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Impact of Random Forest and XGBoost Algorithms on Improving Patient Outcomes Compared to Standard decision-Making Methods in Healthcare Predictive Analytics

Impact of Random Forest and XGBoost Algorithms on Improving ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Senthilvadivu, S. Ramesh, P.S. Narang, Sudha Praveena, N. Shakila, J. Sudha, I. Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE TamilNadu Chennai India Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Jeppiaar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering SIMATS University Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

In the field of healthcare, it is important to bring quick, accurate and prompt predictions in order to serve the needs of patients better, avoid re-hospitalizations, and better allocate resources. With this, this paper proposes an Ml framework for enhancing decision-making in healthcare predictive analytics employing Random Forest and XGBoost algorithms. The first and major problem solved is the inefficiency of the existing decision-making approaches which is caused by the impossibility to analyze and process big and big complex data. The proposed solution uses these machine learning models for activities such as estimating mortality, critical illness, readmission, and length of stay using massive healthcare data set. due to the utilization of sophisticated mechanisms of machine learning, the models provide much more precise predictions to reach better results in terms of early interventions and the usage of resources. The outcomes reveal that Random Forest and XGBoost improve the prediction accuracy and XGBoost can improve most of the measures compared with Random Forest. The findings of the models suggest their usefulness for providing daily operational decision making in clinical contexts, helping enhance the delivery of care and management of resources in healthcare facilities. © 2024 IEEE.

关键词： Predictive analytics

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Automated detection of Oropharyngeal Carcinoma Using Super-Resolution Generative Adversarial Network and Convolutional Neural Network

Automated Detection of Oropharyngeal Carcinoma Using Super-R...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Prasanth, A. Ragavi, B. Jayachitra, S. Kuntavai, T. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Dr.N.G.P Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Dindigul India Adhipara Sakthi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331528126

Oropharyngeal carcinoma is one of the prevalent forms of abnormal cancer. detecting cancer at an early stage and getting proper treatment is the only way to reduce the mortality rate. The key intention of this work is to enhance the image quality and to classify the images with distinct test cases. The first approach is to implement an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) in the input stage for improving the quality of images. The second approach is to implement the U-Net architecture in a proposed method to enhance the accuracy rate and precise outcome for early prediction. After implementing a U-Net architecture, the cancerous image and non-cancerous images are identified and classified accurately with lesser computational time. The proposed methodology executes preprocessing of input images using de-gamma correction. Thereafter, the Convolutional Neural Network (CNN) model are employed to extract all the features pertained from the image which facilitates to classify the input images. The unique validation dataset consists of 1050 images utilized to analyze the proposed ESRGAN-CNN model. The simulated results have been tested under 2 test cases with different accuracy rates along with cancerous and non-cancerous categorized images. When compared to existing works, the suggested work produces a high-resolution image with superior accuracy of 92.51%. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimizing Energy Efficiency in Electric Vehicles Through Fuzzy logic and Neural Network Algorithms 2

Optimizing Energy Efficiency in Electric Vehicles Through Fu...

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2nd International Conference on distributed Computing and Optimization Techniques, ICdCOT 2024

作者： devi, M. Sreedhar P, Siva Satya Priya, S. Geetha Amutha, M. Kanagajothi, d. Sri, R.Kalaivani Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh Gudlavalleru India R. M. D. Engineering College Department of Computer Science and Engineering Chennai Kavaraipettai India Sona College of Technology Department of Ece Tamil Nadu Salem India Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology Department of Mathematics Chennai Avadi India Arunai Engineering College Department of Computer Science Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350382952

The introduction of electric vehicles (EVs) has represented a significant step forward in the development of environmentally sustainable transportation. The study addresses the critical need to enhance energy management systems in electric vehicles (EVs) by overcoming the limitations of traditional algorithms. Existing systems, which rely on rule-based controllers and basic predictive models, have poor flexibility and precision when it comes to dynamically optimizing energy efficiency. To revolutionize energy optimization in EVs, the proposed approach uses a hybrid model that combines fuzzy logic and neural networks. It tackles limits through a comprehensive analysis, the design of a fuzzy logic-based controller, and the development of a neural network model trained on historical data. TensorFlow-implemented simulation results in Python validate the reliability of the model by closely matching predicted energy usage in a range of driving scenarios. Real-world experiments with an instrumented EV prototype highlight the proposed system's advantages even further, giving noteworthy results - 92% energy efficiency, 25 mile range extension, and 95% predicted accuracy. These findings support the system's ability to greatly improve energy efficiency and range extension in electric vehicles, therefore transforming sustainable transportation. © 2024 IEEE.

关键词： Energy management systems

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Quantum Resnet18 for Classifying and Predicting the Maize leaf diseases

Quantum Resnet18 for Classifying and Predicting the Maize Le...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Kujani, T. Bhuvanya, R. Sathya, T. Arivubrakan, P. Usha, V. Thangaselvi, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Tamilnadu Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798350354539

The agriculture industry contributes one-third of the worldwide Gross domestic Product (GdP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including leaf Blight, Grey leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops. © 2024 IEEE.

关键词： Crops

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Efficient Resource Allocation to Improve the Quality of Services in Cloud Computing

Efficient Resource Allocation to Improve the Quality of Serv...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Syed Fiaz, A.S. Anusha Priya, A. dinakaran, S. Sundari, S. Saran Raj, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science Muthayammal College of Arts and Science Tamilnadu Namakal India K S R College of Engineering Tamilnadu Namakal India Department of Computer Science and Engineering Jeppiar University Tamilnadu Chennai India

ISBN: (纸本)9798350305708

In recent days, cloud computing places a significant role in allocating resources in edging the various radio-accessing ubiquitous network methodologies called edge computing;also, it used mainly different users on mobility hence the name called mobile-edge cloud computing. On-demanding edge cloud services, the architecture called very comprehensive techniques which utilize the concept of allocation of resources over the offloading mechanism in a very efficient manner for the users;it also operates the methodology called JCC, which points out Joint Communication and Computation method to allocate the different resource allocation technique to perform over the network operators. The problem addresses the more complex issues over controlling the user admission control for the methodology called JCC, which helps to choose resource allocation over the different gathered sets of users to perfectly satisfy the allocation of resource demands. To make sure the issue stated with the control operation over resource problem is admission control which delivers the problem control of NP-hard. As a result, we devise a low-complexity level approximately the solution precisely for different ranges of ranking criteria. The proposed work uses the critical value method as a powerful tool to make an honest pricing structure for JCC resource distribution to avoid the manipulation that some consumers may engage in when purchasing mobile-edge cloud services. Hence the overall proposed work gives immersive performance for mobile-cloud edge computing in an on-demanding this will make a good evaluation of quality performance. © 2023 IEEE.

关键词： edge computing mobile-edge On-demanding resource distribution

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Evaluation and Implementation of Optimal Classification Algorithms for Credit Card Fraud detection 2

Evaluation and Implementation of Optimal Classification Algo...

引用

2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Sampath Kini, K. Pushpakumar, R. Senthamil Selvan, R. Praveenkumar, G.d. Basi Reddy, A. Nitte Deemed To be University Department of Computer Science and Engineering Karkala India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Information Technology Avadi India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Kongu Engineering College Department of Computer Technology Erode India School of Computing Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350364699

Nowadays, the credit card industry is more concerned about credit card fraud than any other problem. The main idea is to examine optional techniques used for fraud detection and to identify the different types of credit card fraud. Numerous strategies exist for lowering the prevalence of credit card fraud, each tailored to the specific threats faced by banks and the credit card industry. Credit card fraud prevention is the primary goal of implementing these measures. Existing methods have several unresolved issues that cause legitimate credit card customers to be mistakenly labelled as fraudulent. The amount of fraud in the financial sphere may be inferred from this paper's emphasis on integrating the best classification algorithm out of four distinct methods. © 2024 IEEE.

关键词： Credit card fraud faced by bank Financial sphere mistakenly labelled Primary Goal

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Threshold public key-sharing technique in block chain

Threshold public key-sharing technique in block chain

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Pande, Sagar dhanraj Singh, Gurpreet Zekrifa, djabeur Mohamed Seifeddine Kodgire, Shilpa Prashant Patel, Sunil A. le, Viet-Thanh School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Faculty of Computational Science GNA University Phagwara India Centre for Quantum Computation and Communication Technology Sydney Australia Maharashtra Institute of Technology Maharashtra Aurangabad India Computer Engineering Department L.D. College of Engineering Ahmedabad India Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9781032669663

A threshold key-sharing technology-based publicly verifiable key-sharing technology is presented to explore blockchain users’ private keys’ security. After receiving key fragments, participating nodes validate them. When splitting the key, the central splitting node does evil. In the critical recovery stage, the key fragments of the nodes participating in key splicing are publicly verified to prevent them from doing evil. In the critical distribution stage, add Ids to participating nodes to track malicious nodes and update node status in real-time, and a dynamic threshold mechanism is designed. To maintain private key fragment integrity, the owner of the key fragment and the controller node redistribute it to the new participating nodes once the vital fragment node goes offline. Experimental results reveal that this scheme’s private key recovery rate is 80% and has threshold properties, traceability, unforgeability, and recoverability. © 2024 The Author(s).

关键词： Blockchain

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Neural Networks for Natural language Processing

Neural Networks for Natural Language Processing

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： deshpande, Sudhindra B. Velrani, K. Sundara Bhagyalakshmi, A. Vani, M. Sowmya Ananthi, S.N. Sahu, devika Department of Information Science and Engineering KLS Gogte Institute of Technology Udyambag Karnataka Belagavi590006 India Department of Information Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Application Mohan Babu University Andhra Pradesh Tirupati517501 India Department of Computer Science and Engineering S.A Engineering College Tamil Nadu Chennai600077 India Department of Computer Science and Engineering Government Engineering College Chhattisgarh Raipur492001 India

ISBN: (纸本)9798350374933

Artificial neural networks have made Natural language Processing (NlP) a lot different because they can encode language data more efficiently. A lot of different natural language processing tasks, like mood analysis, translation, and text production, have been done very well with these networks are based on the neuronal structure of the human brain. This abstract delves into the fundamental principles of neural networks in natural language processing, with a particular emphasis on notable structures, including the more recent Transformer models, CNNs, and RNNs. The investigation investigates the potential future trajectories, challenges, and applications of improving language comprehension and developing assignments. © 2024 IEEE.

关键词： Natural language processing neural networks neural networks NlP recurrent neural networks RNNs

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Machine learning based human interacted robotic intelligence to detect the different categories of speech 2

Machine learning based human interacted robotic intelligence...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Singh, Ravinjit Yasmeen Srivastava, Aditi Bijeshdhyani, Bijeshdhyani deepa, d. Malaysia Cvr College of Engineering Hyderabad Csit Department India G.L.Bajaj Institute of Technology and Management India Graphic Era Deemed to Be University Department of Management Studies Dehradun India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India

ISBN: (纸本)9798350315905

Realizing artificial intelligence depends heavily on human-robot interaction (HRI) technology. Here, we present dual-function sound actuators built on graphene for human-robot exchanges that use machine learning. (GHRI). The GHRI's triboelectric acoustic detection mechanism and thermoacoustic sound output mechanism allow it to serve as both a prosthetic ear and tongue. Triboelectric materials, electrodes, and thermoacoustic sources made possible by laser-induced graphene contributed to the overall success of the combined device. The GHRI can recognize speech identities, feelings, substance, and other information in the human speech by optimizing the structure parameters to produce high sensitivity and working longevity. With the help of machine learning, a convolutional neural network is used to teach 30 voice categories, with an accuracy of 99.72% and 97.83% in training and test datasets, respectively. In addition, AIs can use GHRI to communicate through recognized voice characteristics. The implications of our research for the future of artificial intelligence in robots are vast. © 2023 IEEE.

关键词： Graphene

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An Industrial IoT driven Approach for Sewage Gas Identification and Reporting System using Intelligent Sensors with logical Controller 1

An Industrial IoT Driven Approach for Sewage Gas Identificat...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Rajasubha, J. Ramya, R. Juliet, A. Hency Janakiraman, V. devi, S. Rukmani Karuppaiah, S. Dhanalakshmi College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Based on a real-time sewage gas level detection and monitoring system, this system seeks to offer intelligent solutions for the monitoring of toxic sewage gases. An alarm is issued to the observer who is looking at the circumstances from a distance whenever a specific threshold is crossed. Following this, the data is transmitted together with different gas ppm levels, indicating if the worker can clean or operate securely in that region. The suggested solution incorporates both the IoT platform and the remotely located IoT monitoring devices. This necessitates finding the appropriate threshold values for septic plants and facilities, as well as calibrating gas sensors for use in industry. The gear is meant to provide the sewage worker advance notification whenever the concentration of toxic gaseous substances grows over time, ensuring their safety. The presence of gas, temperature, and other factors in sewage may be detected using a variety of sensors. By notifying sewage workers as well as cleaners via SMS whenever the threshold value drops below the observed levels, this technology helps save people from harmful infections, which is a social good. A number of harmful gas concentrations are graphed out for real-time monitoring, and this is how it accomplishes its job. In order to assess the efficacy of the suggested model, this paper cross-validates it with the Conventional Sewage Gas detection (CSGd) based on GPRS functioning. The goal of the cross-validation is to create an IoT based Sewage Gas detection (IoTSGd) system that can monitor and analyze toxic gas levels in real-time and notify workers. Firebase receives the measured gas levels. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. An SMS message can alert the authorities if the sewage system is likely to overflow. For the purpose of monitoring, an android app was created and connected with the planned system. © 2024 IEE

关键词： Internet of things

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