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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Avneet Bhatia, Munish Department of Computer Science and Engineering Lovely Professional University Punjab India Department of Computer Applications NIT Kurukshetra Kurukshetra India

ISBN: (纸本)9789811998751

Educational data mining (EDM) contributes cutting-edge methodologies, strategies, and applications to the advancement of the education system, hence playing a crucial part in its development. Utilising machine learning and data mining approaches to explore and utilise educational data, the current advancement gives essential tools for comprehending the student learning environment. Academic institutions in the twenty-first century operate in a highly competitive and complicated environment. Among the prevalent issues faced by universities are performance analysis, the provision of a high-quality education, systems for evaluating the performance of students, and the planning of future activities. Student intervention programmes must be created in these universities in order to address the academic difficulties encountered by students. From 2009 through 2021, the relevant EDM literature relative to predicting student attrition and students at risk is examined in this review. According to the review’s results, several machine learning (ML) methodologies are used to discover and address the fundamental challenges of forecasting students at risk and student withdrawal rate. Furthermore, the bulk of studies make use of data from student college/university database and online learning portals. It was determined that ML techniques play crucial roles in forecasting students at risk and withdrawal rates, hence boosting student performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Surveying Keyword Extractors: Classification, applications, and Empirical Analysis 6

Surveying Keyword Extractors: Classification, Applications, ...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Musunuru, Varun Nagam, Sai Pavan Krishna Thushara, M.G. Villuri, Praneeth Vasantha, Gopikrishna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350369748

Natural Language Processing (NLP) has seen some rapid developments. One of the areas is keyword Extraction which has seen rapid growth. Many varied and complex extractors have been brought to light recently, and several extractors have been upgraded. These can be used in several NLP applications. The analysis and working of some of the newest and benchmark extractors that shed new light in this field have been discussed. The goodness of any extractor can only be measured by how it works on real-life data. Instead of going down the set route, a detour was taken, and the working of keyword extractors on movie plots was observed and discussed. Thus for the analysis part, a new dataset has been created from several movie plots taken from various credible sources like IMDB, Rotten Tomatoes, etc. © 2024 IEEE.

关键词： Natural language processing systems

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A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels

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SN computer science 2025年第1期6卷 1-12页

作者： Surendran, Simi Montresor, Alberto Vinodini Ramesh, Maneesha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Information Engineering and Computer Science University of Trento Trento Italy Amrita Center for Wireless Networks & Applications (AmritaWNA) Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The lack of affordable communication facilities to the shore remains a fundamental problem for fishermen engaged in deep-sea fishing. The Offshore Communication Network (OCN) is a wireless network of fishing vessels, whose goal is to provide Internet over the ocean. However, the dynamic nature of OCNs characterized by extreme weather, the difficulty of deploying additional infrastructure, wave-induced vessel movements, and high mobility causes significant challenges for traditional routing protocols. This paper proposes OCN-AR, a Q-learning-based adaptive routing strategy for ocean networks. The quality of the learning process relies on the reward function, which has been carefully designed to incorporate the most important features, including real-time forecasts of connectivity quality, path probability, link availability duration, and distance to the destination. The routing performance is evaluated through extensive simulations conducted under diverse conditions, including varying mobility scenarios, transmission rates, vessel rocking intensities, and node densities, and is compared against traditional protocols. The results demonstrate that OCN-AR significantly outperforms existing routing approaches, making it a reliable solution for maritime communication. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fishing vessel network Marine communication network Reinforcement learning Routing protocol

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Optimizing University Time Table Schedules for Smart Space Utilization 5

Optimizing University Time Table Schedules for Smart Space U...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Dasari, Chetan Aditya Kumar, Solasa Manikanta Arpitalaxmi, Arpitalaxmi Thushara, M.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350376685

Manual university course timetable scheduling is a time-consuming and error-prone process, often resulting in conflicts and inefficiencies. An optimized timetable is crucial for smooth academic operations, ensuring efficient use of resources and fostering a positive learning environment for students and faculty. We address the challenge of university course scheduling by proposing an optimized time-table scheduling algorithm. This paper will identify suitable approaches to automate timetable generation, considering factors like lecturer availability, student groups, room capacity, and course requirements. Keeping track of all the different factors that affect things like course requirements, teacher availability, room capacity, and fair task distribution is hard. By working on slot-based scheduling methods, we hope to find ways to make the schedule work best for both students and teachers, which will make everyone happier and help them learn more. © 2024 IEEE.

关键词： Students

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Investigating Natural Language Techniques for Accurate Noun and Verb Extraction 2

Investigating Natural Language Techniques for Accurate Noun ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nair, Reshma P. Thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

Natural language processing (NLP) has witnessed significant advancements in recent decades. Automatically classifying parts of speech, like nouns and verbs, from textual input has transformed text analysis and language understanding. Using natural language processing techniques, we explore various methods for identifying noun and verb phrases automatically, with an emphasis on high accuracy. Our study explores rule-based, statistical, and Machine Learning (ML) approaches for determining the nouns and verbs from sentences. The effectiveness of these approaches is clearly evident, especially when NLP libraries such as SpaCy and the Natural Language Toolkit (NLTK) are used. As well as demonstrating their potential applications across diverse language processing tasks and industries, we conduct comparative research to showcase their advantages and disadvantages. The performance of these methods is also examined in terms of retrieving subject and action terms. SpaCy achieves an impressive accuracy of 95% in noun and verb extraction, while Part-Of-Speech (POS) technology tagging delivers an even higher accuracy of 96%. The results obtained with these methods illustrate how nouns, verbs, and names can be classified in text successfully. © 2024 Elsevier B.V.. All rights reserved.

关键词： Extraction

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AST-Based and Token-Based Neural Networks for Source Code Classification: A Comparative Performance Analysis 15

AST-Based and Token-Based Neural Networks for Source Code Cl...

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

作者： Parvathy, R. Thushara, Mg Amrita School of Computing Department of Computer Science & Engineering Amrita vishwa Vidyapeetham Amritapuri India Amrita School of Computing Department of Computer Science & Applications Amrita vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Programming languages function as a medium for expressing instructions in the context of computer program creation. A prevalent convention in programming is categorizing source code into fragments, a strategy that enhances understanding and simplifies the upkeep process. This research study presents a comparative examination of two distinct models utilized to classify source code. The first model employs a neural network approach that heavily relies on Syntax Trees (ASTs). In contrast, the second model is built on a neural network framework that utilizes the tokenization process. The AST-based model employs a methodology that entails decomposing extensive ASTs into smaller clusters of statement trees. These clusters are subsequently transformed into vectors by capturing the lexical and syntactical characteristics of the statements. The naturalness of certain statements is captured by employing a bidirectional Recurrent Neural Network (RNN) model, which utilizes a collection of statement vectors. In contrast, the token-based paradigm diverges from ASTs and emphasizes tokens obtained through tokenization. The tokens undergo preprocessing and vectorization before inputting into a bidirectional RNN model to evaluate the naturalness of the statements. To choose the optimal model, the accuracy of the two models are compared. © 2024 IEEE.

关键词： Recurrent neural networks

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A Hybrid Methodology for Software Development and IT Team Analysis in Manufacturing 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The IT team working in the manufacturing industry may use this paper to learn about the results of the recently established hybrid software development model with the aid of this paper. The IT team’s needs, difficulties, working practices, past working strategies, and the client’s expectations in the manufacturing firm all play a role in determining how the hybrid model is implemented. The IT team was able to accomplish goals such as on-time delivery with quality improvement, more accuracy, reduced maintenance cycle, the involvement of clients, reduced development time, and improved defect removal efficiency by utilizing a new model and adopting appropriate features from Traditional and Agile methodologies. This article presents a guideline on prior outcomes for the IT team at a manufacturing business and results after using a hybrid approach. The results of deploying a hybrid software development paradigm inside an in-house IT team that works in the manufacturing business are the subject of this research, which explores those results. This study intends to evaluate the effects of combining conventional and agile approaches for software development by thoroughly examining software development practices and determining how successful this combination is. The research investigates the subsequent enhancements in project delivery deadlines, product quality, and team cooperation. The findings provide light on the possible advantages and drawbacks of using a hybrid method. The results give organizations who are looking to optimize their software development processes in a manufacturing setting with useful insights that will eventually contribute to increased efficiency and competitiveness in the sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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Hybrid Actor-Action Relation Extraction: A MachineLearning Approach 5

Hybrid Actor-Action Relation Extraction: A MachineLearning A...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Reshma P. Thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India

Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. The field of natural language processing (NLP) and information extraction is always growing. One of the biggest problems that comes up time and time again is finding relationships between entities and actions in textual data. Actor-action relation extraction (AARE) is a hybrid model that combines NLP with rule-based and machine-learning models. It effectively analyzes unstructured data and takes into account different contextual factors, saving time and reducing errors. The proposed approach extracts Actors, actions, entities, and relationships (AARE) from natural language text more accurately and completely using machine learning techniques and rule-based systems. The hybrid model handles unstructured input and adapts to changing linguistic signals using machine learning. The hybrid approach uses Named Entity Recognition, rule-based extraction, and machine learning principles to convert unstructured data into structured format. It uses tokenization, part-of-speech tagging, nlp, and semantic role labeling for relationship categorization. The model has a 93% accuracy rate and is effective in extracting actor-action relations. Future research should focus on improving rule-based techniques, semantic learning, and addressing complexities in UML diagrams. © 2024 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Deep Learning Based Intrusion Detection for IoT Networks 4

Deep Learning Based Intrusion Detection for IoT Networks

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ananthi, P. Nirmaladevi, K. Mothika, G. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Erode India

ISBN: (纸本)9798331529635

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. This study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. The project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. The novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. The TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. The ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance. © 2024 IEEE.

关键词： Network intrusion

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IoT-Based Smart Mask Protection against the Waves of COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11153-11164页

作者： Goar, Vishal Sharma, Aditi Yadav, Nagendra Singh Chowdhury, Subrata Hu, Yu-Chen Govt. Engineering College Bikaner Rajasthan Bikaner India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India Department of Computer Science & Applications SVCET Engineering College Andra Pradesh Chittoor India Department of Computer Science and Information Management Providence University 200 Sec. 7 Boulevard Shalu Dist Taichung City43301 Taiwan

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytic

关键词： COVID-19

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