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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Waghale, Pratibha Damahe, Lalit Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331527822

Camouflaged Object Detection (COD) poses a significant challenge in computer vision due to the minimal contrast between the object and its surroundings. Conventional methods often struggle in such scenarios. This study introduces an approach by integrating DeepLabV3, a cutting-edge semantic segmentation model, with Deep Embedded Clustering (DEC) to enhance COD. From DeepLabV3, the ResNet101 model is fine-tuned to achieve precise segmentation. Then DEC is applied to help with unsupervised feature learning that enables the model to differentiate elusive differences between camouflaged objects and their backgrounds. The proposed method's efficiency is inveterated on the ACOD10K dataset. It shows distinct improvements in quantitative metrics and qualitative evaluations with a Mean Square Error (MSE) of 0.337 compared to other methods. This approach establishes a robust foundation by integrating DEC into deep learning frameworks to tackle the complexities of COD. © 2024 IEEE.

关键词： Semantic Segmentation

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DrownNet: Infant Drowning Alert System using Deep Learning 7

DrownNet: Infant Drowning Alert System using Deep Learning

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Lipika, M.S. Sruthi, S. Subhasri, V.P. Kingsy Grace, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

The Infant Drowning Prevention System (IDPS) is a vital innovation aimed at combating the tragic loss of young lives due to drowning incidents. The Infant Drowning Prevention and Alert System (IDPAS) is a crucial innovation designed to prevent the tragic loss of young lives due to drowning incidents. By leveraging advanced Video Vision Transformers (VVTs), the IDPAS accurately detects potential drowning behaviors in infants and toddlers, ensuring swift alerts to caregivers for timely intervention. Its Smart Watching System, powered by sophisticated deep learning algorithms like CNNs and RNNs, continuously monitors video streams for subtle cues indicative of distress in water. Through ongoing learning from annotated examples and historical data, the IDPAS refines its ability to differentiate between safe interactions and risky situations, enabling rapid alerts via SMS, push notifications, or audible alarms upon detecting potential drowning *** can easily configure settings through a streamlined web interface, while pool managers have a dedicated dashboard for camera setups and emergency contacts. Caregivers receive instant alerts via SMS, email, or in-app notifications, empowering quick responses to risks. data privacy and security are prioritized, to protect sensitive information, ensuring compliance with regulations while significantly enhancing infant *** proposed DrownNet system within the IDPAS offers an impressive 96% accuracy compared to other models in the literature, highlighting its quality and completeness. The integration of high accuracy, real-time monitoring, and user-friendly interface makes the IDPAS an essential tool for drowning prevention, providing comprehensive coverage and reliable alerts to prevent potential drowning incidents effectively. © 2024 IEEE.

关键词： data privacy

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A Systematic Approach for Effective Apgar Score Assessment in 1 and 5 min Using Manifold Machine Learning Algorithms 1

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2nd International Conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Shaambhavi, S. Bhavani, G. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

The Apgar score was first introduced by Dr. Virginia Apgar in 1952, and its mainly used to find the well-being of newborn infants by using the five important aspects such as heartbeat, reflex irritation, color, muscle tone, and respiration. Apgar was an assessment tool that is performed at 1 and 5 min of a child’s birth. There is a potential impact of the score on medical decision making and providing care to newborn infants. Since a low Apgar score results in harmful interventions. So here in this research paper, the analysis and comparison of R2 Score of various machine learning algorithm models is done. Here, this comparison mainly focuses on improving the R2 score of the Apgar value. Additionally, the use of the Apgar score is a very important subject in terms of research and discussion among healthcare professionals. The Apgar score has also been evaluated in various populations including preterm infants and newborns with congenital anomalies. This research paper has analyzed the comparison of 13 different machine learning algorithms and the R2 Score was obtained as 96.5% for the hybrid approach. So, utilizing machine learning to predict the Apgar score will increase R2 Score, boost neonatal research and help us better understand the physiology and health of newborns. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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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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Deep Learning Evolved: Overcoming Sub-Optimal Local Minima with (μ/ρ+λ)-Evolution Strategies

Deep Learning Evolved: Overcoming Sub-Optimal Local Minima w...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Rivas, Pablo School of Engineering and Computer Science Baylor University Department of Computer Science United States

ISBN: (纸本)9798350327595

Integrating Evolution Strategies (ES) and Backprop-agation (BP) within a deep neural network framework presents a significant challenge, as ES has previously only been shown to perform comparably to BP for smaller problems. In this study, we extend the application of ES to high-dimensional problems, using it to initialize the weights of a Deep Neural Network (DNN). Our experiments demonstrate that this novel ES approach can effectively overcome local minima and converge towards near-optimal global solutions. Following ES initialization, we employ traditional BP gradient methods to further refine the weights based on the initial set provided by ES. A key finding of our research is the potential for ES to reduce the computational time required by traditional gradient-based backpropagation learning methods. This efficiency is achieved by providing an initial set of weights close to the global optimum, enabling the network to converge more rapidly than with random or zero-weight initialization approaches. Our approach offers a promising direction for future research in the efficient training of deep neural networks and opens up new possibilities for tackling high-dimensional problems with these networks. © 2023 IEEE.

关键词： deep learning evolution strategies genetic algorithms machine learning

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Fostering Environmental Awareness: A CNN-Powered Plant Identification and Knowledge-Sharing Platform 6

Fostering Environmental Awareness: A CNN-Powered Plant Ident...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Duraimurugan, N. Vaseekaran, K.V. Vikram, V. Department of Computer Science Engineering Rajalakshmi Engineering College Tamil Nadu India

ISBN: (纸本)9798350369748

In cities people often feel out of touch, with nature which leads to awareness and interaction with plants especially when they eat processed foods. Our project aims to change this by creating an app that lets users take pictures of plants and quickly identify ones using a cutting-edge deep learning algorithm, specifically a Convolutional Neural Network (CNN). This app will also have a commenting feature to encourage users to share knowledge and engage with each other. The CNN model is trained on plant data fine tuning its accuracy through neural network processes. We also use filters and activation functions such Relu for better plant identification. Additionally, we include a summarization feature to provide users with information about the plants they find using API. Our goal is to help people reconnect with nature promote eco lifestyles and support sustainability. Through our work we hope to create a balance, between humans and the natural world for a planet. In the field of learning researchers are focusing on classification and identification techniques using CNN technology to promote awareness and sustainability. © 2024 IEEE.

关键词： Convolutional neural networks

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MOOD: Mindfulness fOr sOftware Developers 24

MOOD: Mindfulness fOr sOftware Developers

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18th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2024

作者： Romano, Simone Scanniello, Giuseppe Marchetto, Alessandro Giorgini, Paolo Guidetti, Gloria Converso, Daniela Viotti, Sara Department of Computer Science University of Salerno Italy Department of Information Engineering and Computer Science University of Trento Italy Department of Psychology University of Turin Italy

ISBN: (纸本)9798400710476

Peopleware, which includes anything related to the role of people in Software Development (SD), has been arousing an increasing interest from both the software industry and research community. This interest is due to the current economic system that demands high-quality software products with a short time to market, staying on the budget. This exposes software developers to the risk of experiencing stress, burnout, and reduced motivation, leading, in turn, to reduced job performance, low-quality SD-related artifacts, and increased turnover. Mindfulness represents a promising intervention that might let developers do their best at work, limiting or even preventing the previously mentioned negative outcomes. This paper presents MOOD (Mindfulness fOr sOftware Developers), a research project whose overarching goal is to customize a well-known and validated group-based intervention program, Mindfulness-Based Stress Reduction (MBSR), in the context of SD-related tasks and assess whether it helps developers to improve their well-being and performance, as well as the quality of the SD-related artifacts they produce. © 2024 Owner/Author.

关键词： Budget control

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Design of an Efficient Model for Health Status Prediction Using LSTM, Transformer, and Bayesian Neural Networks

Design of an Efficient Model for Health Status Prediction Us...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Golchha, Rishi Khobragade, Prashant Talekar, Ashish Gh Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350319019

The urgent need for health predictions has led to extensive research, but existing methods often fail to cope with the complexity of health data. While traditional methods struggle to capture the temporal and descriptive relationships expected in multimodal health data, this research offers new methods that use technology tools to overcome these limitations. By integrating long-term memory (LSTM) networks for physical modeling, Transformer architecture with multi-head listening mechanism for feature extraction, and Bayesian neural networks (BNN) for parameter estimation, clearly, our model was effective in predicting health outcomes. LSTM networks are effective at capturing long-term dependencies found in continuous health data and help identify changes in healthy patient recruitment over time. Meanwhile, the multihead Transformer takes care of the translation by focusing on important features in multivariate data, thus improving the accuracy of disease classification. Additionally, integration of Bayesian neural networks can quantify uncertainty, which is important for informed clinical decision making. Tests show that the performance is better than traditional methods, with fewer errors and defects. This principle not only increases the accuracy and reliability of health predictions, but also has the ability to change medical decisions, ultimately improving patient outcomes and healthcare. © 2024 IEEE.

关键词： Forecasting

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Indonesian Dynamic Sign Language Recognition for Individuals with Sensory Disabilities using LSTM 4

Indonesian Dynamic Sign Language Recognition for Individuals...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Yunita, Reni Nababan, Erna Budhiarti Lydia, Maya Silvi Universitas Sumatera Utara Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Computer Science Medan Indonesia

ISBN: (纸本)9798350376111

People with disabilities often face difficulties communicating with the general public, especially regarding sign language. Sign Language Recognition (SLR) is a technology focused on recognizing, interpreting, and translating sign language into text or speech. SLRs can be divided into two types: static and dynamic. This research focuses on dynamic sign language recognition to develop recognition for Indonesian Sign Language, using the Python programming language with libraries such as Mediapipe, cvzone, sklearn, numpy, and the Long Short-Term Memory (LSTM) technique. These findings show the potential of SLR technology in assisting communication for individuals with disabilities in Indonesia. This research significantly contributes to the development of Indonesian Sign Language SLR. © 2024 IEEE.

关键词： Disabled persons

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