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Breast Cancer Diagnosis using Soft Voting Classifier Approach

Breast Cancer Diagnosis using Soft Voting Classifier Approac...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Archana Kaswan, Kuldeep Singh Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350376470

Breast cancer (BC), the leading type of cancer in women, is very important since the necessity of choosing exactly between benign and malignant cases requires highly accurate and precise diagnostic tools. Therefore, it is crucial not only to diagnose it at an early stage but also to use that result for the intended treatments. One of the aims of this study is to forecast breast cancer in an early and accurate manner. The proposed method in our research is the use of a soft voting classifier for automatic assessment of malignancy or benignancy of breast cancer using three ML algorithms: logistic regression, SVM, and decision tree. The Breast Cancer Wisconsin dataset (Original) with 699 instances is used to test and evaluate the proposed approach. The data is balanced using the random oversampling method to minimize the bias. The methodology that is proposed, gives 0.9708 accuracy, 0.9821 precision, 0.9483 recall, and an F-1 score of 0.9649 with an AUC of 0.9678. © 2024 IEEE.

关键词： Lung cancer

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Assessing the Significance of Machine Learning in Forecasting Energy Recovery from Waste 4

Assessing the Significance of Machine Learning in Forecastin...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Badal, Nirzona Binta Anjum, Fahmida Nur, Ismail Mahmud Paul, Tithi Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka1219 Bangladesh

Accurate estimating energy recovery from trash is vital for optimizing the Waste-to-Energy (WTE) mechanisms essential in tackling global waste management processes and energy sustainability issues. This paper analyzes a synthetically expanded dataset generated with the help of SMOTE techniques to compare the performance of four machine learning (ML) models, Decision Tree Regression, Random Forest Regression, CatBoost Regression, and XGBoost Regression. Here, the dataset contains important waste parameters like composition, moisture content, and treatment procedures that help the models forecast energy output with high precision - datasets and evaluate additional machine learning techniques to boost prediction accuracy in industrial WTE systems further. Performance indicators like MAE, RMSE, MAPE, and R2 scores have been assessed here to identify each model's accuracy and computational efficiency. The final result of the analysis states that the ensemble based models, more precisely XG-Boost and CatBoost, outperformed the simpler ones like Decision Tree, where CatBoost achieved the best R2 value of 0.9893 and the minimum MAPE of 12.90 percent. Though using little extra storage, CatBoost showed great performance. The obtained results bring useful insights into efficient model selection for WTE applications. Further studies shall therefore be exclusively focused on validating this study's results in real life conditions. © 2025 The Authors. Published by Elsevier B.V.

关键词： CatBoost Comparative Analysis Energy Recovery Prediction Machine Learning Waste-to-Energy

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Exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis 3rd

Exploring Transformer-Based Approaches for Hyperspectral Ima...

引用

3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Arya, Rajat Kumar Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology-BHU Varanasi India

ISBN: (纸本)9789819743582

Hyperspectral imaging (HSI) is a technique that captures and analyzes multiple spectral bands for each pixel in an image. Convolutional neural networks (CNNs) effectively extract local features for HSI classification but struggle with capturing sequential spectral signatures. To address this, we conducted a comparative study to assess the performance of transformers, which can handle sequential information, in HSI classification. We reviewed a few models in the literature, analyzing their architecture and performance. Using the Indian Pines dataset, we evaluated models like ViT, SSRN-ViT, SpectralFormer, and Double-ViT. Our results show that Double-ViT models outperform transformers for HSI classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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PCLive: Pipelined Restoration of Application Containers for Reduced Service Downtime 15

PCLive: Pipelined Restoration of Application Containers for ...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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Relation Between Electrical Grid Congestion and Bus Characteristics

Relation Between Electrical Grid Congestion and Bus Characte...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Verdaasdonk, Flin Vlasiou, Maria Hoogsteen, Gerwin Hurink, Johann University of Twente Department of Electrical Engineering Mathematics Computer Science Netherlands

ISBN: (纸本)9789531842976

Electrical grids are increasingly congested, which causes stability and safety risks. To get more insights in how congestion can be managed, this paper analyses whether bus characteristics exist that have a consistent influence on grid loading. To study this, a metric for quantifying bus influence on grid loading is introduced and applied in two case studies. The first case study investigates if there are characteristics that correlate with congestion consistently, regardless of grid topology. The results from this case study suggest that none of the evaluated characteristics consistently correlate with grid loading. These results imply that topology should be explicitly considered in congestion management. The second case study investigates the variance in the correlation between bus characteristics and influence, within a single topology, over a variety of conditions. The results of the second case study suggest that this correlation is robust to reasonable changes in bus loads. © 2024 IEEE.

关键词： Sensitivity analysis

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Object Detection in Shelf Image of the Retail store using Single Stage Detector 2

Object Detection in Shelf Image of the Retail store using Si...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Bagyammal, T. Mahesh Madhav, K. Dhiraj Kumar, Y. Arthi Sri, N. Siva Srinivas, V. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly tackles critical issues in retail industry, encompassing inventory management and optimized product placement. The ultimate goal is to boost operational efficiency and elevate customer satisfaction within the retail sector. The proposed methodology involves comprehensive steps, including data collection, annotation, pre-processing, and the deployment of advanced object detection models based on YOLOv5, with a particular emphasis on YOLO due to its real-time capabilities and being known as a single stage detector. By achieving these milestones, the proposed model promises to provide valuable solutions for the retail industry, reducing out-of-stock situations, minimizing manual labour, and delivering enhanced product organization. Thus, ensuring good performance compared to its other object detectors of the same use case. We have modified the architecture at different levels of the State-of-the-art YOLOv5 (Backbone, Head) and compared the models implemented for inference. We found out that Soft-IoU layer added in the head has given the best performance in detecting object with mean average precision of 79.7 © 2024 IEEE.

关键词： Customer satisfaction

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AI Batting Buddy: A Computational and Kinematic Approach for Enhancing Batting Performance and Analysis in Baseball 24

AI Batting Buddy: A Computational and Kinematic Approach for...

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2024 International Conference on Multimedia Retrieval, ICMR 2024

作者： Liu, Kuo-Yu Guo, Ting-Yu Pan, Ta-Shan Tung, Ping-Yi Lin, Yi-Rou Department of Computer Science and Communication Engineering Providence University Taichung Taiwan

ISBN: (纸本)9798400706028

This paper presents a VR and AI integrated baseball batting training system that leverages VR to provide an immersive and realistic training environment and uses AI for performance analysis and personalized feedback. the system compares the user’s performance with professional athletes and provides actionable feedback and training recommendations. Experimental results demonstrate the effectiveness of the proposed system in enhancing baseball batting skills. the paper contributes to the research on the application of VR and AI in sports training and addresses a gap in the literature by exploring the integration of these technologies in baseball batting training. © 2024 Copyright is held by the owner/author(s).

关键词： Virtual reality

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Survey of Malware Detection through Use of Neural Network Models

Survey of Malware Detection through Use of Neural Network Mo...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Bean, Joshua Torri, Stephen A. Mississippi State University Department of Computer Science & Engineering United States

ISBN: (纸本)9798350376739

In general, antivirus software has heavily relied on signature-based detection methods. These antivirus products usually reference a database of known malicious signatures, making them naturally susceptible to previously unseen malware samples. One of the easiest ways for malware authors to produce these new malware samples is by applying various obfuscation techniques to the previously existing malware. This paper will survey a newer approach to malware detection through neural networks by gathering results from recent journal articles and conference papers published in various online databases. These artificial intelligence-oriented solutions show promise in resilience to unfamiliar malware variants. Explicitly focusing on obfuscated malware, it will compare detection rates between signature-based and neural network-based malware detection systems and organize results using various NN models and training features. Overall, the survey finds that signature-based methods detect between 50 and 80 percent of obfuscated malware samples, while all but one of the neural network methods detect between 90 and 99 percent. This considerable increase in performance suggests that neural networks might provide an effective alternate or supplemental detection method for antivirus products. © 2024 IEEE.

关键词： antivirus artificial intelligence malware detection neural networks obfuscation

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Neural Command: Real-Time EEG-Based Brain-computer Interface for Assistive Robotic Control 3

Neural Command: Real-Time EEG-Based Brain-Computer Interface...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Vasker, Nishat Shakib Sakibur Rahman, M.D. Arafat, Yasin Ul Islam, Raihan East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

This paper presents a method to help people with disabilities interact with their surroundings using brainwave signals. Since individuals with disabilities often depend on different types of transportation, this research focuses on enabling the real-time control of a robotic vehicle through non-invasive brain-computer interfaces (BCI).Can brain-computer interfaces (BCIs) based on EEG signals control a robotic car in real time? The MUSE headband is used to capture brain waves, and the signals are processed through PETAL metrics and a server within a Brain Wave Processing Unit. Subsequently, they are transmitted via WiFi/IPv4 to a Vehicle Control Unit with esp8266 microcontroller. The system underwent rigorous evaluation, achieving an 85% control accuracy and an average response time of 300 milliseconds. These results highlight the system's effectiveness in real-time applications, offering precise and rapid response capabilities suitable for assistive technology. This research represents a contribution to the development of advanced BCI-based assistive devices, demonstrating the potential for improved human-machine interaction. The high accuracy and low latency of the system emphasize its suitability for environments where swift and reliable control is crucial, particularly for individuals with mobility impairments. © 2024 IEEE.

关键词： Human robot interaction

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Advancing Cancer Diagnosis with LungColonGuard: A Deep Learning Framework For Lung and Colon Cancer Detection From Histopathological Image 4th

Advancing Cancer Diagnosis with LungColonGuard: A Deep Lear...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Sultana Eshita, Farhana Bakar Siddique Mahi, Abu Kumar Saha, Aloke Murtaza Rashid Al Masud, Shah Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

ISBN: (纸本)9783031733437

Genetic mutations or other changes in DNA can contribute to the uncontrolled cell growth that occurs in the human body, known as cancer. Undoubtedly, lung and colon cancers are fatal diseases that contribute significantly to the death and disability of people worldwide. In the initial stage, cancer cells are mostly smaller in size and have not spread to surrounding cells or other organs of the body. Early detection and diagnosis of these forms of cancer can drastically enhance the possibility of successful treatment and a better outcome. The adoption of ML and DL models in computer-aided systems can massively improve the speed and accuracy of cancer diagnosis. It is also beneficial to examine a significantly large number of patients within significantly shorter time periods. In this study, we present a Convolutional Neural Network based deep neural architecture named LungColonGuard which is developed to accurately identify lung and colon cancer. An assessment of comparison is presented between the LungColonGuard model and several transfer learning models, namely: ResNet50V2, ResNet101V2, and ResNet152V2. The proposed model gained the highest accuracy of 99.50% for colon and lung cancer detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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