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

作者： Tambe, Riya Bharadwaj, Rakhi Vaze, Sanaya Uttarwar, Varad Urane, Samarth Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331300579

The rapid advancements in deep learning techniques have significantly impacted various fields, including mathematics and natural language processing. Mathematical equation identification and solving is a challenging task that has been studied by researchers for many years. Traditional approaches to this task typically rely on rule-based systems, hand-crafted features or manual work. However, these approaches are often limited in their ability to handle complex equations and there are also chances of error in it. The survey begins by discussing the challenges associated with mathematical equation identification and solving, such as the complexity of mathematical notation, variable representation, and equation structure. The paper provides an overview of the existing methodologies, algorithms, and frameworks employed in the development of systems capable of identifying and solving mathematical equations using deep learning techniques. © Grenze Scientific Society, 2024.

关键词： Deep learning

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AeroDetect: Real-Time Small Object Detection in Aerial Images 2nd

AeroDetect: Real-Time Small Object Detection in Aerial Imag...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Singhal, Nikita Pandey, Anuranjan Kumar Kumar, Ankit Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (纸本)9789819995233

Real-time small object detection in aerial images is a challenging task in computer vision because of the complex environmental conditions and fewer pixels occupied by the objects. Along with this getting real-time inference makes this much more difficult. To tackle these challenges we propose an improvement in the classical YOLOv3 network which is capable of removing noise from the images and performing small object detection to while maintaining a high frame frequency. We are using a drone to capture real-time footage from the AIT campus, which is further used for classifying objects like vehicles, pedestrians, etc. Our proposed model "AeroDetect" was able to beat various state-of-the-art models like YOLOv3 and YOLOv5 in various parameters by having higher mAP, and a lesser number of layers and parameters while maintaining a high frame rate which is crucial for our use case. We achieved mAP of 0.402 on the Visdrone dataset as compared to 0.4 and 0.38 by YOLOv3 and YOLOv5, respectively. On the VEDAI dataset, we got mAP of 0.632, whereas YOLOv3 and YOLOv5 achieved 0.578 and 0.604, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Machine Learning-Based Air Pollution Monitoring and Forecasting

Machine Learning-Based Air Pollution Monitoring and Forecast...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： M, Naga Ravindra Babu Durga Satish, M. Prasanthi, B.V. Jagadeesh, S.V.V.D. Janardhana Naidu, J.N.S.S. Pradeep, Immidi Kali Department of Computer Science & Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031513374

Today, the governments of developing countries are focusing more on air pollution. Air pollution occurs because of factors like fuel use in vehicles such as private transport, industries, and, more importantly, the burning of waste like plastic and grass. These are a few of the elements that contribute to air pollution. All of these factors have an impact on air quality. It can be calculated or determined by using PM2.5 and other variables. If this level is high, then it may be a serious problem for people’s health. To control air pollution, continuous pollution monitoring is required. Here, different machine learning models like linear regression, decision tree classifiers, random forest classifiers, and KNN classifiers are used to detect whether the sample of air is polluted or not. On the basis of previous readings of PM2.5, NH3, CO, NO, NOx, and NO2, SO2 is used to predict the future values of the AQI. By collecting datasets of the daily atmospheric conditions of a specific city or town, this system is able to predict the PM2.5 level and also detect the air pollution status. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Air quality

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Satellite Image to Map Translation using GANs 15

Satellite Image to Map Translation using GANs

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

作者： Phatangare, Sheetal Khalifa, Mohammed Mansoor Kharche, Sejal Khatib, Amaan Kshirsagar, Atharva Vishwakarma Institute of Technology Computer Engineering Department Pune India

ISBN: (纸本)9798331300579

Generative adversarial networks (GANs) are a promising method for learning deep representations without spending a lot of time on data for annotated training. They accomplish this by obtaining signals that are back propagated through a competitive mechanism in-transforming two networks. The possible representations of a variety of applications may employ the knowledge gathered by GANs, image synthesis, semantic image editing, fashion categorization, transmission, and image super-resolution. The goal of this research article is to give an overview of GANs. the signal processing community, utilizing well-known whenever feasible, use concepts and analogies. Additionally, to recognize various training techniques and building GANs, we also highlight lingering issues with their theory and utilization. © Grenze Scientific Society, 2024.

关键词： Gallium nitride

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A Comparative Study of Machine Learning Models for Stock Price Prediction 2

A Comparative Study of Machine Learning Models for Stock Pri...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Ashtagi, Rashmi Deshmukh, Amogh Dhomse, Siddhesh Dhumal, Gayatri Dhurve, Siddhartha Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350368413

The stock market has always been evolutionary. It has been an area of interest for researchers to forecasting the stock prices too guide investment decisions and avoiding financial risk since many years. This paper highlights the significance of precise stock price prediction and investigates the use of several machine learning techniques to strengthen the predictive accuracy. The proposed system makes uses two different approaches: Regression, which predicts the company's stock closing price, and Classification predicts whether the closing price of the stocks will increase or decrease the next day. Historical stock data is examined carefully and preprocessed to ensure high quality of data. Using feature engineering appropriate features are extracted to identify the intrinsic patterns and trends within the stock market. Algorithms like Linear Regression, ARIMA, LSTM and Random Forest are implemented and thoroughly evaluated for their efficiency in stock price prediction. After conducting a comparative analysis of the two algorithms, it becomes evident that while LSTM performs exceptionally in regression tasks, identifying the temporal associations and patterns that guide the stock market data in its development, Random Forest provides more firm predictions of the stock price movement. This study provides the extensive analysis of the real-life application of machine learning for discussions on the stock market, thus allowing investors to derive a more reliable resource to refer to when dealing with the challenges of the stock market. © 2024 IEEE.

关键词： Prediction models

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Enhanced Non-Defended Website Fingerprinting Attack Model Using Deep Learning

Enhanced Non-Defended Website Fingerprinting Attack Model Us...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sinha, Minal Dave, Mayank National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350375237

Onion routing (Tor) is a routing protocol that uses multiple-layer encryption to protect privacy and security for authentic users during web browsing. The website fingerprinting (WF) attack, which may be used to identify which websites a user is browsing are one of the vulnerabilities present in the Tor browser, despite its security. There are many modern and advanced attack models to build a WF attack model but in this work, Convolutional Neural Network (CNN) is presented considering two different scenarios Close World (CW) and Open World (OW). In CW scenario, the proposed attack model attains 98.20% accuracy on traffic traces without utilizing any defense model and when the same attack model is used in the OW scenario, it attains 97.10% accuracy. In OW scenario, the precision comes out to be 0.99 and the recall is 0.90, even in the presence of imbalanced data without any defenses. Overall, in this work, the proposed non-defended attack model can identify monitored websites with a precision of 0.99, even in the real OW scenario. © 2024 IEEE.

关键词： Websites

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Satellite Image Processing for Cloud Detection and Movement Analysis 8

Satellite Image Processing for Cloud Detection and Movement ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Dhore, M.L. Kate, Sumedh Khandelwal, Dipkul Khandetod, Arya Kharade, Aishwarya Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350376425

The rapid development of satellite technology has resulted in an exponential growth in the availability of high-resolution satellite images, which presents both opportunities and challenges for image processing and classification. The study provides a comprehensive overview of the state-of-the-art techniques for satellite image processing and classification, with an emphasis on recent developments in machine learning and deep learning approaches. Understanding environmental dynamics, tracking weather trends, and assisting with disaster relief operations all depend on satellite imaging. Unfortunately, clouds frequently obscure these photos, making it challenging to precisely follow atmospheric conditions and analyse the Earth's surface. In order to improve weather forecasting and catastrophe management, the research work creates a satellite image processing system that uses machine learning to identify clouds, eliminate them, and forecast their *** fields, including disaster management, agriculture, urban planning, environmental conservation, climate research, renewable energy, forestry, and geospatial intelligence, are greatly impacted by satellite image processing and cloud detection. Through the provision of enhanced, unobstructed satellite imagery and precise cloud movement forecasts, the project improves capacities for natural resource monitoring and management, better urban development, climate research assistance, energy production optimisation, forest protection, and defence operations support. Accurate satellite data analysis facilitates more informed decision-making, which in turn improves environmental management, readiness for disasters, and strategic planning in these vital areas. © 2024 IEEE.

关键词： Optical flows

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Enhancing Success Prediction In Crowdfunding Platform Using LIME-Explainable AI Method

Enhancing Success Prediction In Crowdfunding Platform Using ...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Chaudhary, Shefali National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350374711

This study focuses on utilizing various machine learning techniques, ranging from supervised models to ensemble boosting methods, to predict the success of crowdfunding campaigns. The project aims to make these models more transparent and interpretable during decision making by using explainable AI (XAI) methods such as LIME. The dataset used for training these models is obtained from the well-known crowdfunding platform, Kickstarter. By leveraging XAI techniques, the project gains insights into the model's functionality, identifying important features that impact predictions positively or negatively. This understanding enables the refinement of machine learning models, leading to more precise predictions. In summary, the project comprises an effective combination of machine learning and explainable AI, demonstrating how these technologies can work together to produce highly precise, accurate, and interpretable success prediction models. © 2024 IEEE.

关键词： Prediction models

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Vision Voice: A Raspberry Pi-Based Text-to-Audio Converter for the Visually Impaired

Vision Voice: A Raspberry Pi-Based Text-to-Audio Converter f...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Thopate, Kaushalya Amrutkar, Kavya Kasliwal, Tarun Karvir, Shaunak Kumbhar, Omkar Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350376470

Vision Voice is an innovative assistive technology project. This project aims to improve the accessibility and quality of life of visually impaired people. It utilizes Raspberry Pi 3 as its core hardware platform and runs on a Python-based code framework. Vision Voice's main goal is to empower the visually impaired by offering smooth text-to-audio conversion technology. It offers an effortless solution for the visually impaired to access and interact with digital content. Users can enter text, which will then be converted to crisp and natural audio output. This breakthrough technology enables blind individuals to access a wide range of resources including digital resources, educational materials, literature, and news. It gives them a sense of empowerment and independence. By focusing on accessibility and user empowerment, Vision Voice embodies the future of assistive technology, bridging the digital divide and ensuring that the visually impaired can fully participate in the digital age. © 2024 IEEE.

关键词： Assistive technology

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Blockchain-Driven Academic Learning Record Management in Higher Education: A Comprehensive Review of Methodologies, Applications, Benefits, and Challenges

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SN computer Science 2025年第5期6卷 1-33页

作者： Chaudhari, Sheetal Shirole, Mahesh Computer Science and Engineering Department Sardar Patel Institute of Technology Andheri Maharashtra Mumbai 400058 India Computer Engineering and Information Technology Department Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai 400031 India

Blockchain technology has the potential to revolutionize the education sector by improving the legitimacy and security of academic procedures. The academic learning record management records the learner’s academic qualifications and achievements. Keeping track of higher education academic learning records is essential to assess students’ credentials and advance their careers. The COVID-19 outbreak highlighted the need for flexible and collaborative learning systems. However, the processes of recording credit accumulation, verification, and transfer are complicated by the disparate credit schemes found in different universities. This creates the need for an academic credit management system. Secure learning record management is necessary because validating the professional and academic accomplishments listed on the resumé currently poses a challenge. Blockchain can remedy this scenario by guaranteeing data privacy, reducing the possibility of hacking, and offering transparent, traceable academic credentials as well as verification of other achievements. Despite these benefits, challenges are still associated with implementing blockchain in academic learning record management. These include handling multiple credentials with a unique identity, absence of smart contracts, on-chain and centralized storage, scalability problems, transaction costs, restricted control over student data, and inadequate student data recording standards. The main objective of this review is to examine recent studies on blockchain-driven academic learning record management by categorizing them into learning record management and academic credit management. This paper identifies research gaps and suggests further studies and practical applications to enhance transparency, efficiency, and trust in the education system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Academic bank of credits Academic credit management Blockchain Education Learning record management NEP 2020

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