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Performance estimation of direct and indirect transmission in V2X communications

Performance estimation of direct and indirect transmission i...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Keramidi, Irene Uzunidis, Dimitris Moscholios, Ioannis Yiannopoulos, Konstantinos Sagias, Nikos Sarigiannidis, Panagiotis University of Peloponnese Dept. Informatics and Telecommunications Tripolis Greece University of West Attica Dept. Electrical and Electronic Engineering Athens Greece University of W. Macedonia Dept. Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350318845

In this paper, we evaluate the performance of a vehicular ad hoc network that enables Vehicle-to-Everything (V2X) communication considering direct and indirect packet transmission. As a performance indicator, we use the blocking probability due to the unavailability of resources. For the blocking probability calculation, we utilize two loss models from the teletraffic theory. The results demonstrate that both the capacity of the V2X links and the number of hops in the multi-hop transmission significantly affect the blocking probability of a request for service and consequently the QoS. © 2024 IEEE.

关键词： Blocking probability

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Translating the unspoken Deep learning approaches to Indian Sign Language recognition using CNN and LSTM networks

Translating the unspoken Deep learning approaches to Indian ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Bhamare, Siddharth Bhamare, Shreeraj Watumull Institue of Technology Computer Engineering Dept. Ulhasnagar India School of Computer Engineering Coep Technological University Pune India

ISBN: (纸本)9798350303452

In India, there are over 5 million deaf and mute people, with many cases going unreported. Communication can be difficult due to the lack of standardized sign language across the country. The Indian Sign Language (ISL) Detection project aims to improve communication by developing a system that can recognize and interpret ISL gestures. The system uses a type of artificial intelligence called machine learning, specifically Convo-lutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. These techniques help the system detect and classify ISL gestures from video input with high accuracy. The project has the potential to provide a more accessible means of communication for millions of deaf individuals in India. © 2023 IEEE.

关键词： Long short-term memory

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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JARVIS - Smart AI Support System 3

JARVIS - Smart AI Support System

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing technology, ODICON 2024

作者： Ramya Manaswi, V. Saketh, Meduri Ravi Tejha, Mandumula Sai Karthikeya, Atluri Sai Prakash, Dharani Chandra Alkhayyat, Ahmad Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science Engineering Hyderabad India Gitam University Dept. of Computer Science Engineering with Specialisation of Cybersecurity Hyderabad India K.G Reddy College of Engineering and Technology Dept. of Electronics & Communication Engineering Telangana Hyderabad India College of Technical Engineering The Islamic University Dept. of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350354379

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating 'Air Canvas' facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI. © 2024 IEEE.

关键词： Face recognition

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Modeling Data Set of Indonesian Heart Sound Signals 7

Modeling Data Set of Indonesian Heart Sound Signals

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7th International Conference of computer and informatics engineering, IC2IE 2024

作者： Oktivasari, Prihatin Sinantya, F. Agitha Nasywa, A. Hk, Firmansyah Setiawan, Evan Sutarna, Nana Mohd Safuan, Syadia Nabilah Najah Saud Al Humairi, Safaa Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia Politeknik Negeri Jakarta Dept. of Electrical Engineering Depok Indonesia Msu University Faculty of Information Sciences and Engineering Selangor Malaysia

ISBN: (纸本)9798331505530

Vital signs, such as heart and respiratory rates, are the main parameters when assessing a person's physical condition. Advanced technologies for monitoring vital signs include electrocardiography (ECG), photoplethysmograph (PPG), or phonocardiography (PCG). While ECG utilizes electrical stimulation that triggers each heartbeat, PPG measures changes in absorbance caused by blood flow, and PCG measures acoustic waves resulting from the mechanical activity of the heart. In recent decades, analysis of heart sound signals, especially for automatic heart sound segmentation and classification, has been widely studied and reported to have potential value for accurately and clinically detecting pathology. However, comparative analysis of algorithms in the literature has been hindered by the lack of high-quality, rigorously validated, and standardized heart sound databases of recordings. In this research, a heart sound database will be created by the public, especially Indonesians, to represent the condition of Asian society, which in the future can be used as a research reference for other researchers as an open data set. It includes healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. Records are collected from a variety of clinical or nonclinical environments and equipment. Recording length varies from a few seconds to several seconds-minutes. This study will report detailed information about subjects/patients including demographics (number, age, and gender), recordings (number, location, state, and length of time), associated synchronously recorded signals, sampling frequency, and type of sensor used. This research aims to measure acoustic waves resulting from the mechanical activity of the heart and the results of heart sound recordings in various patient conditions, along with their pathologies, will be grouped in an open database based on normal and abnormal. In this study, a deep-ranging S1 pattern was

关键词： Electrocardiography

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DeepStock Forecast: Unveiling Market Movements through Advanced Deep Learning Models 4

DeepStock Forecast: Unveiling Market Movements through Advan...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Smruti Rekha Mishra, Debahuti Lenka, Arundhati Shaw, Kailash Gitam Deemed to Be University Dept. Computer Science and Engineering Andhra Pradesh Visakhapatnam India Siksha 'o' Anusandhan Deemed to Be University Odisha Dept. Computer Science and Engineering Odisha India Dept. of Aiml Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350349856

This study uses deep neural network (DNN) methodologies, including auto encoder, deep belief network (DBN), and backpropagation neural network (BPNN), to predict stock prices over 15 and 30 days. Utilizing BSE Sensex and NSE Sensex datasets with diverse technical indicators, the research highlights DNN's superior training accuracy, measured by minimal mean squared error (MSE). The study's key contribution is applying DNN to predict stock market volatility. While acknowledging the limitation of relying solely on training error, the study incorporates additional measures like RMSE, MAPE, MAE, and ARV during testing. Notably, DNN's MAPE results fall within a narrow range (0.0221 to 0.0255), indicating minimal error deviation. In a comprehensive evaluation across all datasets, DNN consistently outperforms DBN and BPNN in both training and testing performance. © 2024 IEEE.

关键词： Deep neural networks

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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation 4

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kolekar, Samruddhi Maheshkumar Yadav, Agnesh Chandra Yadav, Suchita Dash, Daba Prasad Thaper Institue of Engineering and Technology Dept. Electrical Engineering Patiala India India Instite of Technology Patna Dept. Electrical Engineering Patna India Bennett University Dept. of Computer Science and Engineering Gr. Noida India

ISBN: (纸本)9798350364729

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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Towards an integration management maturity 49

Towards an integration management maturity

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49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： Hyrynsalmi, Sonja M. Olsson, Helena Holmstrom Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350342352

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management. © 2023 IEEE.

关键词： Integration

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.

关键词： Parallel processing systems

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