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检索条件"机构=Computer Science and Engineering ABES Engineering College"

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Leveraging Machine Learning and Deep Learning Percepts for Air Pollution Analysis 15

Leveraging Machine Learning and Deep Learning Percepts for A...

引用

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

作者： Gahlot, Mansi Katiyar, Maitreyi Katiyar, Maitri Agarwal, Ayushi Computer Science and Engineering Abes Engineering College India Abes Engineering College Computer Science and Engineering India

ISBN: (纸本)9798331300579

The main aim of this project is to teach the computer to recognize patterns in air quality data, such as how pollution levels change over time. We use the LSTM network to make sense of different things like pollution amounts, weather conditions, and past AQI numbers. What makes this model smart is that it improves at understanding the air by looking at a lot of data. It can handle the tricky and ever-changing nature of the atmosphere. The computer learns from what happened before and adjusts to what's going on right now, which is super useful for predicting air quality in the future. Utilizing deep learning's predictive powers to improve AQI forecast accuracy is the main goal of this research. The LSTM model exhibits higher adaptability to the dynamic character of meteorological circumstances by assimilation of complex patterns included in the air quality data. The model learns the intricate correlations between different contaminants, climatic variables, and AQI through extensive training. But this study isn't just about computers and data;it's about making our cities and lives better. The computer predictions can help our leaders make wise decisions to protect our health, especially in busy cities. It's like having a friend that always watches out for us, letting us know when things might not be great so we can make changes and live in a cleaner and safer environment. © Grenze Scientific Society, 2024.

关键词： Air quality

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Exploring and Analyzing the Different Models of Video Object Detection

Exploring and Analyzing the Different Models of Video Object...

引用

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

作者： Gupta, Malvika Kumari, Pragati Gautam, Vikash Kumar, Vatan Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350376470

Video object detection aims to identify the objects that appear in the frame of video *** challenges in video object detection include the degradation of video frames caused by fast motion,occlusion,scale variations,and the need for real-time processing. To achieve these challenges, we have analyzed var-ious approaches, such as two-stage detectors(e.g.,Faster R-CNN or CNN) and one-stage detectors(e.g.,CenterNet),which adapt to video data, Sequence Level Semantics Aggregation(SELSA) module (working on cluster of frames). The motivation for using deep learning, particularly CNN *** this system is their ability to handle complex scenes,lighting variations, feature extraction,and different object types. The versatile and efficient algorithm for video object detection,offers real-time performance,accuracy, and the ability to detect multiple objects in a single *** this highlights the importance of video object detection and the advancements in technology that make it possible to tackle real-world video analysis tasks effectively. © 2024 IEEE.

关键词： Video analysis

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Intelligent and Smart Traffic System Based on Yo-Lo Version-8 through Video Streaming for Longer Distance Level 5

Intelligent and Smart Traffic System Based on Yo-Lo Version-...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Varshney, Apoorv Arya, Disha Katiyar, Arpit Dubey, Anil Kumar Abes Engineering College Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350383522

The creation of intelligent traffic systems is essential in the age of smart cities and developing technologies in order to guarantee effective and secure transportation networks. This study uses the state-of-the-art YOLO (You Only Look Once) v8 deep learning architecture to give a novel solution to the problems of real-time car and pedestrian recognition in video frames. The suggested method is centered on obtaining useful data from video streams, especially when it comes to recognizing cars and pedestrians in individual frames. In this context, we show that YOLO version 8 is accurate and effective through a series of thorough trials and assessments. YOLO version 8 is used for object recognition after pictures from video frames are captured as part of the research method. Our technology can quickly recognize and track automobiles and people by using real- time visual analysis. We also look at how this technology may be used in driverless cars, traffic control, and spying. The report also explores the technological aspects of YOLO version 8, emphasizing its advancements over earlier versions, including increased resilience, speed, and accuracy. We go over the process of training and fine-tuning needed for best results when it comes to traffic monitoring. We demonstrate the system's capacity to properly recognize cars and pedestrians, even in difficult settings like changing illumination conditions and occlusions, by thorough testing and validation on many datasets. The findings demonstrate how this technology has the ability to completely transform traffic management and improve road safety. Finally, using YOLO version 8 for real-time car and pedestrian recognition in video frames, this research study provides an intelligent traffic system. The technology exhibits encouraging outcomes, laying the path for further advancements in intelligent transportation, traffic optimization, and public safety. © 2024 IEEE.

关键词： Object Detection Pedestrian Detection Vehicle Detection Video Analysis YOLOV8

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Efficacy of Customer Churn Prediction System 6

Efficacy of Customer Churn Prediction System

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Raj, Ritik Gupta, Siddhant Mishra, Ritik Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Customer churn, defined as customer defection is a big problem for businesses in today's competition. This paper takes an in-depth look at the application of machine learning (ML) in customer churn prediction to identify the best models for various industries. quantitative performance metrics of seven commonly used machine learning algorithms: Logistic Regression, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and XGBoost. The theoretical background, application context, and performance evaluation of each model are examined, focusing on precision, accuracy, recall, and F1 score. Evaluation of the performance of the model on different business data is also done to ensure that the application is real in the world. This comparison shows the advantages and disadvantages of each model and guides providers in their selection based on the specific needs of the business and profile features. Additionally, this paper explores future changes in predictions and highlights the changing role of machine learning technologies, especially deep learning, in improving the accuracy of predictions and personalization. This review is very useful for professionals and experts;It provides a better understanding of the current machine learning model for customer churn prediction and provides insight into future research and practical applications. ©2024 IEEE.

关键词： Forecasting

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Prediction of Chronic Kidney Disease Using ML Techniques 5

Prediction of Chronic Kidney Disease Using ML Techniques

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Shukla, Anupam Robin, Fabian Narvaria, Harshit Joshi, Anju Computer Science and Engineering Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350383522

The study of chronic kidney disease of human body is now a significant topic of interest in the domain of biosciences because of the emerging cases of the disease. One of the most dangerous illnesses in the modern world is chronic kidney disease, for which an accurate diagnosis method needs to be developed to identify and stop the disease at an early stage. A condition known as chronic kidney disease causes a person's kidneys to malfunction and eventually become damaged. This leads to the condition where the kidney is unable to filter the blood and remove the waste as they do. Now-a-days machine learning algorithms have become a major tool in which the disease gets diagnosed based on the certain values like blood pressure, urine value and many such things. This leads to the early detection of the disease so that the patient can consult his required doctor and with the help of which his disease could be cured. The application and the study of the applied algorithm will help us to detect the disease will be explored in this study. These algorithms' outputs have a higher degree of accuracy, sensitivity, and specificity and can be used to predict the presence of chronic kidney disease in the necessary human being. The findings will demonstrate how technology can aid in the better management of chronic kidney disease and how this can enhance decision support systems for healthcare in general. © 2024 IEEE.

关键词： Decision tree GUI Data-driven detection Smart disease detection Machine learning methodologies SVM

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From Displacement to Employment: Strategies for Sustainable Integration in Host Nations

From Displacement to Employment: Strategies for Sustainable ...

引用

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

作者： Sawan, K. Patwa, Shreyansh Gupta, Shruti ABES Engineering College Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350376470

Unprecedented global displacement of people due to conflict, persecution and environmental problems requires creative solutions to meet the special educational and employment needs of migrants and refugees. This study examines how digital learning interventions impact displaced people's education and career search results. In our study, we examine the mixed-method experiences of two distinct groups: Group A, which is a control group and receives traditional support, and Group B, which is an experimental group and uses digital learning tools. Demographic profiles, course completion rates, satisfaction levels, and employment status are compared to find patterns and statistically significant differences. Qualitative insights also draw attention to participants' contributions and emerging themes. The findings add to the growing body of knowledge about helping displaced people, shedding light on how digital learning can be the spark that creates better educational and career opportunities. Policymakers, educators, and organizations working to improve the well-being of migrants and refugees worldwide should consider the findings of this study. © 2024 IEEE.

关键词： Digital Learning Displaced Job Recommender

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Behavior-Based Machine Learning Approaches for Malware Detection 2

Behavior-Based Machine Learning Approaches for Malware Detec...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Srivastava, Vivek Khare, Manya Kansal, Manasvi Harsh Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

The present research investigates the implementation of various methods of machine learning to analyze fluid information from executable files for the purpose identify contamination. We extracted feature vectors from a dataset that contained both malicious and benign samples. Standard metrics like Fl-score, recall, and precision were used to train and evaluate a range of classifiers, including Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression. Our test findings show that the models successfully distinguish between harmful and benign files, with Random Forest providing the highest level of consistency in all evaluation measures. These Results demonstrate the promise of machine learning methods to improve systems for detecting malware. © 2025 IEEE.

关键词： Adversarial machine learning

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Privacy-Preserving Speech Analysis- A Review 6

Privacy-Preserving Speech Analysis- A Review

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kumar, Pankaj Shukla, Mayank Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Speech-recognition technology is an essential component of many smart IoT devices. To make these systems secure and privacy-preserving, it is important to consider outsourcing speech-recognition functionality to a third-party storage provider. By removing the speech-recognition process from the device itself, the amount of personal data stored on the device is minimized. Additionally, the provider can ensure the strictest security standards are met when the audio data is stored, including the use of encryption and access control mechanisms. Furthermore, the provider can also ensure that the speech-recognition process is done using the latest technologies to ensure accurate recognition of the audio data. To ensure the end-user's privacy is kept intact, the provider should allow only authorized users to access the audio data. Additionally, the provider should also enable data access logging and audit trails allowing for the tracking of any access to the data. By outsourcing speech-recognition services to a third-party, smart IoT devices can ensure the highest levels of privacy and security while still enabling users to get the most out of their device. The server processes the encrypted data to generate recognition results, such as user commands and voice printing, without compromising the privacy of the user's voice data. To enhance voice data privacy, federated learning can be employed in the design of these models. This approach enables distributed training of the model without the need to transmit the user's voice data to a central server. The adoption of this approach in the IoT industry is gaining momentum as it offers the potential to create smarter and more secure IoT devices that inspire customer trust. ©2024 IEEE.

关键词： Speech recognition

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Price Wise a Deep Learning Approach to Stock Price Prediction 1

Price Wise a Deep Learning Approach to Stock Price Predictio...

引用

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

作者： Bansal, Anshika Singh, Abhishek Roy, Shweta Agarwal, Kadambri Abes Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350377002

Financial markets depend heavily on stock price prediction, which affects both market behaviour and investment decisions. Given the complexity and volatility of financial markets, traditional methods of stock price forecasting sometimes encounter difficulties. Due to their capacity to extract complex patterns from large datasets, deep learning techniques have become more and more popular in recent years. The use of deep learning models to forecast stock values is examined in this research study, and their effectiveness is compared among various deep learning models. The study employs a diverse dataset encompassing historical stock prices. LSTM based models such as Long-Short Term Memory (LSTM), Bidirectional Long Short-Term Memory(Bidirectional-LSTM), Stacked Long Short-Term Memory(Stacked-LSTM) and Tree based models such as Random Forest, ADA Boost, XG Boost are employed to predict future prices. Using historical stock price data, the models are trained and verified, and a variety of performance indicators are used to evaluate how accurate are the predictions. In this research study, we found that stacked LSTM performs better among various other models. © 2024 IEEE.

关键词： Prediction models

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Devnagari Character Recognition using Optical Character Recognition (OCR) 1

Devnagari Character Recognition using Optical Character Reco...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Shukla, Ananya Sharma, Ayush Aggarwal, Akriti Jain, Shikha Abes Engineering College Computer Science Engineering India Abes Engineering College India

ISBN: (纸本)9798350338027

In the age of digital applications, optical character recognition (OCR) is a useful tool. There are many different languages and scripts used around the world, with more than 300 million individuals in India speak Hindi as their first language. OCR previously created for the Devnagari script has a very low recognition rate. In this paper, we propose an OCR system based on a Convolutional Neural Network (CNN) model. It increases the handwritten data's efficiency or accuracy by 98.32%. We do several steps for getting this efficiency like preprocessing of data, grayscale images, binarization, reshaping, normalization, modelling, and Evaluation of the data with the help of plotting. © 2023 IEEE.

关键词： Optical character recognition

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