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Cognitive, Green and Ubiquitous computing (IC-CGU), International Conference on

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

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

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.

关键词： Measurement Cloud computing Pollution Urban areas Ubiquitous computing Real-time systems Fuels

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Heart Failure Prediction using Machine Learning Techniques: A Comparative Analysis

Heart Failure Prediction using Machine Learning Techniques: ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Rithish S Krishnendu S Sarath S Niveditha Bayana Mohith Siva Sai Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Heart failure is a prevalent cardiovascular disease with significant global mortality rates. Accurate prediction methods are imperative for predicting cardiovascular disease and identifying individuals at risk of adverse outcomes, highlighting the crucial need for precise prediction tools. This study employs advanced machine learning techniques to enhance heart failure prediction. We conduct a comparative analysis of various machine learning models, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Adaptive Boosting (AdaBoost), on three diverse datasets. The datasets encompass clinical characteristics, personal habits, and mortality outcomes, providing a comprehensive understanding of heart failure determinants. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess model performance. The results reveal patterns in model behavior across datasets, highlighting the importance of selecting appropriate models for specific data characteristics. Our findings contribute to the building of proper heart failure prediction models, supporting medical institutions in resource allocation and early therapeutic customization.

关键词： Support vector machines Analytical models Adaptation models Logistic regression Accuracy Predictive models Data models Cardiovascular diseases Random forests Biomedical imaging

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Post Quantum Blockchain Assisted Privacy Preserving Protocol for Internet of Medical Things

Post Quantum Blockchain Assisted Privacy Preserving Protocol...

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2023 IEEE World Conference on Applied Intelligence and computing, AIC 2023

作者： Yadav, Dushyant Kumar Yadav, Dinesh Pal, Yogesh Chaudhary, Dharminder Sahu, Hemlal Manasa, A.S.L. Govt. J. Y. Chhattisgarh College Dept. of Mathematics Raipur India Shivalik College of Engineering Dept. of Computer Science and Engineering Raipur India Greater Noida India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350310061

Internet of Medical Things (IoMT) is an advanced ecosystem made with the help of Internet of Things technology, smart sensors, medical equipment, and connected end users. Internet of Medical Things is very helpful to enhance efficiency, accuracy, and qualities traditional healthcare system. A smart IoMT contains end users, heterogeneous network, and private data is transferred through public channel. Therefore, it becomes hard to achieve both security and privacy in the IoMT system. In this article, we have focused on designing privacy preserving authenticated key mechanism between two parties. We have proposed a blockchain assisted authenticated key exchange mechanism for IoMT systems supporting fog computing paradigm. The proposed privacy preserving authenticated key exchange protocol is based post quantum assumption 'ring learning with errors' and physical unclonable function (PUF), respectively. A detailed security analysis ensures that proposed protocol is secure against most of the existing attacks. This protocol is also secure against quantum attacks based on the assumption. © 2023 IEEE.

关键词： Authentication

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Understanding Accessibility Issues in China's Contact Tracing Systems During the COVID-19 Pandemic for Visually Impaired People 22

Understanding Accessibility Issues in China's Contact Tracin...

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4th International Conference on Big Data engineering, BDE 2022

作者： Li, Huaigu Best, Michael L. School of Electrical and Computer Engineering Georgia Institute of Technology United States Georgia Institute of Technology Sam Nunn School of International Affairs School of Interactive Computing United States

ISBN: (纸本)9781450395632

To fight against the COVID-19 pandemic, China has deployed the Health Code Apps and the Trajectory Code Apps as the front-end of the national contact tracing system. To date, accessibility issues for visually impaired people using these apps have not been studied. In this paper we use a mixed-method approach, including qualitative and quantitative studies, to explore these accessibility issues. The qualitative study included interviews and observations with visually impaired users, while quantitative analysis was conducted through surveys. We found that the current contact tracing systems are inaccessible and that most visually impaired people can only use the app with other people's help. The findings serve as a reference point for the potential large-scale adoption of digital contact tracing systems. © 2022 Owner/Author.

关键词： COVID-19

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Anylogic and Python-Based Gunshot Detection System Using CNN and Trilateration

Anylogic and Python-Based Gunshot Detection System Using CNN...

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

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

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Gunshot detection has been applied in fields such as defense and forensic sciences. This plays a very critical role in law enforcement and public safety. Detecting a gunshot begins with automatic detection of the presence of an audio event. These rare events can be analyzed and prevented by using a system that detects the audio and location of gunshots. The success of this system relies ultimately on its accuracy. In this paper, audio data has been collected and a machine learning(ML) model using a Convolution Neural Network (CNN) has been trained that classifies the audio and detects a gunshot acoustic. The prediction of the gunshot location has been implemented using the trilateration method with the use of AI agents as sensors. The model has been trained in Python programming language. This simulation has been implemented in the Java programming language with AnyLogic software as the user interface.

关键词： System testing Gunshot detection systems Scalability Neural networks Refining User interfaces Software

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A Logical Investigation of Stock Market Prediction and Analysis using Supervised Machine Learning Algorithm 1

A Logical Investigation of Stock Market Prediction and Analy...

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

作者： Dhanalakshmi, R. Kumar, V. Vasantha Basha, Saleem Vijayaraghavan, N. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mazoon University College Computing and Informatics Department Muscat Oman KCG College of Technology Department of CSE Chennai India

ISBN: (纸本)9798350336009

In the Field of computer science, artificial intelligence (AI) is a broad field, which is concerned with structuring smart products and machines able to perform tasks which require the intellectual capability of humans. Artificial Intelligence is in need for Stock trading's future. Robotic advisers examine millions of pieces of data using artificial intelligence and carry out the analysis at the best possible price. Market forecasts are more precise and effective thanks to the analysts. For providing higher returns, the trading firms efficiently mitigate the risk using Artificial Intelligence Techniques. Our proposed Stock Market system is a Web Application which uses the Random Forest Algorithm. The Random Forest Algorithm comes under Supervised Learning. With the system, one can forecast the stock prices for the subsequent days. © 2023 IEEE.

关键词： Forecasting

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SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network 38

SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Re...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Weiyu Sun, Ying Xu, Yijie Qiao, Ziyue Yang, Yongkui Xiong, Hui Thrust of Artificial Intelligence HKUST Guangzhou China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Computer Science and Engineering HKUST Hong Kong

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/. © 2024 Neural information processing systems foundation. All rights reserved.

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Transcribing Natural Language to Python Code: A Compiler Based Approach

Transcribing Natural Language to Python Code: A Compiler Bas...

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International Conference on computing and Networking Technology (ICCNT)

作者： Mayank Pandey Naga Sai Shreya Kunda Kc Pranave Meena Belwal Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Coding in a programming language can indeed be a meticulous and a less interesting task, especially when compared to the fluidity of spoken or written communication. Each programming language has its own set of syntax and rules, demanding expertise to effectively write code in it. Through this study, a compiler based approach to translate Natural Language into Python code is proposed. This endeavor could revolutionize access to programming by making it more intuitive and inclusive. The research involves working with different phases of the compiler including the lexical analyser, syntax and the semantic analyser phase in combination with Natural Language Processing techniques. By automating this process, we aim to streamline programming for a wider audience, removing barriers to entry and making coding more accessible to all.

关键词： Codes Semantics computer architecture Syntactics Programming Natural language processing Encoding Python

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A Study on Evaluating the Performance of Robot Motion Using Gradient Generalized Artificial Potential Fields with Obstacles 1

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6th International Conference on Computational Intelligence in Data Mining, ICCIDM 2021

作者： Basha, Syed Muzamil Ahmed, Syed Thouheed Al-Shammari, Naif K. School of Computer Science and Engineering REVA University Bengaluru India School of Computing and Information Technology REVA University Bengaluru India Department of Mechanical Engineering College of Engineering University of Hail Hail Saudi Arabia

ISBN: (数字)9789811694479

ISBN: (纸本)9789811694462

Motion planning (MP) is actually a specialized version with more general Artificial Intelligence (AI) planning problem. The goal of a general purpose planning problem is to come up with a sequence of actions that accomplish a given goal. There are two approaches that utilize random samples. One the Probabilistic Road Map algorithm that sought the construct of a road map. Second, the rapidly exploring random tree procedure, which constructs every evolving trees to explore the free space and forge paths between the start and the goal. Both algorithms have the pleasing property that they work quite well in practice, even on high dimensional configuration spaces. Finally, the behavior of the generalized potential fields with obstacles are discussed. It helps to steer the robot through configuration space by considering the gradient of this artificial potential field. A strength of these potential field methods is that they are relatively simple to implement, and they can often be carried out directly based on sensory input. The finding of the present research is to describe the challenges being faced in the area of motion planning of robot and controlling its speed using gradient in generalized artificial potential fields with obstacles. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilizing EEG Signals

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4938-4947页

作者： Anik, Israq Ahmed Kamal, A.H.M. Kabir, Muhammad Ashad Uddin, Shahadat Moni, Mohammad Ali Jatiya Kabi Kazi Nazrul Islam University Department of Computer Science and Engineering Trishal2224 Bangladesh Jatiya Kabi Kazi Nazrul Islam University Trishal2224 Bangladesh Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia The University of Sydney School of Project Management Faculty of Engineering SydneyNSW2037 Australia Charles Sturt University Institute of Ai and Cyber Future BathurstNSW2795 Australia Charles Sturt University Rural Health Research Institute OrangeNSW2800 Australia

Major depressive disorder (MDD), commonly called depression, is a prevalent psychiatric condition diagnosed via questionnaire-based mental status assessments. However, this method often yields inconsistent and inaccurate results. Furthermore, there is currently a lack of a comprehensive diagnostic framework for MDD that assesses various brainwaves (alpha, theta, gamma, etc.) of electroencephalogram (EEG) signals as potential biomarkers, aiming to identify the most effective one for achieving accurate and robust diagnostic outcomes. To address this issue, we propose an innovative approach employing a deep convolutional neural network (DCNN) for MDD diagnosis utilizing the brainwaves present in EEG signals. Our proposed model, an extended 11-layer 1-D convolutional neural network (Ex-1DCNN), is designed to automatically learn from input EEG signals, foregoing the need for manual feature selection. By harnessing intrinsic brainwave patterns, our model demonstrates adaptability in classifying EEG signals into depressive and healthy categories. We have conducted an extensive analysis to identify optimal brainwave features and epoch duration for accurate MDD diagnosis. Leveraging EEG data from 34 MDD patients and 30 healthy subjects, we have identified that the Gamma brainwave at a 15-s epoch duration is the most effective configuration, achieving an accuracy of 99.60%, sensitivity of 100%, specificity of 99.21%, and an F1 score of 99.60%. This study highlights the potential of deep-learning techniques in streamlining the diagnostic process for MDD and offering a reliable aid to clinicians in MDD diagnosis. © 2024 IEEE.

关键词： Feature extraction

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