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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Computer Science and Engineering Paavai College of Engineering Namakkal India Information Technology KSR College of Technology Thiruchengode India Computer Science and Engineering Excel Engineering College Komarapalayam India

ISBN: (纸本)9798350383867

A recent study revealed that fake articles spread considerably faster than factual content on Twitter, specifically at a 70% higher rate of retweets and reaching the first 1,500 users six times faster. This highlights the urgent need for scalable automated detection solutions to combat the pervasive threat of fake news online. While machine learning methods often rely solely on semantic text analysis, evolving fake news campaigns mimic genuine news, employing tactics like bot networks and sophisticated language imitation. This renders text-based meth- ods increasingly vulnerable. To address this challenge,we propose a heterogeneous deep learning model that incorporates richer information beyond text, specifically dissemination structure features from social networks. These features capture aspects like user interactions, sharing patterns, and network topology, providing valuable insights into the spread of information. Our model leverages the sentiment and affects the enriched Alt dataset for text analysis. Instead of using a popular language model called BERT, we explore alternative pre-trained language models like the Sentence Transformer, which focuses on capturing con- textual sentence embeddings, or the Universal Language Model (ULMFiT), which excels at understanding semantic relationships within text. Additionally, we consider spaCy, a versatile NLP library offering similar functionalities for text processing and feature extraction. While The Quint Dataset Challenge Dataset lacks sentiment annotations, it could be a potential alternative to Alt depending on research goals. This network-aware approach achieves an impressive 95.27% accuracy on The Boom Live dataset and demonstrates generalizability to The Quint dataset with 92.1% accuracy, highlighting its effectiveness in combatting evolving fake news tactics. © 2024 IEEE.

关键词： Latent semantic analysis

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Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 39-56页

作者： S.Kalavathi R.Nedunchelian Department of Information Technology Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science&Engineering Excel Engineering CollegeKomarapalayamNamakkal638183India

Network analysis is a promisingﬁeld in the area of network applications as different types of trafﬁc grow enormously and *** route prediction is a challenging task in the Large Scale Networks(LSN).Various non-self-learning and self-learning approaches have been adopted to predict reliable *** protocols decide how to send all the packets from source to the destination addresses across the network through their *** the current era,dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.A novel method named Reliable Route Prediction Model(RRPM)is proposed toﬁnd the best routes in the given hefty gage network to balance the load of the entire network to advance the network *** task is carried out in two *** theﬁrst phase,Network Embedding(NE)based node classiﬁcation is carried *** second phase involves the network analysis to predict the route of the *** experiment is carried out for average data transmission and rerouting time is measured between RRPM and Routing Information Protocol(RIP)protocol models with before and after failure *** was observed that average transmission time for RIP protocol has measured as 18.5 ms and RRPM protocol has measured as 18.2 *** the proposed RRPM model outperforms well than the traditional routeﬁnding protocols such as RIP and Open Shortest Path First(OSPF).

关键词： Network embedding node classiﬁcation link prediction routing protocols novel RRPM

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Enhancing Multimodal Sentiment Analysis with Deep Learning Techniques to Foster Emotional Intelligence 10

Enhancing Multimodal Sentiment Analysis with Deep Learning T...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kousika, N. Nanda Kumar, N. Baskaran, Elango Abbenaya, M.S. Arthika, G. Ashwanth, M. Sri Krishna College of Engineering and Technology Department of Cse Coimbatore India Excel Engineering College Department of Cse Kumarapalayam India Adhiparasakthi Engineering College Department of ECE Melmaruvathur India

ISBN: (纸本)9798350353068

Sentiment analysis (SA) is a critical process in understanding the emotions associated with data on social networking sites. Building upon this foundational concept, this paper introduces a sophisticated web application aimed at enabling users to submit diverse forms of content, including text, images, videos, and audio, for hate content assessment. This platform employs BERT CNN and VGG16 CNN algorithms for performing a thorough analysis of provided information in various formats. Emphasizing the identification of emotions in text and the detection of hate speech, the methodology extends to incorporate facial emotion detection for images. Through meticulous preprocessing, feature extraction, and ensemble learning, the proposed solution provides a robust framework for accurately identifying and mitigating hate content across various modalities. Experimental tests employing varied datasets validated the usefulness of the strategy, contributing to the advancement of hate content detection systems and fostering a safer online environment. © 2024 IEEE.

关键词： Sentiment analysis

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Cau-Net: Enhancing Medical Image Segmentation With Contour-Guided Attention for Accurate Stroke Prediction 2

Cau-Net: Enhancing Medical Image Segmentation With Contour-G...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Annamalai, Sivakumar Priya, T. Nandhini Deepika, J. Jeevitha, R. Priyanka, B. Richard, Titus Department of Safety and Fire Engineering Excel Engineering College Namakkal Komarapalayam India Department of Electrical and Electronics Engineering Excel Engineering College Namakkal Komarapalayam India Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of English Vels Institute of Science Technology & Advance Studies Chennai India

ISBN: (纸本)9798331504960

Accurate segmentation of medical images is necessary for accurate diagnosis and treatment planning. The achievement of the precise boundaries in complex anatomical structures is difficult. In this paper, a novel hybrid segmentation model called Contour-Guided Attention U-Net (CAU-Net) is proposed by combining the traditional region-based active contour models with the advanced deep learning capabilities of Attention U-Net. CAU-Net employs an active contour model that generates an initial segmentation mask that provides the boundary awareness needed to steer the attention U-Net refinement. The use of attention mechanisms will be allowed with the selective features on the regions of interest identified by active contours in order to refine it to high accuracy of results in segmentation. It was tested on a Kaggle MRI dataset of medical images, including stroke prediction tasks, and performed better in boundary accuracy and overall segmentation quality than standalone Attention U-Net and active contour models. In stroke prediction, the CAU-Net achieved 98.3% segmentation accuracy and a Dice coefficient of 0.87 in detecting stroke lesions and delineating fine anatomical details. From the results, the hybrid approach significantly amplifies the contour adherence in addition to contextual sensitivity such that these are effective methods in such complex segmentation task especially those in medical applications. © 2024 IEEE.

关键词： Image segmentation

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Applying Deep Neural Networks and NLP Techniques for Sentiment Analysis in Social Media Data 2

Applying Deep Neural Networks and NLP Techniques for Sentime...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Nathiya, N. Raguraman, P.J. Srinivasan, K. Paavai College of Engineering Computer Science and Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Excel Engineering College Computer Science and Engineering Namakkal India Paavai College of Engineering Artificial Intelligence and Data Science Namakkal India

ISBN: (纸本)9798350349221

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks - like transformers or recurrent neural networks - with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation. © 2024 IEEE.

关键词： Sentiment analysis

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Design of optimal bidirectional long short term memory based predictive analysis and severity estimation model for diabetes mellitus

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International Journal of Information technology (Singapore) 2023年第1期15卷 447-455页

作者： Annamalai, R. Nedunchelian, R. Department of Information Technology Jeppiaar Institute of Technology Kanchipuram 631 604 India Department of Computer Science and Engineering Excel Engineering College Namakkal 637303 India

Diabetes mellitus (DM) is a metabolic illness affecting millions of people over the globe and it results in severe diseases such as diabetic nephropathy, cardiac stroke, etc. So, the earlier detection of DM becomes essential to avoid the progression of disease to the advanced stage and damage other organs. The recently developed deep learning (DL) models find useful for accurate and prompt disease diagnosis processes. This paper designs an optimal bidirectional long short term memory based predictive analysis and severity estimation (OBLSTM-PASE) model for DM. The major aim of the OBLSTM-PASE technique is to determine the presence of DM and identify the severity level properly. Besides, the OBLSTM-PASE technique involves two major stages namely OBLSTM based DM prediction and density-based spatial clustering of applications with noise (DBSCAN) based severity level estimation. In addition, the optimal hyperparameter selection of the BLSTM model takes place using the salp swarm algorithm (SSA) and thereby improves the overall prediction performance. To validate the better outcomes of the OBLSTM-PASE technique, a set of experiments were carried out on PIMA Indians Diabetes dataset. A comprehensive comparative analysis pointed out the improved performance of the OBLSTM-PASE technique over the other approaches. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Clustering Deep learning Diabetes mellitus Hyperparameter tuning Predictive analysis Salp swarm algorithm Severity estimation

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Enhancing Consumer Sentiment Analysis and Product Improvement with Aspect-Optimized Adaptive GCN (AOA-GCN) 2

Enhancing Consumer Sentiment Analysis and Product Improvemen...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Annamalai, Sivakumar Nandhini Priya, T. Basker, N. Yuvaraj, S. Ramasami, S. Suresh, D. Department of Safety and Fire Engineering Excel Engineering College Namakkal Komarapalayam India Department of Electrical and Electronics Engineering Excel Engineering College Namakkal Komarapalayam India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Mechanical Engineering V.S.B Engineering College Karur India

ISBN: (纸本)9798331504960

The exponential growth in online reviews offers a company the most precious source of information regarding the customers' emotions and potential product improvement areas. However, it is very challenging to derive meaningful information from the unstructured data. The work presents a new model called Aspect-Optimized Adaptive GCN (AOA-GCN), which combines hierarchical structuring with aspect-based optimization for effective consumer sentiment analysis. The AOA-GCN applies a two-level analysis framework that covers both global sentiments and detailed aspect-specific sentiments in order to build a full understanding of the customer review. The model is essentially based on an Adaptive Lightweight GCN with pruning techniques, ensuring that it does not only reduce computations but also takes into account influential aspects along with its high accuracy, even across extended datasets. Dynamic attention in AOA-GCN will help make critical product features in the customer reviews focus on the issues so improvements can be made on those products specifically. Experimental results show that the improved performance is actually as high as 6.45% over global sentiment classification, 9.12% over aspect-based sentiment analysis, and 7.35% over cross-domain generalization, and training time is reduced by 35%. The model also exhibits improved interpretability with clearer insights into customer preferences. The proposed AOA-GCN becomes an effective, efficient, and interpretable business tool for optimizing products through insights from consumers. © 2024 IEEE.

关键词： Sales

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A Smart Cable-Driven Parallel Robot Assistant for Individual Plant Care in Farming

A Smart Cable-Driven Parallel Robot Assistant for Individual...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Sankarananth, S. Arun, R.S. Excel Group Institutions Excel College of Engineering Dept. of Eee Tamil Nadu Namakkal India Excel College of Engineering and Technology Tamil Nadu Namakkal India

ISBN: (纸本)9781665484251

In this paper, an Artificial Intelligence-powered cable-driven robot made by combining a cable-based parallel robot having 6 Degrees of Freedom (DOF) of operation with a delta robot having 3 DOF, capable of assisting in the farming activities like weeding, pesticide and fertilizer spraying, fruit and flower picking, and monitoring of plant health, NPK levels, and soil moisture, named as Farmbot has been proposed. Farmbot is designed in a way such that, it can send data to remote servers for storage and analysis, can be monitored and controlled from a remote place, can be automated to do activities, and can access almost anywhere in the farm field as it is placed above a moving wheeled bot. The Farmbot design is more focused on individual plant care and increasing productivity. A 3D render is provided as a design example of the working model and the simulation results are provided. © 2022 IEEE.

关键词： Internet of things

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An Efficient System for Visually Impaired by using LORA RA01 5

An Efficient System for Visually Impaired by using LORA RA01

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Malini, P. Karpakam, S. Saranya, E. Selvaraju, P. Murugajothi, T. Ranjith, G. Vivekanandha College of Technology for Women Tiruchengode India Hindusthan College of Engineering and Technology Coimbatore India Sri Eshwar College of Engineering Coimbatore India Excel Engineering College Komarapalayam India Psna College of Engineering and Technology Dindigul India K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798350321425

The LORA RA01 technology is offered as a useful device for visually challenged people. By giving visually impaired people a gadget that can identify barriers and warn them of potential threats in their surroundings, this system is intended to improve their mobility and independence. The LORA RA01 technology is the best option for this application since it has minimal power requirements and long-range communication capabilities. A wearable device with a range sensor and a LORA RA01 module, which connects to a centralised server, make up the proposed system. The server analyses the data from the range sensor and uses a haptic feedback method to provide the user feedback in real-time. The system has undergone testing in a variety of settings and has proven to be trustworthy in its ability to identify barriers and give the user prompt feedback. Overall, this approach has a lot of promise for enhancing the mobility and security of people who are blind. © 2023 IEEE.

关键词： Feedback

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Study of Improvement in Mechanical Properties of Chemically Treated Hybrid Fibre-Reinforced Polymer Composites 2nd

Study of Improvement in Mechanical Properties of Chemically ...

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2nd International Conference on Materials, Design and Manufacturing for Sustainable Environment, ICMDMSE 2022

作者： Boopathi, C. Vadivel Vivek, V. Natarajan, N. Balaganesh, R. Siva Department of Mechanical Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Mechanical Engineering Excel Engineering College Komarapalayam India

ISBN: (纸本)9789811930522

Natural fibres such as banana, jute, coir, hemp, sisal, and bamboo are utilised as reinforcements for composite materials at random because of their inherent features such as low-cost, low-density, and low-energy consumption when compared to man-made fibres. Furthermore, they are both renewable and biodegradable, and a wide range of natural fibres are accessible. In this study, mechanical properties such as tensile, flexural, compression, and impact behaviour of a hybrid banana–jute fibre-reinforced polymer composite were examined using compression moulding techniques. Jute and banana fibre were used as reinforcements, and polyester resin was used as the matrix in this investigation. The jute and banana fibres were chemically treated for 48 h with potassium hydroxide (KOH) solution at 5% solution concentration. Fibre composites were made using the compression moulding technique. Mechanical and physical evaluations revealed the impact of surface chemical treatments on reinforcements. An optical microscope (SEM) was used to investigate the impact of chemical alteration on composites. Thermogravimetric analysis was used to determine the thermal stability of hybrid composites. Finally, it is discovered that the hybridization of jute and banana fibres might be highly useful in the production of composites. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermodynamic stability

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