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Frontiers in Artificial Intelligence and applications 3

Frontiers in Artificial Intelligence and Applications

引用

3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Yadav, Jaya Shukla, Shruti Sharma, Kanika Soni, Nupur Agarwal, Shruti Pathak, Prabhash Chandra Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Mangalmay Institute of Management and Technology Department of Computer Application Greater Noida India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The industrial revolution has been the main cause ever since tremendous technological advancement was observed. The ubiquitous deployment of recent information and communication technologies (ICT), namely Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology, is hastening the world's industrial and technological transformation. This technical aggrandizement enhances the working culture and has a favorable impact on the workplace, as per the progressivist perspective. The breakneck pace of technological advancement, as well as AI, has enabled humans to replace manual labor in various industries. As being a domain of science and technology, AI develops machines and programs for computers that are intelligent and can accomplish tasks that would normally require human intelligence abilities. This paper mainly explores the frontiers of artificial intelligence and its applications in various fields. The AI Frontiers promulgate methodical concepts that are peer-reviewed cutting-edge research on the disruptive technological revolution of Artificial Intelligence. Additionally, some key viewpoints in the field of AI have been listed along with the main frontiers, including Machine Learning (ML), Deep Learning (DL), Fuzzy Logic (FL), Natural Language Processor (NLP), and Genetic Algorithm (GA). Furthermore, this paper discussed some common AI applications and a briefing about the current scenario in the worldwide market for artificial intelligence. © 2022 IEEE.

关键词： Genetic algorithms

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Jyoti: An Intelligent App for Women Security 1

引用

4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Pathak, Pooja Choudhary, Parul Department of Mathematics Institute of Applied Sciences GLA University Uttar Pradesh Mathura India Department of Computer Application and Engineering GLA University Uttar Pradesh Mathura India

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

In today’s world of rapid urbanization, a new research chapter in social relations and women’s safety has begun. With this wave of urban transformation comes a growing sense of loneliness and helplessness among certain social groups. Women’s safety is a major issue in this situation, not only for the poor but also for civilized women. Weaknesses in the situation women’s safety plans, practices, and guidelines exist to reduce violence against women and their fear of crime. While alone walking or traveling, women should be aware of their surroundings. The advancement of technology may also contribute to women’s safety. Our proposed application is "Jyoti" for women’s safety. This application helps to prevent female homicide. When a female is in a bad situation, the application will send a message to trusted people and the police will go to current location. This application has unique feature that it sends messages to trusted people at regular intervals while shaking the phone. It also calls for the first trusted contact to assist in an emergency. It will help to reduce female crime. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global positioning system

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Leveraging Machine Learning to Optimize Real-Time Data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. This technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. Through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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Bitcoin-Blockchain Technology: Security Perspective 3

Bitcoin-Blockchain Technology: Security Perspective

引用

3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Ansar, Syed Anas Arya, Swati Aggrawal, Shruti Yadav, Jaya Pathak, Prabhash Chandra Babu Banarasi Das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India University of Lucknow Department of Computer Science Lucknow India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken. © 2022 IEEE.

关键词： Blockchain

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Examining Driver Sleepiness Detection Based on Visual Actions Using Haar Cascading and CNN

Examining Driver Sleepiness Detection Based on Visual Action...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Vandana Rawat Pratibha Manral Lakshya Gupta Nidhi Joshi Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Hill University Dehradun India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Every hour, there are numerous road accidents all over the world. Some of these accidents are caused by drunk driving, lack of sleep, inattention behind the wheel, and a variety of other factors that can be dangerous for both passengers and other road users. The most frequent circumstance is sleeping deprivation, which can cause the driver to be reckless while operating a motor vehicle. Driver drowsiness detection systems are particularly effective in identifying drowsiness by measuring the size of the driver's eyes and the rate at which they blink using a camera and software that is connected to it. The driver fatigue detection system works utterly offline, leveraging the Haar Cascade and Perclos machine learning methodologies, and can warn the driver via alarm if he or she is feeling drowsy.

关键词： Training Visualization Accuracy Sleep Lighting Wheels Software Software measurement Vehicles Testing

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A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph 9th

A Method for Identifying the Timeliness of Manufacturing Dat...

引用

19th International Conference on Advanced Data Mining and applications, ADMA 2023

作者： Liu, Zehua Ding, Xuefeng Jiang, Yuming Hu, Dasha College of Computer Science Sichuan University Chengdu610065 China Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu610065 China

ISBN: (纸本)9783031466601

Timeliness is one of the important indicators of data quality. In industrial production processes, a large amount of dependent data is generated, often resulting in unclear timestamps. Therefore, this article combines the conclusion dependency graph into a process dependency graph to determine the identification order of the timeliness of each process data;By constructing a weighted timeliness graph (WTG) and path single flux, a data timeliness identification method that does not completely rely on timestamps is proposed. Finally, a time-effectiveness identification method based on weighted time-effectiveness graph was discussed through an example and 9 dependency rules, and the effectiveness of the method was verified through a set of experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Big data

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

引用

2023 IEEE International Conference on Memristive Computing and applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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CLAST: Efficient Certificateless Sequential Aggregate Signature Scheme for Industrial Internet of Things Environments

CLAST: Efficient Certificateless Sequential Aggregate Signat...

引用

International Conference on Information, Communication and Networks (ICICN)

作者： Siqi Yang Wenqiang Pan Wenqi Zheng Mingyong Yin Fang Lou Teng Hu Jiajun Xie CAEP Institute of Computer Application China Academy of Engineering Physics

The Internet of Things (IoT) has experienced rapid growth in recent years, drawing significant attention from both academics and industry. As IoT gradually gains popularity in the industrial market, industrial internet of things(IIoT) has emerged as a necessary solution to meet the demands of the times. The limitations posed by the small storage space of certain devices, restricted network throughput coupled with timing and reliability requirements, and the need for safety assurance in the IIoT, all present significant challenges. In particular, security threats are constantly increasing, posing a significant risk to the rapid development of IIoT. Therefore, CLAST scheme was proposed to address these issues, which based on elliptic curve cryptography and bilinear mapping to ensure the security of information. The security analysis of the scheme in the random oracle model was proved, affirming that it satisfies the security requirements for signature verification. Furthermore, the scheme reduces the complexity of the cryptographic operation process and reduces the computational overhead of the signature verification algorithm. These benefits have been substantiated through security analysis performance analysis.

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Exploring Sentiment Analysis Algorithms for Brand-Product Reputation Enhancement 15

Exploring Sentiment Analysis Algorithms for Brand-Product Re...

引用

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

作者： Kumar, Ashwini Sharma, Sunil Nanduri, Sireesha Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Mangement - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper affords an overview of sentiment analysis algorithms for boosting brand-product reputation. The point of interest is on supervised and unsupervised systems getting to know strategies, which might be usually used for predicting sentiment. Specifically, the authors examine 4 forms of algorithms - Naïve Bayes classifiers, support vector machines, recurrent neural networks, and sentiment analysis with deep mastering - and how they can be used for boosting emblem-product popularity. Moreover, the look evaluates the constraints of the algorithms, in addition to capacity destiny studies instructions. The authors finish that while sentiment analysis with deep getting to know can also be the maximum accurate choice, cautious consideration must be made while choosing an algorithm as specific algorithms may also produce distinctive outcomes. This paper presents an informative and comprehensive assessment of sentiment analysis algorithms and their usefulness for boosting logo-product reputation. © 2024 IEEE.

关键词： Support vector machines

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An Effective Prediction of Rainfall Using Machine Learning Technique 5

An Effective Prediction of Rainfall Using Machine Learning T...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Rana, Nisha Guru Prasad, M. Sharma, Ashutosh Pratap, M.S. Anand Kumar, M. Aditya Pai, H. Dept. of Computer Science and Engineering Rishikesh India Graphic Era Deemed University Dept. Computer Science and Engineering Dehradun India Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Dehradun India Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9781665493604

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting. © 2023 IEEE.

关键词： Machine learning

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