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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Kumar, Madisetty Tharun Natarajan, B. Murali, P. Chellammal, P. Dhavakumar, P. Muruganantham, T. Chennai601103 India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India J J College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu India K. Ramakrishnan College of Engineering Dept. of Ece Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

In the domain of deep learning and computer vision, Human Activity Recognition (HAR) holds paramount importance in understanding human movements through sensor data. This research presents a robust HAR system leveraging the VGG-19 deep learning architecture. Acknowledging challenges like limited context understanding and dataset biases, the research emphasizes the ongoing need for mitigation through continued research. Despite these hurdles, the VGG-19 based system showcased promising applications in healthcare and smart environments, enriching human-machine interactions. This research work conducted a comparative analysis between VGG-19 and ResNet-50 using standardized images of 15 human activities, highlighting VGG-19's superiority with an impressive 98.44% accuracy on the test dataset. The research underscores the potential of deep learning, particularly VGG-19, in accurately identifying intricate human activities from images. However, challenges such as contextual understanding and biases persist, prompting the research to emphasize the ongoing efforts to enhance the reliability and ethical soundness of HAR systems. These advancements are crucial for diverse sectors like healthcare and surveillance, ensuring the effective and responsible deployment of HAR technology. © 2024 IEEE.

关键词： computer vision

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Harnessing Security Improvements Using FaceNet Approach for Face Recognition System 13

Harnessing Security Improvements Using FaceNet Approach for ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Bhavya Sri, Pilli Navya, Kurella Saiteja, Kalla Keerthi, P. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350305463

In the realm of education technology, the integration of biometric authentication has garnered significant attention for its potential to streamline and enhance traditional attendance management systems. This research explores the development and implementation of a Face Recognition-Based Attendance System (FRBAS) using the state-of-the-art FaceNet technology. FaceNet, renowned for its accuracy and efficiency in facial recognition, serves as the backbone for the proposed system. The Face Recognition-Based Attendance System (FRBAS) leverages FaceNet technology to not only provide a seamless and secure method for recording attendance but also offers a robust solution for identity verification. By harnessing the power of deep learning, the system ensures high accuracy and reliability in recognizing facial features, reducing the chances of errors associated with traditional methods. Additionally, the integration of biometric authentication not only enhances security but also facilitates a more efficient and timely attendance tracking process, promoting a technologically advanced and streamlined educational environment. © 2024 IEEE.

关键词： Face recognition

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Classification of Disease in Potato Leaves using Deep Learning Approach 1

Classification of Disease in Potato Leaves using Deep Learni...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Prakash, Vallakatti Bhanu Shirisha, Dhanavath Reddy, Gopu Suryaprakash Sowjanya, M. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350372748

Agriculture is the backbone for every country in the world. and Farmers are the ones who predominantly take care of the crops and fields. In the present paper, we mainly focus on the diseases that are occurring in the farm fields mainly on potato leaves. Basically the leaf diseases occur because of the sudden climate change, improper nourishment, over usage of fertilizers, which involves high amount of NPK (Nitrogen, Phosphorus, Potassium). This study proposes a novel algorithm, which will help the farmers to detect the leaf diseases in the early stages, and eradicating them before growing. For this CNN (Convolutional Neural Network) algorithm, in order to detect the diseases that are present on the leaves by analyzing the leaves, perform feature extraction and provides recommendations and suggestions in order to prevent the disease. It mainly analyses the object, extracts the features and detect the diseases. This will help to eliminate the financial losses for the farmers, helps in yielding the more production of crops and also eradicate the disease completely from its roots, so that it won't occur next time. Additionally, this study incorporates the recommendation system in two distinct ways: first, it bases the recommendation on crop cultivation prior to the commencement of agriculture, and second, it bases the recommendation on crop curing following agriculture, that is, throughout the crop's growth phase. © 2024 IEEE.

关键词： Crops

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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ESP32: QEMU Emulation Within a Docker Container 1

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8th Future Technologies Conference, FTC 2023

作者： Howard, Michael Bruce Irvin, R. Department of Computer Science Maseeh College of Engineering and Computer Science Portland State University Portland United States

ISBN: (数字)9783031474576

ISBN: (纸本)9783031474569

The ESP32 is a popular microcontroller from Espressif that can be used in many embedded applications. Robotic joints, smart car chargers, beer vat agitators and automated bread mixers are a few examples where this system-on-a-chip excels. It is cheap to buy and has a number of vendors providing low-cost development board kits that come with the microcontroller and many external connection points with peripherals. There is a large software ecosystem for the ESP32. Espressif maintains an SDK containing many C-language sample projects providing a starting point for a huge variety of software services and I/O needs. Third party projects provide additional sample code as well as support for other programming languages. For example, MicroPython is a mature project with sample code and officially supported by Espressif. The SDK provides tools to not just build an application but also merge a flash image, flash to the microcontroller and monitor the output. Is it possible to build the ESP32 load and emulate on another host OS? This paper qualitatively explores the QEMU emulator and its ability to emulate the ethernet interface for the guest OS. Additionally, we look into the concept of containerizing the entire emulator and ESP32 load package such that a microcontroller flash image can successfully run with a one-step deployment of a Docker container. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Containers

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Responsible AI on National Cybersecurity Strategy with Enhancement of Cybersecurity in Education

Responsible AI on National Cybersecurity Strategy with Enhan...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Tingare, Bhagyashree Ashok Prasanna Lakshmi, G. Khedkar, Vijayshri Suresh Babu, Rakesh Thoppaen Dhar Diwan, Tarun Khatkale, Pravin B D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Department of Computer Science & Engineering Pune India HEXAWARE TECHNOLOGIES INC NJ United States Atal Bihari Vajpayee University Bilaspur India Sanjivani University Maharashtra Kopargaon India

ISBN: (纸本)9798350390049

For national cybersecurity to keep up with the digital transition, AI is more important than ever. Responsible AI improves cybersecurity and education by emphasizing openness and ethics. Ethical design is stressed from the outset in responsible AI. This article analyzes how artificial intelligence (AI) influences the national cybersecurity strategy, focusing on cybersecurity education development and effectiveness. The National Cybersecurity Strategy will frame this article. The major results show that responsible AI improves threat detection and response, cybersecurity infrastructure, talent shortage filling, collaborative research, and public trust. Educational institutions may now offer innovative training programs thanks to artificial intelligence. Completing this aim requires closing the gap between conventional methodologies and modern challenges. Closing gaps is vital. Functional artificial intelligence improves national cybersecurity and educational quality. This analysis shows that this conclusion is possible. This allows experts to properly react to shifting dangers. This is why it matters. © 2024 IEEE.

关键词： AI Integration AI-based Training Programs Cybersecurity Education Cybersecurity Infrastructure Ethical AI Governance National Cybersecurity Strategy Public Trust Responsible AI Skills Gap Threat Detection

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Holistic Knowledge Framework for Improving PC Design using Big data Analytics 7

Holistic Knowledge Framework for Improving PC Design using B...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rajendran, T. Vignesh, O. Priyadharsini, V. Jagadeesh, K. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350397253

Cyberspace is massively expanding every day, and the users of these digital devices are looking for more innovative applications to ease their day-to-day work. The main objective of any device is to use available system resources at a maximum optimum level and give high performance to the users. Especially in personal computers (PC), there is still a massive gap in the user experience and utilization of available system resources. There are several situations where error conditions and system responsiveness lag. Over several years the digital world evolved and provided high-performance computing. Furthermore, the PC design enhancements could be carryout through the participation of PC users. That could be accomplished by collecting the system performance indicators, system behavior and properties, software application usage, user working log, and other wide range of real-time data from the PC. These telemetry data needed to be retrieved from an extensive array of PCs then these datasets would be reposited on the centralized servers. By applying the big data analytical methods and other mining algorithms over these extensive datasets to identify the hidden patterns and discover more knowledge about the key indicators that would help improve PC performance and user experiences. Through this knowledge acquisition model, the organizations and research institutions could develop the most resilient and adoptable PC designs and software applications developments. © 2023 IEEE.

关键词： Personal computers

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Segmentation of Lung Images Using Hybridization of Fuzzy C Means and Morphological Techniques

Segmentation of Lung Images Using Hybridization of Fuzzy C M...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Harshini, I. Sri Dharshini, R. Shanmugalatha, A. Saranya, R. Devaki, M. Josephin Shermila, P. Velammal College of Engineering and Technology Department of Electrical and Electronics Engineering Madurai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541170

One of the serious illnesses that could shorten a patient's life is lung cancer. In addition, it is challenging to detect lung cancer early on and most cases are discovered after the disease has spread to other lung regions. The lung cancer tumoral tissue must be located by radiologist and other medical specialists. Consequently, the suggested work makes it easier to segment tumoral tissue from the CT lung image. Lung image segmentation is a crucial task in the field of medical image analysis because it allows lung abnormalities to be recognized and described. In order to achieve better performance hybrid strategies - which combine several approaches - have grown in popularity recently. Morphological operations are combined with fuzzy c-means clustering in this particular hybrid approach. In this work we provide a state-of-the-art overview of lung image segmentation using hybrid fuzzy c-means and morphological approaches. Hybrid techniques are an intriguing topic for additional research because it has been observed that their application can lead to increased resilience and accuracy when analyzing lung images. © 2024 IEEE.

关键词： Lung cancer

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A modelling analysis to predict the traffic using K-NN algorithm

A modelling analysis to predict the traffic using K-NN algor...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Narmatha, R. Prakash, M. Vennila, V. Tamil Nadu Dharmapuri India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

In this modern world due to Road traffic, many people are unable to reach their destination at the correct time. For example, if a person needed to reach the hospital in critical condition due to road traffic, they are unable to reach the hospital and got stuck in traffic which will lead to death. We lost a lot of people because of this road traffic. So, Road Safety is the most important part of our daily life. Conjointly, this prediction accomplished enhanced the safety and security of women empowerment which acted as a security guard for people especially women, protecting themselves in every circumstance. Here, statistical analysis of road traffic networks of selective locations is implemented to prognosticate road traffic. Alert and divert the people who were identified in the traffic zone and found the location of the people who were identified in the unsafe zone. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Hospitals

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Signs to Speech 2

Signs to Speech

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vardhan, Harsh Harsavardhan, S. Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings. © 2024 IEEE.

关键词： Convolution

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