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Artificial Intelligence of Things (AIoT) Technologies, Benefits and Applications

Artificial Intelligence of Things (AIoT) Technologies, Benef...

引用

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Chowdhury Abida Anjum Era Mahmudur Rahman Syada Tasmia Alvi Dept. of Computer Science Fachhochschule Dortmund Dortmund Germany Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Today’s world is changing rapidly because of technological developments. Among the astonishing evolution, AIoT has become a new addition to machinery growth. This term is a compound of another two dominant infrastructures: Artificial Intelligence (AI) and the Internet of Things (IoT). AIoT intends to invent more efficient and improved IoT operations or services with enhanced data management and analysis capabilities. AI deals with machines, especially computer systems fields like natural language processing, speech recognition, etc. IoT is a system where interrelated devices and machines communicate and transfer data with different sensors without human intervention. AIoT can be more beneficial for both types of technology, where a vast amount of data obtained from IoT devices, will be handled by some powerful AI algorithms. When it needs to handle a lot of data, essential and efficient data processing is required to use the information gathered from IoT devices. Without integrating AI, IoT devices can't aid efficient services. Besides, AIoT is utilized to handle efficiency, scalability, accuracy, and fraud detection in transactions. This paper aims to show the potentiality of AIoT, its recent applications and benefits, and the probable future of this technology.

关键词： Surveys Supply chain management Scalability Medical services Speech recognition Nervous system Sensor systems

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Enhancing Weather Forecasting Accuracy: Integrating Machine Learning Algorithms with Numerical Weather Prediction Models 1

Enhancing Weather Forecasting Accuracy: Integrating Machine ...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Madhuri Singh, Rana Pratap Sharma, Usha Arivukkodi, R. Roy, Ranjeet Kumar Verma, Kartik Dept. of Life Sciences CHRI Campus India Madhav University Dept. of Humanities and S.Science Pindwara India Meenakshi A.H.E.R Dept. of Computer Applications Chennai India Lingayas Vidyapeeth University Dept. of Mathematics Faridabad India Chandigarh University Dept. of Com. Science Engineering Chandigarh India

ISBN: (纸本)9798350356816

Many industries, including agriculture, transportation, & energy, heavily rely on weather forecasts. Earlier weather forecasting techniques were built on numerical weather prediction models, which use physical equations to simulate atmospheric conditions. However, these models have limitations due to measurement uncertainty & the complexity of atmospheric systems. Machine learning techniques have emerged as a promising advancement in weather forecasting. This paper introduces a novel method for weather forecasting using Naive Bayes & Chi-Square algorithms. The theoretical foundations of these algorithms & their application in weather forecasting are discussed. Experimental results on real weather datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Prediction models

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MSMA: Multi-Agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-Source Data Integration 24

MSMA: Multi-Agent Trajectory Prediction in Connected and Aut...

引用

24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Xi Bhadani, Rahul Sun, Zhanbo Head, Larry Dept. of Systems and Industrial Engineering Univ. of Arizona TucsonAZ United States Dept. of Electrical and Computer Engineering Univ. of Alabama in Huntsville HuntsvilleAL United States School of Transportation and Logistics Southwest Jiaotong Univ. Chengdu China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The prediction of surrounding vehicle trajectories is crucial for collision-free path planning. In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing both sensors and communication technologies to perceive its surrounding traffics consisting of autonomous vehicles (AVs), connected vehicles (CVs), and human-driven vehicles (HDVs). Our trajectory prediction task is aimed at all the detected surrounding vehicles. To effectively integrate the multi-source data from both sensor and communication technologies, we propose a deep learning framework called MSMA utilizing a cross-attention module for multi-source data fusion. Vector map data is utilized to provide contextual information. The trajectory data set is collected in CARLA simulator with synthesized data errors introduced. Numerical experiments demonstrate that in a mixed traffic flow scenario, the integration of data from different sources enhances our understanding of the environment. This notably improves trajectory prediction accuracy, particularly in situations with a high CV market penetration rate. © ASCE.

关键词： Data fusion

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Improving Prediction Accuracy in IoT Network Security Against DDoS Attacks by Combining RIPPER and KNN Algorithms 8

Improving Prediction Accuracy in IoT Network Security Agains...

引用

8th International Conference on electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Juliansyah, Handrino Sihombing, Poltak Fahmi, Fahmi Universitas Sumatera Utara Faculty of Computer Science and Information Technology North Sumatera Medan Indonesia Universitas Sumatera Utara Faculty of Engineering Electrical Engineering Dept North Sumatera Medan Indonesia

ISBN: (纸本)9798331531249

The Internet of Things (IoT) is a network of linked devices comprising interconnected devices that communicate through internet, integrating sensors, software and other technologies. As the number of connected devices grows, ensuring IoT security has become increasingly critical, with cyber threats and other related risks arising. A prevalent threat is the Distributed Denial of Service (DDoS) attack, which seeks to incapacitate server systems by overwhelming the network with excessive traffic. This study explores machine learning approach to improve intrusion detection systems (IDS) performance, such as accuracy in identifying and mitigating DDoS attacks. Specifically, it employs a hybrid approach combining the well-known RIPPER algorithm or Repeated Incremental Pruning to Produce Error Reduction and the K-Nearest Neighbors (KNN) algorithm as detection models. The study utilizes the Network Security Layer-Knowledge Discovery in Database (NSL-KDD) dataset, which contains 41 features for classifying DDoS attacks. To optimize the model's performance, the data underwent preprocessing to eliminate irrelevant and redundant features while retaining essential information. The classification results demonstrate a peak accuracy of 97.32% with k=6 and an average accuracy of 96.30% across 15 trials with varying k values from 1 to 15. These findings highlight the effectiveness of the RIPPER-KNN approach in improving the prediction accuracy of DDoS attack detection in IoT networks. © 2024 IEEE.

关键词： Denial-of-service attack

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Share-a-Meal: A Mobile App-based Food Sharing System

Share-a-Meal: A Mobile App-based Food Sharing System

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Mohammad Salah Uddin Fozlay Rabby Ansary Dept. of Computer Science and Engineering East West University Bangladesh

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Many problems have been solved using the internet of things (IoT), Waste management, resource management, traffic management, financial services (e.g. stock rate prediction), logis-tics services, agriculture, etc. are the major sectors where IoT has played a fruitful role. The use of IoT is increasing day by day due to its immense advantages. This paper presented an IoT-based system for sharing leftover food with others or hungry people. The system consists of a mobile application and an embedded system. Cloud service is used as middle-ware between the mobile application and embedded system. Mobile application is used for sending and receiving notifications. The rectangular-shaped box type embedded system is responsible for food collecting and distribution. The development and prototype implementation of our proposed system are discussed throughout this paper. The system helps to share leftovers (food) with others which can reduce food wastage.

关键词： Waste management Embedded systems Prototypes Passwords Mobile applications Internet of Things Resource management Reliability Informatics Mobile computing

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Sentiment Analysis of COVID-19 Tweets 1

Sentiment Analysis of COVID-19 Tweets

引用

1st International Conference on Informatics, ICI 2022

作者： Gupta, Isha Chatterjee, Indranath Gupta, Neha Mriirs Faculty of Computer Applications Faridabad India Tomsyong University Dept. of Computer Science Engineering Busan Korea Republic of

ISBN: (纸本)9781665437219

Sentiment analysis is the field of computer science that studies people's behaviour or attitude towards objects, events, organizations, and products. Organizations make use of this to improve their products. This paper aims to study people's behaviour towards COVID 19. We have taken data from Twitter to analyse the sentiments. The results show that people don't have positive sentiments towards this coronavirus. © 2022 IEEE.

关键词： COVID-19

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Source Identification of DeepFake Videos in Social Media-like Networks using Centrality Measures

Source Identification of DeepFake Videos in Social Media-lik...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Aryan Kumar Bharath Raja Ashlin Furtado Anoushka Vemireddy Prasad B. Honnavalli Sapna V.M. Dept. of Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The widespread occurrence of DeepFake videos on social media platforms makes it extremely difficult to pinpoint their source, which is essential for reducing their propagation and possible damage. This research uses centrality measurements in graph-based social networks to identify the primary accounts responsible for spread of misinformation. PageRank, Closeness, Betweenness, and Degree are some of the centrality metrics used to evaluate the influence and impact of nodes in the network. The approach combines a multi-criteria centrality evaluation, content similarity analysis, and graph pruning to determine the most likely source of detected DeepFake videos. The accuracy and resilience of the suggested framework in identifying sources and assessing information dispersion are shown by experimental findings obtained in a variety of network scenarios. Our results identify the deepfake content as well as determine the source account which released this content onto social media. Identifying and eliminating these accounts from a social media network would ensure safety and help users trust the information received online. Our research is an important first step in thwarting false information and boosting confidence in digital platforms.

关键词： Measurement Bridges Deepfakes Social networking (online) Digital forensics Network analyzers Safety Security Dispersion Resilience

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Control of the Boiler Turbine Unit Based on Active Disturbance Rejection Control 3

Control of the Boiler Turbine Unit Based on Active Disturban...

引用

3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Zrigan, Ahmed F. Shi, Gengjin Altrmal, Hossam A. Elsouri, Khaled S. Libyan Academy for Post Studies Dept. of Electrical & Computer Engineering Tripoli Libya Tsinghua University Beijing China China Power International Development Ltd. State Power Investment Corporation Beijing100080 China Higher Institute of Science and Technologies Dept. of Electrical & Electronics Engineering Tripoli Libya

ISBN: (纸本)9798350319897

The boiler turbine unit was controlled for that operating point using the optimal controller (PSO) and the controller (ADRC), and from that concordance implementation, we can choose the best controller at this operating point. This paper describes the development of operating the boiler unit at an operating point lower than the equilibrium point, by selectively selecting that point to avoid nonlinearity in this process, and it also shows us that this unit is nonlinear. © 2023 IEEE.

关键词： Controllers

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Efficient Semantic Detection and Analysis of Misinformation in CBD-Related Tweets Using FAISS and Mistral NeMo Instruct

Efficient Semantic Detection and Analysis of Misinformation ...

引用

International Conference on Semantic Computing

作者： Jason Turner Mehmet A. Gulum Mehmed Kantardzic Dept. Computer Science & Engineering University of Louisville Louisville KY United States Dept. Of Computer Science DePauw University Greencastle IN United States

ISBN: (数字)9798331524265

ISBN: (纸本)9798331524272

The growing popularity of cannabidiol (CBD) has led to a surge in misinformation, particularly on social media platforms like Twitter, posing risks to public health. This paper presents a scalable method for detecting CBD-related misinformation in a large corpus of tweets. Using approximately 3.7 million tweets collected from 2011 to 2021, we implement a two-step process: first, FAISS (Facebook AI Similarity Search) efficiently identifies tweets semantically similar to false claims extracted from FDA warning letters. Second, Mistral NeMo Instruct, a zero-shot model, classifies tweets as ‘Misinformation’ or ‘Non-Misinformation’, providing justifications for transparency. This approach minimizes computational costs while maintaining accuracy, making it a practical tool for large-scale misinformation detection. The framework is scalable and adaptable, evolving with new FDA data or emerging cannabis research.

关键词： Adaptation models Analytical models Social networking (online) Computational modeling Scalability Semantics Natural language processing Hardware Fake news Public healthcare

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Strategical Implementation of Autonomous Vehicle Navigation System 3

Strategical Implementation of Autonomous Vehicle Navigation ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Mohapatra, Srikanta Kumar Ali, Farida A. Sarangi, Prakash Kumar Sahu, Premananda Mohanty, Jayashree Chitkara University Institute of Engineering and Technology Chitkara University Punjab India University Faculty of Engineering & Technology Odisha India Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab India IIIT Bhubaneswar Dept. of Computer Science and Engineering Odisha India

ISBN: (纸本)9798350303360

Accidents involving motor vehicles are on the increase as our culture grows more technologically advanced. Even with mobile phones, in-car entertainment systems, higher traffic, and improved road infrastructure, the majority of traffic accidents are caused by human error on the part of the driver. Accidents caused by humans may be reduced by technology, which would be beneficial to road safety. The introduction of driverless automobiles may result in increased road safety. Everyone in the car is putting in a lot of effort to achieve this goal. Automakers Audi, BMW, Ford, Google, General Motors, Tesla, and Volkswagen, together with Volvo, are doing research and development on completely autonomous vehicles. The 'Waymo' product from Google is well-known. This task involved the creation of a miniature prototype of an autonomous vehicle based on an Arduino Uno. Present study able to determine the path by graphing the starting point and ending point on Google Maps. From the KML route, *** is able to extract both the latitude and longitude coordinates. Following that, the coordinates were entered into the code for the Arduino uno. Arduino Uno is used along with AGPS sensors to track the realtime movement along with the car's latitude and longitude coordinates and then upload them to the things peak cloud. AGPS sensors are necessary for today's smartphones. The Arduino Uno establishes a connection to the Things peak cloud by means of the esp8266 Wi-Fi module. It then retrieves the most recent data and computes the angle and distance at which our autonomous car should travel based on the latitudes and longitudes of the gmap route that have been stored in an array. © 2023 IEEE.

关键词： Autonomous vehicles

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