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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Swarna G. Saranya A. Vanathi R. Ashwini B. Arunsundar Department of ECE Dr. M.G.R. Educational And Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The recently survey government is said 80 percentage of road accident is done by drunk and drive and the small cities accident range is 50 percentage to 80 percentage on this survey government declare overall of 100 accident and 30 to 35 deaths occurs on the road due to not wearing the seat belt. The Alcohol Sensor we are implement on the steering and this paper is totally depends on embedded System. This research is IoT and Arduino based system. There has been a dramatic rise in the likelihood of traffic accidents caused by the proliferation of automobiles on the road. Drunk driving is a major contributor to traffic accidents worldwide. The major goal of this research is to develop a system that can detect how much alcohol a driver has consumed. The goal of the proposed system is to decrease the frequency of drunken driving accidents by restricting users from operating motor vehicles while under the influence of alcohol or drugs. A NodeMCU ESP8266 WiFi Modules, an Arduino Controller, an Eye Blink Sensor, and a MQ3 Alcohol Detection Sensors are the main components of the suggested model. To ensure everyone’s safety, when the blood alcohol concentration (BAC) rises beyond a certain threshold, the vehicle’s ignition system will be disabled and the appropriate authorities will be notified via the IoT module; this process has previously been carried out using the GSM module. The proposed work is termed as Improved IoT based Driver Safety Model (IIoTDSM), in which it is cross-validated with the conventional model called GSM based Driver Safety Model (GSMDSM) to evaluate the efficiency of the proposed work.

关键词： Surveys GSM Roads Government Urban areas Safety Automobiles

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Multivariate Spearman's p for Aggregating Ranks Using Copulas

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Journal of Machine Learning Research 2016年 17卷 1-30页

作者： Bedo, Justin Ong, Cheng Soon Walter and Eliza Hall Institute 1G Royal Parade ParkvilleVIC3052 Australia Department of Computing and Information Systems University of Melbourne VIC3010 Australia Data61 CSIRO 7 London Circuit CanberraACT2601 Australia Research School of Computer Science Australian National University Department of Electrical and Electronic Engineering VIC3010 Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's p, which measures correlation between a set of ranked lists. Multivariate Spearman's p is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's κ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008. © 2016 Justin Bed}o and Cheng Soon Ong.

关键词： Benchmarking

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Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore 29

Taxi travel time based Geographically Weighted Regression Mo...

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29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Wang, Yi'an Cai, Fangyi Cheng, Shih-Fen Wu, Bo Cao, Kai School of Geographic Sciences East China Normal University Shanghai China East China Normal University Key Laboratory of Geographic Information Science Ministry of Education Shanghai China East China Normal University Key Lab. of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai China National University of Singapore Department of Geography Singapore School of Computing and Information Systems Singapore Management University Singapore School of Geography and Environment Jiangxi Normal University Jiangxi Nanchang China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling. © 2022 IEEE.

关键词： Travel time

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Experimental Evaluation of AI Powered Vehicle Toll Collection System Based on RFID and Internet of Things (IoT) Assistance

Experimental Evaluation of AI Powered Vehicle Toll Collectio...

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Innovative computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Jyothi Pulikanti R. Jayaraj S. Purushotham V. Saranya Devi A.Santhi Mary Antony M.P. Chitra Department of AIMLDS QIS College of Engineering and Technology Ongole Andhra Pradesh Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of EEE Sona College of Technology Salem Department of ECE QIS College of Engineering and Technology Ongole AndhraPradesh Department of EEE Loyola-ICAM College of Engineering and Technology Nungambakkam Loyola Campus Chennai Department of Electronics and Communication Engineering Panimalar Engineering College Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This paper presents the experimental evaluation of an AI-powered vehicle toll collection system, utilizing RFID technology and IoT integration. The system was designed to automate toll collection, reducing transaction time and improving accuracy while minimizing human intervention. The methodology involved RFID-based vehicle identification, real-time data transfer via IoT, and automatic toll deductions from prepaid accounts. The system was evaluated based on transaction speed, accuracy, user satisfaction, and system reliability. Results show that the automated system significantly reduced average transaction times from 32.5 seconds (manual) to 10 seconds (automated) during peak hours. Additionally, the system demonstrated a 100% accuracy rate in detecting unregistered vehicles and low-balance scenarios. User satisfaction was high, with an average rating of 4.6 out of 5. System reliability was also excellent, achieving an average uptime of 99.5% across multiple toll booth locations. The AI-powered toll collection system proved to be highly efficient in automating toll processes, reducing congestion, and improving the user experience. These findings suggest that such systems can effectively modernize toll management, contributing to time savings, reduced operational costs, and enhanced security.

关键词： Human computer interaction Accuracy Manuals data transfer Real-time systems User experience Internet of Things Reliability Security Radiofrequency identification

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Real-time Stream Processing in IoT Environments

Real-time Stream Processing in IoT Environments

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： P. Rajasekar Rupali Shekar Bhosale Indhumathi C Sandeep K V Prasanthi Kumari N Rajendiran M Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Telecommunication Engineering Bharati Vidyapeeth Institute of Technology Navi Mumbai Maharashtra India Department of Computer Science and Business Systems RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electronics & Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India School of Computer Sciences University of Petroleum and Energy Studies Dehradun Uttarakhand 248007 India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions.

关键词： Privacy data analysis Protocols Smart cities Scalability Decision making Real-time systems

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Psychological and personality profiles of political extremists

arXiv

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arXiv 2017年

作者： Alizadeh, Meysam Weber, Ingmar Cioffi-Revilla, Claudio Fortunato, Santo Macy, Michael Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University BloomingtonIN47405 United States Computational Social Science Program Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Qatar Computing Research Institute Doha Qatar Social Dynamics Laboratory Cornell University IthacaNY14853 United States

Global recruitment into radical Islamic movements has spurred renewed interest in the appeal of political extremism. Is the appeal a rational response to material conditions or is it the expression of psychological and personality disorders associated with aggressive behavior, intolerance, conspiratorial imagination, and paranoia? Empirical answers using surveys have been limited by lack of access to extremist groups, while field studies have lacked psychological measures and failed to compare extremists with contrast groups. We revisit the debate over the appeal of extremism in the U.S. context by comparing publicly available Twitter messages written by over 355,000 political extremist followers with messages written by non-extremist U.S. users. Analysis of text-based psychological indicators supports the moral foundation theory which identifies emotion as a critical factor in determining political orientation of individuals. Extremist followers also differ from others in four of the Big Five personality traits. Copyright © 2017, The Authors. All rights reserved.

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Intrusion detection based on phishing detection with machine learning

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Measurement: Sensors 2024年 31卷

作者： Jayaraj, R. Pushpalatha, A. Sangeetha, K. Kamaleshwar, T. Udhaya Shree, S. Damodaran, Deepa Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur TN Chennai India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology TN Coimbatore India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology TN Chennai India Department of Computer Science and Engineering Alpha College of Engineering and Technology Puducherry India VITBS Vellore Institute of Technology Chennai Campus TN India

Machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative Distribution Function gradient (CDF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method. © 2023

关键词： Intrusion detection

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Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of data: Results from Clinical Neuroscience

arXiv

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arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

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Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

arXiv

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings. © 2022, CC BY.

关键词： Reinforcement learning

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Lc-Stream: An Elastic Scheduling Strategy with Latency Constrained in Geo-Distributed Stream computing Environments

SSRN

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SSRN 2023年

作者： Sun, Dawei Wang, Yueru Sui, Jialiang Rong, Jia Gao, Shang Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China Department of Data Science & AI Faculty of IT Monash University VIC3800 Australia School of Information Technology Deakin University Waurn PondsVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

An effective scheduling strategy is critical for achieving better performance in real-time stream processing systems. How to process real-time data stream quickly and efficiently is always a challenging task, especially when clusters are collaborating in a Geo-Distributed computing environment. To address this challenge, we propose an elastic scheduling strategy with latency constrained in Geo-Distributed stream computing environments called Lc-Stream. This paper focuses on the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter-layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo-Distributed computing environment to ensure energy saving under low transmission latency. Our experimental results demonstrate that Lc-Stream reduces the overall latency of typical Geo-Distributed multitasking topology by more than 19%, and increases the throughput by more than 37%. Compared with the existing strategies in Storm, it also helps balance the load between systems and avoid excessive use of computing nodes. © 2023, The Authors. All rights reserved.

关键词： Storms

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