and science Act in the United States has signaled a renewed interest in expanding the domestic semiconductor industry. To fuel this expansion and the new job opportunities it creates, academic institutions and companies alike will need to educate and train a new and growing workforce in the field of semiconductor fabrication. Unfortunately, the ability to provide hands-on learning experiences currently lacks accessibility due to the costs of starting and maintaining a cleanroom. However, extended reality (XR) offers a lower-cost alternative to address this issue. This scoping review seeks to determine the features present in existing XR environments used for semiconductor fabrication process education. Using the framework outlined by Arksey and O’Malley, only five conference articles from an initial pool of 1,116 articles were included in the review. These papers were all published between 2018 and 2023 and only focused on virtual reality devices. Across the five articles, 12 different features were identified. Notable limitations of this work include restricting the scope to six semiconductor fabrication processes, as well as excluding articles that did not specify the XR device used. Overall, the findings show that there has been very little research on the use of XR in the field of semiconductor fabrication education. © 2013 IEEE.

关键词： Teaching

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MediGuide AI using Machine Learning

MediGuide AI using Machine Learning

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： R Pooja Nikitha Elugubanti Mary Shiba C Kalai priya V K Sasirekha S Rathana Sabapathy Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Tiruvallur District Department Of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department Of Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal Tiruvallur District Department Of Computer Science and Business Systems R.M.D Engineering College Kavaraipettai Department Of Computer Science and Engineering Dhaanish Ahmed College of Engineering Vanchuvanchery Chennai District

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Providing diverse patient groups with their specific medication requirements, using a wide range of drugs, poses meaningful challenges for prescribing. Standardized approaches often lead to large waste, many side effects and suboptimal outcomes. Therefore, we need improved, more personalized healthcare. MediGuide AI's drug recommendation system is powered by machine learning and it offers a thorough solution. Optimized data pre-processing and analysis, leading to the desired outcome, is achieved using advanced machine learning algorithms like Naive Bayes, TF-IDF and the Natural Language Toolkit. Highly personalized prescriptions are generated using thorough patient data, including demographics and meaningful clinical factors to achieve substantially better accuracy. Important enhancements include dual recommendation outputs, which provide many drug recommendations alongside a large number of alternative Ayurvedic treatments presented side by side for effortless comparison. An advanced personalized dosage calculation module precisely determines drug dosages using advanced regression models based on age, weight and metabolism. This system helps patients track and record their medications and it also suggests several nearby Ayurvedic shops based on their location. MediGuide AI employs exceptionally powerful algorithms and its advanced tools deliver remarkably precise and exceptionally user-friendly healthcare solutions. Current systems make use of rule-based methods that cannot learn from new data, whereas MediGuide AI leverages a hybrid Naïve Bayes and PassiveAggressiveClassifier model for higher accuracy and real-time adaptability. It also includes Ayurvedic options to ensure personalized healthcare experience.

关键词： Drugs Accuracy Pain Databases Natural languages Machine learning Vectors Bayes methods Medical diagnostic imaging Diseases

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Parallel Median Filter with Arbitrary Window Size and Image Depth

Parallel Median Filter with Arbitrary Window Size and Image ...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Moreno Marzolla Michele Ravaioli Elena Loli Piccolomini Department of Computer Science and Engineering Center for Inter-Department Industrial Research ICT University of Bologna Bologna Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Removing noise in digital images is a fundamental operation that arises in many application domains. In this paper we consider the median filter, a filtering technique that replaces the color of each pixel with the median of those in a square neighborhood of fixed radius. For some use cases, the size of the neighborhood or the image depth may be large, making existing algorithms either too slow, or not applicable at all due to excessive memory requirements. In this paper we describe architecture-specific optimizations that enable the computation of the median filter with arbitrary window size and image depth on multicore processors and GPUs. We report preliminary results that indicate that the parallel implementations are suitable for practical use, with the GPU version outperforming the CPU.

关键词： Histograms Single instruction multiple data Multicore processing Image color analysis Memory management Graphics processing units Filtering algorithms Programming Parallel algorithms Optimization

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Quantum lattice representation of nonlinear classical physics

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Radiation Effects and Defects in Solids 2025年第1-2期180卷 98-102页

作者： Soe, Min Vahala, George Vahala, Linda Ram, Abhay K. Koukoutsis, Efstratios Hizanidis, Kyriakos Department of Mathematics and Physical Sciences Rogers State University ClaremoreOK United States Department of Physics William & Mary WilliamsburgVA United States Department of Electrical & Computer Engineering Old Dominion University NorfolkVA United States Plasma Science and Fusion Center MIT CambridgeMA United States School of Electrical and Computer Engineering National Technical University of Athens Zographou Greece

Using the Madelung transformation on a generalized scalar Gross–Pitaevski equation, a nonlinear continuum fluid equations are derived for a classical fluid. A unitary quantum lattice algorithm is then determined as a second order discrete representation of this Gross–Pitaevski equation and the simulations are compared to those using classical fluid dynamic techniques. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nonlinear equations

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Is Perceptual Encryption Secure? A Security Benchmark for Perceptual Encryption Methods

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Kashyap, Umesh Padhi, Sudev Kumar Ali, Sk. Subidh Indian Institute of Technology Bhilai Department of Computer Science and Engineering Durg491002 India

Perceptual encryption methods are the key enablers for protecting image privacy for deep learning-based applications in the cloud. In perceptual encryption, the image content is obfuscated such that the deep learning models can work on the obfuscated data. The key advantage of perceptual encryption over holomorphic encryption is that, unlike holomorphic encryption, the feature required by the target deep learning model is preserved in the encrypted data. Therefore, the model is not required to be retrained on the encrypted data. Recently, a significant number of perceptual encryption methods have been proposed in the literature, each improving over the others. In this paper, we perform a detailed security analysis of three best-known perceptual encryption methods, namely, Adversarial Visual Information Hiding, Learnable Encryption, and Encryption-then-Compression methods designed to protect the privacy of images. We proposed a new GAN-based security evaluation framework to successfully reconstruct the original images encrypted by these methods, showing clear security flaws. We conducted extensive experiments using different datasets and deep learning models. The results show significant vulnerabilities in the existing key-based perceptual encryption methods. The source code of our work is available at the given link: https://***/umesh-21/Benchmark to perceptual encryption. © 2025 IEEE.

关键词： Generative adversarial networks

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Perceptions of AI Ethics Policies Among Scientists and Engineers in Policy-Related Roles: An Exploratory Investigation

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Digital Society 2025年第1期4卷 1-25页

作者： Weichert, James Zhu, Qin Kim, Dayoung Eldardiry, Hoda Department of Computer Science Virginia Tech Blacksburg USA Department of Engineering Education Virginia Tech Blacksburg USA

The explosive growth of artificial intelligence (AI) over the past few years has focused attention on how diverse stakeholders regulate these technologies to ensure their safe and ethical use. Increasingly, governmental bodies, corporations, and nonprofit organizations are developing strategies and policies for AI governance. While existing literature on ethical AI has focused on the various principles and guidelines that have emerged as a result of these efforts, just how these principles are operationalized and translated to broader policy is still the subject of current research. Specifically, there is a gap in our understanding of how policy practitioners actively engage with, contextualize, or reflect on existing AI ethics policies in their daily professional activities. The perspectives of these policy experts towards AI regulation generally are not fully understood. To this end, this paper explores the perceptions of scientists and engineers in policy-related roles in the US public and nonprofit sectors towards AI ethics policy, both in the US and abroad. We interviewed 15 policy experts and found that although these experts were generally familiar with AI governance efforts within their domains, overall knowledge of guiding frameworks and critical regulatory policies was still limited. There was also a general perception among the experts we interviewed that the US lagged behind other comparable countries in regulating AI, a finding that supports the conclusion of existing literature. Lastly, we conducted a preliminary comparison between the AI ethics policies identified by the policy experts in our study and those emphasized in existing literature, identifying both commonalities and areas of divergence.

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