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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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Subject–Verb Agreement Error Handling Using RNN Architectures 4th

Subject–Verb Agreement Error Handling Using RNN Architectur...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Gautam, Prakhar Thakur, Jitendra Singh Mishra, Ashish Department of Computer Science Engineering Jabalpur Engineering College Jabalpur India Department of Computer Science Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India

ISBN: (纸本)9789819769919

The domain of natural language processing has reached to a level where various tools are available for automatically identifying and correcting grammatical errors in a wide range of languages text. Though these tools are productive, it is still difficult to develop an effective and accurate grammatical error handling system. In this paper, we examine the efficacy of several neural network architectures with a particular focus on subject–verb agreement, a rule of English grammar. This work investigates the capabilities and performance of deep learning models using an annotated dataset designed specifically for English language subject–verb agreement issues. The strengths and weaknesses of well-known models, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Transformers (BERT), are assessed through testing. The study sheds light on how the performances of these models differ from one another in detecting subject–verb agreement errors in English language sentences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Optimizing Hyperparameters for an Adapted Capsule Network in Lumpy Skin Disease Diagnosis 10

Optimizing Hyperparameters for an Adapted Capsule Network in...

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

作者： Mallikarjun, Goddeti Narayana, V.A. Jntuh Department of Computer Science and Engineering Hyderabad Telangana Kukatpally India Cmr College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350384369

Lumpy Skin Disease (LSD) presents a global threat to cattle populations, with over 110,000 cattle deaths reported in India alone. The pressing need for precise diagnostic tools by exploring a Capsule Network adaptation for LSD diagnosis. The proposed model integrates advanced feature extraction layers, including Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), and Histogram of Oriented Gradients (HOG). Achieving optimal performance relies on meticulous hyperparameter tuning, with a focus on parameters such as learning rates, batch sizes, and regularization techniques, underscoring their pivotal role in maximizing diagnostic efficacy. The research delves into the intricacies of hyperparameter choices, aiming to strike a delicate balance for fine-tuning the model. The ultimate goal is to achieve optimal LSD diagnosis, contributing valuable insights to the broader field of deep learning in veterinary diagnostics. The study attains outstanding accuracy at 0.976, accompanied by balanced precision, recall, and F1 scores of 0.965, 0.971, and 0.963, respectively. The findings highlight the adapted Capsule Network's potential as a powerful tool to improve diagnostic accuracy for LSD in cattle, making noteworthy contributions to the broader field of deep learning in veterinary medicine. © 2024 IEEE.

关键词： Local binary pattern

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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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Multivariate Time series Forecasting for Cloud Machine Utilization 3

Multivariate Time series Forecasting for Cloud Machine Utili...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Naik, Archana Sooda, Kavitha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9798331527549

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LSTM, GRU, Prophet, and XGBoost across the diverse datasets. The performance is evaluated using RMSE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMSE value for Informer is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTM is 0.1725, and GRU is 0.1547. © 2025 IEEE.

关键词： Time series

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Fire and Smoke Detection Using Deep Learning 2

Fire and Smoke Detection Using Deep Learning

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Divya, B. Kavitha, S. Muthu Pandeeswari, R. Sreenidhi, R. Ponvaishnavi, M.R. Loshine, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India India Rajalakshmi Engineering College Dept of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Of late, wildfires and commercial fires like fires in a shopping complex, firework factories, and industries, continue to cause extensive destruction throughout the world, frequently causing human fatalities. The solution is divided into two systems, where the first system focuses on the wildfire and the second system focuses on commercial fires. Identifying the fire and smoke correctly plays an important role. Gradient-weighted Class Activation Mapping (Grad-CAM) is used to identify the smoke region in the image. This algorithm is used to assure the smoke or fire in the image. The algorithms like ResNet, CNN (Convolutional neural network), and VGG16 (Visual Geometry Group) algorithms are used in transfer learning. This aims to increase the accuracy of the detection of fire and smoke in forests to avoid mishaps. The GradCAM is an algorithm that finds out the positive score in the image. This confirms the smoke in the image using the heat map generation. The ReLU activation function is used to show the positive pixels. The saliency map is also used in finding out the difference between smoke and fog in case of wildfire. In the case of the building fire shopping complex, firework shops, factories, and industries would also require a better solution because the detection is easier but the rescue process is more difficult in such places. The same procedure is followed for detecting and finding the origin of the fire as the wildfire model. The YOLOv8 algorithm is used for real-time analysis of the building. The model constantly looks for the unusual behavior of the environment and this in turns induces the notification. The additional feature is added to the model for rescue purposes. Fire control and evacuation have become more complex and hence the solution to such a problem is not yet brought into consideration. Even after detecting the fire, most of the people die due to the poor rescue process. This is because the rescue team cannot find the people inside the buildi

关键词： Smoke

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Automated Glaucoma Screening in Retinal Fundus Imagery: Leveraging a Convolutional Neural Network Framework 3

Automated Glaucoma Screening in Retinal Fundus Imagery: Leve...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Babu, Tina Vengateshwaran, M. Rani, K. Sasi Kala Joshi, Ramya Raghunath Nair, Rekha R Rajesh Sharma, R. Alliance University Bengaluru Department of Computer Science and Engineering India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350354171

Glaucoma, a prevalent eye ailment, necessitates early detection and treatment to prevent irreversible vision loss. Conventional screening methods are often time-intensive and require specialized expertise, limiting accessibility, especially in remote areas lacking ophthalmologists. This study introduces a CNN model, harnessing deep learning to analyze retinal images and extract glaucomatous indicators. Evaluation of the CNN's performance on a diverse dataset includes accuracy, sensitivity, specificity, and F-score assessment. Results underscore the CNN's promise as an efficient and dependable tool for automated glaucoma screening. This advancement offers optimism for enhancing early diagnosis and intervention, critical for managing this sight-threatening condition. © 2024 IEEE.

关键词： Convolutional neural networks

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Covid-19 Vaccine Certificate Verification using Sha Algorithm and IPFS in Blockchain technology 5

Covid-19 Vaccine Certificate Verification using Sha Algorith...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Agrawal, Naitik Singh, Yash Veer Sharma, Vishnu Bedi, Vikas Kumar Chhonker, Sumit Computer Science And Engineering Galgotias College Of Engineering And Technology Greater Noida India Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

The COVID-19 pandemic has suddenly arisen as a global health emergency requiring concerted international action. Governments are working to stop the virus from spreading and health organizations rushed to create vaccines that would protect people. Reduce Covid-19 morbidity, reduce strain on healthcare systems, and let economies to expand. The Vaccine Certificate has been adopted as a way forward after the COVID-19 vaccination to assist authorities in formulating policies by restricting cross-border travel. In this work, we employ blockchain technology to construct a safe and verifiable vaccination certificate, which alleviates substantial privacy concerns and eliminates the need for third parties to maintain trust and control the user's data. Different modern technologies, such as smart contracts, are used in our approach. © 2023 IEEE.

关键词： Smart contract

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Task scheduling in cloud computing using Heterogeneous Initial Finish Moment technique 5

Task scheduling in cloud computing using Heterogeneous Initi...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Balajishanmugam, V. Karthikeyan, N. Karthik, S. PPG Institute of Technology Computer Science and Engineering Coimbatore India SNS College of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665493604

Cloud computing is one of the most widely used infrastructures and services for using virtual servers, also known as processing elements. One of the most basic issues with cloud technology is the scheduling algorithm. Over the years, researchers have put forth a variety of approaches to handle non-deterministic polynomial-time complete optimization of data processing, with the best cloud work schedule criteria being persistence. The Heterogeneous Initial Finish Moment (HIFM) procedure is one of these methods and is renowned for producing the best outcomes for task scheduling in a centrally managed setting in a shorter period of time. According to research, HIFM delivers exceptional results in terms of timeline quality and execution time. Nevertheless, a good solution might not always be produced by the selection of the initial empty slot and the common computational complexity. As a result, we offer here various HIFM technique variants that may result in better results. In the processor selection stage, we also customize the process for choosing standby slots for task scheduling, but in the rank generation stage, we use various methods to determine positions. This article suggests improved iterations of the HIFM technique under consumer budgetary constraints in order to decrease the number of iterations of a specific work - flow submitting on virtual servers. Our research shows that improved HIFM algorithms perform better than the conventional HIFM technique in terms of shorter timeline lengths for process trouble running on numerous virtual servers. © 2023 IEEE.

关键词： Cloud computing

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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