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Real-Time Potholes Detection and Prevention Using Deep Learning Techniques 8th

Real-Time Potholes Detection and Prevention Using Deep Learn...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Shabai, Iqbal Mahalle, Parikshit N. Shinde, Gitanjali Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Machine Learning Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9789819766802

Road infrastructure safety and maintenance have received more attention recently due to the significant influence that it has on traffic flow and road user safety. Potholes are one common kind of road defect that seriously endangers drivers. Manual checks take a lot of time and may delay necessary repairs. In response to these challenges, a real-time pothole recognition system utilizing state-of-the-art computer vision and deep learning techniques was developed. The system leverages the Faster R-CNN model for precise pothole identification, enabling fast detection and timely repairs. The automated detection and instantaneous notification of authorities by the system enhances road safety and maintenance efficiency. This ingenious tactic improves the standard of the transportation network and creates roads safer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Ecowatch: A Comprehensive NodeMCU-Based System for Solar Power Oversight 2

Ecowatch: A Comprehensive NodeMCU-Based System for Solar Pow...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Thopate, Kaushalya Kambare, Shweta Manohar Kulkarni, Mukund Gawade, Mayuri Jadhav, Balasaheb Jaiswal, Krishna Nilesh Vishwakarma Institute of Technology Computer Engineering Pune India Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Electronics and Telecommunicarion Pune India

ISBN: (纸本)9798350360523

Globally, solar power systems are becoming more and more popular as a renewable energy source. However, to maximize their effectiveness and guarantee their dependable functioning, these systems must be effectively managed and monitored. In this study, we suggest utilizing the open-source IoT platform NodeMCU to create a solar power monitoring system. Our solution gives owners of solar power systems complete monitoring capabilities by gathering, processing, and visualizing data in real time from solar panels, batteries, and other system components. Because of its scalable and affordable design, the system can be used for both home and business purposes. We show through our research that our NodeMCU-based solar power monitoring system is both practical and efficient. This research introduces an innovative approach to monitoring and optimizing solar power systems through the implementation of NodeMCU in an Internet of Things (IoT) framework. The study's findings significantly contribute to the field of renewable energy management by advocating for a more efficient and scalable solution, thereby fostering broader acceptance and utilization of renewable energy sources to achieve a sustainable and environmentally friendly future. © 2024 IEEE.

关键词： Solar energy

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EcoLux: Revolutionizing Living Spaces with Smart Window Solution 8

EcoLux: Revolutionizing Living Spaces with Smart Window Solu...

引用

8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Amune, Amruta Dhage, Rohit Rajendra Dhage, Sharvari Somani, Tanuj Mahesh Rokade, Mayur Bhivasen Rajguru, Varun Mohan Vishwakarma Institute of Technology Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Department of Chemical Engineering Pune India

ISBN: (纸本)9798350367904

This research study presents a novel automated window shade control system developed using the ESP32 microcontroller. The system integrates a Light Dependent Resistor (LDR) sensor for measuring ambient light intensity with the ESP32's Real-Time Clock (RTC) module to achieve precise day/night detection. By combining LDR measurements with RTC data, the system effectively determines daytime and nighttime conditions, accommodating variations in natural light due to seasonal changes. This dual-parameter approach enhances the system's accuracy in regulating shade positions. Furthermore, the system includes timebased scheduling functionality, allowing users to program shade adjustments independently of real-time light conditions. This capability is facilitated by the ESP32's computational power, thereby increasing operational efficiency and user convenience. © 2024 IEEE.

关键词： Stepping motors

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Disaster Management Ecosystem Using Crowdsourced data, Deep Learning and NLP 5

Disaster Management Ecosystem Using Crowdsourced Data, Deep ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Savale, Vaishali Patil, Siddhi Raut, Atharva Phadke, Mrunmayee Sundkar, Rahul Dongre, Rajkumar Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798331530013

In recent years, the frequency and intensity of natural disasters have increased, necessitating more efficient and responsive disaster management systems. This paper presents a novel disaster management solution leveraging crowdsourced data to enhance disaster response and coordination. The system bridges the gap between citizens and emergency management officials by composing a mobile application for citizens to send real-time disaster alerts and a web application for administrators to monitor and manage these alerts. A customized DBSCAN algorithm, adapted to use haversine distance, is deployed to detect clusters of disaster alerts in real time, indicating potential disaster events. Upon cluster detection, administrators can delineate affected areas on a map, triggering notifications to users within these regions and enabling them to send help requests with images and messages. These help requests are processed using the BLIP model for image captioning and the ChatGPT API for categorization, assigning relevant tags to each request. The system provides administrators with real-time data analysis features, including map filtering and citizen help request category visualization, enabling more informed decision-making. Utilizing a cloud-based architecture ensures scalability, reliability, and efficient processing of large volumes of data. This research demonstrates the system's capability to provide timely alerts and comprehensive situational awareness, ultimately contributing to more efficient disaster management. © 2024 IEEE.

关键词： Information management

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SAGEConv Graph Neural Network Model for Multivariate Regression using Google Quest dataset 2

SAGEConv Graph Neural Network Model for Multivariate Regress...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Ghadekar, Premanand Ingale, Varad Pungliya, Vithika Bhonsle, Roshita Raut, Ankur Purohit, Atharva Department of Information Technology Vishwakarma Institute of Technology Pune411037 India Department of Artificial Intelligence and Data Science Technology Vishwakarma Institute of Technology Pune411037 India

Natural Language Understanding (NLU) has become increasingly important in recent years, with the advent of various applications such as chatbots and sentiment analysis. In the domain of online question-answering platforms, the equitable visibility of valuable responses is paramount. The proposed automated solution employs SAGE Conv Graph Neural Network which uses the SAGEConv operator to extract features from the graph structure to comprehensively evaluate question- answer quality. By incorporating nuanced subjective labels from the Google Quest dataset, our methodology bridges the gap between human comprehension and machine analysis. In contrast to traditional Natural Language Processing (NLP) techniques that struggle with intricate language subtleties, GNNs capitalize on inherent graph structures within text, bolstering precision. Experimentation conducted on Google Quest dataset yields compelling quantitative outcomes. With an impressive R2 score of 0.644 and an exceptionally low MSE score of 0.00050. These results underscore the potency of GNN-driven Natural Language Understanding (NLU) models paves the way for improved NLU accuracy across various domains, marking a substantial advancement in the field. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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An Aviation Industry Recommender System(AIRS) using K-nearest Neighbour and Cosine Similarity 2

An Aviation Industry Recommender System(AIRS) using K-neares...

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Mundargi, Zarinabegam Mulay, Shubham Navale, Dnyaneshwari Talnikar, Vishwam Nawale, Akhilesh Sonkusale, Vishal Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781665475174

The Aviation Industry has flourished recently with a big boom in ***, tourists are confused about selecting the best airline amongst available options. The existing systems just compare the price and analyze past data related to it and predict accordingly. We focus on calculative analysis of multivariate data derived through survey for recommendation. Our approach is to develop a system which will recommend an airline which is supreme amongst *** broad criteria for selection is based on the quality of services, network & passengers per aircraft and factor of safety of the aircraft. With the help of k-nearest neighbor as the main algorithm, assisting with directional features provided by cosine similarity model makes our system robust in predicting output. The architecture also possesses calculations using pivot matrix along with a random number generating method recommending the best airline for given input. Label encoding technique is implemented in preprocessing to resolve and rectify error of conversion of deferring input data types. This paper covers fundamental application of KNN as well as cosine similarity in an intuitive way. © 2023 IEEE.

关键词： Nearest neighbor search

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Intelligent Predictive Maintenance for Smart Building Systems 2

Intelligent Predictive Maintenance for Smart Building System...

引用

2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Pavitha, N. Savithramma, R.M. Ashwini, B.P. Mankawade, Amruta Naik, Yojana Bedmutha, Yashashree Vishwakarma University Department Of Artificial Intelligence Pune India Siddaganga Institute of Technology Vtu Department Of Cse Karnataka India Vishwakarma Institute of Technology Department Of Artificial Intelligence &data science Pune India

ISBN: (纸本)9798350368413

An innovative method for streamlining building maintenance and operations in smart buildings is intelligent predictive maintenance, or IPM. This thorough analysis looks at the main advantages, difficulties, and cutting-edge methods for integrating IPM in smart building settings. Early failure identification, decreased downtime, longer equipment lifespans, and optimised maintenance plans are among the main advantages of IPM. These benefits have the potential to significantly reduce costs while also enhancing building performance and occupant comfort. IPM must also overcome a number of significant obstacles, including issues with data quality, security and privacy, complexity of the model, and interpretability. The paper examines the most recent cuttingedge methods and algorithms being created and used for IPM in smart buildings in order to address these issues. These encompass machine learning, deep learning, statistical techniques, and hybrid methodologies. The assessment, for example, emphasises the potential use of federated learning-a machine learning technique that protects privacy-for anomaly detection in smart buildings. The review also explores the particular difficulties associated with maintaining smart buildings, such as system complexity, occupant behaviour, and maintenance strategy and costs. To overcome these obstacles, IPM implementation must take a deliberate and comprehensive approach. Through the development and implementation of efficient IPM solutions to optimise building operations and maintenance, practitioners and researchers can benefit from the deep understanding gained from this thorough assessment, which will ultimately progress sustainability and smart building technologies. © 2024 IEEE.

关键词： Predictive maintenance

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Comparative Study on Calculating CPU Burst Time using Different Machine Learning Algorithms 14

Comparative Study on Calculating CPU Burst Time using Differ...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Vayadande, Kuldeep Agarwal, Naman Shevate, Srushti Sawakare, Chinmayee Parakh, Piyush Khandare, Snehankit Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. Throughout the study, we discovered that the effectiveness of different machine-learning approaches relies on the applications to which they are put. Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data. © Grenze Scientific Society, 2023.

关键词： Scheduling algorithms

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Smart Resume Analyser 15

Smart Resume Analyser

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rane, Milind Zanwar, Harsh Wagh, Kunal Wakode, Suhani Yadav, Avishkar Kulkarni, Atul Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331300579

Every time job recruiters (HR) have to go through a huge pile of resumes submitted by applicants. which becomes a very burdensome task, with Python, NumPy, and Pandas, a resume parser is to be created as part of the project. A structured format will be used to store the data that the parser extracts from resumes, including name, contact information, education, work experience, and abilities. Pandas will be used to store the data in a data frame, and NumPy will be used to manipulate the data. The parser will recognise and extract pertinent information from resumes using methods of natural language processing, improving the accuracy and efficiency of the procedure. By swiftly extracting pertinent information from resumes, the initiative will assist in automating the hiring process and save time for recruiters and HR personnel. © Grenze Scientific Society, 2024.

关键词： Python

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Improving Image Quality of Noisy Images Through Denoising and Style GAN Technique 7

Improving Image Quality of Noisy Images Through Denoising an...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ghadekar, Premanand Gundawar, Ayush Kamnapure, Somesh Manjramkar, Devang Gujarathi, Ishan Deore, Dhananjay Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350304268

This research proposes an approach to enhance the denoising and upscaling performance of noisy images using Generative Adversarial Networks (GANs), particularly Style GAN architecture. Denoising and upscaling noisy images are crucial in many computer vision applications, and GANs have shown remarkable effectiveness in creating high-quality images. However, training Style GAN requires huge amount of data and is computationally expensive. To address this issue, this study proposes using various filters such as mean, median, and weighted median to pre-process the noisy images before feeding them to Style GAN. The proposed approach achieves superior denoising and up scaling compared with other system in terms of FID and inception score, and further exploration of hyperparameters and variations of the Style GAN architecture can lead to even better results. © 2023 IEEE.

关键词： Deep learning Diffusion Modelling Image Processing median filtering Style Gan Super resolution

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