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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Ajeeb Sagar Vinothkumar Kolluru Ujjwal Jaiswal Gaurav Kumavat Shreyas Rajendra Hole Ankit Kumar Computer science Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of Data science Stevens Institute of Technology Hoboken U.S.A. Computer Science and Engineering PES UNIVERSITY Bangalore India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The phenomenon of Antimicrobial Resistance (AMR) is emerging strategically rather quickly and efficiently on a global scale. Because of due AMR Corona-virus outbreaks, previously effective antibiotics are becoming obsolete. The situation is only becoming dire as a result of chronic infections, increased deaths and subsequent healthcare expenditures. Increasingly, Artificial Intelligence solutions and Machine Learning strategies are contributing towards optimizing large volume data sets and offer introduction of new intervention methodologies. Within the scope of the current work, we aim to focus on data set amalgamation through Open-Source genomic data along with phenotype and environmental data sets within ml frameworks. While genomic datasets explain portion of resistance around genes, phenotype datasets gather ‘recorded’ datasets of resistance patterns and Environmental datasets monitor the distribution of AMR loops. Opportunities for improving model ethics, integration of data, and addressable generalization gaps to subsequently increase prediction accuracy are studied in focus. Predictability in AMR detection through leveraging ai increases accuracy and optimizes clinical decision-making alongside public health responsiveness. With the help of exact models, it is also possible to predict alterations in the resistance evolution timeline with new resistance genes and while clinically useful to determine right treatment options, they can aid clinical decisions. ai and ml mechanisms enable streamlined AMR surveillance, effectively guiding the direction for inline policy and containment control centers for antibiotic sustainability frameworks. ai, along with ml developments and emergence, is looked upon to take the lead in delays in AMR deployment. ml would intensively work in maintaining the effectiveness of antibiotics and redefining infection healthcare management.

关键词： Ethics Phenotypes Explainable ai Antibiotics Genomics Predictive models Data models Bioinformatics Public healthcare Immune system

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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Design and Development of Chip Design for Digital Electronic System: Evolution and Trends 1

Design and Development of Chip Design for Digital Electronic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Deep, Pragya Anand, John Rawat, Himani Kumar, Pankaj Kubba, Akshaya Karn, Ankriti IIMT College of Engineering Department of AI & DS Greater Noida India UP Greater Noida India Tula's Institute Department of Electronics and Communication Engineering Dehradun India Amity University Department of Statistics Kolkata India Chandigarh University Deparment of Computer Science Punjab Mohali India Tula's Institute Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331518578

System on-a-chip (SoC) achievement relies upon a very much planned, incorporated come nearer from a few disciplines, including application, plan, and gadget. From the viewpoint of the gadget, quick progressing VLSI innovation makes it conceivable to incorporate billions of semiconductors on a solitary chip, empowering the mix of various capabilities on a solitary chip. Since they rely upon these electrical interconnects at the actual level, on-chip correspondence models like transports and networks on-chip (NoC), which are utilized to empower between part correspondence in multi-processor systems on-chip (MPSoC) plans, are dependent upon the full scope of troubles and impediments that influence copper-based electrical interconnects. Various examination projects have begun looking at imaginative association choices, for example, on-chip optical interconnects, remote interconnects, and carbon nanotube-based interconnects, trying to beat the downsides of traditional copper-based electrical interconnects. An outline and flow research status for these three promising association advancements are introduced in this review. It additionally discusses the issues that should be fixed for every one of these advances before they can ultimately be utilized to supplant copper-based electrical interconnects. © 2025 IEEE.

关键词： Network-on-chip

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BCViMer 1.0: A Deep Network Pipeline for the Identification of Breast Cancer Using MRI Data 3

BCViMer 1.0: A Deep Network Pipeline for the Identification ...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Das, Tejaswini Ratha, Ashis Kumar Dash, Sushree Sasmita Pandey, Rakesh Mohanty, Sabyasachi Nayak, Debasish Swapnesh Kumar Utkal University Department of Statistics Bhubaneswar India School of Computer science and Engineering IILM University Uttar Pradesh Greater Noida India IIMT College of Engineering Department of AI & DS Greater Noida India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798331533205

Breast cancer ranks among the foremost causes of cancer-related deaths in women globally, with early diagnosis via advanced imaging techniques significantly enhancing patient outcomes. Convolutional neural networks (CNNs) demonstrate considerable potential in the automation of breast cancer detection. Nonetheless, their constrained capacity to capture global image features may hinder their performance. This study presents BCViMer 1.0, a deep learning pipeline utilizing the Vision Transformer (ViT) to overcome existing limitations and enhance breast cancer classification accuracy from MRI images. This research aimed to assess the efficacy of BCViMer 1.0 relative to established CNN architectures, specifically ResNet-50, ResNet-101, VGG-16, and VGG-19. Experimental results indicate that BCViMer 1.0 excels these CNN models, attaining an average classification accuracy that is 4.15% higher. The enhanced performance of BCViMer 1.0 is due to its capacity to capture long-range dependencies and global context in MRI images, facilitated by the self-attention mechanism of the ViT. In conclusion, BCViMer 1.0 exhibits superior diagnostic accuracy compared to conventional CNN-based models, representing a significant advancement in breast cancer detection. This study emphasizes the capabilities of Vision Transformers in medical imaging and establishes a basis for future investigation in clinical applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Transforming Agricultural Supply Chains: The Power of Blockchain Technology 3

Transforming Agricultural Supply Chains: The Power of Blockc...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kumar, Gotte Ranjith Lovely Professional University School of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Sr University School of Cs & Ai Department of Cse Telangana Warangal506371 India

ISBN: (纸本)9798331515911

Implementing blockchain technology in agricultural supply chains has the potential to revolutionize and increase transparency, efficiency and trust between all parties. A variation of the white paper discussed how blockchain can be a transformative tech in addressing major challenges especially on traceability, fraud prevention and sustainability on agriculture. Blockchain (for those hearing about this for the first time) can provide a system where every step of even complex supply chains is perfectly recorded, enabling transactions to take place in a secure way and offering transparency, immutability, accountability. Case studies and real-world examples showcased throughout the paper depict how blockchain could enhance operational efficiencies, boost food safety, authenticate products, and cut waste across agriculture. While very promising, the implementation of blockchain technology in agriculture is not without its own issues;this includes obligatory industry standards as well as highly costly technical implementations. The paper also highlights the potential for blockchain to contribute to sustainable agriculture by allowing better tracking of environmental footprints and ethical sourcing. In summary, we propose that the flexibility of blockchain technology renders it an ideal approach to revolutionizing supply chains in the agriculture sector, as we continue down a trajectory towards increased levels of visibility and productivity with improved environmental outcomes. This paper contributes towards a more comprehensive understanding of the role of blockchain in scenario-based selection as it addresses both opportunities and challenges due to adoption of block chain which gives technology and agriculture a new future(directions). © 2025 IEEE.

关键词： Sustainable agriculture

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Comparision of Principal Component Analysis, K-means, and Generative Adversarial Network for Compression and reconstruction of medical image

Comparision of Principal Component Analysis, K-means, and Ge...

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Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Anusha Prashanth Shetty Deepthi L Raksha Puthran Surendra Shetty Mohammad Anzil Parveez Shravan Kumar Department of Master of Computer Applications NMAM Insitute of Technology Nitte (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering (AI&ML) Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Medical images are essential in healthcare, but their large file sizes present challenges for storage and transmission. This study proposes a framework for compressing color medical images while preserving key visual information. We apply three compression models: Principal Component Analysis (PCA), K-Means clustering, and Generative Adversarial Networks (GANs), and evaluate their performance based on Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). K-Means with 100 clusters outperforms the other models, achieving the highest compression (CR of 1.20), with excellent image quality (PSNR of 66.77 dB, SSIM of 1.0000). PCA shows moderate performance with a CR of 2.45, while GAN provides the least effective compression and image quality. These results demonstrate that K-Means offers the best trade-off between compression and image fidelity.

关键词： Measurement Image quality Analytical models Image coding PSNR Medical services Generative adversarial networks Image reconstruction Principal component analysis Biomedical imaging

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Autoencoder-LSTM Ensembles for Predictive Analysis of Financial Churn Patterns

Autoencoder-LSTM Ensembles for Predictive Analysis of Financ...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Raynukaazhakarsamy Rahul Pradhan Nadhema Ahmed Jaff G Venu Gopal Mariam Fadel Ali P. Nagaraj Department of Computer Applications Nehru Arts and Science College (Autonomous) Coimbatore India Department of Computer Engineering & Applications GLA University Mathura India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Computer Science and Engineering (AI & ML) Geethanjali College of Engineering and Technology Telangana India Department of Radiology Techniques Uruk University Baghdad Iraq Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

In the financial sector, customer churn is a significant problem that influences the organization’s revenue growth. Churn means that customers have left their current providers which is a negative factor for the providers or financial institutions; therefore, accurate churn prediction helps institutions to increase customer loyalty and satisfaction by improving the quality of service. In this paper, a new method of using Autoencoder and LSTM network for predicting financial churn patterns is introduced. The Autoencoder is used to reduce dimensions and for unsupervised learning acts as a pass through that allows the model to learn the latent patterns in customer behaviour and transactions. Since the LSTM has demonstrated its efficacy in analyzing temporal dependencies of a sequence in time series, it is used to predict churn by analyzing sequential customer activity. The ensemble approach combines the merits of the two models improving the predictive power and especially the stability in a situation of data scarcity and high noise levels. Experimental comparison with conventional predictive models such as decision trees and logistic regression reveal that the Autoencoder-LSTM is more accurate and sensitive than traditional models by comparison. In other words, the proposed model enhances the early identification of potential churners and such institutions can then take appropriate measures of containing it, and in the process, offer relevant retention solutions. The paper describes the benefits of utilizing deep learning models for churn prediction for the financial industry and provides information on how such methods might be used to improve customer retention. Possible future research avenues consist of improving the model interpretability and generalizing the approach to other industries with comparable churn prediction requirements. The accuracy obtained from the proposed method is higher at 99.7% compared to other methods.

关键词： Accuracy Computational modeling Autoencoders Time series analysis Predictive models Stability analysis Churn Predictive analytics Long short term memory Unsupervised learning

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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LLM-based Methodology for Expanding 3D Metadata in Point Cloud 27

LLM-based Methodology for Expanding 3D Metadata in Point Clo...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kim, Geon-Woo Kim, Joo-Chang Department of AI Computer Science and Engineering Kyonggi University Suwon16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University Suwon16227 Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Data assimilation

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Leying Zhang, Wangyou Chen, Zhengyang Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios. © 2025 IEEE.

关键词： background preservation background removal flow-matching text-to-speech

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