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NLP Based Sentiment Analysis Using Particle Swarm Optimization with Logistic Regression for Digital Education 6

NLP Based Sentiment Analysis Using Particle Swarm Optimizati...

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6th International Conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2024

作者： Priyadharshini, G. Clara Barathi Balaji, Prasanalakshmi Chaurasia, Mousmi Ajay Das, Subhranginee Dutta, Sushama Rani Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India King Khalid University Department of Computer Science Abha Saudi Arabia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505790

Identification and categorization of user sentiments from a text passage is known as sentiment analysis into several sentiments. For example, using feelings like happiness, sadness, anger, or positive, negative, or neutral to gauge how the user feels about a certain topic or item. These days, a variety of websites and applications collectively referred to as online educational platforms allow users to obtain a higher degree of education across a range of areas. For this reason, digital education provides a platform for the dissemination of knowledge. Among the most active areas within NLP, web mining, and text mining research is sentiment analysis. It is significant in a variety of subjects, including as the social sciences and management sciences, where evaluating the efficacy of learning technology depends heavily on student input. Due to the proliferation of educational institutions, many students have become enthralled with online learning portals that provide cost-free courses. Every year, a huge number of students enrol in these expansive online courses and then revisit their opinions regarding the course material and level of instruction. Additionally, share ideas on blogs on how to raise the standard of instruction by expressing both good and negative opinions. However, an assortment of ML techniques are available to assess these ratings and ascertain which virtual education platform is the most user-friendly and optimal. This study examined natural language processing (NLP)-based sentiment analysis and social media evaluations that were manually gathered from educational applications. To categorise the reviewer's comment for digital education, Particle Swarm Optimisation with Logistic Regression (PSOLR) outperformed all other existing Naïve Bayes, Decision Tree, and Logistic Regression algorithms in terms of accuracy (93.75%). © 2024 IEEE.

关键词： Students

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Guarding Against Command and Control (C2) Agents Utilizing Real-World applications for Communication Channels 5

Guarding Against Command and Control (C2) Agents Utilizing R...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Vidhun, K. Kannimoola, Jinesh M. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350361155

Command and Control (C2) agents are a critical component of many cyberattacks, enabling adversaries to maintain covert control over compromised systems. In recent years, attackers have increasingly leveraged real-world applications, such as social media and collaborative platforms, as C2 channels to evade detection. This paper proposes a behavioural-based network-level filtering approach to enhance cybersecurity defences against these threats. The suggested method includes creating a way to look at network traffic and find patterns and oddities that point to C2 agent activity, as well as setting up network-level filtering to spot malicious traffic and attempts to communicate from C2 agents. This network-level filtering approach aims to provide a proactive and effective defence against C2 attacks, particularly those utilising non-traditional communication channels. Initial results, obtained through testing a Random Forest model on real-world traffic, demonstrate promising outcomes, with the model successfully distinguishing between benign and malicious activities. © 2024 IEEE.

关键词： Cybersecurity

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Smart Healthcare Systems: IoT and AI for Heart Disease Prediction 5

Smart Healthcare Systems: IoT and AI for Heart Disease Predi...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Vishnu, K. Sebastian, Thomas Binu, P.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350379945

Fast food consumption and changes in lifestyle are associated with an increase in heart related problems. This study looks at how cloud computing, the Internet of Things and machine learning can be combined for CVD risk prediction and real-time monitoring. The IoT-ML-Cloud paradigm aims to increment the precision of Cardio Vascular Disease prediction by merging these technologies. The system comprises three main modules: Prediction, Data collection and User engagement. Utilizing Arduino and ESP32 modules. An Android application facilitates patient data input and real-time monitoring and predictions using ML models deployed on the Microsoft Azure cloud. Bluetooth technology utilised to facilitates real-time data transfer to the cloud. This paper proposes Bi-LSTM algorithm with an accuracy of 96.55% for the heart disease prediction. Along with highlighting significant discoveries from numerous ML algorithms in the diagnosis of Cardio Vascular Disease, it also highlights the significance of cloud computing and data mining for large-scale medical data analysis. © 2024 IEEE.

关键词： Electronic health record

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A Comprehensive Exploration of Digital Twins in Smart City applications 15

A Comprehensive Exploration of Digital Twins in Smart City A...

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

作者： Menon, Remya R.K. Saikrishna, S. Sai Ganesh, P.V. Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amrita School of Computing Amritapuri India

ISBN: (纸本)9798331300579

The notion of a Digital Twin (DT), acting as a virtual replica interconnected with its physical counterpart through real-time data exchange, finds extensive applications in real world scenarios. As industries experience a surge in interest in DT, companies and other organisations who are interested in DT define their own methods to leverage the technology. This paper aims to consolidate and summarise these varied definitions of DT prevalent in the literature. The exploration spans from the foundational concepts to the anticipated future of DT, emphasising the need for a standardised architecture to guide its effective application across diverse ***, the incorporation of Machine Learning (ML) into the DT framework and the combination of DT and The interconnected network of smart devices, known as the Internet of Things are examined, emphasising the revolutionary possibilities of these technologies. This study provides a thorough overview of the changing DT landscape by demonstrating the architecture, variants, kinds, and integration of several communication protocols and data representation. The use of DT in the development of smart cities is examined in the paper's last section. The digital twin city, characterised by the integration of dynamic digital technology and static 3D models, emerges as a critical element for achieving comprehensive urban intelligence, planning, and related services. © Grenze Scientific Society, 2024.

关键词： Smart city

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EdgeGuard: Real-Time Rule-Based Spam Detection for IOTA Tangle on Edge Devices 5

EdgeGuard: Real-Time Rule-Based Spam Detection for IOTA Tang...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Arunodhai, V. Susan, Leena P. Kannimoola, Jinesh M. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350361155

Our research paper addresses the pressing security challenges emerging within the rapidly expanding Internet of Things (IoT) landscape, with a specific focus on the IOTA Tangle-a distributed ledger based on Directed Acyclic Graph (DAG) technology. The proliferation of IoT devices has heightened the demand for secure and efficient transaction processing on distributed ledgers, but this surge also amplifies the risk of spam transactions that can compromise the integrity and efficiency of the ledger. In response to this imperative, we introduce EdgeGuard-a sophisticated rule-based spam detection system implemented on edge devices. This real-time system dynamically analyzes transaction data, applying predefined rules to differentiate between legitimate transactions and spam. Immediate action is taken to prevent spam transactions from infiltrating the IOTA Tangle, and the use of edge devices enhances the efficiency of spam detection by alleviating the load on servers. Notably, the system includes integration with real-time alerts using a messaging channel to notify system owners when spam transactions are identified. The effectiveness of EdgeGuard is rigorously validated through real-world data scenarios, demonstrating its capacity to thwart potentially harmful transactions. This research paper significantly contributes to the broader discourse on decentralized security measures, emphasizing the paramount importance of fortifying distributed ledgers. This is particularly crucial in critical IoT applications where unauthorized transactions could yield severe consequences. EdgeGuard stands as a robust solution, embodying innovation in tackling the evolving challenges posed by the intersection of IoT and distributed ledger technologies. © 2024 IEEE.

关键词： Internet of things

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Hate Speech Analysis using Supervised Machine Learning Techniques

Hate Speech Analysis using Supervised Machine Learning Techn...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Chitra, K. Karthikeyan, M. Pradeep, M. Suriya, Prakash T D Viswanathan, T.J. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India Computer Applications Kongu Engineering College Erode India

ISBN: (纸本)9798350348217

Hate speech is a serious issue that can have harmful consequences for individuals and groups. In an effort to combat this issue, automated methods for identifying and categorising hate speech in text and other media have been developed using machine learning and deep learning techniques. These methods involve training machine learning algorithms on large datasets of labelled text, audio or visual data, and then using these algorithms to identify and classify new, unseen examples of hate speech. Machine learning approaches can be effective at detecting and classifying hate speech, but they also have limitations and can be prone to errors, particularly if the training data is biased or if the algorithms are not carefully designed and evaluated. It is important to thoughtfully examine the ethical and social consequences of using machine learning to identify hate speech and to ensure that these methods are transparent, fair, and accountable. © 2023 IEEE.

关键词： Support vector machines

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Implementation of Yolo-Based Object Detection and Sensor Integration Model for Visually Challenged Person

Implementation of Yolo-Based Object Detection and Sensor Int...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kamarunisha, M. Jayasri, K. Sharin, S. Priyadharshini, M. Anusuya, T. Malathi, R. Dhanalakshmi Srinivasan College of Arts and Science for Women Autonomous Department of Computer Applications Perambalur India

ISBN: (纸本)9798331543617

It is suggested that visually impaired individuals use an assistive device to receive automatic direction and navigation. The gadget may provide obstacle-free navigation and use real-time image processing to detect impediments. The goal of the strategy is to maximise information flow by completing all activities in real-time and gathering all the information regarding path impediments. The suggested navigation stick is an inexpensive item, obstacle, and obstacle identifier. For safety and navigation, the suggested assistance system for the blind makes use of cutting-edge technology such as tilt and ultrasonic sensors, as well as YOLO(YouOnly Look Once) object recognition. It updates its location via GPS, haptic feedback, and real-time object detection. The user interface is independent and accessible through non-visual means. The suggested work focuses on a multi-label method for detecting items in indoor and outdoor settings utilising vision technology. It improves accuracy and cuts down on recognition time by using classification and clustering techniques. © 2024 IEEE.

关键词： Ultrasonic sensors

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Structure-Based Code Assessment Model (SBCAM) for Introductory Programming Courses 2024

Structure-Based Code Assessment Model (SBCAM) for Introducto...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Rao, Vineeth V. Rajesh, Sanjay Thushara, M.G. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798400709722

Automated assessment of student code remains a significant challenge in introductory programming education. To address this, we propose a Structure-Based Code Assessment Model (SBCAM), leveraging tools that support Abstract Syntax Tree (AST) analysis techniques, along with innovative feature extraction methodologies. Our model streamlines code assessment by acquiring source code directly from instructors and transforming it into standardized templates. These templates serve as a benchmark for evaluating student submissions, and ensuring adherence to assignment specifications. By automating the assessment process of student submissions using instructor solution, SBCAM reduces manual grading time and provides instructors with valuable insights through comprehensive scoring mechanisms. Ultimately, SBCAM aims to enhance efficiency, accuracy, and instructor productivity, while empowering students to improve their coding skills effectively from the ground up. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Upper bound for Sorting Permutations with Balanced-Starburst 2024

Upper bound for Sorting Permutations with Balanced-Starburst

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16th International Conference on Contemporary Computing, IC3 2024

作者： Ashtapadhi, S.V. Pillai, Devi Indulekha, T.S. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798400709722

In today's rapidly evolving landscape, where genetic research and the optimization of computer networks hold immense importance, understanding genetic mutations and fine-tuning network performance are critical endeavors with profound impacts. Permutations serve as an essential tool in both genomic studies and computer networks, providing versatile methods for understanding genetic variations and enhancing network efficiency. Permutations are used in genomics to represent genomes, where the source genome is denoted by π1 and the target genome by π2. The genomic distance, also known as inter-species distance, quantifies the minimum modifications required to transform π1 into π2. Similarly, permutations are used in computer networks to represent nodes, often visualised through graphs like the Cayley graph, where the graph's diameter determines the maximum delay between network nodes. Sorting permutation refers to the transformation of a permutation π to its form of identity l. Various operations such as adjacent swaps, block transpositions, prefix transpositions, and transposition trees can be applied for sorting permutations. This research particularly focuses on transposition trees. Sorting of permutations using transposition trees is an NP-hard problem in general. Although, there are effective algorithms that optimally sort certain types of transposition trees such as star, path and brooms, developing such algorithms for more general transposition trees remains a challenge. A notable structure is the balamneed star burst, formed by connecting the centers of k stars (k> 2) to a common center vertex (c), for which optimal algorithms and upper bounds are yet unknown. In this paper, we explore the sorting of permutations with the balanced starburst transposition tree, aiming to establish an upper bound. © 2024 Copyright held by the owner/author(s).

关键词： Stars

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Modeling Concept Drift Detection as Machine Learning Model Using Overlapping Window and Kolmogorov–Smirnov Test 3rd

Modeling Concept Drift Detection as Machine Learning Model U...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data sciences, MIND 2021

作者： Jafseer, K.T. Shailesh, S. Sreekumar, A. Department of Computer Applications Cochin University of Science and Technology Cochin India Department of Computer Science Cochin University of Science and Technology Kalamassery India

ISBN: (纸本)9789811958670

Nowadays the large volume of data from different sources especially as streaming data opens us various opportunities for streaming analytics. Concept drift is one of the challenging streaming analytic problems which observes the changes in the distribution of the data over time, and detecting and adapting these attracted many researchers. In this work, we modeled concept drift detection as a machine learning problem. We have followed a semi-supervised learning approach by utilizing a statistical test, the Kolmogorov–Smirnov test which determines the variation of two time series distributions. The core work is to build a classifier that is capable of predicting the given window of data stream holds drift or not. As we have no labels representing drift or not drift for the stream windows, we have explicitly labeled some beginning parts of the stream using the Kolmogorov–Smirnov test and utilized these for building the classifier. Using this classifier, further, we can detect the drift. Also, we have used overlapping windows to avoid information loss. For building the classifier we have applied various classification models like Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. Among them KNN model has a low false-positive rate and outperformed others with an accuracy 96%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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