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Early Forest Fire Prediction System Using Wireless Sensor Network 2

Early Forest Fire Prediction System Using Wireless Sensor Ne...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Thangam, S. Sri Sai Samhitha, J. Adarsh Sagar, K. Yaswanth, Julakanti Sai Haritha, Kundula Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350372847

Wireless sensor network (WSN), is a collection of sensors that is used for monitoring the activities in a given environment. They are used in the forests for fire detection wherea large number of sensor nodes are placed to detect the fire inits early stages. Forest fires are the main reason which affect the wildlife and many living creatures. The project performs a comparative analysis between the traditional system and the new system developed used to predict forest fires early using the Wireless sensor network for detecting forest fires with the goal of monitoring the scale and intensity of the wildfire ignited at forests. The solution involves deploying the fire sensing sensors across the forest area, calculating Fire Whether Index (FWI) of the regions, establishing data communications between the sensors using the protocol to create dynamic routing paths across the Wireless Sensor Network [7]. These paths are developed based on several factors such as fire, energy, weather index and security. Tracing the multi dynamic route is established using star topology where the red alert zones send the alert message to all the nearby base stations. Using Net Logo simulator this project analyses the performance of the traditional systems and new systems based on network power, fire detection efficacy and the overall power consumed by the fires alerting systems. © 2024 IEEE.

关键词： Fires

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Adaptive Multi-Level Feedback Round-Robin 15

Adaptive Multi-Level Feedback Round-Robin

引用

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

作者： Prajana, Mamidi Rajkumar, Harsha Sannidhi, Akepati Sai Vidyasri, Kammari Krishnan, Sajitha Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

Scheduling is vital in operating systems, controlling process resource access. Round Robin stands out among traditional algorithms due to its improved performance, unlike First Come First Serve (FCFS) or Shortest Job First (SJF). While the Priority-based scheduling algorithm executes processes depending on their priority, the proposed algorithm is particularly designed for time-sharing systems. The paper aims to improve system performance, reduce latency, and enhance overall responsiveness in multitasking environments. The time quantum assigned ensures fair allocation of CPU to all the processes getting executed. Based on their burst time and assigned priorities, different processes can be divided within multi-level queues. Multilevel queues adjust task priorities based on the time execution. The use of priority for each process enables prioritization of the important processes in each queue. Decrementing the priority based on the time taken to execute, is an efficient solution to reduce the starvation time of the other processes. It shows a reduction in turn-around time. The implementation will also include comprehensive performance evaluations and benchmarking against traditional scheduling algorithms, demonstrating the superiority and effectiveness of the proposed scheduler in real-world scenarios. © 2024 IEEE.

关键词： Scheduling algorithms

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An Implementation of Feature Selection for Detecting LOIC-based DDoS Attack

An Implementation of Feature Selection for Detecting LOIC-ba...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chen, Shih-Chieh Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Machine learning method is efficient and effective in detecting DDoS attacks, but it all begins from identifying and selecting their important features. This paper presents an implementation of feature selection for DDoS detection based on Random Forest method. In our implementation, we use a LOIC software flood DDoS requests to a target computer, then control the target to extract the features from the captured IP packets, and finally calculate their Gini feature importance and ranking for subsequent feature selection. © 2023 IEEE.

关键词： Feature Selection

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Asthma Assessment Device for Pediatric Patients: A Proof of Concept

Asthma Assessment Device for Pediatric Patients: A Proof of ...

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Al-Ghafran, Muneera Ahmadizadeh, Fatemeh Al-Naimi, Asma Abualsaud, Khalid Yaacoub, Elias College of Engineering Dept. of Computer Science & Engineering Qatar University Doha Qatar

ISBN: (纸本)9798350335590

Asthma is a common lung illness that causes breathing difficulties. It affects people of all ages and generally begins in childhood;however, it can also appear in adults for the first time. There is presently no cure, but there are basic treatments that can help keep the symptoms under control. This paper aims to monitor asthma attacks for pediatrics under twelve years according to the following parameters: oxygen blood level, respiratory rate, and pulse rate. The resulting measures would be compared to pre-determined health standards. The respiratory rate is calculated and deduced by analyzing the result of air pressure sensor. The pulse rate and oxygen level are obtained from an oximeter. These measurements were compared with the normal health status to decide if the child has an asthma attack or not. The result is given within two minutes. The values of the three parameters and the final status of asthma are displayed on the screen. The main aim of the paper is to propose a monitoring device that can be used in a hospital or at home where parents can constantly check the status of their children in a short period of time without having to go to the hospital. The success of this device is getting accuracy higher than 85%. © 2023 IEEE.

关键词： Hospitals

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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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Seamless Attendance Logging Application: A Technological Revolution in Logbook Capture 9

Seamless Attendance Logging Application: A Technological Rev...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Raj, Bhupathi Shwejan Sujith, Bolisetty Krishna, Janagama Vamshi Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350394474

In this age of swift technological progress, Most educational establishments seek novel approaches that may further boost productivity. One such challenge has been the manual attendance tracking system in colleges. This research paper presents the development and implementation of an automated college attendance system powered by deep learning-based text extraction models. The primary objective of this system is to remove the labor-intensive process of taking attendance, improve accuracy, and provide valuable insights into student attendance patterns. The research begins by utilizing state-of-the-art deep learning techniques to extract roll numbers from images of attendance sheets. A Convolutional neural network is utilized for situating and identifying text within images. The system then processes this data and marks students as present or absent based on predefined criteria. The findings demonstrate that the automated attendance system, with its high degree of precision, makes a major dent in the time and energy formerly needed for keeping attendance logs, streamlining the whole process significantly. Furthermore, by enabling the ongoing monitoring and analysis of data in real time, educators and administrators are empowered to make evidence-informed decisions about student attendance through insights continuously gained from observant assessment and evaluation. © 2024 IEEE.

关键词： Students

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A Case Study of Detecting HULK-based DDoS Traffic with Random Forest Classifier

A Case Study of Detecting HULK-based DDoS Traffic with Rando...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chang, Tsu-Chen Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time. © 2023 IEEE.

关键词： HTTP

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A Systematic Survey on Diabetic Retinopathy and Deep Learning Approaches 2

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

引用

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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Improvising energy efficiency of heterogeneous servers using AWS services and Machine Learning approaches 15

Improvising energy efficiency of heterogeneous servers using...

引用

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

作者： Reddy, P. Praneeth Shruthi, P. Sai Tharneesh, P. Beena, B.M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The rapid expansion of data centers has led to a substantial increase in energy consumption, prompting a need for more energy-efficient solutions. This paper presents a comprehensive approach to enhancing the energy efficiency of heterogeneous servers by leveraging Amazon Web Services (AWS) and Machine Learning (ML) techniques. Our proposed solution involves developing an ML model that uses a dataset of VM cloud which consists of various parameters like the cloud ID, timestamp, CPU performance, memory usage, network traffic, energy efficiency and so on. The model uses regression algorithms like Linear Regression, Decision Tree Regressor, K-Neighbor Regressor, Polynomial Regression, and AdaBoost Regressor to predict the energy-efficiency of a system based on the relevant features. The model will be evaluated on the basis of Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R2 Score, and Mean Absolute Percentage Error and Linear Regression model is noted to provide the least error scores. This model helps in reducing server workloads and dynamically manages resource allocation to minimize energy usage while maintaining optimal performance. The developed ML model is then deployed on the AWS EC2 free tier instance by encapsulating the ML models within a Docker container. This study provides a scalable and economical approach to enhance the energy efficiency in the data centers, with a potential impact on the wide range of cloud -based infrastructures. © 2024 IEEE.

关键词： AWS EC2 Container Docker Energy efficiency Green computing Regression

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Comparative Study of Cryptographic Algorithms in Post Quantum Computing Landscape 5

Comparative Study of Cryptographic Algorithms in Post Quantu...

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

作者： Sahoo, Anshuman Indira Kumar, A.K. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

As the advent of quantum computing looms, the cryptographic landscape faces unprecedented challenges that could render traditional algorithms like AES, RSA, and ECC vulnerable. This paper delves into a comparative study of these classical cryptographic algorithms, examining their foundational principles and evaluating their resilience against quantum computing threats, particularly Shor's and Grover's algorithms. Through a combination of theoretical analysis and practical simulations, we assess the potential weaknesses and strengths of AES, RSA, and ECC in the post-quantum era. The study highlights the need for quantum-resistant cryptographic methods and explores how current algorithms can be adapted or replaced to withstand the sophisticated computational capabilities of quantum computers. By bridging the gap between classical and quantum cryptography, this research offers crucial insights into the future of secure digital communication, underscoring the urgency for innovation in cryptographic practices to safeguard data in an increasingly quantum-dominated world. © 2024 IEEE.

关键词： Encryption algorithms

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