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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

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4th International Conference on Technological Advancements in computational sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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Comment Compass: An Approach to Analyze YouTube Comments through Web Scraping, Sentiment Analysis, and Generative AI for Actionable Insights 1

Comment Compass: An Approach to Analyze YouTube Comments thr...

引用

1st International Conference on Pioneering Developments in Computer science and Digital Technologies, IC2SDT 2024

作者： Rathod, Manav Yadav, Ankita Phutane, Soham Bhise, Suvarna Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798350365016

In this paper, we introduce 'Comment Compass,' a novel system designed to revolutionize the analysis of YouTube video comments by content creators. The hypothesis driving this research is that integrating web scraping, sentiment analysis, and generative AI can provide content creators with deeper insights into their audience's sentiments and perspectives, thereby enhancing content strategy and engagement. Methodologically, 'Comment Compass' employs Python-based web scraping to extract comments from YouTube videos. Sentiment analysis is conducted using the SentimentIntensityAnalyzer from the Natural Language Toolkit (NLTK) library, which produces a pie chart depicting the distribution of positive, negative, and neutral comments. Additionally, the system leverages Generative models to generate further insights from the comments, such as future content ideas, criticisms, and inquiries. Our results indicate that 'Comment Compass' offers content creators actionable insights that can significantly optimize content strategies for better audience engagement and impact. This work demonstrates the substantial potential of combining web scraping, sentiment analysis, and generative AI, contributing valuable knowledge to these fields. The findings are contextualized within existing literature, underscoring the innovative contributions of this research. © 2024 IEEE.

关键词： Video analysis

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Enhanced Building Prediction for Smart City Surveillance 1

Enhanced Building Prediction for Smart City Surveillance

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Saravanan, Lekshmipriya Natarajan, Mathivathani KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades. © 2023 IEEE.

关键词： Intelligent buildings

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Deep Learning 1

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Ranganathan, Preethi Akshaya Natarajan, Karthika KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

One of the most expensive natural disasters that affect people, earthquakes occur suddenly. As a result, earthquake prediction has grown in importance and difficulty for humanity. Although numerous existing approaches attempt to handle this problem, the majority rely on seismic indicators created by geologists or feature vectors extracted by the deep learning techniques to describe an earthquake for the earthquake prediction. Combining these two categories of characteristics to enhance ultimate Earthquake prediction performance is still a challenge. In order to achieve this, we put forth a deep learning model that successfully fuses explicit and implicit information for earthquake prediction. Here, we use a Convolutional Neural Network to extract implicit features while using eight precursory pattern-based indicators as the explicit features. After that, an attention-based approach is suggested to effectively combine these two categories of traits. A dynamic loss function is the additionally created to address the imbalance of category in seismic data. On a test picture dataset, the performances of multiple CNN models are contrasted. In order to classify earthquake damage, the model is further developed as a web application. Damage assessment values, which are determined using the Convolutional Neural Network model and gradient-weighted class activation mappings, are used to determine the extent of the damage. The web-based program can efficiently and automatically categorize earthquake-related structural damage, making it appropriate for decision-making in emergency response, resource allocation, and policy formation. © 2023 IEEE.

关键词： Earthquakes

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning. 2

Earthquake Damage Prediction and Rapid Assessment of Buildin...

引用

2nd International Conference for Advancement in Technology, ICONAT 2023

ISBN: (纸本)9781665475174

A quick forecast of the region's structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model's outcomes show that elastic net regression is the most effective predictive model for predicting building damage. © 2023 IEEE.

关键词： Earthquakes

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Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

Multi-Agent Collision Avoidance Method Using Fuzzy Risk Esti...

引用

2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Dundar, Yigit Can Bremdal, Bernt Arild Hakansson, Anne Karlsen, Randi UiT - the Arctic University of Norway Dept. of Computer Science Tromsø Norway UiT - the Arctic University of Norway Dept. of Computer Science and Computational Engineering Narvik Norway

ISBN: (纸本)9798350322972

Automated vehicles within Industry 4.0 are used as logistics units where they can move resources from one place to another safely and efficiently. The automated vehicles can be tasked to work in unknown environments where collision-free navigation is challenging due to uncertainty and lack of environmental information. Collisions can damage equipment and may even cause harm to human workers sharing the same space. In order to carry out tasks and avoid collisions in unknown environments, automated vehicles need means of self regulation and environmental awareness through sensors. This paper presents a multi-agent collision avoidance method using fuzzy risk estimation and information sharing for automated vehicles navigating unknown environments. The automated vehicles exchange information about their current state and GPS location with other automated vehicles and use fuzzy collision risk estimation based on sensor data to avoid collisions with static obstacles and each other. Additionally, the automated vehicles collectively learn about their work environment over time by creating a virtual map of the environment through shared information. To test and evaluate the method, a series of simulated tests are carried out. The results from tests show that the automated vehicles are able to avoid collisions while also accurately mapping an initially unknown work environment using the method. © 2023 IEEE.

关键词： Multi agent systems

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Advance Monitoring System for Mountain Climber's Health

Advance Monitoring System for Mountain Climber's Health

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Surender, R. Gowtham, P. Reddy, Likhith Sabapathy, Sundaresan Sathyabama Institute of Science and Technology Dept. of ECE Chennai India Sathyabama Institute of Science and Technology Dept. of EC Chennai India School of AI Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331540685

This paper presents an innovative solution for continuously monitoring the health of mountain climbers in real-time. The proposed system collects vital health parameters such as pulse rate, body temperature, live location, and the orientation of the climber's body. In cases where the climber's body temperature drops below a certain threshold, a Peltier device is employed to initiate heating and restore the body temperature to a safe level. The gathered data is transmitted wirelessly using long range (LoRa) technology for efficient long-distance communication. A receiver module captures the transmitted data and displays it on a liquid crystal display (LCD) for immediate observation. Additionally, the receiver module is connected to the Blynk cloud platform via the internet, enabling remote access to the collected data from anywhere in the world. This advanced monitoring system ensures the safety and well-being of mountain climbers by providing real-time health status updates and intervention capabilities, thereby enhancing the overall safety measures in mountainous regions. © 2024 IEEE.

关键词： Liquid crystal displays

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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