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A Proposed Model for machine learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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Non-Invasive Blood Glucose Estimation Using Photo Plethysmography and Extreme learning machine Algorithms 3

Non-Invasive Blood Glucose Estimation Using Photo Plethysmog...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Narasimharao, Mudra Swain, Biswaranjan Nayak, Praveen Priyaranjan Bhuyan, Satyanarayan Siksha 'o' Anusandhan Deemed to Be University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

The current research describes a non-invasive blood sugar level estimation device that uses Photo Plethysmography (PPG). Prior research has demonstrated that using a light-sensitive sensor improves blood sugar level prediction. The PPG signal of the individuals is captured using a constructed light sensor-based data-gathering device. The primary advancement of the results paper is the examination of many PPG signal characteristics through the efficient estimate of BGL values using the Only One Pulsed Assessment approach. Regarding the ADC conversion, the sampling rate of the frequency was set at 1 kHz with an accuracy of twelve bits during the dataset collection process. This PPG dataset segment contains 219 sample points. Features such as time and frequency domain characteristics are used in BGL value estimation. Using the previously suggested feature sets, a machine learning network is developed, and a BGL value estimate is carried out. The PPG medical data are also obtained, and both of them are trained to predict patient diabetes or nondiabetes using ELM algorithms. The method that produces the best results is taken into consideration. The top classifier was the ELM, which had a training time of 9 seconds and an efficiency of 99.6%. Based on the research, the Extreme learning machine (ELM) achieved more accurately in the machine learning methods. © 2024 IEEE.

关键词： Glucose sensors

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data-Driven Aerospace Equipment Based Materials Research Methods 3

Data-Driven Aerospace Equipment Based Materials Research Met...

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3rd international Symposium on Aerospace engineering and Systems, ISAES 2024

作者： Zhang, Xinyue Li, Bifeng School of Weapon Engineering Naval University of Engineering Wuhan China

ISBN: (纸本)9798350350418

Aiming to address the challenges faced by aerospace equipment encountering a series of problems such as high overload, high temperature, and susceptibility to oxidative corrosion, as well as the low efficiency of traditional experimental research and development methods during actual usage, this paper utilizes first-principle density functional theory calculations and machine learning technology to establish a material database and predict material properties. Through first-principle calculations, we can gather a large amount of material data to construct a comprehensive material database;the use of machine learning technology based on the material database to predict the materials we need to save a lot of R & D time. The results indicate that the predicted values are in line with the actual range of values. © 2024 IEEE.

关键词： Aluminum alloys

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Research on Multimodal Optimization Mechanism based on machine learning 3

Research on Multimodal Optimization Mechanism based on Machi...

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3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Chen, Qianyu Tang, Runxuan School of Information Science and Technology ShanghaiTech University Shanghai China School of Information Science and Technology Shanghai Normal University Shanghai China

ISBN: (纸本)9798331506582

As modern engineering optimisation problems become increasingly complex, a significant number of them exhibit multimodal characteristics. This implies that there are multiple local optimal solutions and a single global optimal solution. This not only increases the difficulty of problem-solving, but also necessitates the development of more sophisticated algorithms for optimisation. In this work, by constructing intelligent agents to continuously optimize their behavior strategies in the search process of multimodal optimization problems, they use environmental feedback to continuously optimize their behavior strategies, and at the same time simulate the process of natural selection and genetic variation to generate multiple candidate solutions, so as to conduct extensive searches on a global scale. Deep neural networks are used to build policy networks and value networks for reinforcement learning agents, enabling agents to accurately predict the potential benefits of different search strategies through a large amount of training data. Through real-time monitoring and analysis of feedback information, the model dynamically adjusts the search strategy to balance exploration and utilization, avoid falling into the local optimal solution, and finally find the global optimal solution. Experimental analysis shows that the model performs well in dealing with complex multimodal optimization problems, and significantly improves the efficiency and effect of solving. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multimodal Content Moderation System using Adversarial machine learning 3

A Multimodal Content Moderation System using Adversarial Mac...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

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Proceedings - 2024 3rd international conference on Artificial Intelligence and Software engineering, ICAISE 2024

Proceedings - 2024 3rd International Conference on Artificia...

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3rd international conference on Artificial Intelligence and Software engineering, ICAISE 2024

ISBN: (纸本)9798331532864

The proceedings contain 11 papers. The topics discussed include: improved YOLOv5-based method for date palm leaf disease recognition;three real-time pathfinding strategies in games: from a ideas to machine learning approaches;software metrics selection– the case of C#;automated seismic horizon picking using vision transformer;a regionally generalized machine learning framework towards census-enabled multi-factor non-communicable disease analyses;and enhancing the customer experience in Philippine food court establishments with an integrated self-service ordering system.

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Diabetes Classification Using XG Boost Classification Techniques Through machine learning based SMOTE Analysis 3

Diabetes Classification Using XG Boost Classification Techni...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Gill, Kanwarpartap Singh Anand, Vatsala Upadhyay, Deepak Dangi, Sarishma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

Diabetes is a prevalent and enduring metabolic condition that has substantial health ramifications. The timely and precise categorization of patients into diabetes and non-diabetic groups is of utmost importance in order to facilitate efficient disease treatment. machine learning methodologies, such as the XGBoost algorithm, have been recognised as very effective instruments in the field of diabetes categorization. This study investigates the use of XGBoost, a gradient boosting algorithm, in combination with the Synthetic Minority Over-sampling Technique (SMOTE) to tackle the issue of data imbalance in the categorization of diabetes. The use of algorithms in machine learning is of significant importance in the field of medical research and healthcare, since it enables the generation of predictions by analysing patient data. Within the domain of diabetes categorization, the task at hand is the development of a model that can effectively forecast an individual's diabetes state by considering pertinent factors such as age, body mass index (BMI), glucose levels, and genetic background. The XGBoost method is extensively used in the field of machine learning because to its notable efficiency and accuracy, especially in jobs involving categorization. The main aim of this study is to use the XGBoost classifier in order to categorise the diabetic illness. This will be achieved by using SMOTE Analysis to address the issue of imbalanced data and then analysing both balanced and unbalanced datasets. The forthcoming scholars in this particular area of investigation will get advantages from the notable degree of precision shown in this research. © 2024 IEEE.

关键词： Diseases

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Proceedings - 2024 3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

Proceedings - 2024 3rd International Conference on Sentiment...

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3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

ISBN: (纸本)9798350396157

The proceedings contain 121 papers. The topics discussed include: ***: a cutting-edge solution for informed meal selection;a hybrid learning model for detecting attacks in cloud computing;a real time prediction model for round winners of counter strike: global offensive;social media censoring using of visual sentimental analyzer;a comparative analysis of machine learning and deep learning algorithms for skin cancer detection and classification: exploring performance across multiple parameters;an extensive review of intelligent deep learning based anomaly detection and classification of 3D MRI brain imaging data;evaluating human emotions using image processing and brain waves;and hybrid machine learning based shill bidding fraud detection system using recursive feature elimination method.

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Lung Cancer Prediction and Risk Assessment: A machine learning Approach Integrating Symptoms and Etiological Factors 3

Lung Cancer Prediction and Risk Assessment: A Machine Learni...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain Sarkar, Md. Mosharrof Afrin, Sadiha Reza, Md Tuhin Bokshi, Md Abid Hasan Roni Mim, Sharmin Sultana Faridpur Engineering College Department of Computer Science and Engineering Faridpur Bangladesh Faridpur Engineering College Department of Electrical and Electronic Engineering Faridpur Bangladesh

ISBN: (纸本)9798331534400

Lung cancer requires accurate risk assessment and early detection due to its high prevalence and fatality rate. The simultaneous analysis of clinical symptoms and etiological factors using machine learning revolutionizes our understanding of lung cancer and patient outcomes. machine learning models are built on comprehensive patient data, including lifestyle, symptoms, and 12 variables. We used ten models for precise lung cancer diagnosis, including Decision Tree (DT), K-nearest neighbors (KNN), Support Vector machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), XGBoost, Gradient Boost, CatBoost and AdaBoost. At the National Institute of Cancer Research & Hospital (NICRH) in Mohakhali, TB Gate Road, Dhaka, Bangladesh, we collected 346 data directly from patients using questionnaires, with permission from the director. For every model, we performed parameter tuning to determine the ideal ratio of complexity to accuracy. Notably, with the best accuracy of 98.43% and an excellent area under the curve (AUC) of 0.983, the Decision Tree (DT) performed better than any other classifier. The motivation behind this research is to predict the risk assignment of lung cancer using various machine learning models. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Decentralized Federated learning with User Feedback Loops: A Novel Approach for Personalized and Adaptive learning in IoT Environments 3

Enhancing Decentralized Federated Learning with User Feedbac...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Sarker, Arpita Jesser, Alexander Speidel, Markus Heilbronn Univ HHN Inst Intelligent Cyber Phys Syst ICPS Heilbronn Germany Heilbronn Univ HHN Heilbronn Germany

ISBN: (纸本)9798350372977;9798350372984

The burgeoning field of the Internet of Things (IoT) demands innovative approaches to data privacy and user-centric services. This study introduces a novel cross-platform application engineered using the Flutter framework, which orchestrates federated learning (FL) and natural language processing (NLP) to facilitate personalized event discovery within IoT environments. The application features an NLP-based chatbot for user interaction and employs Dendrite, a second-generation Matrix homeserver, to manage decentralized communication. Central to the system's design is the stringent upholding of data privacy: user data, including browsing history and application usage patterns, is processed locally on user devices to construct profiles that inform personalized event suggestions. The system's deployment of end-to-end encrypted communication underscores its commitment to user privacy and security. This integration of FL and NLP showcases a significant leap forward in the realm of privacy-preserving, personalized applications, charting a new course for user engagement in IoT.

关键词： federated learning natural language processing internet of things Flutter Dendrite artificial intelligence decentralized communication personalized event discovery end-to-end encryption

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