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Investigation into a Medical Diagnostic System for Chronic Kidney Disease: Preparation and Execution 10

Investigation into a Medical Diagnostic System for Chronic K...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sharma, Arvind Singh, Dalwinder School of Computer Application Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

This abstract provides a concise overview of a study focused on the development and execution of a medical diagnostic system specifically designed for Chronic Kidney Disease (CKD). The primary objective of this study is to contribute to the advancement of early detection and intervention strategies for chronic kidney disease (CKD), a prevalent and potentially incapacitating condition. The inquiry process encompasses several sequential stages, including the initial planning phase and subsequent execution phase. During this stage, comprehensive literature studies are conducted to examine the various approaches used for diagnosing chronic kidney disease (CKD), as well as the risk factors associated with its development and the biomarkers used for its detection. This literature review aids in identifying the deficiencies within the diagnostic system and highlights the potential utility of advanced techniques such as machine learning and data analytics. The development and implementation of the diagnostic system occur during the execution phase. A comprehensive database is established by the consolidation of data from various sources, including patient records, laboratory findings, and diagnostic imaging. State-of-the-art algorithms and machine learning approaches are utilized to conduct comprehensive data analysis, pattern identification, and the construction of prediction models for the diagnosis of Chronic Kidney Disease (CKD). This article provides a comprehensive examination of various healthcare systems pertaining to the management of chronic kidney disease. Additionally, it presents a systematic review of recent research undertaken by multiple scientists during the past five years. © 2023 IEEE.

关键词： Biomarkers chronic kidney disease (CKD) Diagnosis Preparation Early detection Execution Literature review Machine learning Risk factors

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Enhanced License Plate Detection using YOLOv8n: An Approach for Efficient Vehicle Identification 3

Enhanced License Plate Detection using YOLOv8n: An Approach ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Chandok, Vishesh Maurya, Sudhanshu Mirza, Kashish Kumar Yadav, Ajay Thomas Varghese, Sneha Garg, Rachit Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Bhimtal Campus India United Institute of Management Department of Computer Application Prayagraj India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350384598

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age. © 2024 IEEE.

关键词： Traffic signals

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Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction 4

Leveraging the Strengths of Long Short-Term Memory and Gated...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jena, Prem Purusottam Das, Kaberi Mishra, Debahuti Lenka, Arundhati Mishra, Sashikala Mallick, Pradeep Kumar Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Application Bhubaneswar India Pune India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

The study recognizes the multifaceted nature of the stock market and the challenges inherent in predicting market movements accurately. Focused on the Indian stock market, the research harnesses the capabilities of deep learning, specifically the long short-term memory (LSTM) and gated recurrent unit (GRU) architectures, known for their prowess in processing sequential data with predictions over a 10-day horizon. The models are systematically evaluated, considering parameters such as root mean square error (RMSE), mean absolute error (MAE), regression variance, and R2-score, with a comparative analysis highlighting the effectiveness of the proposed hybrid models - LSTM-GRU and GRU-LSTM. The experimental analysis reveals that the GRU-LSTM model demonstrates superior performance on the HDFC bank Ltd. dataset, while the LSTM-GRU model excels on the TATA Motors Ltd. dataset, aligning with the predictive performance and accuracy metrics observed in earlier experiments. © 2024 IEEE.

关键词： Long short-term memory

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A Comparative Analysis of Machine Learning Models for Colon Cancer Classification

A Comparative Analysis of Machine Learning Models for Colon ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Swain, Rashmi Ranjan Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

One of the trickiest machine learning challenges is the automatic classification of cancer cells. Colon Cancer can strike anyone at any age, but it often strikes older persons. In recent years, several investigations have focused on developing machine learning algorithms that can automatically classify genes of various types of a particular disease, especially colon cancer. The classification of cancer has improved due to improvements in computational methods like machine learning (ML), which required little human interaction and inefficient computers. Since the classification of gene expression data has been extensively studied in conjunction with the advancement of gene technology, a wide variety of techniques, primarily neural network-related, have been applied to the analysis of medical data, which is primarily concerned with the large dimension and very small in quantity. In this work, five different ML techniques were implemented for the classification of colon cancer in different age groups. The implemented ML techniques are Random Forest (RF), Decision Tree (DT), Support vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN). It is observed that most of the implemented model has an average accuracy of more than 95%. © 2023 IEEE.

关键词： Decision trees

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A Novel Procedure for Recognising Human Activity Using Machine Learning 4

A Novel Procedure for Recognising Human Activity Using Machi...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Singh, Neelam Bhatt, Mansi Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350300888

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity Recognition (HAR) is one of these sensors' applications. Given the dependence on general Machine Learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. The study focuses on Human Activity Recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used. © 2023 IEEE.

关键词： Long short-term memory

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Remotely Controlled Electronic Noticeboard Using NodeMCU 14

Remotely Controlled Electronic Noticeboard Using NodeMCU

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Ayush Singh, Gurpreet Rawat, Vandana Negi, Deepti Joshi, Aditya Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350335095

In today's digital age, traditional notice boards are being replaced by more dynamic and interactive solutions. This research paper presents the design and implementation of a remotely controlled notice board using the NodeMCU ESP8266 microcontroller and an 8x8 matrix display. The system allows users to remotely update and display messages or notifications in real-time, enhancing communication efficiency and flexibility. By leveraging the capabilities of the NodeMCU ESP8266 and the visual output of the 8x8 matrix display, this solution offers an innovative approach to notice board management. The paper discusses the hardware and software components, the communication protocol, and explores the potential applications of this technology in various settings such as educational institutions, offices, and public spaces. The findings demonstrate the effectiveness and reliability of the proposed solution and emphasize its significance in improving communication processes. This research also highlights potential future enhancements and expansions, paving the way for further advancements in the field of remotely controlled notice boards. © 2023 IEEE.

关键词： Remote control

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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Enhanced SMOTE Strategy for Handling Imbalanced Data in Machine Learning Classification

Enhanced SMOTE Strategy for Handling Imbalanced Data in Mach...

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

作者： Tiwari, Shivam Bhardwaj, Barkha Rathore, Yogesh Singh Vats, Satvik Vishwakarma, Pramod Department of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science and Engineering NIET Greater Noida India Computer Science and Application Sharda University Greater Noida India Computer Science & Engineering Graphic Era Hill University Dehradun India Chandigarh University Gharuan Punjab Mohali India

ISBN: (纸本)9798350305708

Most real-time datasets have class instances scattered across the data in a non-uniform fashion. Certain classes have an abnormally high number of instances when compared to the rest of the classes. The term "class imbalance problem" is used to describe this occurrence (CIP). Due to its inaccuracy in projecting weak class data samples, this skewed information impacts the reliability of forecasts as a whole. Many businesses make use of CIP's data mining experts who employ CIP. Both ML and deep learning have substantial difficulties with the classification of skewed data (DL). Several researchers are curious in how to overcome the major hurdle presented by the widespread adoption of sample techniques to improve classifier performance. In this research, we provide a survey of state-of-the-art approaches currently being used in studies of data classification, and we contrast and compare the numerous algorithms used at each stage of the process. Talk about the challenges and limitations that come up in investigations of imbalanced data classification. Taken as a whole, this work not only provides an in-depth analysis of the uneven data categorization industry but also inspires more study in this space. © 2023 IEEE.

关键词： balanced data classification deep learning each sampling method has a connection to the issue of class imbalance Machine learning

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Conventional Neural Network for Maths Handwritten Digit Categorization 6

Conventional Neural Network for Maths Handwritten Digit Cate...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Garg, Shivya Kuchhal, Navya Barmola, Jigyasa Rawat, Vandana Singh, Neelam Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350304480

Handwritten digit recognition plays a crucial role in various sectors such as document processing, automatic form identification, and postal automation. This task involves accurately identifying and classifying handwritten digits into their respective numerical values. This presentation provides a summary of current developments in machine learning techniques for handwritten digit recognition. The primary objective of this research is to develop an advanced system for recognizing handwritten digits by harnessing state-of-the-art machine learning techniques. To train and test the suggested system, a sizable dataset of handwritten digits is used. We are utilizing the MNIST dataset here. To improve the quality of the input pictures and guarantee uniform feature representation, preprocessing techniques like image enhancement and normalization are used. Experimental results indicate that the CNN model surpasses all other investigated techniques in accuracy, boasting an impressive accuracy rate of over 98%. The capacity of the CNN model to automatically deduce pertinent characteristics from the input pictures, catching both local and global patterns, is credited with its better performance. Additionally, the model can successfully handle spatial relationships in the digit pictures because to CNNs'usage of convolutional layers. A number of practical applications, such as computerized form processing, digital document analysis, and intelligent character recognition, are possible with the suggested handwritten digit recognition system. The CNN model's high accuracy enables accurate digit categorization, enhancing efficiency and accuracy across a variety of applications. Future research can concentrate on investigating ensemble approaches, data augmentation methods, and fine-tuning techniques to improve the handwritten digit recognition system's performance even further. © 2023 IEEE.

关键词： CNN digit recognition epochs Handwritten recognition hidden layers machine learning neural network

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A Comprehensive Study of a Conversational AI-Driven Healthcare Bot with Pose Estimation for Precision Fitness Monitoring and Personalized Guidance

A Comprehensive Study of a Conversational AI-Driven Healthca...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Sinha, Anurag Kumar, Biresh Kundu, Roumo Sharma, Amit Sharma, Mukesh Raza, Asif ICFAI University ICFAI Tech School Computer Science Department Jharkhand Ranchi India Amity University Department of Computer Engineering and Application Jharkhand India Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Lovely Professional University Department of Computer Science Phagwara India Amity University Department of Master of Computer Application Jharkhand Ranchi India

ISBN: (纸本)9798350374100

This research investigates a new healthcare paradigm through the analysis of a conversational AI-powered healthcare bot that has posture estimation capabilities for accurate fitness tracking and customized recommendations. User motions during workout routines are accurately and thoroughly assessed thanks to the incorporation of powerful posture estimation technology. With the help of individualized exercise advice, this thorough study explores the capabilities of the system and shows how it may transform wellness practices. The healthcare bot provides an innovative method of fitness tracking by using conversational AI and accurate posture prediction to customize recommendations based on user requirements. This novel combination promises to enhance wellbeing and provide consumers with a more efficient and customized workout experience. The study emphasizes this point. © 2024 IEEE.

关键词： Health care

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