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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Reddy, Harthikeswar Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering & Computer science Malaysia

ISBN: (纸本)9798350381689

The surge in digital transactions has paved the way for an alarming rise in credit card fraud, compelling the need for robust detection systems. The swift progress of technology has transformed customer payment habits, driving them toward a cashless society, with the rise of digital payments making it easier for fraudsters to commit fraud. The impact of credit card fraud on revenue can be significant. No matter the scale of the businesses, credit card fraud will have a significant effect on the businesses. This paper addresses the escalating threat by proposing a fraud detection model utilizing the 'IEEE CIS Credit Fraud Detection' dataset from Kaggle, constructing a machine learning model aimed at enhancing the accuracy of fraudulent transaction identification, presenting an integrated approach to credit card fraud detection that combines user separation and innovative techniques for improved accuracy and real-world impact, ultimately strengthening the global financial ecosystem. © 2024 IEEE.

关键词： Electronic money

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TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation 25

TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesio...

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25th International Conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Iqbal, Shahzaib Zeeshan, Muhammad Mehmood, Mehwish Khan, Tariq Razzak, Imran Department of Electrical Engineering Islamabad Pakistan School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Early detection of skin cancer relies on precise segmentation of dermoscopic images of skin lesions. However, this task is challenging due to the irregular shape of the lesion, the lack of sharp borders, and the presence of artefacts such as marker colours and hair follicles. Recent methods for melanoma segmentation are U-Nets and fully connected networks (FCNs). As the depth of these neural network models increases, they can face issues like the vanishing gradient problem and parameter redundancy, potentially leading to a decrease in the Jaccard index of the segmentation model. In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions. The proposed TESL-Net involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using bi-convolutionallong-short-term memory (Bi-ConvLSTM) networks and a Swin transformer. This enables the model to account for the uncertainty of segmentation over time and capture contextual channel relationships in the data. We evaluated the efficacy of TESL-Net in three commonly used datasets (ISIC 2016, ISIC 2017, and ISIC 2018) for the segmentation of skin lesions. The proposed TESL-Net achieves state-of-the-art performance, as evidenced by a significantly elevated Jaccard index demonstrated by empirical results. © 2024 IEEE.

关键词： Convolutional neural networks

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

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

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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Hierarchical Fault-Tolerant NoC Architecture for Reliable Communication 17

Hierarchical Fault-Tolerant NoC Architecture for Reliable Co...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Khalil, Kasem Mohaidat, Tamador Sherif, Ahmed Bayoumi, Magdy University of Mississippi Department of Electrical and Computer Engineering MS United States University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA United States

ISBN: (纸本)9798331530471

As multi-core systems continue to grow in complexity, Network-on-Chip (NoC) architectures have emerged as a scalable and efficient solution for managing on-chip communication. However, ensuring reliable communication in the presence of router faults remains a critical challenge, particularly for applications in safety-critical systems where failure can lead to catastrophic consequences. This paper proposes a novel fault-tolerant NoC architecture designed to improve reliability in multi-core systems. The proposed method divides the NoC into multiple regions, each managed by a dedicated Region Router (RR) and interconnected via a Centralized Router (CR). This hierarchical structure ensures that even in the presence of multiple faulty routers, data packets can be rerouted effectively, maintaining system functionality. We implement the proposed method on a Xilinx Virtex-7 FPGA and evaluate its performance in terms of latency, throughput, power consumption, and resource utilization. The experimental results demonstrate that the proposed method achieves a 15% improvement in system reliability with only a 3.2% increase in resource utilization, compared to the baseline NoC. Additionally, Mean Time to Failure (MTTF) is extended by 25%, while the latency remains within an acceptable range of 3.2 ns. These findings highlight the potential of our approach for use in safety-critical and high-performance computing systems, offering enhanced fault tolerance without significantly compromising performance. © 2024 IEEE.

关键词： Hierarchical systems

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Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer 2

Deep Convolutional Neural Networks for Early-Stage Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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Poetry Form Identification - A Comparative Analysis of Embedding Models and Classifiers 2

Poetry Form Identification - A Comparative Analysis of Embed...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Rohan, Gamidi Manideep, Thotapalli Sri Surya Saragadam, Sneha Pati, Peeta Basa Sail, Smita Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of English Bengaluru India

ISBN: (纸本)9798350368413

In recent years, automated text classification has advanced significantly, yet poetry classification remains a relatively under-explored domain. By employing Machine Learning and pre-trained language models, the proposed paper aims to automate poetry classification and provide a comparative analysis for utilizing GPT2 (Generative Pre-Trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly Optimized BERT Pre-Training Approach). We have employed tokenization, sequence padding, and fine-tuning on a diverse dataset of poetry forms, including Sonnet, Haiku, Limerick, Acrostic, Ballad, Epigram, Sestina, and Villanelle. The proposed model is trained and evaluated on a dedicated data set using the application of AdamW Optimizer, and strives for robust classification performance. This research study yields promising results, with the Support Vector Machine (SVM) classifier achieving the highest accuracy of 93.25 percent using the top 20 percent ranked features. This study contributes to the field of automated poetry analysis by integrating various embedding models and classifiers, presenting notable advancements in the categorization of poetry. © 2024 IEEE.

关键词： Support vector machines

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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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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Covid-19 Classification using Fine-tuned EfficientNet Architecture 9

Covid-19 Classification using Fine-tuned EfficientNet Archit...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sruthi, Srigiri Emadaboina, Siddharth MacHavarapu, Pradyumna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering And Computer Science Malaysia

ISBN: (纸本)9798350394474

This work utilizes an Efficient Net-based deep learning architecture, leveraging its efficiency and scalability for image classification tasks. We employ a Dataset of chest X-ray images, including COVID-19 cases as well as other relevant Pathology, to train and evaluate the model. The training process involves data augmentation, model optimization, and early stopping to ensure robust and generalizable performance. It specifies the image size, channels, and class count, and then proceeds to create a pre-trained model using the EfficientNetB0 architecture. The model is built upon the pre-trained EfficientNetB0 model, with additional layers including Layer Normalization, Dense, and Dropout layers for fine-tuning on the Covid-19 data. Subsequently, the model is rigorously evaluated using standard performance metrics, including precision, recall, F1-score, and accuracy, and has proven to outperform other state-of-art models by a 3% increase in F1-score value. © 2024 IEEE.

关键词： Deep learning

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An ensemble deep learning framework for foetal plane identification

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1377-1386页

作者： Thomas, Seena Harikumar, Sandhya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India

Antenatal (prenatal) care stipulates periodic monitoring of the foetus in alleviating risk factors and improving pregnancy outcomes. Foetal images generated from the ultrasound during prenatal care can be classified into different planes, such as the brain, abdomen, femur, etc., and can be further used for the abnormality detection, disease diagnosis, and foetal growth estimation of the foetus. Therefore, recognizing the foetal plane is one of the initial steps to automate antenatal care. This paper proposes an Ensemble Convolution Neural Network (ECNN) model that combines the base models to classify the foetal planes by using an open-access database containing 12,400 images with six foetal planes. Prior studies on this database involved state-of-the-art CNN methods, and Densenet-169 pre-trained model gave an accuracy of 93.6%. Three pre-trained CNN models (base learners) are trained using the transfer learning approach in the proposed method. The predicted features derived from the base learners are then used to train a Deep Neural Network (DNN) based meta-learner to achieve high classification rates. The stacked ensemble model resulted in an accuracy of 96 % , which is better than the accuracy of the individual pre-trained models. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Antenatal care Classification Deep neural network Ensembled convolution neural network Foetal planes

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