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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Kaushik, Hariharan Natarajan, B. Annamalai, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai India

ISBN: (纸本)9789819778614

The urgency for precise diagnostics during the COVID-19 pandemic has driven advancements in imaging and deep learning tools. However, progress is impeded by limited access to medical imaging data. This study employs cutting-edge deep learning techniques to identify chest diseases, including COVID-19 and various lung conditions. State-of-the-art Generative Adversarial Networks (GANs), including CycleGAN, Super-Resolution GAN (SRGAN), and Deep Convolutional GAN (DCGAN), are used to synthesize medical images, augmenting the dataset and enhancing model robustness. VGG-19 achieves a remarkable 91% accuracy and an F1-score of 0.98 across diverse chest-related diseases. Saliency Maps and Grad-CAM graphics improve interpretability, showcasing the model’s healthcare potential. The study encompasses diseases such as Pneumonia and COVID-19, contributing to a comprehensive classification task. These findings underscore the impact of deep learning in medical imaging, promising improved patient care and healthcare accessibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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ESOA-Based 1D-CNN Classification for Amazon Textual Review Sentiment Analysis of Mobile Phones

ESOA-Based 1D-CNN Classification for Amazon Textual Review S...

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International Conference on computing and Machine Learning, CML 2024

作者： Kosala, N. Nirmalrani, V. School of Computing Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789819775484

A trend toward the use of digital platforms for product marketing and profit margin enhancement is evident in the contemporary corporate environment. As a result, using deep learning methods to evaluate consumer sentiment in tweets based on product reviews becomes essential. The study's proposal is to use a big dataset and a one-dimensional convolutional neural network (1D-CNN) for the classification process in order to extract lexical characteristics for sentiment analysis. In order to do this, analysis is done on customer review datasets of Amazon items with an emphasis on reviews pertaining to mobile phones. To improve the performance and accuracy of the classifier, data preprocessing is first done. A lexical method is then used to efficiently compress the text via feature extraction. For the purpose of correctly assessing review emotions, efficient classification is essential. So, classification is done using the 1D-CNN method. The Egret Swarm Optimization Algorithm is used to adjust the 1D-CNN model's hyperparameters (ESOA). After this procedure, the research evaluates the 1D-CNN method's performance in relation to other techniques currently in use. The research concludes that the suggested method performs well overall, achieving 98.77% accuracy, 97.37% precision, 97.42% sensitivity, and 97.21% F-measure, respectively. These performance measures outperform those of current algorithms created in earlier research. The sentiment analysis of mobile phone evaluations inside the Amazon e-commerce system shows improved accuracy and efficiency when using the suggested 1D-CNN neural network approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

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computer Systems science & engineering 2023年第3期44卷 2147-2163页

作者： V.Joseph Raymond R.Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119TamilnaduIndia School of Computing SRM Institute of Science and TechnologyChennai603203TamilnaduIndia

Android devices are popularly available in the commercial market at different price levels for various levels of *** Android stack is more vulnerable compared to other platforms because of its open-source *** are many android malware detection techniques available to exploit the source code andfind associated components during execution *** obtain a better result we create a hybrid technique merging static and dynamic *** this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing *** the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given *** Android Sensitive Permission is one major key point to be considered while detecting *** select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or *** goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

关键词： Zero-day exploit hybrid analysis principal component analysis supervised learning smart cities

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Harnessing LLMs for Medical Insights:NER Extraction from Summarized Medical Text 15

Harnessing LLMs for Medical Insights:NER Extraction from Sum...

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

作者： Sasikala, D. Sudarshan, R. Sivasathya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Pondicherry University School of Engineering & Technology Department of Computer Science Pondicherry India

ISBN: (纸本)9798350370249

The rapid growth of medical text presents a significant challenge for healthcare professionals to stay updated with current research and clinical guidelines. Effective text summarization is essential to facilitate access to knowledge, improve clinical decision-making, and ultimately enhance patient outcomes. This study aims to fine-tune and assess a medical text summarization system utilizing large language models (LLMs). The objective is to produce concise, coherent summaries of medical literature, prioritizing clinical relevance. Additionally, automated named entity recognition (NER) is employed to extract vital entities from the summaries generated by GPT3, T5, PEGASUS LLMs. These LLMs are trained on benchmark PubMed dataset which are evaluated under standard metrics such as ROUGE (1, 2, L) and BERTScore metrics. The model pipeline demonstrates summarization and NER performance over precision of 6 5% on medical text data. The resulting entity-annotated summaries provide condensed representations, enabling efficient information extraction and knowledge discovery. This automated approach accelerates biomedical research by large-scale medical text analysis. © 2024 IEEE.

关键词： GPT3 large language model medical text summarization Named Entity Recognition PEGASUS T5

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Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis

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International Journal of Information Technology (Singapore) 2024年 1-17页

作者： R, Annamalai Sindhu, Kolla Gnapika Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

In the realm of maternal healthcare, accurate fetal plane detection is of paramount importance. This paper introduces a novel approach that leverages ensemble techniques to enhance the precision and dependability of fetal plane classification. We address two pivotal classification tasks: first, the categorization of fetal planes into six distinct classes, encompassing critical regions such as the fetal abdomen, brain, femur, thorax, maternal cervix, and other areas. The second task focuses on the nuanced classification of fetal brain planes, further refined into trans-thalamic, trans-cerebellum, and trans-ventricular subtypes. To address these challenges, we harnessed the power of six distinct pre-trained models, rigorously training each for 50 epochs. Our results underscore the consistent superiority of InceptionResNetV2, DenseNet121, and Xception. Through a pioneering ensemble approach, we synergistically harnessed their capabilities, leading to enhanced classification performance. This research promises to augment the precision of maternal-fetal plane classification, with the potential to revolutionize clinical practice and healthcare research. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： DenseNet121 Ensemble learning Fetal planes Histogram equalization InseptionResNetV2 Median filter Ultrasonography

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

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

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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QViLa: Quantum Infused Vision-Language Model for Enhanced Multimodal Understanding

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SN computer science 2024年第8期5卷 1023页

作者： Mukesh, K. Jayaprakash, S.L. Kumar, R. Prasanna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamilnadu Chennai 601103 India

Vision-language models have emerged as transformative tools, revolutionizing the integration of visual and textual information, forging pathways for nuanced interpretations across various applications. The evolution of these models underscores the challenge of achieving seamless modality fusion, particularly in aligning raw pixel values of images with high-level semantics of text, a bottleneck that often hinders optimal cross-modal representations. Addressing this, our research introduces a quantum-enhanced multimodal framework. The main objective of our proposed work is the integration of classical vision-language transformers with quantum-augmented layer, aimed at enhancing the fusion of extracted feature embeddings, thereby bridging the modality gap. The quantum computing techniques, offers an innovative approach to information processing, which paves the way for richer and more intricate visual-textual representations. Furthermore, the shared self-attention mechanism accentuates the model’s ability to detect complex modality interactions. The quantum-enhanced framework is empirically evaluated on the VQA v2 dataset. This evaluation not only considers accuracy across diverse question categories but also the model’s computational efficiency, emphasizing the pivotal contributions of quantum computations in achieving heightened accuracy levels. Further exploration into the influence of different quantum feature maps aided in identifying the most optimal model variant. Our findings highlight the quantum layer’s pivotal role in improving the efficacy of classical vision language models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multimodal learning Quantum computing Vision language model Visual reasoning

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

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computer Systems science & engineering 2023年第9期46卷 2797-2808页

作者： D.Elangovan V.Subedha School of Computing Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennai600118India Computer Science and Engineering Panimalar Institute of TechnologyChennai600069India

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and *** people share their views and ideas around the world through social media like Facebook and *** goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s *** on if they provide a positive or negative perspective on a given topic,text documents or sentences can be *** compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature *** firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of *** account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关键词： Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis

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