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Safe Route Prediction Based on AQI and Machine Learning

Safe Route Prediction Based on AQI and Machine Learning

引用

2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Shreyank Kumar V, Sharath Shashwath Vishwaraj, T. Pramod, T.C. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798350300826

Pollution is the most indispensable and upsetting issues faced in today's day to day life in the world. Analyzing air quality is crucial for understanding and addressing the potential impacts on human health., ecosystems., and the economy. In this paper., safe route prediction system is designed for Tumakauru city commuters based on Air Quality Index (AQI) integration and by leveraging machine learning models and map functionality. Multiple machine learning models, such as XGBoost, LR, MLP classifier, RFC, and Lasso, are utilized for accurate AQI predictions. The system integrates Google Maps to input source and destination locations, retrieve possible routes, and mark danger zones based on predefined AQI thresholds. Safer routes are suggested by eliminating paths passing through identified danger zones. The system enhances user decision-making and promotes health and environmental sustainability. The results demonstrate the models' accuracy in predicting AQI values and categorizing air quality levels. The system's practical implications include minimizing health risks and reducing air pollution exposure. Overall, the proposed solution introduces a comprehensive solution for safe commuting based on AQI predictions. © 2023 IEEE.

关键词： Air quality

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Resilient Fog-Based Adaptive Traffic Control System 14

Resilient Fog-Based Adaptive Traffic Control System

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Matange, Ashwini Taneja, Varun Chaumal, Aarya Buwa, Pallavi Abraham, Jibi Coep Tech Dept. of Computer Engineering and Information Technology Pune India

ISBN: (纸本)9798350335095

Traffic congestion is a growing problem in India, largely caused by the increasing number of vehicles. To address this, creating an adaptive traffic control system has become essential. To present a solution, this paper aims to create a resilient, adaptive traffic control system that dynamically adjusts signal timings based on the current traffic density. The paper proposes introducing a Fog layer at each traffic junction to capture real-time video feeds and process them locally, with the final decision about dynamic signal control being taken in the Cloud. The validity of the solution is tested through simulations in SUMO. A Machine Learning model is trained in the Cloud to predict expected vehicle numbers to set adaptive signal times for contingency situations. The paper aims to demonstrate the resilience and performance of the system with the adaptive traffic signal control algorithm, potentially offering a solution to traffic congestion in India. © 2023 IEEE.

关键词： Traffic congestion

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Cryptosight: Decrypting Future Trends with LSTM, PyCaret and Jinja 2.0 1

Cryptosight: Decrypting Future Trends with LSTM, PyCaret and...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Mhatre, Soham Motiram Mishra, Brajesh Kumar Raja, Ishan Kumar Goswami, Bigit Krishna Agrawal, Sneha Upadhyay, Pranjal National Institute of Technology Dept. of Electronics and Communication Engineering Nagaland Dimapur India National Institute of Technology Dept. of Mechanical Engineering Nagaland Dimapur India National Institute of Technology Dept. of Electrical & Electronics Engineering Nagaland Dimapur India National Institute of Technology Dept. of Computer Science and Engineering Nagaland Dimapur India

ISBN: (纸本)9798350377002

The primary objective of this research is to develop and evaluate combined cryptocurrency price prediction models using Long Short-Term Memory (LSTM) neural networks along with traditional time series models and machine learning techniques like Random Forest and Linear Regression. The LSTM models will capture long-term dependencies in sequential cryptocurrency price data, while the other models provide complementary predictive capabilities. This multi-model approach aims to leverage the strengths of different techniques to improve overall prediction accuracy. Additionally, this research seeks to explore the practical application of these predictions in real-world cryptocurrency trading scenarios. The study will collect historical price data and relevant news for major cryptocurrencies from reputable sources. Data preprocessing, feature engineering, model training, hyper-parameter tuning, and evaluation using metrics like RMSE, MAE, and R-squared will be performed. The predictive performance of the LSTM-based models will be compared against baseline models, with statistical significance testing. Interpretability analysis will provide insights into the factors driving price movements. Furthermore, the research will investigate integrating the predictions into trading algorithms and evaluate their efficacy through back-testing on historical data. Limitations, challenges, and future research directions in cryptocurrency price prediction will also be discussed. © 2024 IEEE.

关键词： Prediction models

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

引用

22nd International Learning and technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Multi-label Bengali Abusive Comments Classification using Problem Transformation Method 20

Multi-label Bengali Abusive Comments Classification using Pr...

引用

20th International Conference on Electrical engineering, Computing Science and Automatic Control, CCE 2023

作者： Khan, Tahsin Tasnia Hassan, Abid Ahamed, Md Faysal Islam, Samiul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh North South University Dept. of Electrical & Computer Engineering Dhaka Bangladesh International Standard University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306767

Sentiment analysis on the Bengali language is limited to binary and multi-class classifications. This paper focuses on the multi-label classification of the Bengali abusive social media comments dataset having 10220 rows of different social media negative comments using the problem transformation method. The texts are categorized into five labels: toxic, threat, obscene, insult, and racism. Three different multi-label approaches: Binary Relevance, Label Powerset, and Classifier Chain combined with three popular machine learning algorithms: Multinomial Naive Bayes, Random Forest, and Logistic Regression are implemented in this research. This study evaluates the performance of these models by applying them to various classification scenarios, considering five-label, four-label, three-label, and two-label classifications independently. Our research outcomes are evaluated with some performance indicators: Accuracy, Precision, Recall, F1-Scores, Confusion matrix, Macro-average, and Hamming score. The combination of Label Powerset and Logistic Regression outperformed the other two classifier-model combinations because of their quick adaptation to the dataset with an accuracy of 88.07% for five label classifications, 90.12% for four label classifications, 90.56% considering three label classifications and 92.67% for two label classification individually. With 88.07% accuracy, the Label Powerset with Logistic Regression approach can be applied to classify five types of sentiment in a single text. © 2023 IEEE.

关键词： Classification (of information)

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Detection of Various Plant Leaf Diseases Using Deep Learning Techniques 2

Detection of Various Plant Leaf Diseases Using Deep Learning...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Harshavardhan, K. Krishna, P J V Abhinav Geetha, Angelina Hindustan Institute of Technology and Science Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

To sustain healthy crop yields and avoid extensive plant disease damage, it is essential to diagnose plant leaves. Traditional diagnostic techniques, however, can be laborious and complicated. Deep learning methods like ResNet 34 have recently come to light as a potential strategy for automating plant leaf diagnosis. Deep convolutional neural network ResNet 34 is capable of correctly classifying photos of plant leaves based on patterns and characteristics. A ResNet 34 model may be taught to recognize different leaf illnesses and other anomalies with excellent accuracy by training it on a huge dataset of tagged plant leaf photos. The ResNet 34 model's output may be seen on a website, enabling users to upload pictures of plant leaves and get a diagnosis in a matter of seconds. Also, the website might include details on the particular ailment or issue found, along with suggested remedies and prevention actions. The way of identifying and treating plant illnesses may be completely altered by this method of diagnosing plant leaves, which would ultimately result in healthier crops and a more sustainable future. Farmers, agronomists, and other stakeholders will find it simpler to recognize and effectively treat plant diseases thanks to the usage of ResNet 34 and a website for output display, which offers an accessible and user-friendly interface for plant diagnostics. © 2023 IEEE.

关键词： Diagnosis

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Malaria Detection Using Neural Network

Malaria Detection Using Neural Network

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Kavitha, P. Manimala, G. Sanchana, R. Anushree, U. Rmk Engineering College Department of Computer Science and Engineering India Sri Sairam Engineering College Department of Computer Science and Engineering India Sri Sairam Institute of Technology Dept. of Information Technology India

ISBN: (纸本)9798350394580

Malaria is a serious and occasionally fatal disease brought on by a parasite that frequently infects a particular species of mosquito that feeds on people. Malaria frequently results in severe illness in its victims, including high temperatures, chills that make you shiver, and flu-like symptoms. The four different forms of malaria parasites that infect humans are most likely to result in serious infections and, if untreated, death. Malaria can be devastating, but symptoms and deaths from malaria are typically preventable. Deep Learning models, or to be extra unique,ConvolutionalNeuralNetworks(CNN)modelsarepr oposedonthistaskAcomparisonoftheproposed and contemporary algorithms exhibits that the accuracy of malaria disorder classificationprimarily based on CNN's is higher than other algorithms. If the CNN approach is supported by the addition of more feature extraction techniques and accurately classifying malaria disease in the image, it is projected that the achievement of the acquired results will increase. © 2023 IEEE.

关键词： Cnn deeplearning malariadisease TensorFlow

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Human Skin Diseases Detection and Classification using CNN 3

Human Skin Diseases Detection and Classification using CNN

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Ahmed, Shamim Abiduzzaman, M.D. Rajib, Mahedi Hasan Rahaman, Rafat Hussain, Salam Rashid, Hafsha Binta Wadud, Anwar Hussen Amir-Ul-Haque Bhuiya, T.M. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

Recently skin disease has turned into a threat to normal human life when it becomes cancer. If there do any negligible steps during treatment it will become severe and also may cause the reason of human death. So, necessary steps in an early stage of the diagnosis are mandatory to avoid the worst scenario like human death, especially when the disease is Melanoma skin cancer. Because the human death rate of Melanoma skin cancer has become high during this recent era. To detect skin cancer disease there is some common treatment strategy which is done by expert healthcare professional like doctor or dermatologist, as well as these procedures are also very lengthy and time-consuming. Therefore, recently some automated computer vision or image processing with artificial neural network-based methods are introduced but during the implementation level, these methods are highly cost-effective thoughthey provide better accuracy during prediction. In this paper, we proposed a deep learning-based CNN method for classifying skin diseases. To implement our proposed model, we gathered approximately 2102 image datasets from a massive repository of diverse source images, particularly dermatoscopic images of pigmented lesions. Our experiment results show an 89.45 percent accuracy in detecting skin cancer disease. We hope our proposed method will help to diagnose this skin cancer disease more efficient way than other methods that have already been used. © 2023 IEEE.

关键词： Diagnosis

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Topic Modeling Based Clustering of Disaster Tweets Using BERTopic

Topic Modeling Based Clustering of Disaster Tweets Using BER...

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2024 MIT Art, Design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Rachel J, Julanta Leela Bhuvaneswari, A. Kumudha, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India T.John Institute of Technology Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350362879

The essence of catastrophe management is in the capacity to promptly and effectively addressing unforeseen circumstances, as well as to implement premeditated and flexible strategies in accordance with established protocols and norms. Twitter has become a prominent means of disaster communication, particularly in nations where it has a substantial presence. Twitter can provide valuable information regarding the effects of infrastructure on emergency management in the event of significant disasters. However, the process of sifting through numerous unrelated tweets can be laborious and time-consuming. Prior research has successfully recognized the various categories of communications present on social media platforms during times of disaster. However, there is a dearth of recommended methodologies aimed at effectively extracting valuable information from these messages. In this study, we propose a framework that may be efficiently utilized to promptly deliver disaster effect information derived from social media sources. The methodology involves the application of certain techniques to process the unprocessed Twitter data by employing keyword filtering, location-based filtering, and analysis of tweet properties. Subsequently, Bidirectional Encoder Representations from Transformers (BERT) technique is utilized to categorize the tweets originating from disaster-Affected regions into distinct subjects that are relevant and valuable for emergency management. BERT analysis yielded a total of 376 themes within the dataset. Among these topics, a significant proportion of 196 were found to be closely associated with the impacts of disasters. These impacts encompassed many aspects such as outages, closures, flooded roadways, and damaged infrastructure. © 2024 IEEE.

关键词： Disasters

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Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction

Cognate Transformer for Automated Phonological Reconstructio...

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Mahesh Akavarapu, V.S.D.S. Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891760608

Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of daughter languages. Computational approaches to historical linguistics attempt to automate the task by learning models on available linguistic data. Several ideas and techniques drawn from computational biology have been successfully applied in the area of computational historical linguistics. Following these lines, we adapt MSA Transformer, a protein language model, to the problem of automated phonological reconstruction. MSA Transformer trains on multiple sequence alignments as input and is, thus, apt for application on aligned cognate words. We, hence, name our model as Cognate Transformer. We also apply the model on another associated task, namely, cognate reflex prediction, where a reflex word in a daughter language is predicted based on cognate words from other daughter languages. We show that our model outperforms the existing models on both tasks, especially when it is pre-trained on masked word prediction task. © 2023 Association for Computational Linguistics.

关键词： Forecasting

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