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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Yenkikar, Anuradha Babu, C. Narendra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Karnataka Bangalore India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350396485

Since its breakout, the coronavirus (covid-19) has wreaked devastation all over the world. There hasn't been a nation that hasn't been affected by it, and India is no exception. Finding a treatment for this sickness and stopping its spread is one of the hardest problems humanities has ever faced. World Health organization (WHO) figures show that the mortality rate and the number of people with covid-19 are both increased exponentially in some countries during the peak waves. In this study, we use machine learning approaches to forecast the number of covid-19 confirmations, recoveries, and mortality cases over a period of time and assess the coronavirus outbreak in India. The polynomial regression (PR), support vector regression (SVR), and an autoregressive integrated moving average (ARIMA) model are three techniques that are used. The findings demonstrate that, in terms of prediction outcomes, the ARIMA model provides the least Root Mean Squared Error (RMSE), closely followed by polynomial regression. SVR doesn't perform well since predictions are either too low or too high. Overall, the proposed system can significantly aid in comprehending the pattern of spread in other nations and assist governmental bodies in taking action to lessen its effects in future. © 2023 IEEE.

关键词： Mean square error

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Harnessing the Potential of the Internet of Things: Navigating Choices Between Fog and Cloud Infrastructure in Agriculture 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Singh, Malikhan Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Ather, Danish Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic era Hill University Dehradun India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven decision-making tools has led to the emergence of a more efficient and sustainable farming landscape. Central to this transformation is the decision of where and how to process and store the vast amounts of data generated by these devices. This paper delves into the practical implications of fog and cloud computing systems in the agricultural domain taking Delhi NCR and region as a sample area by considering factors like efficiency, scalability, and real-world challenges faced by farmers. The aim here is to provide a comprehensive guide to stakeholders in making an informed choice between fog and cloud infrastructure for their IoT-enabled agricultural endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

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computer Modeling in engineering & sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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Towards Harmless Rawlsian Fairness Regardless of Demographic Prior 38

Towards Harmless Rawlsian Fairness Regardless of Demographic...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024 Neural information processing systems foundation. All rights reserved.

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High Accurate Gold Prize Rate Prediction Using Random Forest Regression Algorithm 7

High Accurate Gold Prize Rate Prediction Using Random Forest...

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7th IET Smart Cities Symposium, SCS 2023

作者： Sivasangari, A. Deepa, R. Geetha Rani, K. Surendran, R. Tamilvizhi, T. School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai600123 India

ISBN: (纸本)9781839539831

In the past, one of the precious resources utilized to fund trading purchases was gold. The nations that have gold resources are regarded as rich countries. The nation's gold reserves show how strong its economy is. Not only have government departments participated in gold reserves, but also a number of private individuals and businesses. The routine of the world's top financial prudence affects gold rates. An increase in investors has been drawn to gold due to the recent fluctuations in price. Transactions involving gold are risky, and price fluctuations in gold are unpredictable due to the unpredictability of the gold market. The suggested uses machine learning to forecast the price of gold. Generally, machine learning is used to predict economic variables. To train a prediction model, we use supervised learning algorithms such as multiple linear regression and random forest regression. © The Institution of engineering & Technology 2023.

关键词： Multiple linear regression

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The Role of Ensemble Learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques 1

The Role of Ensemble Learning in Advancing Child Fetal Healt...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Swain, Satyaprakash Patra, Kumar Janardan Jayasingh, Suvendra Kumar Mohanty, Mihir Narayan Pattanayak, Binod Kumar Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Electronics and Communication Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350361780

The field of healthcare has witnessed significant advancements in leveraging technology for in-depth analysis of fetal health data, offering profound insights into child development. We meticulously gathered an extensive dataset comprising 1500 instances of child fetal health data sourced from New Born and Pediatric Health Centre, Cuttack, Odisha. We proposed a robust analytical pipeline for effectively classifying and predicting fetal health outcomes. The process initiated with meticulous data preprocessing and refinement, ensuring data integrity and quality. We use clustering techniques unveiled inherent patterns and groupings within the dataset, setting the stage for in-depth analysis and feature extraction. Further enriching the analysis, Principal Component Analysis (PCA) is deployed to streamline data dimensionality while retaining essential information. For prediction we use ensemble learning techniques. To predict the condition, we used voting ensemble method encompassing Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), after predictive models are successfully constructed, showcasing notable accuracies of 92.28%, 83.89% and 82.22%. These results underscore the effectiveness of these models in predicting fetal health outcomes based on the comprehensive dataset. To further refine model performance, a meticulous GridSearchCV approach exhaustively explored optimal parameter values within predefined parameter grids, significantly enhancing predictive capabilities. Additionally, leveraging a bagging method, particularly Random Forest (RF), Logistic Regression (LR), and Gradient Boosting, further bolstered predictive accuracies. These bagging models demonstrated exceptional achieving accuracy of 94.02%, 93.22% and 92.28%. The systematic approach involving clustering, dimensionality reduction, ensemble learning, and parameter optimization resulted in highly accurate predictive models for fetal health assessment. Our research not only highligh

关键词： Decision trees

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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science China(Information sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph $\mathcal{CONGEST}$ model communication complexity round complexity

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Comparison of Machine Learning Algorithm for Stock Price Prediction Using Sentiment Analysis 5

Comparison of Machine Learning Algorithm for Stock Price Pre...

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5th International Conference on Emerging Smart computing and Informatics, ESCI 2023

ISBN: (纸本)9781665475242

Forecasting companies' stock market prices are one the interesting topics for analysts and researchers. Although a company's stock price can be unpredictable, long-term forecasts can help but it is dependent on many factors such as the company's business model, change in leadership, and investors' mood. It has been found to be insufficient to predict stock values just on the basis of historical data or textual information. Previous research in sentiment analysis have shown a strong correlation between the movement of stock prices and the publication of news stories. At different levels, a number of sentiment analysis research have been attempted utilizing methods. In this paper, we made a comparison of various Machine Learning methods on five datasets of financial news related to the company and domains in which the company. Encouraging results are obtained using 13 models i.e., Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Naive Bayes, Logistic Regression, LSTM, ARIMA, Logistic Regression, Support Vector Machines, Decision Tree, Boosted Tree, and ensemble method which depict polarity of news articles being positive or negative and the accuracies are 93.90%, 92.31 %, 92.27%, 85.44%, 84.65%, 84.65%, 94.73%, 90.13%, 82%, 83%, 72%, 70%, 95.11 % respectively. © 2023 IEEE.

关键词： Financial markets

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A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

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computer Modeling in engineering & sciences 2024年第9期140卷 2131-2164页

作者： Madiha Hameed Aneela Zameer Muhammad Asif Zahoor Raja Department of Computer Sciences Pakistan Institute of Engineering and Applied SciencesNilore50700Islamabad Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002TaiwanChina Directorate of IT Services University of GujratGujrat50700Pakistan

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer *** datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis *** challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human *** cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left *** paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their *** notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion *** review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation *** explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset *** leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model ***,ViTs alleviate augmentation issues by effectively leveraging original *** a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and *** study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological ***,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic

关键词： Medical image skin cancer classification skin cancer segmentation international skin imaging collaboration convolutional neural network deep learning

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