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2024 International Conference on computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Sharma, Utkarsh Subramonian, Krishnan Dsouza, Aaron Sughosh, P. Savitha, G. Girisha, S. Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Civil Engineering Manipal576104 India

ISBN: (纸本)9798350388534

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the 'PKLot' dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development. © 2024 IEEE.

关键词： Traffic congestion

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Strong Baseline with Auto-encoder for Scale-Invariant Person Re-identification 6

Strong Baseline with Auto-encoder for Scale-Invariant Person...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Bilakeri, Shavantrevva Kotegar, Karunakar Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Karnataka Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Person Re-identification associates the same person images captured by disjoint cameras. The different training strategies and precise hyper-parameter selection are required for the construction of an effective person re-identification. However, in the current work, the tests are carried out by trial and error, which takes a long time to reach an optimal solution. We experiment with various training strategies with the metric learning model serving as the baseline to address this issue. Existing person re-identification benchmarks have insufficient samples for training the ReID model. Also, the previous approaches fail when the same individual appears in varied sizes. To solve this issue, an auto-encoder module is used to create a single image in three distinct scales to enhance the sample size and tackle the scale variation problem of the person reidentification. In addition, a performance comparison is made between the baseline and the baseline with auto-encoder to demonstrate the influence of an auto-encoder as a data augmentation for the person re-identification. The adoption of auto-encoder module and bestperforming training techniques to a baseline have enhanced the rank1 and mAP on Market1501, DukeMTMC-reID datasets. © 2022 IEEE.

关键词： computer vision

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FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation 2

FedERA: Framework for Federated Learning with Diversified Ed...

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2nd IEEE International Conference on Federated Learning Technologies and applications, FLTA 2024

作者： Borthakur, Anupam Kasliwal, Aditya Manna, Asim Dewan, Dipyan Sheet, Debdoot Centre of Excellence in Artificial Intelligence Indian Institute of Technology Kharagpur West Bengal India Dept. of Data Science and Computer Application Manipal Institute of Technology Karnataka Manipal India

ISBN: (纸本)9798350354812

Federated Learning (FL) is a distributed machine learning (ML) approach that allows multiple devices to train a model while keeping training data private on edge devices. However, existing FL libraries are incapable of: (i) supporting a wide range of existing FL algorithms, (ii) handling diverse public as well as custom datasets, (iii) discarding unreliable updates from malicious edge devices, (iv) facilitating edge-device training, and (v) implementing model compression to reduce communication overhead. With the rapid progression in AI, there is an increment in the computation power while training the models on edge devices which results in more carbon emission (CE). However, in most of the existing FL frameworks, there is no module for estimating CE. In this paper, we present FedERA, a modular and fully customizable open-source FL framework aiming to address these issues especially also by offering extensive support on heterogeneous edge devices and by incorporating both standalone and distributed training approaches. The integration of new software modules unforeseen in existing FL frameworks not only amplifies the scope of its usability but also fosters environment-friendly FL. We believe that FedERA will be a valuable tool for researchers and practitioners working in FL. ©2024 IEEE.

关键词： Federated learning

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A Quantitative Study on the FaceNet System 4th

A Quantitative Study on the FaceNet System

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Gopakumar, Rajesh Kotegar, Karunakar A. Vishal Anand, M. Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819942831

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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Opinion mining using ensemble model for restaurant feedback analysis

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Discover Computing 2025年第1期28卷 1-27页

作者： Kamath, Rohan S. Kaushik, M. R. Ramakrishna, M. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

The research work introduces a new method for sentiment analysis in the context of restaurant evaluations, emphasizing the optimization of the trade-off between processing time and accuracy. A majority voting classifier is used in our research to evaluate and merge multiple machine learning models, such as Decision Tree, Support Vector Machine, Random Forest, Logistic Regression, K-Nearest Neighbors, Multinomial Naive Bayes, and an Ensemble Method. Thorough preparation of the data, feature extraction, and analysis are all part of the study. Models are then trained and evaluated using performance metrics like accuracy, precision, recall, and F1 score. The main contribution of this work is the integration of models with optimized hyperparameters into an ensemble framework, which greatly improves model performance. Our results show that the ensemble method-the combination of Support Vector Machine, Logistic Regression, and Multinomial Naive Bayes-performs better than both single and multiple models in terms of accuracy and efficiency. Thus, it is determined that the ensemble approach is the best way to perform sentiment analysis on restaurant evaluations, giving restaurant owners valuable business knowledge and important insights for raising customer happiness. By demonstrating the enhanced performance and usefulness of an improved ensemble model in actual food and beverage industry settings, this study contributes to the field of sentiment analysis.

关键词： Classification models Ensemble Machine learning Processing time Restaurant reviews Opinion mining

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Disguise Face Classification Using EfficientNet Deep Learning 1st

Disguise Face Classification Using EfficientNet Deep Learnin...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Padmashree, G. Wagle, Shruti G. Karunakar, A.K. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789811954023

With the rise in popularity of social media and intelligent gadgets, one essential biometrics for identifying people is their face. The efficiency of existing automatic face recognition systems has decreased due to factors such as face ageing, conceals, and pose variations. Face recognition algorithms must be more accurate for recognizing faces hidden behind masks and makeup, as security and surveillance requirements become more stringent. Disguise face classification acts as a standalone early warning system in such scenarios. The major goal of this research was to see how the EfficientNet family of models compares to the current state-of-the-art architecture in terms of disguise face classification. For disguise face classification, the EfficientNet deep learning architecture was proposed in this paper. The models were trained and tested using the Disguise Faces in Wild (DFW) 2018 data set. The transfer learning method was used to train EfficientNet and other deep learning models. Results obtained proved that EfficientNet-B3 has outperformed other EfficientNet architectures with 92.2% precision and 93.9% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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AgriLLM:Harnessing Transformers for Framer Queries 3

AgriLLM:Harnessing Transformers for Framer Queries

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3rd Workshop on NLP for Positive Impact, NLP4PI 2024, held in conjunction with the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal India Department of Data Science & Computer Applications Manipal Academy of Higher Education Manipal India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

ISBN: (纸本)9798891761759

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. © 2024 Association for Computational Linguistics.

关键词： Distribution transformers

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Physiological Signals based Student Grades Prediction using Machine Learning 21

Physiological Signals based Student Grades Prediction using ...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Agarwal, Vanshikha Ahmad, Naeem Hasan, Md Gulzarul Manipal Academy of Higher Education PSPH Department of Data Science Manipal India National Institute of Technology Department of Computer Applications Raipur India

ISBN: (纸本)9798350358230

Amidst today's fast-paced world, the escalating levels of stress among students have become a significant concern, and it appears to be inversely related to their academic performance. There have been several types of research on the link between stress and grade prediction. Our study aims to leverage the available dataset about the stress level in such a way that can predict students' grades to comment on the student's performance. The dataset is collected using the wearable device during three exams containing electrodermal activity, skin temperature, and heart rate from 7680 student subjects. Our approach involves employing a variety of machine learning and deep learning algorithms, including Decision Trees Regressor, CNN Regressor, KNN Regressor, Random Forest Regressor, and Support Vector Regressor models. Through our investigation, we aim to demonstrate the potential of wearable devices in extracting meaningful insights from physiological signals to predict students' academic outcomes. The challenges associated with wearable observations present unique opportunities for addressing and mitigating stress-related issues among students, and this study harnesses the power of a publicly available dataset to contribute to this important area of research. © 2023 IEEE.

关键词： Students

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in Engineering and technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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