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Harnessing Transformative Potential of UAV-Based AI-Powered Data Analysis for Emission Monitoring

Harnessing Transformative Potential of UAV-Based AI-Powered ...

引用

International Conference on Computational Intelligence and Networks (CINE)

作者： Mayank Namdev Jay Dholaria Meet Rathi Dept. of Computer Science Engineering Manipal University Jaipur India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

With the growing pressure to reduce environmental pollution, the combination of Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence (AI) offers a viable path forward for modernizing emission monitoring. This research investigates the revolutionary potential of UAV-based AI-powered data analysis for emissions monitoring, with a particular emphasis on the comparison of XGBoost and CatBoost algorithms. Real-time monitoring, identification, and analysis of pollutants can now be done with remarkable accuracy and efficiency thanks to UAVs outfitted with modern sensors and AI algorithms. This novel methodology has significant advantages over typical monitoring approaches, including increased spatial coverage, lower costs, and higher data quality. Furthermore, AI integration provides predictive analytics and anomaly identification, allowing for proactive environmental risk mitigation interventions. Despite its enormous promise, challenges such as regulatory frameworks, privacy concerns, and technological restrictions must be solved before widespread usage. Overall, the combination of UAVs and AI, as well as the comparison of XGBoost and CatBoost, hold enormous promise for revolutionizing emission monitoring by providing a scalable and versatile solution to protect environmental quality and human health.

关键词： Privacy Data analysis Pollution Prediction algorithms Real-time systems Sensors Artificial intelligence Predictive analytics Monitoring Risk mitigation

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Performance Measurement of Distributed Systems via Single-Host Parallel Requesting using (Single, Multi and Pool) Threads 3

Performance Measurement of Distributed Systems via Single-Ho...

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3rd Information Technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Jghef, Yousif Sufyan Zeebaree, Subhi R. M. Ageed, Zainab Salih Shukur, Hanan M. College of Engineering Knowledge University Computer Engineering Dept. Erbil Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq

ISBN: (纸本)9798350334968

Distributed systems and cloud computing are essential in IT (ICT). Scholars have not contemplated merging distributed systems and cloud computing to measure execution time in milliseconds and capacity in gigabytes. Distributed Systems and Cloud Computing Execute Time and Capacity Methodology This title covered two networking types. Multi-scenario distributed networking begins. A TCP socket links a client-server application (Transmission Control Protocol). This example also networks between computers. In another example, Remote Method Invocation Technology (RMI) links subservers to the main server to handle all client inquiries. Second-generation networking involves distributed systems and cloud computing. Google's API downloaded files to my machine and sent them to the main server. Other examples include using numerous computers or a client server software on the same machine. Multi-client and server threads helped the system find correct results. Pool threads were used elsewhere. This thesis examines execution time and file capacity. The system showed how cloud computing affects distributed systems in two ways to apply many scenarios and draw conclusions (execution time and capacity). This project utilizes TCP sockets, RMI, and API. Threads and a hash table software structure were used to load data into the system between the client and the server. © 2022 IEEE.

关键词： Cloud computing

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Machine Learning - Driven Approaches for Detection and Analysis of Phishing attacks: A Robust Framework for Cybersecurity

Machine Learning - Driven Approaches for Detection and Analy...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： N. D. S. S. Sarvan Venkata Sai Ram Reddy Darshan Jain Guntupalli Rakesh Dinesh Kumar Anguraj Dept. of Computer Science Engineering KL University Guntur India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Phishing is the attempt to trick someone into disclosing confidential and private information by using internet communication, such as bank account details, passwords, credit/debit card details, and usernames. The accuracy of foreseeing such phishing assaults can be substantially enhanced through utilizing these algorithms. The primary objective of this research work is to enhance internet security by providing a dependable and productive method for detecting phishing websites. To achieve this, a machine learning-based approach was employed, utilizing various models trained on a dataset of phishing website attributes. The study evaluated a range of machine learning algorithms and classifiers, including Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, Decision Tree, Random Forest, Bagging, Voting, and AdaBoost Classifiers. These evaluation parameters are used to determine the efficiency of the machine learning models including F1 Score, Accuracy, Precision, and Recall values. By training the dataset with key variables from credible websites, the Bagging Classifier algorithm demonstrated a substantial improvement, achieving an accuracy rate of 98.4% resulting in a significant enhancement in phishing website detection compared to other methods.

关键词： Training Machine learning algorithms Phishing Organizations Predictive models Boosting Prediction algorithms

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Approaches for Identifying Suicide Ideation in Social Media Texts: Comprehensive Review

Approaches for Identifying Suicide Ideation in Social Media ...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Jayshri Suresh Sonawane Dinesh Jain Dept. of Computer Science and Engineering SAGE University Indore India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In contemporary society, Suicide has become a notable concern within the realm of public health. Suicidal ideation (SI) encompasses thoughts about ending one's life. Factors contributing to suicidal thoughts and actions are diverse, ranging from personal life events to enduring emotional distress. We investigate the issue of identifying such ideation on Twitter, emphasizing the usefulness of a collection of characteristics taken from the literature as well as the function of discussion context in this process. Numerous machine learning algorithms have been put forth to identify SI from internet channels, but because feature engineering and classification techniques are still lacking, the accuracy of these algorithms is still low. Therefore, by using novel deep learning and natural language processing-based feature engineering technique accuracy can be increased.

关键词： Deep learning Machine learning algorithms Accuracy Social networking (online) Reviews Feature extraction Natural language processing Distance measurement Classification algorithms Public healthcare

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Design of Violence Event Detection System Based on CCTVs by Human Body Pose Recognition

Design of Violence Event Detection System Based on CCTVs by ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chang, Kuei-Chung Liao, Yi-Ching National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Taiwan National Changhua University of Education Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Violent incidents have happened regularly and usually cause tragedies due to without rescued immediately. Therefore, we expect to study a violence detection and alarm system based on human body pose recognition of images captured from public CCTVs. The system uses OpenPose for real-time skeleton recognition to detect poses based on the abnormal angles or speeds of movement between the various joints of the limbs to determine whether it is violent behavior or not. If possible violent events are detected, the system will trigger an alarm and notify the police or security guard to deter the attacker. Victims will be able to get assistance as soon as possible through this recognition system even if they have obstacles for calling help by themselves. Experimental results show that the target violence behaviors can be detected successfully. © 2023 IEEE.

关键词： Gesture recognition

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An In-depth Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction

An In-Depth Exploration of Machine Learning Algorithms and P...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Inderdeep Kaur Aleem Ali Dept. of Computer Science and Engineering Chandigarh University Kharar India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research.

关键词： Performance evaluation Technological innovation Machine learning algorithms Machine learning Medical services Predictive models Prediction algorithms

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Cyberbullying Detection and Severity Classification Using Bi-LSTM

Cyberbullying Detection and Severity Classification Using Bi...

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International Conference on Communication and Electronics Systems (ICCES)

作者： K. Nirmala Devi Vani Rajasekar P. Jayanthi Kavin Balasubramani Kaviya Kandasamy Keerthana Gowrisankar dept. of Computer Science Kongu Engineering College Erode India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with lingering emotional and psychological repercussions. In social media platforms cyber bullying is becoming more and more common, which has raised the need for efficient tools to identify and remove harmful content. Based on the frequency of objectionable words, a system was created for this research to categorize tweets into four groups: neither, low, medium, or high. A Kaggle dataset was first cleaned and prepared by pre-processing with Keras and Natural Language Toolkit (NLTK). The offending severity level of the tweets was determined using a fuzzy logic with a triangular membership function. Several models were trained using that categorized datasets like Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Encoder Representations from Transformers (BERT-Base) and DistilBERT. Among those models Bi-LSTM showed a high degree of accuracy with a precision score of 95.52%, recall of 95.48%, accuracy score of95.48%, and F1 score of 95.49%.

关键词： Training Accuracy Cyberbullying Psychology Bidirectional control Transformers Encoding Tokenization Long short term memory Videos

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Brain Tumor Detection using EEG Waveforms

Brain Tumor Detection using EEG Waveforms

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Ms Asha Rani Borah Ashok K Rakshith M P Rakesh M Chethan R M Dept of Computer Science New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Brain tumors present a significant health challenge, demanding early and accurate diagnostic methods. This study utilizes the EEG wave patterns for non-invasive brain tumor detection. This research study aims to analyze EEG data using Convolutional Neural Networks (CNNs) and Support Vector Machine (SVM) to identify the tumor-related wave patterns. The proposed methodology involves preprocessing EEG signals, extracting relevant features, and training both CNN and SVM models to classify normal and tumor-related EEG patterns. The interpretability techniques are also incorporated to interpret the neural signs contributing to tumor detection. This enhances the proposed model's transparency and establishes a connection between detected patterns and clinically relevant information. While recognizing the challenges such as data variability and the necessity for large, well-annotated datasets, this study highlights the potential of EEG-based detection method

关键词： Support vector machines Training Accuracy Tomography Brain modeling Feature extraction Electroencephalography

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Performance Evaluation of Clinical Models on Sequential Clinical Text for AI Powered Medical Applications

Performance Evaluation of Clinical Models on Sequential Clin...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Bhavya Javagal Sonal Sharma Dept of computer science and engineering Jain Deemed to be university Bangalore India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Clinical terminologies are used by the experts to evaluate or conclude specific results based on the symptoms of the patients. The clinical terminologies used by the experts can be termed as aspects, aspect extraction is the major sub-task in any sentimental analysis. For the extraction of aspect from a given sentence, the existing natural language processing methods mainly focuses on the results generated by a pre-trained model. These pre-trained models work very well for normal English, however when it comes to clinical terminologies there exists a deficiency in generating the specific results. To address the issues related to clinical terminology. There exist multiple clinical models such as Clinical Big bird, Clinician Bert, Clinical Longformer which are transformer-based learning model which perform better against these clinical data. These models perform better on large dataset. In this paper, we would like to evaluate the performance of these Bio models with sequential clinical text and compare the performance of these models and evaluate context and coherence. The best results of these models will be presented to artificial bee colony for further optimization and integrate with an Artificial Intelligence based applications.

关键词： Performance evaluation Terminology Biological system modeling Computational modeling Transformers Data models Natural language processing

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Weather Forecasting Web application

Weather Forecasting Web application

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Gowrav N Dhanush V Chandrashekhar Pomu Chavan Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Weather Refers to the Atmosphere state of certain area and time in terms of temperature, clouds, winds, dry state, sun and rain. The most important geophysical phenomenon that affects us is weather. Weather fluctuates greatly and is impacted by a number of elements including cloudiness, climate, and other variables. The primary goal of the project is to develop an app that can deliver weather forecasts for each city on the planet. We wish to use the ReactJS java script technology to create a web application.

关键词： Wind Java Rain Planets Clouds Urban areas Government Weather forecasting Sun Meteorology

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