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International Conference on Advanced Computer Control, ICACC

作者： Nafla Iqbal Aiswarya Mohan Ann Rose Syjo Dept of Artificial Intelligence and Data Science Rajagiri School of Engineering and Technology Kakkanad India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

The rapid advancement of music playback technology, particularly within mobile platforms, has fundamentally transformed user engagement with music. Despite significant improvements in music retrieval techniques over the past decade, the evolution of music recommendation systems remains in its early stages. With users having access to extensive online music libraries, the task of selecting preferred tracks from an overwhelming array of options presents a considerable challenge. Individual musical tastes vary widely, and the influx of new users and music necessitates a system that can adapt *** music recommendation systems face the dual challenge of managing vast amounts of data while delivering accurate results, making them resource-intensive. However, the high precision of these models drives their increasing application in commercial settings. This paper proposes a framework aimed at enhancing recommendation accuracy by tailoring suggestions to align more closely with user preferences.A comprehensive analysis of traditional and advanced music recommendation methodologies is presented, focusing on two user-centric approaches: user preference and emotion-based metrics. These methodologies are critically compared, leveraging the Million Song dataset (MSD) for training the recommendation system. Additionally, an Android application has been developed to collect empirical data on user experiences, facilitating a deeper understanding of user interactions and satisfaction with the recommendation framework. This research aims to improve user experience in music discovery by providing more relevant and personalized recommendations.

关键词： Measurement Training Accuracy Collaborative filtering Music Metadata User experience Libraries User preference Recommender systems

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Turmeric Leaf Disease Detection Using Optimized Deep Learning Model 2

Turmeric Leaf Disease Detection Using Optimized Deep Learnin...

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2nd International Conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Selvaraj, R. Devasena, M.S. Geetha Satheesh, T. Karpagavadivu, K. Ravindaran, F. Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Artificial Intelligence and Data Science Dr.N.G.P. Institute of Technology Coimbatore India Karpagam College of Engineering Coimbatore India

ISBN: (纸本)9781837242672

Turmeric is popular for its medicinal, culinary, and non-culinary uses. Turmeric cultivation is affected due to disease in their leaves. Farmers conventionally identify the leaf diseases and provide pesticides to avoid further loss. However, manual identification of leaf disease over large farms is time-consuming and requires human effort. Also, in several types of leaf disease, the specific type that occurs in the plant should be distinguished;thus, proper knowledge of farming is required. To simplify this and to know the type of disease, smart agricultural practices are followed worldwide. One of the intelligent techniques adopted in farming is leaf disease detection. By providing the filed image to a learning algorithm, the type of disease can be detected accurately without manual interventions. ML and DL are used in this smart agricultural practice. To improve the performance further, an optimized deep learning model is presented in this research work. A Novel gazelle optimization algorithm is combined with deep learning MobileNetV3 network to attain enhanced disease detection performance. Experimentation on our own dataset with 5000 samples, including leaf spot, leaf blight, leaf rot, and curl disease-affected leaves, the proposed model performance is evaluated and observed with a maximum accuracy of 96.8%, a precision of 97.1% over conventional disease detection models. © The Institution of engineering & technology 2024.

关键词： Deep learning

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Grey wolf based feature reduction for intrusion detection in WSN using LSTM

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International Journal of Information technology (Singapore) 2022年第7期14卷 3719-3724页

作者： Karthic, S. Manoj Kumar, S. Senthil Prakash, P.N. Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India

In the recent days all the digital devices including devices used in Healthcare, Personal Digital Assistance etc. are connected to the network, consequently data is exposed to various types of attacks. Wide range of attacks has breached the public networks so far and still the number of attacks keeps increasing. Thus, intrusion detection systems (IDS) are developed to monitor and secure the data from such cyber-attacks. Every time the intruders use different approach to hack the networks. In order to effectively secure the networks from cyber-attacks, Machine Learning methodology is being adopted. The network data usually have large number of features and using all the features in identifying the attack will affect the performance of the IDS in terms of accuracy and processing speed. Hence there is a need for an effective IDS which adopts feature selection and classification methodology. In this paper, an enhanced Intrusion Detection scheme is suggested by employing the Greyish Wolf Optimizer (GWO) with LSTM based Recurrent Neural Networks (GWO LSTM). Further, the NSL KDD and UNSW-NB15 datasets are utilized to exhibit the overall performance of suggested scheme and also equate it along with other pre-existing strategies. The results clearly shows that our proposed method adds significant improvement in performance with accuracy of 96.1% and 97.4% for NSL-KDD and UNSW-NB15 datasets respectively. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Dimensionality reduction Grey wolf optimizer Intrusion detection system LSTM networks

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Efficient Way of Heart Disease Prediction and Analysis using different Ensemble Algorithm: A Comparative Study 6

Efficient Way of Heart Disease Prediction and Analysis using...

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Ebin, N.L. Raja, P. Jawahar Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665482714

This work presents a variety of voting ensemble models to measure human heart disease. This work aims to give confidence to doctors to diagnose heart disease accurately. S o that they can begin treatment in an earlier and more effective manner, thereby saving a valuable human life. The dataset can be trained by different models like Voting Ensemble, Light GBM, Stack Ensemble, XG Boost Classifier, Random Forest, and Logistic Regression. The work considered the 12 attributes and trained the model with different algorithms. Finally, the result shows that the systolic blood pressure parameter is the primary reason for heart disease compared to any other parameter in the data set. Considering all the different algorithms, the voting ensemble method produced more than 80 % accuracy. © 2022 IEEE.

关键词： Classification (of information)

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Jiacheng Liu, Jiaxuan Ji, Shengwei Yang, Hongbin School of Computer Science and Information Engineering Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230027 China School of Artificial Intelligence and Big Data Hefei University China

ISBN: (纸本)9798350337020

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets. © 2023 IEEE.

关键词： Information extraction Reliable pattern Writing style data

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Comparing the Effectiveness of GAN-based Methods for Enhancing EuroSat Satellite Imagery Classification

Comparing the Effectiveness of GAN-based Methods for Enhanci...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Aryan Sharad Gurav Yathharth Munesh Karanjikar Omkar Arun Bhikle Himani Deshpande Department of Artificial Intelligence and Data Science Thadomal Shahani Engineering College Mumbai India Department of Artificial Intelligence and Data Science Vidyavardhini's College of Engineering and Technology Vasai Mumbai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Addressing the challenge of limited data in remote sensing, this research investigates the application of Generative Adversarial Networks (GANs) to augment the EuroSat dataset. Four GAN variants (Info-GAN, C-GAN, CDC-GAN, and AC- GAN) were employed to expand a reduced EuroSat dataset by 50%. The augmented datasets were subsequently used to train Convolutional Neural Networks (CNNs) for image classification. Comprehensive evaluation metrics (accuracy, recall, precision, Fl-score, AUC, IS, and FID) were employed to assess the performance of the generated images and the trained models. Results indicate that CDC-GAN exhibited the highest classification accuracy and MCC, while AC-GAN excelled in ROC-AUC, precision, and recall. This study underscores the potential of GAN s as a valuable tool for enhancing remote sensing image datasets and improving classification performance.

关键词： Measurement Accuracy Lead Generative adversarial networks data augmentation Robustness Satellite images Remote sensing Optimization Image classification

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Level Up: Exploring the Dynamics of Student Achievement through Game-Based Learning 15

Level Up: Exploring the Dynamics of Student Achievement thro...

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

作者： Zuhaib, Affan Katta, Abhinav Prathima, T. Prasuna, P.M. Department of Artificial Intelligence and Data Science CBIT Telangana Hyderabad India Department of IT Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of CSE Ramachandra College of Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798331300579

This research is designed to solve the lack of tracking knowledge in games based on learning from a data- driven approach. Specifically, the project will use data from educational games to predict student outcomes in real time and offer personalized learning tailored to each student's learning needs. Machine learning will be used to evaluate game data and build predictive models for the project. The project will bridge this gap by using a data-driven approach to predict student achievement over time. By analysing data from educational games, the project will provide personalized learning to meet the needs of each student. By providing real-time feedback to students, the program aims to help them identify areas where they need to strengthen their knowledge, empowering them to make decisions when they want to learn more about their education. To achieve this goal, the project will use analysing techniques to evaluate data from game logs and develop predictive models. These models will provide a real-time view of students and help them identify areas where they need to improve their knowledge. The data used by this project is one of the largest open datasets of game engines. It provides valuable sources of tracking information for educational games. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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artificial intelligence based Facial Emotion Recognition with Deep Neural GAN Augmentation 8

Artificial Intelligence based Facial Emotion Recognition wit...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Menaka, D. Priya, D. Karthika Lenus, C. Reeda Ashwini, A. Shermila, P. Josephin Prathaban, Banu Priya Noorul Islam Centre for Higher Education Department of Electronics and Instrumentation Engineering Kumaracoil India Easwari Engineering College Department of Artificial Intelligence and Data Science Ramapuram Chennai India S.A.Engineering College Department of Physics Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India R.M.K.College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Networking and Communication Chennai India

ISBN: (纸本)9798350367904

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. The findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. The proposed strategy is based on the generation of artificial 'emotional' images on the face using Deep Neural Self Attention GANs (DNSAGANs). This way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. This larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FER2013 dataset is used. The training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. The percentage of accuracy is about 88.64 % for 100 epochs using the proposed optimizer techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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Face Mask Detection Using Support Vector Machine

Face Mask Detection Using Support Vector Machine

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Divya Priya, D. Subbulakshmi, R. Purushotham, P. Kiran, Ajmeera Arulananth, T.S. Kavya, S.P. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore India MLR Institute of Technology Department of Electroncis and Communication Engineering Hyderabad India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350348217

According to a report published by the World Health Organization (WHO), the only two primary ways in which the COVID-19 virus can be spread are through breathing contaminated air and through direct physical contact. Wearing a mask is one of the bare minimum preventative measures that can be taken against the transmission of certain viral infections, including COVID-19. Therefore, it is much more important to wear a mask properly in very crowded areas such as public parks, supermarkets, and shopping malls. This is because the risk of spreading an infectious disease is significantly higher in these settings. Face Mask Detection is a technique that we developed so that we could avoid problems like these. It can determine whether or not a person is wearing a mask on their face. There have only been a handful of scant studies done on face mask detection. © 2023 IEEE.

关键词： SARS

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