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2024 International Conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Kumar, Ayush Utekar, Swarali Viji, D. College Of Engineering And Technology Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350366846

Blockchain technology provides a safe, transparent, and effective platform that might completely transform public financing. Each transaction is tamper-proof since it is permanently stored with unique identities and timestamps. Furthermore, agreements are automated via smart contracts, which simplifies procedures and lowers errors usually related to human verification. This encourages accountability and confidence inside the system. For platforms that receive public financing, transparency is crucial. This need is met by this blockchain-based online service, which offers a safe and decentralized environment. Donors can simply monitor the initiatives they support and make sure their money is going to where it's supposed to. Should a project be cancelled, the program enables contributors to receive their money back right away. The application caters to two user groups: campaign creators and donors. Donors have access to other fundraising campaigns, project updates, and insightful information. The website gives campaign designers the ability to monitor the status of their initiative and compare their results to those of other campaigns. This blockchain-powered online application provides a stable, open, and safe environment for public fundraising. By empowering both creators and donors, it promotes an ecosystem that is more reliable and efficient. © 2024 IEEE.

关键词： Smart contract

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AI-Driven Insights: A Survey on Innovative Approach for Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods 3

AI-Driven Insights: A Survey on Innovative Approach for Lung...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Bhargav, A. Lakshmi Ashokkumar, C. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science & Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798331532420

The lungs are vital for exchanging oxygen and carbon dioxide, providing oxygen to cells for energy production, and removing waste carbon dioxide from the *** also help filter and protect the body from harmful particles and pathogens in the *** lung function is essential for overall health and *** cancer ranks as the second most common cause of cancer-related fatalities *** emphasizes the challenge of early detection due to the lack of symptoms in the initial *** contributes to a high mortality rate compared to other lung *** identification and precise prediction of lung cancer serves as vital for improving patient outcomes. Machine learning (ML) techniques have shown great promise in revolutionizing lung cancer prediction by leveraging various data sources such as medical images, clinical data, and genomic information. This paper presents a review of lung cancer prediction using ML, DL, and AI *** classification of lung cancer through medical imaging is essential for physicians to determine appropriate treatment options, ultimately aiming to reduce cancer *** goal is to compare the performance and accuracy of convolutional neural networks (CNNs), Random forest algorithm, ensemble extreme boosting (XGBoost) algorithm,Support Vector Machine (SVM), AdaBoost classification model (ADB-C), Long Short-Term Memory Networks (LSTM), incremental multiple resolution residual network (iMRRN) models while also providing explanations for their predictions. © 2024 IEEE.

关键词： Lung cancer

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Advanced 3D-printed antibacterial nano 58S bioglass/AgNPs/CeO2-based scaffolds for bone tissue engineering

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Ceramics International 2025年第15期51卷 20939-20953页

作者： Singh, Indrajeet Kong, Ing Viswakarma, Ashok Balani, Kantesh Advanced Polymer and Composite Materials Laboratory Department of Engineering School of Computing Engineering and Mathematical Sciences La Trobe University BendigoVIC3550 Australia Department of Materials Science and Engineering Indian Institute of Technology Kanpur208016 India Department of Mechanical Engineering Indian Institute of Technology Kanpur208016 India

58S bioglass (BG) has been considered an effective bioceramic in bone tissue engineering due to its good osteoinductive and osteoconductive properties;however, its poor antibacterial and printing properties remain a challenge in the development of ideal patient-specific, fully interconnected, bone-like scaffolds. To address this issue, this study presents novel 3D-printed BG/AgNPs/CeO2 (BGAC3D) composite scaffolds. These scaffolds exhibit a fully interconnected pore network (≈99 %) with a porosity of ≈64 % and optimally designed pore and strut sizes of 404 ± 15 μm and 394 ± 30 μm, respectively, making them suitable for bone healing applications. Adding AgNPs and ceria nanoparticles significantly increased the shear-thinning properties of Pluronic F-127/BG/AgNPs/CeO2-based ink, as compared to Pluronic F-127/BG-based ink. As a result, BGAC3D scaffold showed improved dimensional accuracy during printing relative to pristine BG. Nanoindentation studies revealed substantial increase in the mechanical properties of BGAC3D scaffolds, with elastic modulus, hardness, and compressive strength showing enhancement of 76 % (57.9 ± 8 GPa), 24 % (3.1 ± 1 GPa), and 275 % (1.5 ± 0.4 MPa), respectively. The antibacterial properties of BGAC3D scaffolds were investigated using disc diffusion and bacterial adhesion assays, which demonstrated an exceptional bactericidal efficacy of 99.3 % against Escherichia coli (E. coli) and 99.4 % against Staphylococcus aureus (S. aureus) compared to pristine BG. The cytocompatibility of the scaffolds was assessed using resazurin assays with mouse BMSCs and MTT assays with mouse fibroblasts, revealing enhanced cell viability for the BGAC3D composition compared to BG. Additionally, both BG and BGAC3D composites showed similar apatite mineralization on their surfaces, as observed under a scanning electron microscope. These findings indicate that BGAC3D scaffolds possess the necessary mechanical, antibacterial, and cytocompatibility properties, making th

关键词： Escherichia coli

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The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

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Water Supply 2024年第11期24卷 3848-3861页

作者： Mohammadi, Maryam Gato-Trinidad, Shirley Kuok, Kuok King Department of Civil and Construction Engineering Swinburne University of Technology HawthornVIC3122 Australia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Sarawak93350 Malaysia

Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management. © 2024 The Authors.

关键词： Demand response

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Detection and Classification of Brain Tumor Using Deep Learning Model 1

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4th International Conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2023

作者： Routhu, Manoj Sitaram Govindaraju, Rohith Vigna Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

Accurate and efficient brain tumor classification is paramount for timely clinical diagnosis and effective treatment planning. In this groundbreaking research, we introduce an innovative Convolutional Neural Network (CNN) architecture intricately integrated with customized preprocessing techniques, resulting in an exceptional classification accuracy of 98.5% on the challenging br35H dataset. By harnessing the power of MRI scans and leveraging diverse datasets, our model expedites brain tumor assessments and sets the stage for advanced classification methodologies. With the global incidence of brain tumors rising, the need for technology-driven diagnosis becomes increasingly evident, and CNNs emerge as pivotal tools in enhancing diagnostic precision. This study not only underscores the profound significance of CNN models but also transcends geographical boundaries, reducing the frequency of misdiagnoses and ultimately empowering global healthcare. This paper comprehensively explores our methodology, delving into the intricate details of data collection processes, model development strategies, and experimental findings. Moreover, it sheds light on the implications of deploying CNN models in medical imaging. By contributing to the ongoing discourse on transformative healthcare technologies, this research aims to propel the adoption of CNN-based approaches, ushering in a new era of precise and efficient brain tumor classification for the benefit of healthcare professionals, patients, and society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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Alzheimer's Disease Prediction Using Machine Learning 7

Alzheimer's Disease Prediction Using Machine Learning

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mishra, Shashwat Sharma, Viplav Ramya, G. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350372816

Alzheimer's disease is a progressive and debilitating neurodegenerative disorder that poses a significant public health challenge worldwide. Early detection and prediction of Alzheimer's disease are critical for effective treatment and care. This research paper presents a novel approach for predicting Alzheimer's disease using machine learning methodologies. Our study leverages a diverse dataset that includes clinical, genetic, and neuroimaging data from individuals at various stages of the disease. Through rigorous preprocessing and feature selection processes, we extract relevant features and apply advanced machine learning algorithms. Our results demonstrate the efficacy of this approach in accurately predicting the onset and progression of Alzheimer's disease. The model's high accuracy, sensitivity, and specificity suggest its potential as a valuable tool for clinicians and researchers. Furthermore, our study highlights key factors contributing to the prediction and offers insights into the underlying biological processes of the disease. This research holds promise for early intervention and personalized patient care, ultimately contributing to improved management of Alzheimer's disease and enhancing the quality of life for affected individuals and their families. © 2024 IEEE.

关键词： Adversarial machine learning

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Cricket Performance Analysis System Using Machine Learning Techniques 5

Cricket Performance Analysis System Using Machine Learning T...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mishra, Suraj Garg, Animesh Batra, Vansh Saxena, Devansh Om Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications 603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications India

ISBN: (纸本)9798331530013

The Cricket Performance Analysis System based on big data tools, like Hadoop and Apache Spark, gives realtime deep insights into performances from players and teams and conditions in matches based on large-scale cricket match data. It scales up speedily and finds patterns using distributed computing capabilities of Spark, which makes it possible to take decisions around performance improvements and strategies. The system applies a set of machine learning techniques including K-means clustering, Logistic Regression, and Random Forest classifiers to be used for the analysis of player performance metrics and match outcomes. Thus, through the conversion of raw data into actionable insights, teams can improve their strategies, make better improvements in performance, and thus gain an upper hand against other competing professional cricket teams. As a result, teams are best positioned to make adjustments in good time and direct finer tactics toward further enhancement of overall performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence Models in Power Generation for Energy Consumption Prediction 4

Artificial Intelligence Models in Power Generation for Energ...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Tan, Lit Yang Chen Kwan, Charis Shwu Ajibade, Samuel-Soma M. Ramly, Athirah Mohd School of Engineering and Technology Sunway University Department of Computing and Information Systems Selangor Malaysia School of Architecture Computing and Engineering University of East London Department of Computing London United Kingdom

ISBN: (纸本)9798350353266

The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models, often used in different fields, to predict energy-related patterns, providing a unique and specialized viewpoint. The synergy of academicians, AI experts, and industry professionals in the energy sector has resulted in the creation of customized AI models to optimize operational efficiency. By customizing various AI models to suit the distinct attributes of energy scenarios and datasets, these models are positioned to transform energy management methods. This study examines the utilization of AI models to enhance energy efficiency in power generation in Malaysia. The project seeks to predict future power consumption in various sectors, analyze growth rates, and identify sectors with investment potential by developing a Linear Regression model. In addition, a thorough power plan is developed using the estimated energy usage. A comparative analysis is performed to determine the most appropriate model for this particular scenario, which will improve decision-making in the energy sector. The results of this study present promising opportunities for further investigation. By broadening the study's focus to encompass a broader array of AI models and their assessment of performance, it is possible to gain useful insights for predicting power generation. Furthermore, the integration of real-time data streams and the inclusion of feedback loops in the AI models could improve their ability to adapt and increase their accuracy as time progresses. © 2024 IEEE.

关键词： Prediction models

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Gesture Recognition for Phrase Interpretation: A Deep Learning Approach

Gesture Recognition for Phrase Interpretation: A Deep Learni...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Puyam, Kelvin Singh, Nishant Aruna, M. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350375237

Gesture recognition holds paramount significance in facilitating communication for individuals utilizing sign language to convey phrases and expressions. We present an innovative approach to gesture recognition in this article tailored for interpreting hand signs to derive meaningful phrases, such as 'I love you,' 'please,' 'sorry,' and 'goodbye.' Our proposed methodology integrates a multi-box detector (SSD) with dynamic hand gesture analysis to achieve real-time and accurate recognition of hand signs across various languages and cultural contexts. By harnessing the power of deep learning SSD for object detection, our system ensures swift responses while maintaining precision. Additionally, the incorporation of dynamic hand gesture analysis techniques enhances the system's adaptability to diverse sign styles and linguistic nuances, thus fostering better communication access. By means of meticulous experimentation, we establish the effectiveness of our methodology in precisely identifying and deciphering real-time sign language gestures, attaining noteworthy results in terms of both speed and accuracy. This study enhances assistive technology for hard-of-hearing and deaf communities, thereby promoting accessibility and inclusiveness in communication environments that are accommodated to the linguistic requirements of a variety of individuals. © 2024 IEEE.

关键词： Gesture recognition

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