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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...

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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

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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 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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A Cross-Language Analysis on Sarcasm Detection

A Cross-Language Analysis on Sarcasm Detection

引用

International Conference on Computer and Information Technology (ICCIT)

作者： Nuzat Tasnim Rudra Pratap Deb Nath Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Cross-language sarcasm detection is valuable for assessing if a model effective in one language works equally well in another, potentially saving significant time and effort in developing language-specific models. This study proposes the following null hypothesis: Performance variations in Sarcasm Detection models due to single model component changes (e.g., embeddings or classifiers) are independent of the target language. We evaluated 27 models on English and Bangla datasets, two languages with distinct sentence structures, and analyzed performance differences using the Wilcoxon test. Based on the F1-score, the test revealed p value of 0.21, implying that there are no significant difference between the two datasets, failing to reject the hypothesis. Nonetheless, further research on cross-language sarcasm detection is needed to ensure a comprehensive grasp of this field and its potential applications.

关键词： Measurement Analytical models Ethics Computational modeling Training data Computer architecture Natural language processing Multilingual Cultural differences Information technology

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Publishing Bangladesh Socio-economic data as Linked and Multidimensional data

Publishing Bangladesh Socio-economic Data as Linked and Mult...

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International Conference on Computer and Information Technology (ICCIT)

作者： Rudra Pratap Deb Nath Amrit Bhattacharjee Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Inspired by the vision of achieving a ‘Smart Bangladesh,’ the Bangladesh Bureau of Statistics consistently endeavors to manage and disseminate socio-economic data on the web. However, the existing datasets face several challenges: 1) they are presented in an unsustainable, read-only, static, and aggregated format, 2) they lack adherence to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, and 3) they are not conducive to the interactive analysis and integration with other data sources. In this paper, we propose a comprehensive solution by outlining a step-by-step process for constructing a knowledge graph specifically tailored to the Bangladesh census data. The resulting knowledge graph is presented using the Resource Description Framework (RDF) model, enriched with multidimensional semantics, facilitating Online Analytical Processing (OLAP)-like analysis and aligning with the FAIR and linked data principles. It has a size of 6.31 GB, is linked to 772 external resources, and contains approximately 39 million RDF triples. Our evaluation focuses on assessing both the integration process and the quality of the generated knowledge graph in terms of FAIR principles and OLAP operability.

关键词： Publishing Soft sensors Linked data Semantics knowledge graphs Resource description framework Socioeconomics Information technology Interoperability Faces

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Presenting Bangladesh Education data in Analytical and Semantic Format

Presenting Bangladesh Education Data in Analytical and Seman...

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International Conference on Computer and Information Technology (ICCIT)

作者： Tonmoy Chandro Das Rudra Pratap Deb Nath Rezaul Karim Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In an era where data plays a pivotal role in shaping policy and driving progress, especially in education, the need for well-informed decision-making is more important than ever. Like other governments, Bangladesh regularly publishes education data as part of open data policies to promote transparency and accessibility. However, while Bangladesh’s educational data is rich in key metrics, it faces limitations such as 1) a lack of a unified analytical format and insufficient aggregation, which restricts its usability for Business Intelligence, Online Analytical Processing (OLAP), and gaining a complete view of the data to extract meaningful insights; 2) limited interoperability and accessibility. In this study, we model Bangladesh’s education data across multiple dimensions, integrating diverse sources into a multidimensional framework using data Warehousing technology. This enables OLAP-like analysis for comprehensive insights. To improve interoperability and accessibility, the data is integrated into a knowledge Graph called BEdKG. BEdKG is modeled with the Resource Description Framework (RDF) model and composed of 337,308 RDF triples, adheres to Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, and is linked to 72 external resources. Finally, evaluations through a query interface show that BEdKG, with a size of 45.3 MB, effectively addresses the issue of insufficient aggregation by providing interactive query responses and performing operations over 99% faster than manual methods.

关键词： Education Decision making Warehousing knowledge graphs Resource description framework data models Business intelligence Usability Interoperability Semantic technology

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