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Real Time Accident Alert System in Hilly Region using LoRa Technology 5

Real Time Accident Alert System in Hilly Region using LoRa T...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thangam, S. Gurupriya, M. Kothari, Osho Sah, Subodh Narayan Shah, Kriti Priya Dharmaraj, Kadam Prajwal Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331504403

In hilly regions, ensuring road safety is particularly challenging due to the unique terrain and the potential for more severe consequences in the event of accidents. This study aims to improve road safety in hilly regions by utilizing three distinct accident detection parameters. The first parameter, tilt sensors (MPU6050), monitors vehicle inclination, triggering an alert system when sudden changes in the tilt angle indicate a rollover or collision. The second parameter, vibration sensors, identifies abrupt jolts or impacts, triggering the alert system when unusual vibrations occur. The third parameter, GPS technology, provides accurate positioning and location data for notifying nearby helping stations and first responders, facilitating rapid and precise responses in emergency situations. To ensure efficient communication of accident alerts over long distances, LoRa (Long-Range Radio) technology is integrated into the system. This communication enables swift transmission of accident alerts to the nearest helping station. The central interface for accident detection and alerting is the Blynk mobile app, accessible to a wide range of users, including vehicle operators and first responders. This study presents a significant advancement in enhancing road safety and emergency response in hilly regions, where accidents can have serious consequences. Additionally, we plan to incorporate sending an emergency alert message to the receiver's number, nearby hospital, and police station using a GSM module to further enhance emergency response. © 2024 IEEE.

关键词： Global positioning system

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Mpox Classifier: A Deep Transfer Learning Model for Monkeypox Disease Detection and Classification 1

Mpox Classifier: A Deep Transfer Learning Model for Monkeypo...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Nayak, Ashish Kumar Bisoyi, Sukant Kishoro Banerjee, Abhishek Mahanta, Debashish Swain, Ashutosh C.V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

As humans recover from COVID-19, there is a new pandemic threat posed by the monkeypox virus. A daily report of new instances of monkeypox is made, but it is not as deadly or highly contagious as COVID-19. There's a good chance of a worldwide epidemic if nothing is done. Medical imaging is starting to show potential for using deep learning (DL) techniques to identify a patient's ailments. Because a picture has been utilized to learn more about the disease, the human skin infected with the monkeypox virus and the affected skin area may be used to identify the disease early. But as of right now, there isn't a reliable, publicly accessible MonkeyPox database that can be used for training and testing DL models. Furthermore, a suggested and assessed CNN model was the Mpox Classifier, which is an improved DenseNet-201 deep transfer learning model. Based on the initial and enhanced datasets, this research proposes a deep convolutional neural network with 94 and 99.1 percentage accuracy rates, respectively, for properly identifying monkeypox sickness. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-time Urban Parking Space Management using Fog Computing 1

Real-time Urban Parking Space Management using Fog Computing

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Thangam, S. Snehitha, Munaga Sai Kumar, Kotyada Mohan Kiran Reddy, Koti Leela Sai Praneeth Deepthi, C. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350361780

The rise in the number of private automobiles, especially in populated zones leads to many issues like traffic congestion, waste of time and fuel as they spend more time finding a parking spot. Vehicles release toxic gases, as they are ideal in traffic thus the number of pollutants gets increased. Smart parking systems help with these problems. They reduce traffic congestion which leads to less environmental pollution and saves fuel. A cloud-based parking system has been proposed, but it requires high latency and high bandwidth. Fog Computing gives us a better solution than cloud computing. With the help of fog computing smart car parking systems, the proposed model can clear all these issues. The driver gets information about the parking spots with minimal latency and energy consumption. By guiding the drivers where the parking spot is free it uses the space optimally. It also reduces the need to expand the parking area since land acquisition in urban areas is tough. A latency-reducing approach was developed using iFogSim and metrics like latency, total energy consumed, and total time taken were considered for a comparative analysis between the fog environment and the cloud environment. The developed fog-based system demonstrated better results than the cloud-based approach. © 2024 IEEE.

关键词： Fog computing

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A Systematic Survey on Diabetic Retinopathy and Deep Learning Approaches 2

A Systematic Survey on Diabetic Retinopathy and Deep Learnin...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sadhana, U. Beena, B.M. Amrita Vishwa Vidyapeetham Amrita School of computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350368413

Diabetic retinopathy (DR) is a significant threat to the vision of human beings;it is affected by patients and needs to be detected in the early stage to reduce the severity of vision. In recent research, the DR become interdisciplinary medical and computer science research. In this paper, based on the importance of this research, our research systematically reviewed the deep learning algorithms for automated DR detection, highlighting datasets, model implementations, and challenges. The study explored the Deep Learning approaches in DR management by assessing performance and clinical importance. In this review, our research also included the comparative study of various model outputs based on the different datasets, needful preprocessing techniques, and model refinement for accurate DR detection. We also discussed the integration of advanced models into clinical practice to improve DR management and reduce vision loss associated with diabetes. DR remains a significant global health problem, and advanced detection methods are used to mitigate vision loss. This literature review systematically examines the role of DL algorithms such as EfficientNet and MobileNet in enhancing DR detection. Through rigorous analysis of IDRiD (Indian Diabetic Retinopathy Image Dataset) datasets, model implementations, and challenges, the study synthesizes current research to explain critical directions and understandings. Our research concluded that integrating Deep learning into clinical research holds a guarantee for improving the accuracy and efficiency of DR detection, thereby improving patient results and quality of life. This review delivers valuable wisdom for researchers and clinicians aiming to advance DR detection methodologies and implement innovative solutions for this condition's early diagnosis and management. © 2024 IEEE.

关键词： Clinical research

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Heart Stroke Prediction Using Machine Learning and Explainable AI Insights

Heart Stroke Prediction Using Machine Learning and Explainab...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Bhanusri, Kodati Reddy, Karukonda Nithin Gnaneswarachary, Paluvayi Nair, Priyanka C. Sampath, Nalini Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382693

Heart stroke is a critical worldwide medical problem that requires early and accurate prediction in order to provide proper treatment. Recent advancements in machine learning, utilizing models such as XGBoost, Decision Tree, KNN, SVC, and Random Forest, have demonstrated promise in predicting patients at risks from medical data like Age, BMI, Glucose Levels, Hypertension, Heart disease etc., The aim is to evaluate the effectiveness of these machine learning models in predicting people at risk. Metrics like recall, precision, accuracy, and F1-score are examined to assess how well these models perform in predicting the risk of heart strokes. In addition, the research will look at the primary features of medical data that influence model predictions, using Explainable AI techniques such as SHAP. The overall objective is to provide effective and exact approaches for early detection of heart stroke risks, allowing patients to receive timely and important medical care when it is most needed. © 2024 IEEE.

关键词： Prediction models

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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Design and Implementation of an Android-Driven Smart Farming Assistant 15

Design and Implementation of an Android-Driven Smart Farming...

引用

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

作者： Chandra, Hothur Sai Pavan, Goli Sai Sri Sai, Kadiyala Karthik Sri Chandra, Karuturi Prudhvi Sai Sampath, Nalini Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350370249

The Smart Farming Assistant project is a helpful friend for farmers, using modern technologies to make farming easier and smarter. It's an Android app that uses advanced technologies like deep learning to give farmers important insights and help them manage their crops better. One of its main features is the ability to recognize diseases in paddy fields, helping farmers identify and diagnose issues through analyzing pictures. Additionally, the app provides access to many helpful articles and resources, offering educational content to improve farmer's knowledge and practices. These resources include tips and tricks for cultivation, fertilizer calculator, ensuring farmers have the latest information and tools to improve their farming methods. © 2024 IEEE.

关键词： Smart agriculture

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Real Estate Price Prediction Using PySpark MLlib 1

Real Estate Price Prediction Using PySpark MLlib

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Dharmaraj, Kadam Prajwal Gupta, Pradeep Kumar Ajith, Keerthana Kolli, Mahi Khare, Sangita Panda, Niharika Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350377002

Prices of real estates in metropolitan landscapes harbour immense importance in steering the complexities of dynamic city environments. In India, the real estate sector contributes around 6-7% to India's GDP. Therefore, accurate forecasting of real estate prices is a factor in making informed decisions, affecting various stakeholders. Many techniques were used for this in past several years, like Hedonic models, Repeat-Sales models, Rule-Based Systems and Heuristics, etc. This study leverages the power of machine learning and big data to develop a robust framework for predicting housing prices in metropolitan landscapes. We employ PySpark, a powerful big data processing framework with built-in MLlib library (machine learning library), to analyse large-scale housing data encompassing various cities in India & to predict the prices for the same. By implementing a comparative analysis of prominent regression models - Random Forest, Linear Regression, Decision Tree, and Gradient-Boosted Tree - our approach identifies the most effective algorithms for real estate price prediction using MLlib library. Also, this study highlights the need for scalable solutions to manage an increasing number of data sources and emphasizes the PySpark library which will simplify big data handling and enable parallel computing. This study paves the way for utilizing advanced machine learning techniques and big data platforms to gain valuable insights into real estate markets. Our findings emphasize the critical role of combining these powerful tools to navigate the complex dynamics of urban housing and predict prices with greater accuracy. © 2024 IEEE.

关键词： Decision trees

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Comparative Analysis of Bellman-Ford and Dijkstra Algorithms in Software Defined Networking 5

Comparative Analysis of Bellman-Ford and Dijkstra Algorithms...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Tammanashastri, Prabhav Ravi Rajagopal, Shinu M. Bananjee, Sahen Shivadutt Amrita Vishwa Vidyapeetam Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350389609

One of the key functions of the internet is packet routing of data. A routing protocol outlines the channel of communication between routers that is utilized to connect networks. Numerous routing protocols, including OSPF and RIP are used on the internet. Each protocol has a unique packet routing function. Bellman-Ford and Dijkstra are SDN routing algorithms;the former accommodates negative weights, suiting more complex network topologies, while the latter works efficiently for non-negative weights, ensuring faster convergence. RIP's reliance on the Bellman-Ford algorithm leads to slower convergence and potential routing cycles in bigger networks. OSPF, while stronger with Dijkstra's algorithm, can introduce high communication overhead due to maintaining complete network topology data. Bellman-Ford employs a dynamic programming approach, handling diverse network conditions, whereas Dijkstra's greedy strategy excels in finding the shortest path in positively weighted networks. respectively. © 2024 IEEE.

关键词： Network topology

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Low-Cost Network Jammer for Wi-Fi Network in Civilian Areas 5

Low-Cost Network Jammer for Wi-Fi Network in Civilian Areas

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thangam, S. Gupta, Pradeep Kumar Kartthikeyan, N. Mukil, L.D. Niranjan, D.K. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331504403

This application presents a novel approach to address the challenge of intercepting unmanned aerial vehicles (UAVs) operating within the 2.4 GHz frequency band. Jamming device operating at 2.4 GHz, with an Arduino Nano microcontroller and nRF24L01 module included. The device is designed to accurately search available wireless networks in a specific frequency range, focused on intercepting connections between drones in this spectrum Real-time system identifies networks and selects the channels, thus disconnecting targeted frequency devices Security measures in the presence of counter-drone activities are required This technology is an important tool for growth. Using Arduino Nano and nRF24L01 modules, the jamming device offers a flexible and scalable solution designed for network security professionals. The 2.4 GHz jammer provides precise interference, allowing targeted disruption of drones operating in a specific frequency band. In addition, the paper provides a detailed introduction to well-known drones operating in the 2.4 GHz frequency range, such as the DJI Phantom series and the Parrot Bebop series, and understanding these drones is crucial for planning effective countermeasures. The paper includes implementation data and results of experimental tests, providing insights into the efficiency and feasibility of a 2.4 GHz jammer designed to combat interference in UAV networks in a specific frequency band to be used. © 2024 IEEE.

关键词： Antennas

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