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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

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

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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A Novel Approach using Machine Learning and NLP for Revolutionizing Pali Manuscript Conservation 4

A Novel Approach using Machine Learning and NLP for Revoluti...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Gudadhe, Sangita R. Bardekar, Aashish A. Ranit, Amitkumar B. Sipna COET Amravati Dept. of Computer Science & Engineering Amravati India Prof. Ram Meghe Cem Dept.of Civil Engineering Badanera Amravati India

ISBN: (纸本)9798331540364

The preservation and analysis of ancient Pali manuscripts are crucial for understanding India's cultural heritage. However, the unique shapes of Pali characters present significant challenges for accurate identification and classification. This research proposes a novel system that utilizes machine learning, image processing, and natural language processing (NLP) to convert Pali characters into understandable formats and decipher the Pali Prakrit language. By combining computer science and linguistics, the proposed model aims to effectively extract data from Pali literature. The proposed model was validated using the Government of India dataset, demonstrating improved character recognition accuracy and reduced memory footprint. This research contributes to the preservation of ancient Pali manuscripts and facilitates the study and understanding of this valuable cultural heritage. © 2024 IEEE.

关键词： Adversarial machine learning

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Driver Fatigue Countermeasure System 4

Driver Fatigue Countermeasure System

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Gonuguntla, Ahalya Rani Charan, Vemula Sai Likhitha, Ventrapati Srivalli, Yeddu Shivdeep, Yarmally Saikiran, Veerbhadrapolla Dept.of Computer Science Engineering B V Raju Institute of Technology TelanganaMedak Narsapur India

ISBN: (纸本)9798350305258

One of the major contributing factors to traffic accidents is driver weariness and drowsiness. Automated driver sleepiness detection is a significant computer vision challenge since accidents involving fatigued drivers have resulted in millions of fatalities in recent years. A real-time driver sleepiness detection system is created to address this issue and is based on Facial Landmark Detection. A significant barrier still exists in accurately identifying driver fatigue and forecasting it in real-time scenarios. In the proposed method, drowsiness detection is done by the fusion of eye state recognition and yawn detection. This study describes a non-intrusive driver fatigue detection system that can identify upcoming driver drowsiness and send out timely warnings that could be helpful. The system relies on visual cue, here mouth and eye to characterize the driver's alertness *** detect the mouth and eyes from the full face region, the system uses a Haar cascade classifier function and is followed by extraction of mouth and eyes from images. It also uses image processing techniques to extract the driver's mouth and eyes to determine their states. The proposed model is built using Python language and Open CV. © 2023 IEEE.

关键词： Driver Fatigue Eye Aspect Ratio(EAR) Eye Detection Facial Landmarks Haar Cascade Classifier Mouth Aspect Ratio(MAR) Mouth Detection Threshold value

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT 10

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Munoz-Lara, Rodrigo Cespedes, Sandra Diaz, Marcos University of Chile Dept.of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng. MontrealQC Canada

ISBN: (纸本)9798350373011

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites. © 2024 IEEE.

关键词： Internet of things

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Deep Learning Techniques for Speech Emotion Recognition 1

Deep Learning Techniques for Speech Emotion Recognition

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Bhavani, R. Muni, T Vijay Tata, Ravi Kumar Narasimharao, Jonnadula Murali, K. Kaur, Harpreet Kings Engineering College Dept.of Information Technology Chennai India Koneru Lakshmaiah Education Foundation Dept.of Electrical and Electronics Engineering Vaddeswaram India Koneru Lakshmaiah Education Foundation Dept.of Computer Science and Engineering Vaddeswaram India CMR Technical Campus Medchal Dept.of Computer Science and Engineering Hyderabad India VijayaInstitute of Technology for Women Dept.of Electronics and Communication Engineering Enikepadu India Lovely Professional University Dept.of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665450461

The identification of feelings sent via one's voice has evolved from a niche field into an essential part of humancomputer interaction (HCI). These systems seek to facilitate more natural interactions between humans and machines by favouring direct voice interaction over the use of conventional input devices in order to comprehend verbal information and to make it simpler for human listeners to respond. A few examples include the use of emotion patterns derived from speech in medical applications, dialogue systems for spoken languages such as those used in cell phone call centres, onboard driving systems in automobiles, and the usage of such patterns in other contexts. In the research that has been done on speech emotion recognition (SER), a variety of methods, many of which are considered to be industry standards in speech analysis and classification, have been applied to the process of extracting emotions from signals. The most crucial stages of the speech emotion recognition (SER) process are the phases that deal with the extraction of features and the categorization of features. For the purpose of speech processing, researchers have developed a variety of features, including prosodic features, vocal traction features, and other hybrid features. The second step involves the categorization of features through the application of deep learning methods. These methods have just lately been put up as a potential replacement for SER's more conventional methods. © 2022 IEEE.

关键词： Speech processing

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On the Enhancement of Energy Efficiency of Wireless Rechargeable Sensor Networks through Mobile Charger Scheduling 2

On the Enhancement of Energy Efficiency of Wireless Recharge...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kamaruzzaman, Md. Chandra, Abhijit Azharuddin, Md. The Neotia University Dept.of Robotics and Automation Kolkata India Jadavpur University Dept. of Instrumentation and Electronics Engineering Kolkata India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350382952

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme. © 2024 IEEE.

关键词： Energy efficiency

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Optimizing AODV Routing Protocol to Improve Quality of Service Performance for V2V Communication

Optimizing AODV Routing Protocol to Improve Quality of Servi...

引用

2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Bintoro, Ketut Bayu Yogha Priyambodo, Tri Kuntoro Musthofa, Muhammad Nurul Gadjah Mada University Faculty of Mathematics and Computer Sciences Dept.of Computer Science and Electronics Yogyakarta Indonesia Universitas Trilogi Faculty of Sciences Technology and Design Dept. of Informatic Engineering Jakarta Indonesia

ISBN: (纸本)9798350364101

The study focuses on Quality of Service (QoS) issues in learning automata-based AODV (LA-AODV) for V2V communication protocols. It analyzes and compares the LAAODV protocol with the original AODV protocol in dynamic traffic situations. The study evaluates critical metrics such as the number of vehicles, average speed, congestion spots, and vehicle time loss across various traffic scenarios, including smooth traffic, steady flow traffic, and heavy traffic. NS3 and SUMO are used as essential tools for this evaluation. The study finds that the LA-AODV protocol performs better in smooth traffic conditions than AODV. It demonstrates a 38.27% higher packet delivery ratio (PDR), an 85.26% lower packet loss ratio (PLR), and a remarkable 56.62% increase in average throughput compared to AODV. Both protocols exhibit similar performance in steady flow traffic with high PDRs (97.60% and 96.87%, respectively) and low PLRs (2% and 3%, respectively). However, AODV outshines LA-AODV with a higher PDR (97.60%)) than LA-AODV (96.87%) in heavy traffic. LA-AODV, on the other hand, excels in average throughput by 17.24% in heavy traffic. The study offers valuable insights for improving V2V communication protocols and enhancing communication efficiency to handle dynamic and traffic situations. © 2024 IEEE.

关键词： Quality of service

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Audio-Music Fingerprinting Recognition 3

Audio-Music Fingerprinting Recognition

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Girisha, G.S. Murthy, Chinmaya Chirayu, S.M. Kavalli, Dayanand Divya, J. Dayananda Sagar University Dept.of Cse Bengaluru India Dayananda Sagar University Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350322842

Audio fingerprinting is the method involved with addressing a sound sign minimally with the aid of isolating vital highlights of the sound substance a part of the good sized makes use of of acoustic fingerprinting includes substance-based sound healing broadcast watching and so forth it lets in gazing the sound free of its arrangement and with out the requirement for metadata it really works by using studying frequency styles and tracking down a fit internal its statistics set of tunes this utility tries to understand the songs through the use of a time-frequency graph primarily based on an audio fingerprint that is known as a spectrogram the software program utilizes a cell phone implicit microphone that assembles a concise instance of a legitimate that is played it analyzes the outside sound and seeks a comparable suit on a database in which thousands and thousands of songs are saved based totally on an acoustic fingerprint when the software reveals a in shape it retrieves records such as the album track name original music and so forth. © 2023 IEEE.

关键词： Audio acoustics

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Detection of Lung Inflammation using Chest X-ray with Deep Learning Algorithm 7

Detection of Lung Inflammation using Chest X-ray with Deep L...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Singh, K. Purushotham Kumar, B. Niranjan Anil, A. Rajendran, Selvi Dept.of Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Lung Inflammation causes the passing of around 700,000 youngsters consistently and influences 7% of the worldwide populace. Chest X-ray images are principally utilized for the analysis of this infection. Notwithstanding, in any event, for a prepared radiologist, it is a moving errand to look at chest X-ray. There is a need to further develop the determination precision. In this work, an effective model is developed for the recognition of pneumonia from the computerized chest X-ray pictures is proposed. Several methods are utilized to expand the preparation of dataset in a decent manner. Consequently, it is more advantageous to fabricate a computerized indicator to anticipate the pneumonia utilizing the large information profound learning strategies. Among the wide range of various procedures, Convolutional Neural Networks stand tall and high in this forecast alongside different classifiers. Convolutional brain network comprising of convolution and pooling layers and completely associated Softmax layers toward the finish to give the last forecast. © 2022 IEEE.

关键词： Forecasting

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Care Compass: Monitoring made easy, Care made effective 3

Care Compass: Monitoring made easy, Care made effective

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manhas, Prabal Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Chandigarh University Punjab India Chandigarh University Dept.of Computer Science and Engineering Punjab Mohali India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331515911

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today's youth. The 21st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals. © 2025 IEEE.

关键词： Electronic health record

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