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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Raja, Sabarish Raja Ramesh Antony Vigil, M.S. Pattaiah, Muthukumar Sudarson, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9783031699818

Social media platforms which include Twitter, Reddit, Instagram, and YouTube have emerged as key information sources during emergencies or natural disasters. However, in the case of Twitter, it might be challenging to distinguish the tweets that are genuinely informative due to the overwhelming number of tweets that are sent during these occurrences. This study proposes a method for classifying the tweets as "informative" and "non-informative" using natural language processing. The technique considers the tweets’ subjects, hashtags segmentation, and locations for pre-processing. BERT, RoBERTa, LSTM, and DistilBERT are four different NLP models that have been trained and tested. The findings will indicate the highest classification accuracy for determining if a tweet is informative or not. Our findings reveal that DistilBERT achieved the highest accuracy of 92.0% in testing and 89% in precision of the Twitter dataset. © IFIP International Federation for Information Processing 2024.

关键词： Tweets

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CLSTM-DQN Fusion for AI-Driven Broken Chord Melodies 3

CLSTM-DQN Fusion for AI-Driven Broken Chord Melodies

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Gokul Srinivas, K. Madhavan, S. Manoj, S.J. Sathya Priya, S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India

ISBN: (纸本)9798350396157

This research study introduces an innovative approach by employing LSTM networks to generate broken chord compositions, capturing nuanced harmonies and rhythms inherent in this fragmented structure. Trained on annotated sequences, the model adeptly retains the arpeggiated essence of broken chords. Leveraging LSTM networks, attention mechanisms, and meticulous architectural refinements, this method substantially elevates the quality and ingenuity of the compositions generated. The research significantly contributes to AI-driven music generation by offering robust composition support and personalized music recommendations. © 2024 IEEE.

关键词： Long short-term memory

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Novel Framework for Dengue Classification and Early Recovery using Machine Learning Algorithms 18

Novel Framework for Dengue Classification and Early Recovery...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Faritha Banu, J. Hariprasad, G. Archana, T. Srivatsan, Prahadees Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9789380544519

Dengue fever is a fast-emerging public health hazard that primarily affects tropical and sub-tropical regions. Arthropods that feed on blood, such as fleas, parasites, and mosquitoes, are the carriers of this disease, which can infect humans and cause significant illness. Five serotypes of the dengue virus - DEN V1, DEN V2, DEN V3, DEN V4, and DEN V5 are spread by Aedes aegypti mosquitoes. Currently, this illness is spreading to many other countries. A delayed diagnosis combined with the wrong method of treatment increases the chance of death. Based on the severity of the infection, dengue is classified into Dengue Hemorrhagic Fever (DHF) or Dengue Shock Syndrome (DSS) which are categorized into febrile, critical, and recovery phases. A framework model is essential to analyze the severity of the infection and identify the phase of severity. Among various machine learning algorithms, this study uses Support Vector Machine and Random Forest for their proficiency in handling complex data. Through rigorous feature engineering and data preprocessing, critical information for precise discrimination was extracted and examined for DHF phases. Experimental results demonstrate both models are competent, but SVM is more suitable for the proposed prediction model with an accuracy 95%, precision of 93%, recall of 94%, and F-measure metrics of 94%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Dengue Dengue Haemorrhagic Fever Dengue Shock Syndrome Support Vector Machine and Random Forest

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Comparative Methods of Implementation for Different Question Answering Systems 5

Comparative Methods of Implementation for Different Question...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Naveen, C. Amutha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798350385649

This research introduces an innovative Question Answering (QA) system tailored explicitly for government department inquiries regarding individuals. Harnessing the prowess of cutting-edge language models such as BERT and T5 (Text-to-Text Transfer Transformer), the system excels in understanding complex queries within diverse governmental domains. Moreover, it incorporates a specialized Knowledge Graph meticulously curated with interconnected information about people across various departments. By integrating BERT and T5 for versatile query comprehension and answer generation alongside a comprehensive People-centric Knowledge Graph, this system aims to revolutionize information retrieval within government entities. The seamless fusion of these technologies promises accurate, contextually rich responses, optimizing operational efficiency across government departments and fostering streamlined access to crucial information. © 2024 IEEE.

关键词： Knowledge graph

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Exploring Data Mining Techniques for Early Autism Detection using Random Forest Algorithm 15

Exploring Data Mining Techniques for Early Autism Detection ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ayyadurai, M. Sujatha, K. Deeptha, R. Preethi, D. Dinesh, M. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai India Saveetha University Chennai India

ISBN: (纸本)9798350370249

Autism, a neurological disorder, manifests uniquely in areas such as verbal and nonverbal communication, social interactions, behavioral adaptability, and specific interests. The results collected indicate that health professionals should receive more training in areas like psychology and developmental disorders and be able to detect Autism Spectrum Disorder (ASD) at a much earlier stage. They also provide pertinent information for reflection on the methods currently employed in evaluating children's development and diagnosing ASD. This study collects information from a specific set of children with autism spectrum disorder from kaggle and is based on real, up-to-date facts. The input and output dataset will have certain common characteristics that will determine whether or not a child currently has ASD or has any signs of the same shortly. Random Forests, also known as Random Decision Forests, is a widely used ensemble learning method for tasks like classification and regression. This technique operates by constructing numerous decision trees during the training process. This classification is used to classify the dataset collected, into categories and hence segregate the children examined as "with" and "without" ASD with high accuracy about 88%. ©2024 IEEE.

关键词： Diseases

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Learning to Generate: Text Guided Visual Feature Extraction for Image Captioning Using Joint Two-Phase Learning Model 1st

Learning to Generate: Text Guided Visual Feature Extraction ...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Lakshmi, R. Rawal, Aditya Gurumurthy, Rohan S. Kulshreshtha, Abhijeet Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India

ISBN: (纸本)9783031759567

Image Captioning can be defined as the ability of the machine to identify the features and environment to provide a caption that describes the situation in detail. Over the years, various authors have proposed unique solutions to either the problem of caption generation or an alteration in the caption generation mechanisms. The problem associated with these solutions is that, while they have provided a higher accuracy, they fail to properly correlate the features extracted to the caption, which could potentially decrease the descriptiveness of the caption. This may result in ambiguous captions that could be difficult to comprehend. In our project, we aim to address these issues by integrating a joint two- phase learning model, which will help in identification and classification of features present in the image, to generate a caption. From our results, we have seen an 89% accuracy in the generated captions. As a result, we will potentially be able to increase the implementation of this application in a wide range of domains such as security and education, without having to concern ourselves with limited accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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Efficient spectrum allocation for secondary users in cognitive radio network using round robin priority algorithm along with reservation channels

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16715-16728页

作者： Suganthi, N. Meenakshi, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Ramapuram Tamil Nadu Chennai600119 India Tamil Nadu Chennai India

Cognitive Radio Network provides opportunistic spectrum access to unlicensed users in the absence of licensed users using dynamic spectrum access technology. The key challenge here is to impartially share the existing spectrum to all the secondary users in the network. A Round Robin Priority (RRP) algorithm along with reservation spectrum is proposed in this paper to impartially schedule available spectrum band and to provide continuous uninterrupted service to all secondary users. The QoS of secondary users are evaluated and compared with traditional spectrum allocation methods. The QoS parameters considered are blocking probability, the forced termination probability, spectrum utilization, average waiting time and the average delay of secondary users. The proposed Round Robin Priority Algorithm with Reservation scheme results reveals 22.82% reduction in Blocking Probability, 15.28% reduction in Forced Termination Probability, 12.54% reduction in average waiting time and 24.58% increase in Spectrum utilization when compared with traditional Constant Channel Aggregation Scheme. On comparing RRP with reservation with Variable Channel Aggregation schemes shows 19.32% reduction in Blocking Probability, 12.74% reduction in Forced Termination Probability and 10.57% increase in Spectrum Utilization. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blocking probability

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A Comparative Analysis of the Imputing of Missing Data on Air Pollution 2

A Comparative Analysis of the Imputing of Missing Data on Ai...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Gayathri, M. Kavitha, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution in the environment over the course of the past few years. Data analyst is commonly encountering the issue of data missing. It is crucial to handle them efficiently to get more accurate and dependable data analysis findings. This study aims to improve understanding of data missing mechanisms, data imputation techniques, and examine the efficacy of frequently utilized data imputation techniques on quantitative datasets. Analysts and data scientists frequently face the challenge of missing data in their investigations. Therefore, it is essential to handle them in an efficient manner so as to gain improved and more trustworthy results through the analysis of the data. The objective of this research is to enhance comprehension of data missing processes and data imputed methods, while also evaluating the effectiveness of data imputed approaches often employed in quantitative datasets. A complete comparison of 5 different data imputation methods is presented in this article. These approaches include mean imputation, median imputation, mode imputation, kNN imputation and Linear Regression. For the purpose of analysing and contrasting the efficacy of the various data imputation approaches, we have utilized five distinct numeric datasets that were gathered from several air pollution repositories. Standardized Root Mean Square Error (RMSE) is the technique for assessing the efficacy of data imputation techniques. According on the findings of the analysis, the kNN imputation approach offers superior performance than the other methods. There is no correlation between the length of the dataset or the number of values that are missing in the dataset and the accuracy of the data imputed approach. © 2024 IEEE.

关键词： Air pollution

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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