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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Elavarasi, D. Kavitha, R. Aanjankumar, S. Department of Computer Science and Engineering Mount Zion College of Engineering and Technology Pudukkottai India Information Technology Velammal College of Engineering Madurai India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798350340235

The abstract aims to provide a concise overview of recent challenges and techniques in predicting heart disease, leading to the proposed objectives of the study. Recent advancements in heart disease prediction have seen the emergence of various techniques, each facing distinct challenges. Notably, traditional systems grapple with the increasing volume and complexity of healthcare data. Recent techniques, such as deep learning and ensemble methods, showcase promising results but encounter challenges related to interpretability and resource-intensive training. This study addresses these challenges and presents a novel objective. Firstly, it navigates the interpretability hurdle in complex models by employing feature selection methods, including the Elephant Search Algorithm (ESA). ESA draws inspiration from natural behaviors but faces challenges in handling large datasets and computational complexity. Secondly, the study integrates ESA with the proven Support Vector Machine (SVM) for efficient heart disease prediction. The primary objective is to optimize feature selection, enhancing both accuracy and interpretability, while overcoming the challenges posed by the intricate nature of cardiovascular datasets. By delineating these challenges and objectives, this research contributes to the ongoing discourse on improving clinical decision support systems for heart disease prediction. © 2023 IEEE.

关键词： Clinical Decision Support System Elephant Search Algorithm Support Vector Machine

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Password Manager with Multi-Factor Authentication 1

Password Manager with Multi-Factor Authentication

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Basha, Saleem Vellore Institute of Technology School of Computer Science and Engineering Chennai India KCG College of Technology Department of Mathematics Chennai India Mazoon University College Computing and Informatics Department muscat Oman

ISBN: (纸本)9798350336009

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user's data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication. © 2023 IEEE.

关键词： Cryptography

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Early Forest Fire Prediction System Using Wireless Sensor Network

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

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Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

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

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

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.

关键词： Wireless communication Wireless sensor networks Analytical models Forests Computational modeling Predictive models Sensors

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Interpretable Deep Learning for Facial Feature Detection: A Comprehensive Study on Face and Eyes Recognition with LIME Explanations

Interpretable Deep Learning for Facial Feature Detection: A ...

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International Conference for Convergence of Technology (I2CT)

作者： Nikunj Jain Jawed Hawari Priyanshu Jha Priyanka C Nair Nalini Sampath Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Facial recognition has been nowadays a key role in computer field. The rapid advancement of Machine Learning techniques has been revolutionizing the field of computer vision. This paper presents a comprehensive investigation into the application of Machine Learning techniques. The study comprehensively reviews the various state-of-the-art facial and ocular algorithms. These modern learning techniques help to concatenate the various model which exist. A detailed examination of dataset and evaluation metrics are used for assessing detection performance is also presented to provide a foundation for comparative analysis. This article is used for designing and implementing of novel models for face and eye detection. Inception v3 was used to assist in removing overfitting using efficient convolution and parallel layers. This paper contributes to the ongoing discourse on Machine learning approaches to face and eye recognition by providing new examples, performance reviews, and ethical considerations. This article focuses on using the capabilities Multi-task Cascaded Convolutional Networks (MTCNN) for precise and efficient face and eye detection. Additionally, the study incorporates interpretability methodologies such as LIME(Local Interpretable Model-agnostic Explanations). The results of this study have important implications for the development of more accurate, more efficient, and more responsible facial and visual recognition systems, widely used in various industries.

关键词： Training Measurement Industries Visualization Machine learning algorithms Reviews Face recognition

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Enhancing Cloud Virtualization Security with a Z-Score Based Conjugate Self-Organizing Migration ML Model 15

Enhancing Cloud Virtualization Security with a Z-Score Based...

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

作者： Sidana, Sagar Thakur, Sujata Negi Sinha, Anurag Indhumathi, N. Raj, Petheru Al-Khayyat, Ahmed Modi, Vyom Mishra, Aditya Kumari, Anita Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India Graphic Era Hill University Department of computer science and engineering India Ignou School of Computing and information Science New Delhi India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Marwari Education Foundations Group Of Institutions Rajkot India Gla University Department of computer science and engineering Applications Mathura India Rkdf University Department f English Literature India

ISBN: (纸本)9798350370249

This study presents a new machine learning model for detecting malware in cloud computing settings called Conjugate Self-Organizing Migration (CSOM), which is based on Z-score. Feature vectors are standardized by the model through the use of Z-score normalization, which improves its flexibility with changing data distributions. By merging self-organizing migratory processes with statistical analysis, the CSOM model effectively reduces the threat of growing malware. Using Z-score normalization to guarantee correct representation of changing data, the imaging approach depicts the cloud environment as a dynamic, self-organizing landscape. Through conjugate migration techniques, the CSOM model dynamically modifies synaptic weights to minimize false positives and enable the identification of tiny fluctuations in harmful actions. The Z-score-based CSOM model is effective in detecting malware at high accuracy rates in a variety of dynamic cloud environments, as demonstrated by the experimental results. The model demonstrates its flexibility in responding to changing cyberthreats with its strong performance in real-world settings. Finally, with its better accuracy and adaptability, the suggested Z-score-based CSOM model offers a potential approach for cloud computing virus detection. In order to handle the constantly shifting environment of cloud-based cyber threats, further research may examine scalability and more refinement. © 2024 IEEE.

关键词： Cloud computing Conjugate Self-Organizing Migration Machine Learning Model Malware Detection Z-score

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Optimizing Compiler for Quantum computing Using Qiskit Terra

Optimizing Compiler for Quantum Computing Using Qiskit Terra

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International Conference on computing and Networking Technology (ICCNT)

作者： Peddi Gowtham Balaji Peraboina Chamikar Omkar Chowdary Meena Belwal Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The recent times of fast development of quantum computers has been followed by many researchers who want to run the larger and more difficult quantum algorithms on it and they have promised such speedup several times. Notwithstanding this, commercial quantum computers are currently fraught with instability and errors. This paper is centered on development of the customized compiler for quantum computing with aid of Qiskit Terra, a QIS framework. We target central classes such as circuit representation structures, programming techniques based on OpenQASM, challenges in the QC architecture, high-level routing optimization, and the structure of the Qiskit Terra compiler. In every circuit, the optimization is provided by methods of Qiskit Terra, including Trivial Layout, Noise Adaptive Layout, Dense Layout, and Custom Layout. These optimization levels address many problems that emerge when circuits are being compiled and executed, and thus, the optimization levels seek to boost circuit efficiency, reduce the count of gates, and enhance the overall performance. The introduction of numerous optimization passes starting from unroller passes that translate gates, the layout passes that map virtual to physical qubits, and the optimization algorithm aimed at the runtime reduction. Modern quantum compilation technologies are being currently developed based on economic (and theoretical) analyses to underpin the investigation and development of optimal quantum computation procedures.

关键词： computers Quantum computing Runtime Layout Software algorithms Quantum mechanics Logic gates Routing Optimization Software development management

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency Departments 1

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Shibu, N.V. Sujatha, M. Suresh, M. Xavier Sindhuja, K. Raj, T. Saju Srinivasan, C. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology. © 2024 IEEE.

关键词： Data-driven triage Emergency medicine Healthcare efficiency Healthcare optimization Real-time decision support Resource management

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FDGLib: A Communication Library for Efficient Large-Scale Graph Processing in FPGA-Accelerated Data Centers

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Journal of computer Science & Technology 2021年第5期36卷 1051-1070页

作者： Yu-Wei Wu Qing-Gang Wang Long Zheng Xiao-Fei Liao Hai Jin Wen-Bin Jiang Ran Zheng Kan Hu National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the rapid growth of real-world graphs,the size of which can easily exceed the on-chip(board)storage capacity of an accelerator,processing large-scale graphs on a single Field Programmable Gate Array(FPGA)becomes *** multi-FPGA acceleration is of great necessity and *** cloud providers(e.g.,Amazon,Microsoft,and Baidu)now expose FPGAs to users in their data centers,providing opportunities to accelerate large-scale graph *** this paper,we present a communication library,called FDGLib,which can easily scale out any existing single FPGA-based graph accelerator to a distributed version in a data center,with minimal hardware engineering *** provides six APIs that can be easily used and integrated into any FPGA-based graph accelerator with only a few lines of code *** the torus-based FPGA interconnection in data centers,FDGLib also improves communication efficiency using simple yet effective torus-friendly graph partition and placement *** interface FDGLib into AccuGraph,a state-of-the-art graph *** results on a 32-node Microsoft Catapult-like data center show that the distributed AccuGraph can be 2.32x and 4.77x faster than a state-of-the-art distributed FPGA-based graph accelerator ForeGraph and a distributed CPU-based graph system Gemini,with better scalability.

关键词： data center accelerator graph processing distributed architecture communication optimization

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Research on Target Localization of Automatic Picking Robot for Rock Sugar Orange 23

Research on Target Localization of Automatic Picking Robot f...

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2023 International Conference on computer, Vision and Intelligent Technology, ICCVIT 2023

作者： Guo, Yuchen Liu, Dong Li, Qingbo Jiang, Hang Peng, Taomo Zeng, Xiongjian Liu, Ailin School of Computer and Artificial Intelligence Xiangnan University Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423000 China

ISBN: (纸本)9798400708701

In the production process of rock sugar oranges, picking is the most labor-intensive step. Intelligent replacement of traditional manual labor, timely and rapid picking of rock sugar oranges, improving fruit picking quality, is of great significance for expanding the rock sugar orange market. How to effectively recognize and locate rock sugar oranges is a key technology in machine vision in the research of automatic picking robots. A rock sugar orange positioning method is proposed. Firstly, the rock sugar orange image is preprocessed, binarized using Otsu adaptive threshold segmentation, and the positioning parameters and radius of the rock sugar orange are obtained using Hough transform. The K-means clustering pattern classification method is used to classify different circles, and the clustering results are corrected using a circle parameter correction model. © 2023 ACM.

关键词： computer vision

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IoT Enabled Music Therapy System for Regulation of Hypertension and Mental Wellness

IoT Enabled Music Therapy System for Regulation of Hypertens...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Swetha Sridhar Samrudhi S. Kamble M. Yagnasri Priya Sanjivani P. Shendre Thangam S Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Music therapy is widely considered as a useful form of expression and therapy for individuals coping with chronic conditions such as high blood pressure and mental health disorders. However, traditional approaches to music therapy often lack real-time data on user’s physiological states and limited accessibility. The proposed Internet of Things (IoT)-based system integrates a wearable Pulse Rate Sensor (PRS) and Galvanic Skin Response (GSR) sensor for data collection and machine learning algorithms to offer effective music therapy interventions. By incorporating the use of the Random Forest Classifier to analyze the sensor data and detect the presence of stress and continuous passive intervention with the help of ThingSpeak, this system aims to enhance accessibility and effectiveness in managing chronic conditions. Through this innovative approach, individuals can experience reduced stress responses, decreased anxiety levels, lower blood pressure and heart rate, and improved mental well-being.

关键词： Machine learning algorithms Medical treatment Mental health Skin Regulation Real-time systems Physiology Information and communication technology Internet of Things Random forests

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