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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ayyadurai, M. Sujatha, K. Deeptha, R. Preethi, D. Dinesh, M. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai India Saveetha University Chennai India

ISBN: (纸本)9798350370249

Autism, a neurological disorder, manifests uniquely in areas such as verbal and nonverbal communication, social interactions, behavioral adaptability, and specific interests. The results collected indicate that health professionals should receive more training in areas like psychology and developmental disorders and be able to detect Autism Spectrum Disorder (ASD) at a much earlier stage. They also provide pertinent information for reflection on the methods currently employed in evaluating children's development and diagnosing ASD. This study collects information from a specific set of children with autism spectrum disorder from kaggle and is based on real, up-to-date facts. The input and output dataset will have certain common characteristics that will determine whether or not a child currently has ASD or has any signs of the same shortly. Random Forests, also known as Random Decision Forests, is a widely used ensemble learning method for tasks like classification and regression. This technique operates by constructing numerous decision trees during the training process. This classification is used to classify the dataset collected, into categories and hence segregate the children examined as "with" and "without" ASD with high accuracy about 88%. ©2024 IEEE.

关键词： Diseases

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Leveraging Attention Mechanisms to Enhance EfficientNet for Precise Analysis of Chest CT Images 2nd

Leveraging Attention Mechanisms to Enhance EfficientNet for...

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2nd International Conference on Big data, IoT and Machine Learning, BIM 2023

作者： Rakibul Haque, Md. Al Mamun, Md. Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Accurate and timely analysis of chest CT images is crucial for effectively diagnosing and treating a wide range of respiratory, cardiovascular, and infectious diseases, making it a vital component of modern medicine. Manual interpretation of chest CT can be time-consuming, prone to subjective variability, and potentially error-prone, highlighting the need for automation to improve efficiency and accuracy in analyzing large volumes of images. Researchers are increasingly using convolutional neural networks (CNNs) for chest CT image analysis due to their ability to learn complex features and patterns from large datasets. Despite significant advancements in analytical techniques, the challenge of determining which regions of interest to focus on and how to assign appropriate levels of importance to various features during chest CT image analysis remains an ongoing concern in the field. Our paper introduces a novel approach, utilizing soft and channel attention mechanisms in conjunction with an improved version of EfficientNetB0 to efficiently extract and prioritize critical features essential for accurately detecting and diagnosing lung-related ailments from chest CT scans. We have evaluated the proposed approach on a large dataset of CT scans, with the objective of accurately identifying COVID-19, non-COVID-19, and Community-Acquired Pneumonia (CAP) cases. Experimental results demonstrated exceptional performance, with achieved accuracy of 99.41%, surpassing other state-of-the-art methods in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Large datasets

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Revolutionizing Agriculture: Empowering Farmers through Drone Training Simulations for Precision Agriculture 10

Revolutionizing Agriculture: Empowering Farmers through Dron...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Akhilesh, V. Boobesh, K.S. Danush, S.V. Madhumathi, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Traditional agricultural practices in India have historically relied on manual labor and conventional techniques;however, initiatives like the 'Kisan Drone Scheme' aim to modernize farming practices by leveraging drones to reduce labor and time. Despite its potential benefits, this scheme faces challenges related to farmer awareness and training, posing a risk of inefficient drone operations. To address this issue, this project utilizes cutting-edge technology to revolutionize agricultural training. By leveraging various platforms, this project has developed a comprehensive solution focused on addressing the limitations of traditional training methods. A detailed 3D model of a pesticide drone is created with dynamic animations, to provide farmers with a realistic and interactive learning experience. Through the training model developed, farmers can engage with the drone model, learning how to operate it effectively and safely in virtual environments. The implementation plan is thorough and methodical, encompassing meticulous stages of modeling, animation, and testing to ensure the effectiveness and reliability of the Virtual Reality (VR) training solution. By delivering an immersive learning experience, the ultimate objective of this project is to empower farmers with the knowledge and skills necessary to optimize drone utilization, ultimately leading to enhanced agricultural productivity and sustainability. © 2024 IEEE.

关键词： Drones

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Enhancing Classroom Accessibility for Deaf Students through Sign Language Recognition 10

Enhancing Classroom Accessibility for Deaf Students through ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Monisha, K.B. Sushmitha, G. Shanthalakshmi Revathy, J. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This paper addresses the communication barriers faced by the deaf and voiceless community by introducing a system that translates sign language into spoken or written language. The primary aim is to foster better communication between these communities and the hearing population, ultimately supporting academic success for individuals with hearing impairments. By utilizing the deep learning model VGG19, the system is able to recognize sign language with an astounding 99.97% accuracy rate, which is important for guaranteeing fair access to education. Because VGG19 can efficiently extract complicated features from images, a property that is essential for precisely interpreting the intricate hand gestures and motions that are a part of sign language communication, it is used in sign language recognition. It highlights the importance of real-time captions and interpretations as transformative tools in educational environments. Furthermore, the paper explores the implementation of this technology, outlining its benefits for educators and deaf students alike. Through examination of challenges, and future directions, it underscores the growing significance of sign language recognition in education, emphasizing its role in promoting inclusivity and enhancing learning experiences. © 2024 IEEE.

关键词： Students

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Guardian PI : Raspberry Based Night Survaillance Camera 4

Guardian PI : Raspberry Based Night Survaillance Camera

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Lalitha, V. Venkat Raj, M. Nirmal Raaghav, N. Ramanan, D. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Personal safety, especially for women during nighttime, remains a pressing concern. The "Guardian Pi"is an innovative solution that leveragesthe Raspberry Pi 4 Model B+ to provide real-time surveillance and emergency response. Equipped with a 360-degree HD camera and a high-frequency sound sensor, the system captures and analyzes environmental data, including audio and video, to detect potential threats. Upon identifying an emergency, the system instantly alerts pre-registered contacts and nearby police stations via email using Simple Mail Transfer Protocol (SMTP), providing continuous updates every second. The software, developed in Python, integrates key libraries such as GPIO for hardware interfacing, ensuring a reliable and user-friendly security solution. Guardian PiU aims to enhance nighttime safety by offering a proactive and efficient surveillance system for emergency situations. © 2024 IEEE.

关键词： Acoustic devices

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Adapting Single-Speaker Models for Multi-Speaker Environments 1st

Adapting Single-Speaker Models for Multi-Speaker Environment...

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1st International Conference on Artificial Intelligence Towards Smart Sustainable Society, AISSS 2023

作者： Vidhyadhar Gowda, M. Sujith Kumar, M.D. Sumukha, K. Yatin Chaya Kumari, H.A. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India

ISBN: (纸本)9789819775910

The area of speech, language, and machine learning research has extensively investigated text-to-speech (TTS), sometimes referred to as speech synthesis. Its importance has grown due to its applicability in many different sectors. Neural network-based TTS has significantly improved the quality of synthesized speech, mainly to the breakthroughs in deep learning and artificial intelligence. This study presents an in-depth analysis of neural TTS. We present a text-to-speech (TTS) synthesis system based on neural networks that can produce spoken audio in the voices from multiple speakers, even the ones that are not perceived during training. Our TTS system, with a speaker encoder, Tacotron 2-based synthesis network, and WaveNet-based vocoder, adeptly captures unseen speaker characteristics. It effectively transfers knowledge for synthesizing natural speech from new speakers, emphasizing the importance of diverse training sets and facilitating speech synthesis for dissimilar, novel voices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Efficiency & Innovation in Recruitment usingAI & Android

Efficiency & Innovation in Recruitment usingAI & Android

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Chalana, Kanika Gautam, Ashwin Ravi Raj, Anand Daivik, J.S. Amrutha, H.R. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The goal of screening resumes is to determine the top applicants for a position and to inform users of their resume score and areas for improvement. The literature on existing approaches has been analyzed, and it has been discovered that the traditional systems like manual screening may result in false assumptions and the wasting of human potential, but they lackrobustness Regarding processing, accuracy and efficiency. To acquire accurate results, software must utilized to process natural language and machine learning techniques to match and rate the candidates in real-time by ranking their resumes. The input would be the resumes and output would be a ranked candidate's resumes on the admin side and recommendations for the user side. Instantaneous real-time output results are acquired by employing Object character recognition. In The system that is being suggested the authors used OCR model and android principles along with python scripts. This system has the many benefits including security, interpretability, high accuracy, lightweight model, and quick processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Looking Backwards (and Forwards): NSF Secure and Trustworthy Computing 20-Year Retrospective Panel Transcription

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IEEE Security and Privacy 2023年第2期21卷 32-42页

作者： Landwehr, Carl Reiter, Michael K. Williams, Laurie Tsudik, Gene Jaeger, Trent Kohno, Tadayoshi Kapadia, Apu University of Michigan Electrical Engineering Ann ArborMI48109-2122 United States Duke University Computer Science and Electrical and Computer Engineering DurhamNC27708 United States North Carolina State University Computer Science Department of the College of Engineering RaleighNC27513 United States University of California Computer Science Irvine United States The Pennsylvania State University Computer Science and Engineering University ParkPA16802 United States University of Washington Paul G. Allen School of Computer Science and Engineering SeattleWA98195 United States Indiana University Bloomington Department of Computer Science BloomingtonIN47408 United States

The U.S. National science Foundation (NSF) celebrated the 20th anniversary of its research funding programs in cybersecurity, and more generally, secure and trustworthy computing, with a panel session at its conference held in June, 2022. The panel members, distinguished researchers in different research areas of trustworthy computing, were asked to comment on what has been learned, what perhaps should be 'unlearned what still needs to be learned, and the status of education and training in their respective areas of expertise. Laurie Williams covered enterprise security and measuring security, Gene Tsudik commented on cryptographic security, Trent Jaeger addressed computing infrastructure security, Tadayoshi Kohno reviewed security in cyberphysical systems, and Apu Kapadia provided insights on human-centered security. Michael K. Reiter chaired the panel and moderated questions from the audience. This report provides a brief summary of NSF's research programs in the area and an edited transcript of the panel discussion. © 2003-2012 IEEE.

关键词： Personnel training

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mL-mB LBP Fused Net: Multi-layer-Multi-block LBP Image Fusion Facial Expression Recognition 5th

mL-mB LBP Fused Net: Multi-layer-Multi-block LBP Image Fusio...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Mukhopadhyay, Moutan Ghosh, Ankush Shaw, Rabindra Nath Dey, Aniruddha Department of Computer Science & Engineering MAKAUT West Bengal Kolkata India Chandigarh University Punjab Mohali India Department of Computer Science & Engineering MSIT West Bengal Kolkata India

ISBN: (纸本)9789819791118

Facial emotion recognition (FER) systems have gained popularity due to their applications in various fields, including healthcare, cognitive science, video conferencing, and driver safety. However, recognizing facial expressions automatically from images presents challenges due to inter-person variability and the difficulty in accurately distinguishing between different emotions. The research investigates the application of a modified Local Binary Pattern (LBP) in facial emotion detection, focusing on the multilayer multi-block local binary pattern (mL-mB LBP). The process involves converting facial expression images into 3×3 identical blocks, which are then used to generate center pixels using the LBP descriptor block-wise. The final layer is partitioned into 3×3 blocks to generate central pixels. The study introduces a customized convolutional neural network (CNN)-based framework for emotion recognition, which uses the mL-mB LBP image data to accurately predict emotions. The CNN model is trained using multi-level multi-block images. The experimental study findings show that the accuracy of the CNN model is significantly improved when trained using multi-block multi-level images with all layers fused. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image fusion

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Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves 27

Comparing Optical Flow and Deep Learning to Enable Computati...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Bouraffa, Tayssir Carlson, Elias Kjellberg Wessman, Erik Nourir, Ali Lamart, Pierre Berger, Christian Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Volvo Cars Gothenburg Sweden University of Gothenburg Department of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798331505929

Gathering data and identifying events in various traffic situations remain an essential challenge for the systematic evaluation of a perception system's performance. Analyzing large-scale, typically unstructured, multi- modal, time-series data obtained from video, radar, and LiDAR is computationally demanding, particularly when meta-information or annotations are missing. We compare Optical Flow (OF) and Deep Learning (DL) to feed computationally efficient event detection via space-filling curves on video data from a forward-facing, in-vehicle camera. Our first approach leverages unexpected disturbances in the OF field from vehicle surroundings;the second approach is a DL model trained on human visual attention to predict a driver's gaze to spot potential event locations. We feed these results to a space-filling curve to reduce dimensionality and achieve computationally efficient event retrieval. We systematically evaluate our concept by obtaining characteristic patterns for both approaches from a large-scale virtual dataset (SMIRK) and applied our findings to the Zenseact Open dataset (ZOD), a large multi-modal, real-world dataset, collected over two years in 14 different European countries. Our results yield that the OF approach excels in specificity and reduces false positives, while the DL approach demonstrates superior sensitivity. Both approaches offer comparable processing speed, making them suitable for real-time applications. © 2024 IEEE.

关键词： Optical flows

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