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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Dhongade, Shubhangi Tiwaskar, Shweta Koturwar, Abhijeet Motiramani, Raksha Zarekar, Devyani Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350348460

Air quality emerges as a critical global concern affecting millions of individuals. This paper explores into the development and application of data-driven models for air quality prediction, Motivated by air quality's profound impact on both the environment and human well-being, we leverage deep learning and machine learning methodologies to enhance the precision of air quality forecasts. Dataset used comprising over 33,360 entries. This dataset encompasses diverse characteristics, including PM2.5, PM10, SO2, NO2, CO, O3, etc., with pollutant levels documented on an hourly basis. To gauge air quality levels, we employ various models such as the Random Forest model, Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). A thorough evaluation and performance comparison of these models is conducted using Kaggle's air quality dataset. The study seeks to not only address the immediate challenge of air quality prediction but also holds implications for broader environmental and public health considerations on a global scale. © 2024 IEEE.

关键词： Artificial Neural Network Deep learning K-Nearest Neighbors and Support Vector Machine Long Short-Term Memory Random Forest model

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Early Detection of Alzheimer's Disease based on the State-Of-The-Art Deep Learning Approach 7

Early Detection of Alzheimer's Disease based on the State-Of...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Bagade, Virendra Godse, S.P. Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798331527822

In this paper, we introduce a deep convolutional neural network architecture, code-named Inception, which sets a new benchmark for classification and detection tasks. Early detection of Alzheimer's disease (AD) is critical, as the brain is the primary organ affected, and early intervention can help slow disease progression. Traditionally, AD diagnosis relies heavily on MRI scans, a process that is both time-consuming and resource-intensive. However, recent advancements in artificial intelligence (AI) present promising solutions for more efficient detection. This study examines the application of various Convolutional Neural Network (CNN) models for detecting AD in MRI images. A comprehensive comparison was performed on four distinct CNN architectures - Inception-V3, ResNet-152, VGG16, and VGG19 - using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The images were categorized into four classes: Non-Demented, Very Mild Demented, Mild Demented, and Moderate *** convolutional neural networks (CNNs) excel at identifying advanced dementia stages (Stages 5-7), accurately classifying the earliest, most elusive stages (Stages 2-4) remains a significant challenge. This paper tackles this crucial gap by systematically comparing various CNN models to achieve promising classification accuracy for these crucial early stages of dementia. Through rigorous analysis, we aim to advance the field of dementia detection by enabling earlier diagnosis and intervention, ultimately improving patient outcomes. Therefore, the application of machine learning technology can assist healthcare professionals in enhancing their efforts to combat Alzheimer's, a serious medical condition. © 2024 IEEE.

关键词： Convolutional neural networks

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College Administration Through Chatbot 8th

College Administration Through Chatbot

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

作者： Sharma, Yogesh K. Khan, Rais Abdul Hamid Shirse, Sumedh Jamage, Rajvardhan Shembekar, Anjali Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Sandip University Maharashtra Nashik India Department of Information Technology Vishwakarma Institute Of Information Technology Pune India

ISBN: (纸本)9789819766802

The College Administration System is a comprehensive digital platform designed to enhance student engagement and provide easy access to information about various college departments and activities. This system is developed to streamline administrative processes and create a user-friendly environment for students. It offers a wide range of features, allowing students to access departmental information, stay updated on college activities, and participate in various events and programs. The College Administration System aims to improve the overall student experience by simplifying administrative processes, enhancing communication, and providing easy access to departmental information and activities. By offering a user-friendly interface and a wealth of information, the system empowers students to make the most of their college years and actively participate in the vibrant college community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Decoding Student Sentiments: Academic Emotion Prediction with CNN-ResNet 5

Decoding Student Sentiments: Academic Emotion Prediction wit...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Rathi, Snehal Khedkar, Vijayshri Ghule, Gauri Diwan, Niraj Atre, Kushagra Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Pune India Vishwakarma Institute of Information Technology Department of Entc Pune India

ISBN: (纸本)9798331504403

Student emotions significantly influence the learning process, yet traditional methods of monitoring emotional well-being are challenging. This research explores the potential of technology to understand and respond to student emotions in real-time. By analyzing facial expressions, body language, and physiological signals, the study aims to develop computer models capable of accurately predicting student emotions. This information can empower teachers to adapt their teaching methods to address the unique emotional needs of each student, creating a more engaging and effective learning environment. Machine learning and deep learning models, such as CNN and ResNet, are employed to analyze student data and predict emotions. The models are trained on a dataset of student facial expressions and body language to enhance accuracy and effectiveness. This research contributes to the development of innovative tools for improving student engagement and well-being in the classroom. By understanding and addressing student emotions, educators can create more personalized and supportive learning environments. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Based Safety Jacket System for Mining Professionals 8th

IoT-Based Safety Jacket System for Mining Professionals

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Suryawanshi, Ranjeetsingh Mukadam, Mehvish Manakshe, Aman Raut, Om Kolhe, Siddhant Department of Computer Engineering Vishwakarma Institute of Technology Maharashtra Pune411037 India Department of Information Technology Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819757855

The safety of mining professionals working in underground mines is of paramount importance. To enhance the security of these professionals, this research paper proposes the use of an Internet of Things (IoT)-based safety system to monitor the underground environment. We have used an Arduino board and a collection of sensors to monitor parameters like temperature, humidity, air pressure, etc., as part of our safety system. The sensors are set to a threshold value and connected to an Arduino kit, which processes the data and sounds an alarm in case of any abnormal conditions. This research paper discusses the implementation of this system and its effectiveness in detecting potentially hazardous situations in underground mines. A device like this one could help alert miners and give them sufficient time to get to safety or call for help. The experiment results show that the system is able to detect the presence of harmful gasses that could potentially result in a fire. This system is reliable and can help prevent accidents and fatalities in underground mines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alert system IoT Smart safety jacket Wearable technology

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Using Contact Tracing technology for Communicable Disease Tracking

Using Contact Tracing Technology for Communicable Disease Tr...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dandavate, Prajkta Dutta, Atharva Kulkarni, Atharva Raut, Atharva Atkari, Darshan Auti, Sakshi Joshi, Atharva Vishwakarma Institute of Technology Computer Engineering - AI Pune India Vishwakarma Institute of Technology Computer Engineering Pune India Vishwakarma Institute of Technology Computer Science - AI/DS Pune India Vishwakarma Institute of Technology Computer Science Pune India Vishwakarma Institute of Technology Computer Engineering AIDS Pune India

ISBN: (纸本)9798331517953

Having recently tackled the COVID-19 pandemic, a new dimension to healthcare management has emerged. Irreparable damages were done due to the inadequacy of the existing systems and the lack of knowledge in most of the population in this field. Effective tracking and alerting of communicable diseases has never been of more significance. Our project harnesses advanced contact tracing technology to help users monitor and better manage their health and reduce the risk of exposure to communicable diseases. The app gives real time notifications to the user anytime they are near a potential disease carrier alerting them thereby avoiding potential infections. The innovated system has a questionnaire consisting of simple yes or no questions for symptom detection and provides precautionary measures based on the user's responses. The app also utilizes a radar giving the users better knowledge about their surroundings. Additionally, the app offers online consultations with doctors. Moreover, it provides emergency helpline access to the users. All these features help in more effective tracking of healthcare for not only emergency situations like pandemics, but further provides a complete solution for day-to-day management of healthcare also informing the users about safety measures so they can be well prepared if they ever develop an illness. © 2024 IEEE.

关键词： COVID-19

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IoT-Enabled Smart Helmet: Enhancing Safety for Motorcycle Riders Through Alcohol, Drowsiness, and Helmet Detection 8th

IoT-Enabled Smart Helmet: Enhancing Safety for Motorcycle Ri...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Suryawanshi, Ranjeetsingh Jagtap, Sahil Mane, Nishka Madhikar, Sarthak Munde, Yash Choudhary, Nitin Department of Computer Engineering Vishwakarma Institute of Technology Maharashtra Pune411037 India Department of Information Technology Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819757855

Driving under the influence of alcohol and drowsiness are significant contributors to motorcycle accidents. Consider a case, a man rides his bike despite consuming alcohol, which can lead to two possible outcomes. One possibility is that, by luck, he reaches his destination safely. The other, unfortunately, involves the high consumption of alcohol impairing his abilities, causing drowsiness and slowed reactions. As a result, he loses control of the motorbike and collides with a pedestrian. Sustaining a severe head injury, he is hospitalized, and due to a damaged phone, his family remains unaware of the accident. Concerned about his drinking habits, the family members worry for his well-being. However, due to the severity of the head injury, he passes away, and the family is informed after 24 h. From above it can be understood that drinking and driving can harm individuals, pedestrians, other drivers, and families. If the person had been wearing a helmet, he could have avoided the injury and loss of life. However, if the individual had been using a smart helmet equipped with an alcohol detector and drowsiness detector, the outcome may have been different. Additionally, if the helmet had GPS functionality, the family could have received an alert message with the location. So here comes the idea for a smart helmet—an alcohol and drowsiness detector. The smart helmet alcohol detector is a device that can be worn by motorcycle riders. If the riders would wear the helmet, then the vehicle would start, or else it would not. Two infrared sensors and an alcohol sensor will be built into the helmet. One infrared sensor will detect the rider’s face, and if he wears the helmet, the bike will start. A second infrared sensor will detect drowsiness, and if the rider is sleepy, the alarm will beep. If someone is wearing a helmet, the alcohol sensor will determine whether they are under the influence of alcohol or not. If he drinks alcohol beyond the threshold then the helmet will a

关键词： Motorcycles

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Enhancing Accuracy of Handwritten Character Segmentation Using Region-Based Convolutional Neural Networks (R-CNN) 1

Enhancing Accuracy of Handwritten Character Segmentation Usi...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Varpe, Kanchan Sakhare, Sachin Joshi, Rahul Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Automation of franchise-based data storage, management and analysis using Amazon Web Services

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International Journal of Cloud Computing 2023年第6期12卷 575-585页

作者： Oswal, Shreya Gundawar, Shubham Modani, Varun Shingare, Ashwini Department of Computer Engineering Vishwakarma Institute of Technology Pune411037 India

The wave of computer automation in business has revolutionised the way companies and employees interact with their customers and each other. Robotic process automation (RPA) not only mimics human actions involving complex, high volume and routine tasks but has also extended the creative problem-solving capabilities and productivity of human beings and deliver superior business results (Leshob et al., 2018;Ghosh, 2018). Amazon Web Services (AWS) has created a dramatic cultural shift in infrastructure provisioning from a fairly manual process of physical machines and software configuration. This paper proposes to use Amazon AWS to automate the task of scheduled uploading of data from different franchises, managing the database, analysing the data and storing the data on the supervisor’s machine. This reduces the redundant tasks of daily uploading data to company servers, analysing the data and downloading the data. The analysed data can be used by the company to improve the basic functioning and acknowledge various issues and problems. Thus, it aims to offer infrastructure as a service (IaaS) by providing virtualised computing resources over the internet. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Robotics

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Damage classification and segmentation in extended shear tab connection using convolutional neural networks and transfer learning

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Asian Journal of Civil engineering 2025年第1期26卷 221-236页

作者： Satarkar, Priti R. Dixit, Pradnya R. Londhe, Shreenivas N. Department of Civil Engineering Vishwakarma Institute of Information Technology Pune India

The joint is one of the most critical parts of a building structure. In steel buildings extended shear tab (EST) connection is becoming an attractive alternative for light to moderate end shear connections due to its simplicity and economy. Detection and prediction of cracks in connections perform an important job in the maintenance of steel structures. Currently, the structural inspections of such joints are conducted by manually which is a tedious and expensive. It is essential to detect damages in joints in order to ensure structural safety. In this study a two-phase convolutional neural network (CNN) using transfer learning is presented for detection and segmentation of cracks and damages in component parts of an EST connection. Different pretrained networks using transfer learning, including AlexNet, GoogLeNet, ResNet-101 and VGG-16 are considered for crack and damage detection. The undamaged, cracked and damaged images of component parts of sixteen EST connections were generated through the finite-element simulation which were used to develop the CNN model. Segmentation and detection results show that VGG-16 model appears to give the best results with 100% precision and accuracy, followed by ResNet-101, and AlexNet, and finally GoogLeNet gives the least performance among the four methods selected. This study will be resourceful for quick reference for those who are working in structural health monitoring field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Extended shear tab connection Finite element analysis Steel structures Transfer learning

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