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Diabetic Foot Ulcer Classification Using Deep Learning Approach 8th

Diabetic Foot Ulcer Classification Using Deep Learning Appro...

引用

8th IFIP TC 12 international conference on Computer, communication and Signal Processing, ICCCSP 2024

作者： Sivakamasundari, P. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

Podiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause "Diabetic Peripheral Neuropathy," a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. The basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. This work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set. © IFIP international Federation for information Processing 2025.

关键词： Diagnosis

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Performance analysis of 6T SRAM in different technologies using FinFET process 3

Performance analysis of 6T SRAM in different technologies us...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Nayana, D.K. Deepthi Murthy, T.S. Hegde, Nayana Reva University School of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798331515911

Static Random Access Memory (SRAM) is an essential component of contemporary system-on-a-chip (SoC) devices and processors. To achieve high density, SRAM bit cells must be built using ever-tinier components. This study looks at the design and operation of a 6T SRAM cell at many technology nodes, from 180 nm down to 18 nm (Fin Field-Effect Transistor) FinFET (Complementary Metal-Oxide-Semiconductor) CMOS. It looks into how scale affects stability, process variation tolerance, soft mistakes, and device aging. Significant difficulties are seen at smaller nodes, such as heightened vulnerability to soft mistakes and decreased stability as a result of process variances and aging devices. The report also emphasizes how FinFET CMOS technology has advanced to 18 nm, resolving certain scaling problems with earlier nodes. SRAM cells gain from increased density as technology advances, but they also confront significant stability and reliability issues. In order to guarantee the lifetime and continuous performance of SRAM in cutting-edge semiconductor technologies, future research should concentrate on reducing these problems. © 2025 IEEE.

关键词： CMOS integrated circuits

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Enhancing 6G communication Ecosystems with Blockchain technology: A Comprehensive Survey 14

Enhancing 6G Communication Ecosystems with Blockchain Techno...

引用

14th IEEE international conference on communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Chandan Singh, Murari Kumar Mishra, Renu Maini, Tarun Chakraverti, Ashish Kumar Dept. of ECE ITS Engineering College U.P Greater Noida India Dept. of CSE Sharda University U.P. Greater Noida India Dept. of CSE Galgotia University U.P Greater Noida India

ISBN: (纸本)9798331531935

The landscape of the communication system is undergoing a rapid evolution, emphasizing the need for intelligence, efficiency, and adaptability to enable cloud computing, Internet-of-Everything, and cyber-physical systems. With the rapidly growing complexities and interconnectivities of the systems, assurance of security and privacy is of paramount importance. The inherent nature of blockchain technology is its immutability, decentralization, anonymity, and transparency. It has the potential to establish trust between distributed networks through secure access management, robust authentication, improved integrity, confidentiality, and optimized resource allocation. Implementation of blockchain technology in 6G communication systems is likely to introduce dramatic innovations, enabling digital economy growth and enhancing societal functioning and connectivity. This article delves into the history of the evolution of mobile communications from 1G to 6G, the significance of blockchain technology in 6G, and the latest advances in terahertz antennas that can make an immense contribution to the field of 6G technology. © 2025 IEEE.

关键词： Internet of Everything

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A distributed machine learning dynamic remote proof scheme that resists collusion attacks 4

A distributed machine learning dynamic remote proof scheme t...

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4th international conference on Network communication and information Security, ICNCIS 2024

作者： Wang, Ke Wu, Qiuxin Qin, Yu School of Applied Science Beijing Information Science and Technology University Beijing100192 China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Science Beijing100190 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Based on the security of distributed machine learning data and model based on remote proof, a cross-domain dynamic remote proof scheme based on ring signature improves the efficiency of computer cluster proof, the application of dynamic signature, realizes the real-time measurement of the confidence of computer cluster, and strengthens the real-time protection of data and model. Computers with different security domain may adopt different remote proof mechanism, and convert the proof information of different proof methods into unified proof information, thus realizes the cross-domain dynamic remote proof between computer clusters. Noise is added when the cloud server distributes data to a distributed computer cluster. Due to the particularity of distributed architecture and the unsolution of indefinite equations, the enemy cannot conspire with one or more computers to steal data or the model parameters trained by endpoints. Finally, the safety of this method is verified through safety analysis and experimental analysis, and the computational overhead required in the proof process is reduced compared with other methods. © 2025 SPIE.

关键词： Authentication

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Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network 5

Development of an Arabic Sign Language Recognition System Ut...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Kale, Meenal R. Kaur, Chamandeep Kumar, Indrajeet Mahdi, Haider Sharif Uprikar, Vijay Sankari, V. Yeshwantrao Chavan College of Engineering Department of Humanities Nagpur India Jazan University Department of Computer Science & Information Technology Jizan Saudi Arabia Department of Cse Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Datta Meghe Institute of Management Studies Nagpur India K. Ramakrishnan College of Engineering Department of Ai&ds Trichy India

ISBN: (纸本)9798350356236

The ability to recognize sign language movements, especially Arabic Sign Language (ArSL), is necessary to increase the conversation accessibility for the those who are hard of hearing or deaf. However, there are a number of drawbacks to the current ASLR techniques, such as their dependency on manually created features, lack of datasets, and inefficiencies in real-time applications. This study suggests a novel method that utilize Deep Convolutional Neural Network (DCNN) model designed especially for ASLR applications to overcome these difficulties. The suggested DCNN overcomes the drawbacks of conventional handmade feature-based techniques by automatically extracting discriminative features from unprocessed sign language images using hierarchical feature learning. Preprocessing techniques such as normalization and scaling are applied to improve the 7,857 completely labeled images in the RGB Arabic Alphabet Sign Language (AASL) the data set, which is provided for ASLR model evaluation and training. The results of the study demonstrate how successful the recommended DCNN model is, as evidenced by its astounding 99% accuracy on the AASL dataset. Comparison with similar approaches like VGG-16, ResNet-18, and other Efficient Net based architecture, shows the accuracy to which the proposed method can classify Arabic sign language alphabets. The findings presented evidence for how the chosen architecture for DCNNs can significantly enhance ASLR improvement, open up opportunities for improving communication interaction with Deaf and hard-of-hearing people and their integration. In doing the proposed work, we use Python. © 2025 IEEE.

关键词： Convolutional neural networks

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Fog computing-based Real-Time Emotion Recognition using Physiological Signals 27

Fog Computing-based Real-Time Emotion Recognition using Phys...

引用

27th international conference on Advanced communications technology, ICACT 2025

作者： Erzurumluoglu, Omur Fatmanur Ozlem, Kadir Atalay, Asli Tuncay Atalay, Ozgur Ince, Gokhan Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Textile Technologies and Design Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9791188428137

Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system's ability to support multiple users simultaneously. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fog computing

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An efficient formal verification method for concurrent programs 4

An efficient formal verification method for concurrent progr...

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4th international conference on Network communication and information Security, ICNCIS 2024

作者： Zhang, Jianyu Zhang, Long Zhang, Jingjing Yang, Feng University of Electronic Science and Technology of China 2006 Xiyuan Avenue Western District High-Tech Zone Chengdu611731 China National Key Laboratory of Science and Technology on Information System Security No. 13 Dachen Road Fengtai District Beijing100141 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Formal methods (FM) are a pivotal technology for ensuring the safety and reliability of software systems. These methods are centered around the application of precise mathematical principles to verify the security and dependability of software. Formal methods have been widely employed in the verification of critical systems, with the increasing significance of concurrent programs in the context of the growing prevalence of parallel computing architectures like multi-core processors. However, the efficiency of formal methods in verifying concurrent programs is often low, which hinders their broader adoption and application. Addressing this issue, this study proposes an efficient formal verification method tailored for concurrent programs. This method bifurcates the verification process of concurrent programs into two stages: proving sequential correctness and verifying other attributes. In the first stage, leveraging theorem-proving strategies and large language model (LLM) technology, the method assists in proving the sequential correctness of concurrent programs. The second stage, based on model checking technology, involves transforming the program to narrow down the verification scope before using model checking tools to verify other security attributes of the program. Experimental results have shown that the proposed method significantly reduces the state space that needs to be searched during verification, thereby enhancing the efficiency of concurrent program verification. © 2025 SPIE.

关键词： Model checking

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Predictive Modeling for Identifying Early Warning Signs of Underperformance in Vocational Education 5

Predictive Modeling for Identifying Early Warning Signs of U...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Hettiarachchi, D.S.S. Harshanath, S.M.B. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331530983

This study focuses on developing a predictive modeling system to identify early signs of underperformance in vocational education, critical for building a skilled workforce. Addressing challenges like high dropout rates and inadequate graduate preparedness, the system utilizes machine learning techniques such as Neural Networks, Decision Trees, and Logistic Regression. Implemented in Python, it analyzes key features like academic records, attendance, engagement, and socioeconomic factors. Preprocessing steps, such as data cleaning and feature engineering, were implemented, and transfer learning was employed to adapt the model. This combination of feature engineering and transfer learning enables the transfer of knowledge from academic settings to vocational education by identifying and leveraging shared characteristics between the two domains. The system provides real time insights through automated reports and notifications, enabling targeted interventions to improve retention and graduation rates. This scalable approach advances educational technology and informs policies to enhance vocational education outcomes. © 2025 IEEE.

关键词： Apprentices

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-Network on 6G-enabled Mobile Edge Networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th international conference on communication Systems and NETworkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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Gesture Showdown: Rock Paper Scissors with AI Vision

Gesture Showdown: Rock Paper Scissors with AI Vision

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2025 IEEE international conference on Computational, communication and information technology, ICCCIT 2025

作者： Hemal Babu, H. Yeshwanth, C.D. Shravan, N. Bose, K. Regin Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Institute of Technology Department of AI&DS and Cloud Computing Chennai India

ISBN: (纸本)9798331512965

This paper presents a gesture-based RockPaper-Scissors game that leverages computer vision and hand gesture recognition to enable intuitive human-computer interaction. Utilizing MediaPipe's pre-trained hand tracking module, the system detects and identifies 21 hand landmarks in real-time to classify gestures as rock, paper or scissors based on angular relationships between key points. OpenCV is used for webcam integration and video frame preprocessing, ensuring seamless interaction. The game compares the recognized gesture with a computer-generated move, dynamically displaying the results and resetting after each round for continuous play. The system identifies gestures through precise angle calculations, differentiating between closed fists, open palms and extended fingers. Sample outputs demonstrate real-time gameplay scenarios, highlighting gesture recognition accuracy and user engagement. Future enhancements include refining recognition accuracy, adding multiplayer functionality, introducing customizable gestures, and deploying the game on web and mobile platforms for broader accessibility. This project showcases the potential of integrating AI and computer vision in creating engaging and user-friendly applications. © 2025 IEEE.

关键词： Gesture recognition

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