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检索条件"机构=School of E&tc Engineering"

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empowering Students: Web-based essentials at Your Fingertips 15

Empowering Students: Web-based Essentials at Your Fingertips

引用

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

作者： Gurnule, Mayur Gour, Vaibhav Sharnagat, Abhijeet Kaif, Mohammad Rudrawar, Shilpa School of E&TC Engineering 1 MIT Academy of Engineering Pune India

ISBN: (纸本)9798331300579

In today's fast-paced world, students at MIT Academy of engineering in Alandi often find themselves in need of various essential services. This paper presents a web app, "Home Service on-demand," offering student-related services like accommodation, textbook procurement, and furniture acquisition. The system uses React and *** to simplify access to these essential services. © Grenze Scientific Society, 2024.

关键词： Students

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H Yperparameter Tunning to Improve Deep Learning Model Performance 4

H Yperparameter Tunning to Improve Deep Learning Model Perfo...

引用

4th International Conference on Sustainable expert Systems, ICSeS 2024

作者： Bhandari, Manav Pawar, Mrunmayee Kulkarni, Smita School of E&tc Engineering Mit Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331540364

Deep learning models have become a powerful tool in various domains, including fraud detection in credit card transactions. In this paper, we explore the crucial aspect of hyperparameter tuning to enhance the performance of deep learning models in the context of credit card fraud detection. We investigate four different combinations of optimization algorithms (Adam and Adagrad) and weight initializers (Xavier and He) with and without batch normalization. Through comprehensive experimentation using Convolutional Neural Networks (CNNs) on a credit card fraud detection dataset, we find that the combi-nation of Adam optimization with batch normalization and the He initializer consistently yields the highest accuracy compared to the other configurations. Our study not only highlights the significance of selecting appropriate hyperparameters but also provides insights into achieving superior performance in fraud detection tasks. © 2024 Ieee.

关键词： Convolutional neural networks

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Real-Time AI-Based Face-Mask Detection 5th

Real-Time AI-Based Face-Mask Detection

引用

5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Sawant, Prathamesh Bokefode, Sagar Deshmukh, Shrikant Aswathy, M.A. Yadav, Vipin P. Department of Electronics and Telecommunication Engineering School of E&TC Engineering MIT Academy of Engineering Pune Alandi India Department of Electronics Engineering School of E&TC Engineering MIT Academy of Engineering Pune Alandi India

ISBN: (纸本)9789819735228

The 2019 coronavirus (COVID-19) has recently caused a serious worldwide health disaster due to its rapid proliferation. To address the problem of spreading COVID-19, the World Health Organization (WHO) published a few strategies. A COVID-19 preventative strategy is to cover your face with a mask or a piece of cloth in public and busy settings. Manually observing and segregating people wearing masks from those who are not wearing masks is really difficult. Therefore, the primary goal of this research study is to construct a face mask detection model as an embedded model by using two architectures, viz., Mobile Net and YOLO. When someone ignored social norms or did not wear masks, it was revealed. As a final step, the authors deployed the hybrid model on a Raspberry Pi to act as a prototype that will detect whether an individual has put on a mask or not. The proposed model achieved a 99% confidence rate. The comparison of multiple face detection and face mask classification methods is another feature of this work. The accuracy of the system is 99%. As a result, our approach really records people wearing or not wearing masks. As a result, our technology detects whether an individual wore a mask or not in a real-time situation and permits access only to the individual who wore a mask. As a future scope, the current Raspberry Pi module along with the Microsoft camera can be deployed in a range of retail establishments, an office complex, or at airport gates. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Web-Based Framework for the Prediction of Type 1 Diabetes in Youth Using eHR’s Data 5th

Web-Based Framework for the Prediction of Type 1 Diabetes in...

引用

5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Sontakke, Rohini Shinde, Pallavi Avhad, Vilas Kadam, Yash Yadav, Vipin Aswathy, M.A. Department of Electronics and Telecommunication Engineering School of E&TC Engineering MIT Academy of Engineering Pune412105 India Department of Electronics Engineering School of E&TC Engineering MIT Academy of Engineering Pune412105 India

ISBN: (纸本)9789819735228

The study proposed a predictive model for evaluating the risk of Type 1 diabetes in individuals aged 5–30 using an easily navigable web platform available in many regional languages, including Hindi, Marathi, Bangla, and Kannada. The web page's default language is english. The suggested methodology determines the patient's risk of Type 1 diabetes by compiling the symptoms that they independently describe. The suggested framework achieves a maximum accuracy of 94% and an average accuracy of 89%. The platform facilitates user interaction and basic data entry, hence ensuring accessibility for users with varying linguistic backgrounds. The research investigation demonstrates the efficacy of the strategy in promoting early detection, which pushes preventative healthcare measures. Customized risk assessments are provided by the system, which focuses on a variety of symptoms. The importance of early intervention and better patient outcomes is emphasized in the study. This work aligns well with the expanding area of healthcare analytics and offers a useful tool for preventative health initiatives. The multilingual platform addresses the common health problem of Type 1 diabetes in children and young people while improving user engagement and adaptability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision support systems

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Fall Detection Using HOG Feature extraction and Adaptive Boosting Technique 1

Fall Detection Using HOG Feature Extraction and Adaptive Boo...

引用

1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Devle, Satwik Jadhav, Yogesh Kokate, Krishna Chikmurge, Diptee Barve, Sunita School of E&tc Engineering Mit Academy of Engineering Pune India

ISBN: (纸本)9798350315769

This research holds significant importance as it focuses on the development of a reliable and accurate fall detection system, addressing a critical need for the elderly and individuals with disabilities who are more vulnerable to fall-related incidents. The objective of this study is to utilize the Fall Detection Dataset from Kaggle to create an effective fall detection system using the Histogram of Oriented Gradients (HOG) method for feature extraction from accelerometer and gyroscope measurements. To enhance accuracy, various classification algorithms, such as LogisticRegression, K-nearest neighbors (KNN), and decision tree (DT), are explored. Additionally, ensemble learning AdaBoosting along with cross-validation techniques are employed to further improve the model's performance. By overcoming the limitations of existing fall detection systems, this research aims to provide valuable insights and contribute to the development of trustworthy fall detection systems that can offer timely assistance and enhance the safety and well-being of vulnerable individuals. © 2023 Ieee.

关键词： Fall detection

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Improving Potato Disease Classification with Convolutional Neural Networks and Feature Fusion 3

Improving Potato Disease Classification with Convolutional N...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Deshmukh, Sakshi Parkhi, Anushka Gandhi, Shravani Ghusse, Diptee MIT Academy of Engineering School of E&TC Engineering Pune India MIT Academy of Engineering School of Computer Engineering Pune India

ISBN: (纸本)9798331523893

Accurate prediction of potato diseases plays a vital role in maintaining crop health and maximizing agricultural yield. This study presents a deep learning approach utilizing Convolutional Neural Networks (CNNs) integrated with advanced feature fusion strategies to enhance the precision of disease classification. The methodology employs three feature fusion strategies - early, Intermediate, and Late Fusion - on a benchmark dataset of 2,152 potato leaf images categorized into early Blight, Late Blight, and Healthy. The workflow includes data preprocessing, feature extraction using CNNs, and classification through fully connected *** indicate that Late Fusion outperforms other strategies, achieving the highest classification accuracy and robust generalization despite class imbalance. It effectively distinguishes between early and Late Blight, while early Fusion shows better performance for Healthy leaves. The study highlights the importance of feature fusion and data augmentation in enhancing disease diagnosis, paving the way for scalable and efficient agricultural solutions. © 2025 Ieee.

关键词： Convolutional neural networks

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Anomaly-Based Intrusion Detection System for IoT environment Using Machine Learning 56

Anomaly-Based Intrusion Detection System for IoT Environment...

引用

56th International Carnahan Conference on Security Technology, ICCST 2023

作者： Patil, Arya MacHale, Digvijay Goswami, Dipanshu Muley, Parimal Rajarapollu, Prachi School of E&tc Engineering Mit Academy of Engineering Pune India

ISBN: (纸本)9798350315875

IoT solutions have the potential to revolutionize our work and daily lives by providing the valuable data and insights. From enhancing the safety of roads, automobiles, and houses to fundamentally improving the way we produce and consume things. Regardless of the IoT system's advantages, some high-profile cyber attacks are preventing many firms from implementing IoT technologies. The current landscape of IoT ecosystem is characterized by complexity. Virtually any industry's equipment and objects can now be interconnected and configured to transmit data to cloud applications and back-end systems through cellular networks. Throughout the entire IoT journey, there is an inherent risk to digital security, with numerous hackers ready to exploit any vulnerabilities in the system. So, the need for security is very much important in IoT. In this paper, IoT system has been built to collect the dataset and perform intrusion to provide details of the data before and after the intrusion. Various machine learning models are used to classify tempered data. © 2023 Ieee.

关键词： Cybersecurity

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Predicting Human Body Prakriti with Ayurvedic Tridosha Features using ensemble Learning Models

Predicting Human Body Prakriti with Ayurvedic Tridosha Featu...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Bhosale, Akshay Girase, Piyush Waghmare, Atharva Barve, Sunit Chikmurge, Diptee Mit Academy of Engineering School of Computer Engineering Pune India Mit Academy of Engineering School of E&tc Engineering Pune India

ISBN: (纸本)9798350360660

Ayurveda, an ancient medical system, provides insight into personal health through the concept of Prakriti, which determines a person's constitution. Integrating these traditional concepts with modern medical practice is a huge challenge. The research has addressed this problem by developing a machine learning-based Prakriti estimation method. Advanced algorithms, including random forests and ensemble models, were used to analyze a rich dataset of biological traits not easily altered by current cosmetic or technological interventions. This approach achieved a remarkable accuracy of 98%, validating the effectiveness of integrating machine learning and Ayurvedic principles. Future research will focus on expanding the dataset to include a wider range of biological features and improving feature selection methods to increase the generalizability and applicability of the model in various clinical settings, thereby improving the relationship between traditional Ayurvedic knowledge and modern medical practice. The gap must be narrowed further. © 2024 Ieee.

关键词： Adversarial machine learning

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IoT enabled Battery Management System (BMS) with Active Balancing 5th

IoT Enabled Battery Management System (BMS) with Active Bala...

引用

5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Suryawanshi, Anuradha Singh, Ashwin Ochani, Nidhi Verma, Vikrant Jaiverdhan School of E&TC Engineering MIT Academy of Engineering Pune India Department of ECE NIT Uttarakhand Srinagar India

ISBN: (纸本)9789819735228

In this Battery Management System (BMS) project, we present the design and implementation of an advanced BMS tailored for efficient management of battery packs. The system integrates active balancing and charging techniques to ensure uniform cell voltages and prolonged battery lifespan. Voltage and temperature monitoring modules are incorporated to provide real-time data for accurate analysis of the battery pack’s health and performance. State of Charge (SoC) and State of Health (SoH) calculations are employed to estimate the remaining energy capacity and overall health of the battery pack. Through the seamless integration of these functionalities, the proposed BMS enhances the safety, efficiency, and longevity of battery systems, making it a valuable solution for various energy storage applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Battery management systems

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Marathi Speech Quality Assessment 27

Marathi Speech Quality Assessment

引用

27th International Computer Science and engineering Conference, ICSeC 2023

作者： Kemalkar, Aniket K. Bairagi, Vinayak K. Aissms Institute of Information & Technology Dept. E&tc Pune India School of E&tc Engg. Mit Academy of Engineering Pune India

ISBN: (纸本)9798350342109

Speech quality assessment is useful for checking quality of speech which is required for maintaining quality of service in the network. Subjective and Objective speech quality assessment are the basic two types of speech quality assessment. Listening tests are performed to evaluate speech quality in subjective speech quality assessment (SSQA). In objective speech quality assessment (OSQA) mathematical modeling of human perception of speech and mapping is performed to predict quality of Marathi language speech signal. In this paper OSQA subtype intrusive speech quality assessment (ISQA) method by using perceptual evaluation of speech quality (PeSQ) for Marathi language speech signal is elaborated. Non- intrusive speech quality assessment (NISQA) method for Marathi language speech signal using support vector machine (SVM) and deep learning techniques artificial neural network (ANN) and convolution neural network- long short-term memory (CNN-LSTM) is elaborated in this paper. Mel-frequency cepstral coefficient features of speech signal are calculated for performing non-intrusive assessment. The main objective of the paper is to develop objective non- intrusive speech quality assessment for Marathi language. © 2023 Ieee.

关键词： Long short-term memory

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