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Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI

Procedia Computer Science 2024年 233卷 45-55页

作者： E Kasthuri S. Subbulakshmi Rajasree Sreedharan Research Scholar Department of Computer Science and Engineering Assistant Professor Department of Computer Science and Applications Amrita School of Computing Amritapuri India Associate Professor Division of Nephrology Department of Pediatrics Medical College of Wisconsin Milwaukee Wisconsin USA

Anemia among young people has positive correlation with susceptibility and impede cognitive development. As per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia.

关键词： Explainable Artificial Intelligence Machine learning Anemia Data preprocessing

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Gene Therapy Bio-factory: Mathematical Modeling of the Human Eye Pharmacokinetics 1

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1st Southern Georgia Mathematics Conference, SGMC 2021

作者： Carichino, Lucia Guidoboni, Giovanna Kansara, Viral Ciulla, Thomas Harris, Alon School of Mathematics and Statistics Rochester Institute of Technology RochesterNY United States College of Engineering and Computing Professor Electrical and Computer Engineering The University of Maine OronoME United States Clearside Biomedical Inc. AlpharettaGA United States Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (数字)9783031697104

ISBN: (纸本)9783031697098

Neovascular age-related macular degeneration (nAMD) is one of the leading causes of irreversible blindness in the industrialized world. In nAMD, abnormal blood vessel growth occurs under the macula, leading to exudation and scarring, distorting central vision. Current nAMD standard of care involves repeated intraocular injections of drugs that target the vascular endothelial growth factor (VEGF) signaling pathway (anti-VEGF drugs). These treatments have a high burden on patients, caregivers and physicians. Gene therapy based long-lasting production of anti-VEGF proteins in the eye (bio-factory approach) may address some of the limitations of current therapies. We adapt a previously developed three-compartment (retina, vitreous humor, aqueous humor) pharmacokinetic model in rabbits to simulate the pharmacokinetics of anti-VEGF proteins in the human eye delivered by intravitreal injection or produced via the gene therapy bio-factory approach. The model is used to estimate the protein levels in the vitreous humor and retina, and the retina anti-VEGF production rate starting from clinically measured anti-VEGF protein levels in the aqueous, using gene therapy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Gene therapy

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

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Procedia Computer Science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

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Client-Level Monitoring Approach to Secure Data in a Cloud Environment

Client-Level Monitoring Approach to Secure Data in a Cloud E...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： P. Rajakumar N. Partheeban Ms. Indervati Himanshu Kumar Pius Mlawa Utkarsh Gautam Assistant Professor School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India

Cloud computing imparts huge storage at low cost, because the internet is so widely used around the world, massive amounts of data are stored on local devices and integrated and stored on a common platform for easy access, sharing, and, high availability. The cloud environment provides a convenient way for normal users and enterprises to migrate their apps, data, and storage to the cloud-based server for storage due to the high availability of its services and the flexibility of computing processes. Securing information is the primary challenge in cloud computing. Securing the data from unauthorized modification and access is one of the key challenges of the cloud environment. In order to secure user data, different security algorithms were adapted. This paper provides a client-level monitoring approach to secure data in a cloud environment. The monitoring approach consists of the user or client system, monitor, and cloud storage. The user uploads the encrypted form of documents onto the cloud storage and the cloud storage stores the encrypted form of user documents, and the monitor periodically monitors or the user directs the monitor to check the integrity of user data.

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A Survey on Cyber Attack Detection: Techniques, Datasets, and Challenge

A Survey on Cyber Attack Detection: Techniques, Datasets, an...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： G Geetha Manjula Rajagopal K Purna Chand Department of Computing Technologies School of Computing (Research Scholor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computing Technologies School of Computing (Assistant Professor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering(Data Science) (Professor) B V Raju Institute of Technology Narsapur India

Networking technology is expanded along with cyber threats. Cyber threats are the instances which adversely influences the functional operations of the system or network. It results in various issues such as illegal access, data leakage, data stolen, DDoS attacks, etc. To overcome such issues, several intrusion detection systems are implemented over the past years. Though, these intrusion detection systems exhibit several limitations while trying to achieve excellent efficiency in attack detection. In this survey, totally 50 cyber-attack detection based research works are analysed under several perspectives. The analysis is done on several IDS techniques implemented in existing works. The reviewed learning-based approaches are explained in two major categories as ML and DL approaches. Here, the ML algorithms are further classified into three types: supervised, unsupervised and reinforcement learning approaches. Moreover, the existing works are also analysed in accordance with optimization algorithms and it is classified as metaheuristic and stochastic algorithms. In which, population-based algorithms and swarm-based algorithms are the categories classified under metaheuristic algorithms. In addition, the analyses are also performed on datasets usage and cyber-attack mitigation models. Furthermore, the analysis on existing works is done on performance analysis. And it is analysed under performance of cyber-attack detection models with specific description about maximum and minimum performance attained by recent works on cyber-attack detection models. Finally, the chronological review and challenges involved in existing cyber-attack detection models are discussed.

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A Novel Modified Shuffled Frog Leaping Algorithm (MSFLA) MPPT Controller for Photovoltaic Systems

A Novel Modified Shuffled Frog Leaping Algorithm (MSFLA) MPP...

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Trends in Electrical, Electronics, Computer engineering Conference (TEECCON)

作者： P Suneetha K Kumar V Lakshmi Devi V Sekhar A Basi Reddy Assistant Professor Dept. of EEE Sri Venkateswara College of Engineering Tirupati India Professor Dept. of EEE Sri Venkateswara College of Engineering Tirupati India Dept. of EEE Kuppam Engineering College Kuppam India Assistant Professor Dept. of CSE School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

The exhaustion of fossil fuel reserves and increasing environmental issues have intensified the need for renewable energy. The focus on enhancing solar energy efficiency is particularly strong among various renewable sources. In photovoltaic (PV) systems, temperature and solar irradiance are crucial factors that significantly affect performance, making it essential to operate these systems at their maximum power point. Specifically for PV systems operating in partial shade, this study introduces a new method for improving the performance of Maximum Power Point Tracking (MPPT) controllers. The proposed method integrates a Modified Shuffled Frog Leaping Algorithm (MSFLA) with the MPPT controller to maximize power extraction from PV panels. Inspired by the social dynamics of frog populations, the MSFLA has been tailored to address the complexities of partial shading, which often results in multiple local maxima on the power-voltage curve. Comparative analysis reveals that MSFLA achieves the highest tracking speed and output power accuracy among bio-inspired optimization algorithms, achieving an efficiency of 99.24%.

关键词： Maximum power point trackers Photovoltaic systems Solar irradiance Renewable energy sources Temperature Heuristic algorithms Market research Control systems Fossil fuels Optimization

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Groundwater Delineation Using RS and GIS for Gurgaon Region

Groundwater Delineation Using RS and GIS for Gurgaon Region

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： R.K. Jain Department of Civil Engineering Professor Faculty of Engineering & Computing Sciences Teerthanker Mahaveer University Moradabad

ISBN: (纸本)9781665487351

Water is the most widely consumed natural resource on the earth. Due to continuous use and unmindful wastage, the water table is declining. To protect this information of ground water potential is needed. Remote Sensing with Geographic Information System and Multi criteria decision analysis techniques is used. Analytical Hierarchy Process comes under Multi Criteria Decision Analysis and it is executed for defining weights for different criteria

关键词： Natural resources Earth Analytic hierarchy process Market research Decision analysis Water resources Remote sensing

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Automated Detection of Multiple Sclerosis Lesions in MRI with SegNet 1

Automated Detection of Multiple Sclerosis Lesions in MRI wit...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Agrawal, Pankaj Rose, Anthony Dewangan, Deepak Kumar Singh, Kamred Udham Somasekar, J. Gla University Department of Computer Engineering & Apllications UP Mathura India Department of Ele. & Tele. Raipur Institute of Technology Raipur India Pune India Department of Computer Science and Engineering Gwalior India School of Computing Graphic Era Hill University Dehradun India Professor of Cse Department Bangalore India

ISBN: (纸本)9798350330915

Using deep neural networks, the proposed approach segments MRI images into classes and achieves good visual quality as well as similarity to the ground truth image. For the proposed process the dice similarity index is near 0.8. Dice similarity index is calculated for each class i.e. for WM, GM, CSF. Also the ground truth images are compared with the reference images and segmented MRI images that are used in the paper. © 2023 IEEE.

关键词： CNN Deep learning MRI Multiple Sclerosis Lesions SegNet

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

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

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

关键词： Generative adversarial networks

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Mitigating Credit Card Fraud: A Machine Learning Perspective

Mitigating Credit Card Fraud: A Machine Learning Perspective

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Recent Trends in Microelectronics, Automation, computing and Communications Systems (ICMACC), International Conference on

作者： Shivam Bhushan Indrajeet Gupta Angayarkanni S A Balakiruthiga B Pandimurugan V Rajaram V Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Associate Professor Computer Science and engineering Acharya Institute of Technology Bangalore India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape

关键词： Machine learning algorithms Accuracy Credit cards Data models Real-time systems Fraud Microelectronics Random forests Tuning Testing

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