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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Byeon, Keunho Lim, Jeewoo Lee, Jaeung Han, Chiwon Bay, SungYun Park, Giho Kwak, Jin Tae School of Electrical Engineering Korea University Seoul Korea Republic of Sejong University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

Vision Transformer (ViT) has been successfully applied to various vision tasks, outperforming convolutional neural networks due to their ability to capture global dependencies through the self-attention mechanism. However, it has been shown that ViT struggles with high model complexity and low computational efficiency, and thus it becomes less practical for deployment on edge devices. To address these issues, various studies have sought to enhance the efficiency of ViT, in particular focusing on hybrid models and self-attention mechanism. Herein, we propose a lightweight look-ahead multi-context ViT, which integrates convolutional neural networks and ViT in a hybrid, sequential design to achieve compact size and superior performance on vision tasks. The proposed model introduces a novel self-attention mechanism, which is built based upon a modified Nyström method with advanced landmark selection to provide multi-scale and multi-context information. Experimental results on image recognition dataset demonstrate the superior performance and the effectiveness of the proposed model. © 2025 IEEE.

关键词： Convolutional neural networks

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A deep learning approach for image and text classification using neutrosophy

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International Journal of Information Technology (Singapore) 2024年第2期16卷 853-859页

作者： Wajid, Mohd Anas Zafar, Aasim Wajid, Mohammad Saif School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science Aligarh Muslim University Aligarh India School of Engineering and Sciences Tecnológico de Monterrey Monterrey Mexico

The proliferation of data on the web and on personal computers is a direct result of the proliferation of new technologies and gadgets. Most of these pieces of information are collected through a number of different methods (text, image, video, etc.). This type of information is also vital for e-commerce websites. The products on these websites feature both images and descriptions in text form, making them multimodal in nature. Earlier classification and information retrieval algorithms focused largely on a single modality. This study leverages multimodal data for categorization utilising neutrosophic fuzzy sets for uncertainty management for information retrieval tasks. This work employs image and text data and, inspired by prior ways of embedding text over an image, seeks to classify the images using neutrosophic classification algorithms. Neurosophic convolutional neural networks (NCNNs) are used to learn feature representations of the generated images for classification tasks. We demonstrate how a pipeline based on NCNN may be applied to learn representations of the unique fusion method. Traditional convolutional neural networks are subject to unexpected noisy conditions in the test phase, and as a result, their performance for the categorization of noisy data degrades. Comparing our technique against individual sources on multi-modal classification dataset provides good results. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Convolutional neutral network Early fusion Fuzzy logic Information retrieval Late fusion Multimodal data Neutrosophic logic

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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Sentiment Analysis: Aspect-based analysis for Market Insights and Customer Feedback Valuation 2

Sentiment Analysis: Aspect-based analysis for Market Insight...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Priya, S. Vishnu Varshaa, B. Prabu, M. Amrita Vishwa Vidyapeetam Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

E-commerce platforms have been witnessing a rapid expansion leading to an overwhelming amount of customer reviews which can be a source to obtain valuable insights on performance of the product and customer satisfaction. This paper delves on a machine learning approach to perform sentimental analysis on customer reviews. With a major focus on aspect based sentimental analysis(ABSA) for customer feedback valuation on various headset brands. With flipkart reviews dataset, the experiment aims to develop a model that can classify the overall sentiment of the consumer into positive or negative and dives deeper into product aspects such as sound quality, battery life, and comfort. This involves data preprocessing, sentimental scoring, aspect extraction and model evaluation using BERT. This will help us have strong framework for identifying actionable insights, allowing businesses to enhance marketing strategies and improve customer service. In the future the project can be developed further to include multilingual datasets and refining the accuracy of the model, examining its application to various social media platform © 2024 IEEE.

关键词： Sales

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Enhanced Fake News Detection Leveraging a Hybrid BERT-BiLSTM Model 2

Enhanced Fake News Detection Leveraging a Hybrid BERT-BiLSTM...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Prabu, M. Thinesh, L.B. Sreekumar, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Fake news is phenomena that deserves global attention especially with the above issues of social media being the largest source that influences people's opinions, attitudes or decisions. Authenticating news in the upswing is energy-consuming and highly priced which is why there has been a growing focus in the field. Different means of solutions in issues related to the faking of news has been sought including: content-based classification, contexts based classification, images based classification, sentiment analysis, and mixed approaches. This work offers a method of fake news detection where news headlines are considered, which falls under the content-based classification scheme. The Architecture incorporates BERT and encompasses an LSTM layer as well. Training and evaluation were done on FakeNewsNet - PolitiFact , GossipCop datasets and ISOT dataset. The method propounded was tested against benchmark classification models while the impact of the LSTM layer on classification was tested by a BERT model that was subjected to the same conditions. © 2024 IEEE.

关键词： Natural language processing systems

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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Diabetes Prediction Using Machine Learning Algorithm: A Comparative Analysis 10

Diabetes Prediction Using Machine Learning Algorithm: A Comp...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Bardia, Viksith Sophiya, E. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

Diabetes prediction is an essential task in healthcare that could be achieved through Machine Learning models. Several factors contribute to diabetes such as overweight, high cholesterol levels or frequent urination. This research includes variety of data sources for predicting diabetes including The RTML dataset consisting data from Bangladeshi women and PIMA Indian dataset. These both combined thereby enriching the dataset's variety and data sources. The mutual information feature selection approach was integrated into this research to find the most relevant features in the dataset. To tackle the issue of class imbalance over-sampling Technique (SMOTE) is utilized to rectify the uneven distribution between diabetic and non-diabetic populations. The classification accuracy of the different Machine Learning(ML) algorithms is compared which include algorithms like random forest, logistic regression, voting classifier, KNN, neural networks, Ada Boost, XgBoost and voting classifier. After testing all the algorithms, the model achieved a cross validation score of 85%, accuracy of 83.22% and AUC of 0.86 using the XgBoost algorithm. The use of explainable AI techniques like the LIME framework helped provide interpretable explanations of the model's predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Personalized Medicine - An Innovative Feature engineering for Tailored Drug Design 7

Advancing Personalized Medicine - An Innovative Feature Engi...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Mallavarapu, Jashritha Reddy Navanithi, V.K. Bejjanki, Mohit Manomay Mithun Menon, M. Jeyakumar, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

This paper introduces a pioneering approach integrating Advanced Encryption Standard (AES) security algorithms with multi-objective drug design, aimed at personalized medicine and optimized drug discovery. By leveraging comprehensive patient profiles including genetic markers and medical histories, machine learning and optimization algorithms generate personalized treatment plans and drug efficacy scores. Key objectives include establishing a robust multi-objective optimization framework, enhancing drug efficacy, ensuring data security with AES encryption, and elevating patient care quality. The methodology emphasizes customizing medicines based on individual patient data, employing AES security measures, and optimizing treatments for effectiveness, paving the way for a patient-centric, secure healthcare paradigm beyond traditional drug design methodologies. © 2024 IEEE.

关键词： Drug discovery

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Symbolic Regression Using Dynamic Structured Grammatical Evolution with Digit Concatenation and Linear Scaling 2nd

Symbolic Regression Using Dynamic Structured Grammatical Evo...

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2nd International Conference on Advances in Data-driven computing and Intelligent Systems, ADCIS 2023

作者： Geethani, V. Velayutham, C. Shunmuga Balraj, Aditya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995301

Symbolic Regression’s vast search space can lead to computational inefficiencies. However, Grammatical Evolution (GE) narrows down the search by focusing on solutions adhering to specific grammar and guiding the algorithm toward more promising regions. Dynamic Structured Grammatical Evolution (DSGE), a variant of GE, enables the evolution of variable-length structures, addressing the issue of finding concise and interpretable solutions as problem complexity increases. Integrating constants is challenging, as disruption of values may occur. DSGE, by using digit concatenation as a constant creation method, enables better preservation and accurate representation of constant values during the evolutionary process. The evolved equations can overfit in certain cases. The search algorithm mitigates overfitting by incorporating Linear Scaling with error minimization, prioritizing equations with lower errors to promote better generalization and produce more reliable models. Experiments were demonstrated on the Feynman equations, one of the benchmark datasets. The combined methodology results in capturing the exact and symbolically equivalent equations. These findings highlight the potential of the proposed approach for symbolic regression and related problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Regression analysis

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Transformer Based Sentiment Analysis on Code Mixed Data 5

Transformer Based Sentiment Analysis on Code Mixed Data

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

作者： Sampath, Koyyalagunta Krishna Supriya, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Karnataka Bengaluru560035 India

In India, a country known for its linguistic diversity, code mixing is a common practice, and it has a profound impact on the way people communicate through various mediums, including social media platforms and everyday conversations. The prevalence of code-mixing in social media platforms presents a substantial hurdle for machine translation and language processing tasks. The abundance of unstructured text in code-mixed form on these platforms highlights a crucial research domain within NLP. The blending of Hindi and English, known as Hinglish, and other mixed case text like Malayalam-English, Tamil-English, Telugu-English are particularly prevalent among the younger generation while communication in social media and requires appropriate processing to aid comprehension by both monolingual users and language processing models. Manual translation of this type of data proves to be laborious due to challenges like limited vocabulary, potential misunderstandings of context, grammatical errors, biases, and various other issues. Additionally, existing translation models tend to perform more effectively on monolingual language rather than code-mixed data. Therefore, it is more desirable to build models that can translate code-mixed data. This study tries to convert code-mixed Hinglish, Malayalam-English, Tamil-English, Telugu-English language in Romanised script to monolingual English which can further be given as input to NLP applications like Sentiment Analysis. This is achieved by finetuning pretrained models like IndicLID for Language Identification (LID) module and use an ensemble approach for transliteration + translation using Indictrans and IndicXlit for code mixed machine translation which will be given as input to classification algorithm which performs Sentiment Analysis and predict the sentiment. It is observed that this approach of translation of code-mixed test perform better than traditional machine translation for Indian languages Hindi, Tamil, Telugu and M

关键词： Sentiment analysis

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