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Safe Route Prediction Based on AQI and Machine Learning

Safe Route Prediction Based on AQI and Machine Learning

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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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Explaining Instructional Factors Affecting M-Leaming Using the Moderator of Social Media: An Extension of the IS and TAM Models 7

Explaining Instructional Factors Affecting M-Leaming Using t...

引用

7th International Conference on E-Society, E-Education and E-technology, ESET 2023

作者： Bamaga, Arwa Zafar, Bassam Terzis, Sotirios King Abdulaziz University Jeddah Dept. of Computer and Information Technology Saudi Arabia University of Strathclyde Glassgow Dept. of Computer and Information Science United Kingdom

ISBN: (纸本)9798350344608

Information and communication technology (ICT) is a term which refers to the incorporation of electronic technologies to manage knowledge and information. It has transformed the traditional education system into an efficient and flexible e-learning context. E-learning enables the higher-education institutions to create a traditional-like virtual classroom. The universities must stay abreast with the current technology to evaluate the quality factors that affect the e-learning process. This paper presents a theoretical study of the quality factors affecting e-learning in the mobile environment and presents two research contributions. First, we propose a model which suggests to add social media as a moderator that effects the relationship between user's satisfaction and user's intention-To-use. Second, we propose to add instruction quality attribute to the existing variables of well-recognized TAM and Extended IS models which enhance the e-learning experience within the mobile environment in higher education institutions in Saudi Arabia. © 2023 IEEE.

关键词： E-learning

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Questify: Your Intelligent Web Page Assistant for Quick Answers 3

Questify: Your Intelligent Web Page Assistant for Quick Answ...

引用

3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Jha, Aashish Purohit, Madhavi Samanta, Gaurav Jadhav, Dipti Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Retaining and recalling information from web pages can often feel like a daunting task, especially when faced with the vast amount of information contained within an article. However, researchers have come up with an innovative solution to tackle this. In this study, we propose a hybrid question-answering system that uses Natural Language Processing (NLP) to logically describe the data on a web page and then react to user inquiries. The system consists of a web scraper, named entity recognition module, and classifiers to learn more about the *** process begins with a web scraper, which gets the text from a certain online page and makes sure that the required data is gathered. The system learns more about the information by identifying and classifying items like persons, locations, organizations, or other pertinent phrases. The question-answering module uses a knowledge graph to provide accurate and comprehensive answers to user queries. Using benchmark datasets, we evaluated the system's performance to confirm the efficacy of their strategy. Across several datasets, the F1 score, which is a metric for precision and recall, varied from 0.7 to 0.95. The system can serve as a useful resource for information retrieval from web sites, according to these results, which show that it is able to provide users trustworthy responses to their questions. © 2023 IEEE.

关键词： Benchmarking

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W-Net Plus: Dnn for Spatial Saliency Prediction in Videos 1

W-Net Plus: Dnn for Spatial Saliency Prediction in Videos

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Sharma, Gaurav Singh, Maheep Kumain, Sandeep Chand National Institute of Technology Dept. of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350345957

Deep neural networks (DNNs) have been employed recently to identify saliency in images and videos. Different models have been suggested to predict spatiotemporal as well as temporal and spatial saliency. In this study, a model called W-net Plus is proposed for predicting spatial saliency in videos. The newly proposed model not only translates information from the encoder to the decoder but also from the decoder to the encoder, and it is inspired by U -net architecture. The proposed model has been evaluated by the author(s) on various data sets, and they have obtained noteworthy findings for the F - Max, structural similarity (S-measure), and Mean Absolute Error (MAE) measurement parameters, demonstrating its effectiveness and survivability. No stage of the training involves the utilization of transfer learning in the work. © 2023 IEEE.

关键词： Deep neural networks

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique 3

Faulty Classes Prediction in Object-Oriented Programming Usi...

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

作者： Mahfuz, Nagib Shill, Pintu Chandra North Western University Dept. of Computer Science and Engineering Khulna Bangladesh Khulna University of Engineering and Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350345360

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score. © 2023 IEEE.

关键词： Object oriented programming

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Brain Tumor Detection with Ensemble of Convolutional Neural Networks and Vision Transformer 2

Brain Tumor Detection with Ensemble of Convolutional Neural ...

引用

2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yurdakul, Mustafa Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept. Kirikkale Turkey Selçuk University Technology Faculty Computer Engineering Dept. Konya Turkey

ISBN: (纸本)9798350373363

Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional methods rely on specialists meticulously examining MRI scan images. However, this approach is not only time-consuming but also carries a significant risk of error. Therefore, there is a need for more effective methods to detect brain tumors from MRI images. In this study, an ensemble model was proposed for classifying tumor types using MRI scans. Initially, sixteen well-known Convolutional Neural Network (CNN) models and four Vision Transformer (ViT) models were trained on the Brain Tumor Dataset, which contains 3264 MRI scan images. Subsequently, by combining the top three high-performing models, we achieved a robust classification performance. Experimental results demonstrate that our proposed model provides a satisfactory performance comparedto existing methods. © 2023 IEEE.

关键词： Convolutional neural networks

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An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

An Ensemble ML Model to Predict the Wastage of Food: Towards...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Masuk Shopnil, Md Masrur Husna, Asmaul Sultana, Shaheena Islam, Muhammad Nazrul Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh NotreDame University Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns;then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination;as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost;while all other possible combinations showed around 80-82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings;as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation. © 2023 IEEE.

关键词： Forecasting

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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Automated Attendance Management System 5

Automated Attendance Management System

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Goyal, Pranshu Nigam, Vishwas Khan, Shahnaw A.J. Singh, Shashi Kant Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering India

ISBN: (纸本)9798350330861

the manual process of recording and managing attendance in educational institutions and organizations is often time-consuming, prone to errors, and inefficient. To address these challenges, there is a growing need for automated attendance management systems. This research paper presents the design and implementation of an Automated Attendance Management System (AAMS) using modern technologies. The AAMS utilizes various technologies such as biometric recognition, RFID (Radio Frequency Identification), and computer vision to accurately and efficiently track attendance. The system aims to simplify the attendance recording process for both students and instructors, reduce administrative burden, and improve overall efficiency. The proposed AAMS incorporates biometric recognition techniques, such as fingerprint scanning or facial recognition, to identify and verify the identity of individuals. © 2023 IEEE.

关键词： Efficiency

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Institutional Accreditation Data Analysis And Decision-Making System 3

Institutional Accreditation Data Analysis And Decision-Makin...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Dsouza, Chris Gupta, Rhea Pradhan, Sarah Shaikh, Phiroj Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

This paper highlights the implementation of the National Accreditation System's fifth criteria and its application to ensure quality assurance standards are met by the University. The authors provide an overview of recent advancements in the Institute's Special Faculty Administrative Region and examine how they relate to neighbouring institutes' engineering practices. The paper concludes with the author's observations on the interplay between different institutes in India with regard to accreditation criteria. Specifically, the paper delves into the contents of Self-Assessment Reports, the process of developing them, challenges encountered during their creation, as well as the advantages and features of the Accreditation Data Management © 2023 IEEE.

关键词： Accreditation

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