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Predictive Modeling for Osteoporosis Risk Assessment from DXA Scans 10

Predictive Modeling for Osteoporosis Risk Assessment from DX...

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

作者： Varshney, Neeraj Pokhariya, Hemant Singh Shrivastava, Anurag Shalini, N. Kumar, Mukesh Singh, Pankaj Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

A common skeletal condition marked by decreased bone density, osteoporosis is a major global health concern, especially for elderly populations. This study aims to provide a novel method for evaluating osteoporosis risk by combining relevant clinical data with information from Dual-energy X-ray Absorptiometry (DXA) scans. Using a deductive technique and an interpretive attitude, we compiled a large dataset of clinical data and DXA scans from various patient groups. To ensure data integrity, sophisticated data preparation procedures were used, such as alignment, standardization, and management of missing data. To extract pertinent data, characteristic engineering and selection procedures were used, producing a cleaned dataset for modeling. The comprehensive predictive model was created using machine learning techniques such as support vector machines, random forests, and logistic regression. Thorough analysis produced encouraging findings, with R-squared values showing that the model fit the data very well. Furthermore, interpretability methods like Partial Dependence Plots (PDPs) and Shapley Additive explanations (SHAP) values offered insightful information about the significance and connections between features. © 2023 IEEE.

关键词： DXA data fractures learning medical imaging osteoporosis risk

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Comparative Study of Machine Learning Techniques on Chronic Kidney Disease Prediction 3

Comparative Study of Machine Learning Techniques on Chronic ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Panigrahi, Soumyarashmi Adhikary, Dibya Ranjan Das Pattanayak, Binod Kumar Mohanty, Bibhuprasad Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Siksha 'o' Anusandhan Deemed to Be University Department of Computer Science & Engineering Bhubaneswar India Siksha 'o' Anusandhan Deemed to Be University Department of Electronics & Communication Engineering Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

A malfunctioning kidney can cause the increase of waste in the blood, adversely affecting various bodily processes and systems. ML techniques can assist in classifying patients as having chronic kidney disease (CKD) or not. Although previous researchers have conducted several studies, they have often neglected important steps like data preprocessing and feature engineering. In this paper, we have addressed these gaps by implementing various data preprocessing and feature engineering techniques such as Principal Component Analysis (PCA) on the CKD dataset. By carefully selecting features from our dataset, we identified the top-performing classification models which are RFC, SVC (Polynomial Kernel), XG Boost Classifier (XGBC) and Gradient Boost Classifier (GBC) gave the best performance in terms of accuracy. To evaluate the effectiveness of these models, we have utilized various performance metrics. The results obtained have significant contribution to the understanding and management of CKD, ultimately helping in the early detection and treatment of this widespread and critical health issue. © 2023 IEEE.

关键词： Principal component analysis

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Sentiments Analysis and Text Summarization of Medicine Reviews Using Deep Sequential Iterative Model with Attention Encoder

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Journal of Mobile Multimedia 2022年第4期18卷 1281-1300页

作者： Prabhu, B. Rajalaxmi Seema, S. Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bengaluru India Department of Master of Computer Applications M.S. Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering NMAM Institute of Technology VTU Belagavi India

In this day and age of data innovation, vast amounts of data are being generated daily, much of it is useless to the general public unless it is correctly handled. This year has seen a huge increase in social networks' relevance, resulting in vast quantities of data provided by users. This vast amount of information is gathered from a variety of sources, including company websites, customer blogs, and item reviews. The text outline is probably the most significant point of view in daily life. Analysis of text sentiment is a method for assessing, summarising, and drawing conclusions about the most important content. In the field of sentiment analysis, attention methods have been crucial since they make use of sentiment lexicons to gather a huge amount of sentiment polarity *** an attention mechanism that acts as a link between linguistic information with a strong emotional component and deep learning algorithms, it may be possible to boost text sentiment significantly. In the LSTM model, word sequence addictions can be captured over the long term. For the first time, scientists have developed an attention model that combines LSTM with an incredibly deep RNN model to tackle the problem of sentiment analysis in the real world. The iterative method trains the first set of word embeddings using a Word to Vector technique. With the Word2Vec algorithm, text strings are converted into a numerical value vector and distance between words and comparative words based on meaning are calculated. Using the attention strategy has the added benefit of potentially enhancing machine learning's ability to learn sentiment representations. The attention model is more scalable and adaptable than previous approaches. The major objective of this work is to assess feelings and develop an abstracted content outline, decide on the semantic summarization of different materials and effectively process the data. © 2022 River Publishers.

关键词： attention model decoder encoder Polarity summarization

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Sentiment Analysis on the Elevated Expressway in Bangladesh: A Machine Learning and Deep Learning Approach 2

Sentiment Analysis on the Elevated Expressway in Bangladesh:...

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2nd International Conference on Power, Electrical, Electronics and Industrial applications, PEEIACON 2024

作者： Jeba, Samiha Maisha Khan, Rajib Ahmed, Md Shoaib Aurpa, Tanjim Taharat Dash, Satya Ranjan International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Boise State University Department of Computer Science Idaho Boise United States Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Educational Technology Bangladesh School of Computer Applications Kiit University Odisha India

ISBN: (纸本)9798331517984

User comments on social media or any video streaming platforms show how people feel about something, including different opinions, feelings, and problems of people interacting with it. It can assist people and organizations in making wellinformed choices by offering insightful information on how people feel about a topic or issue. The Elevated Expressway is a crucial infrastructure in Bangladesh and a controversial project that has sparked public debate and criticism. The 46.73 km road project connecting Hazrat Shahjalal International Airport to Kutubkhali on the Dhaka-Chittagong Highway has eased the commute time from the Airport to Farmgate from over an hour to 10 minutes. Regarding this project, People's comments on YouTube as a digital platform for voicing their opinions and contributing to a shared narrative that goes beyond traditional media. In this work, we examine users' sentiments about Elevated Expressway from YouTube comments, employing various Machine Learning (ML) algorithms and Deep Learning (ML) architecture. Based on these analyses, the BERT architecture arises as the most satisfying model, with an accuracy of 84.34% and a macro f1 score of 80.14% for classifying public sentiment as positive, negative, or neutral. © 2024 IEEE.

关键词： Video streaming

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An Analysis of Irrigation Management for Crops using Machine Learning Algorithms

An Analysis of Irrigation Management for Crops using Machine...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： R. Thamilselvan P. Natesan R. R. Rajalaxmi Prasanth D Nithish Swaminathan S Yuvapriya R Computer Science and Engineering Kongu Engineering College Perundurai Erode India Department of Computer Applications Kongu Engineering College Perundurai

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural production by providing water to crops in regions where rainfall alone is insufficient to meet their needs. This abstract provides an overview of crop irrigation, its importance, and various irrigation methods used in agriculture. Irrigation is a critical component of agricultural production, and advancements in machine learning algorithms have the potential to optimize irrigation practices for crop cultivation. Additionally, machine learning has developed in several industries to improve quality. Machine learning algorithms like Random Forest, K-Nearest Neighbor, Support Vector Machine and Decision Tree are used to find the various results. Internet of Things also plays a vital role in irrigation by making use of the sensors. This review's investigation was conducted using a variety of machine learning algorithms.

关键词： Performance evaluation Support vector machines Irrigation Machine learning algorithms Production Vectors Real-time systems

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An Artificial Intelligence Network based-Host Intrusion Detection System for Internet of Things Devices 4

An Artificial Intelligence Network based-Host Intrusion Dete...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Jain, Ashish Srinivasa Rao, B. Chattopadhyay, Saumitra Kumar, Aniruddh Muthuraman, M.S. Manjula, A. Bsss College Department of Computer Applications Madhya Pradesh Bhopal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Telangana Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Electronics Government Polytechnic Aurai Uttar Pradesh Bhadohi India Psna College of Engineering and Technology Department of Mathematics Tamilnadu Dindigul India Jyothishmathi Institute of Technology and Science Karimnagar India

ISBN: (纸本)9798350300093

Internet of Things (IoT) is currently employed in almost all the areas, including applications in smart cities, smart homes, e-Wellbeing, and others. Due to its wider utilization, IoT security has become a serious concern. A secure Intrusion Detection System (IDS) for the Internet of Things is often built using artificial intelligence (AI) and its subsets, deep learning (DL), and machine learning (ML). Industrial IoT devices, which are readily available, are regularly used by researchers and industry experts. This research study investigates the possibility of deploying a DL-Based Host-IDS (DL-HIDS) on specific commercial IoT devices. In this study, an optimized Convolutional Neural Network (O-CNN) based on DL is used. The proposed model's efficiency is evaluated by utilizing performance metrics like recall, precision, accuracy, and f1score. The proposed model's effectiveness is verified by analyzing the promising results obtained from the implementation of the proposed DL-HIDS on various existing models. © 2023 IEEE.

关键词： Internet of things

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Leveraging XGBoost for Predictive Analytics in Healthcare: Enhancing Disease Diagnosis 7

Leveraging XGBoost for Predictive Analytics in Healthcare: E...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Kotiyal, Arnav Habelalmateen, Mohammed I. Rana, Ajay Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Departyment of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in healthcare predictive analytics to improve the speed and precision of illness detection. Thanks to its built-in regularisation algorithms that avoid overfitting and its capacity to handle big datasets, XGBoost has proved useful in a broad variety of prediction applications. It is a scalable and high-performance machine learning algorithm. In order to improve patient outcomes, healthcare providers must ensure that patients are diagnosed with chronic and life-threatening conditions including cancer, heart disease, and diabetes as soon as possible. To test how well XGBoost performs in comparison to more conventional machine learning techniques, the research makes use of healthcare datasets collected from the actual world. The effectiveness of the model in predicting illness outcomes is evaluated using critical metrics including accuracy, precision, recall, and F1-score. Based on the findings, XGBoost is the best classifier for real-time healthcare applications because to its quicker processing efficiency and greater predictive ability. Additionally, the article delves into the model's interpretability and how it may be integrated into clinical decision support systems. By demonstrating how XGBoost may improve diagnostic accuracy and bolster individualised treatment plans, this study adds to the expanding area of AI-driven healthcare. © 2024 IEEE.

关键词： Predictive analytics

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Vision-Based Locomotion Control for Humanoid Robots: A Study on Vision-Guided Walking Strategies

Vision-Based Locomotion Control for Humanoid Robots: A Study...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Pandey, Saroj Kumar Kumar, Lalan Kumar, Gaurav Kumar, Ankit Singh, Kamred Udham Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Recently, there has been a lot of research on using vision to direct humanoid robots as they walk. Focusing on the major methodologies, difficulties, and prospects, this review article presents an overview of the current state of study in the vision-guided walking of humanoid robots. In the first part of the study, we introduce vision-guided walking and go through the several methods that have been developed to execute it, such as visual serving, visual odometer, and visual simultaneous localization and mapping. The difficulties of vision-guided walking are then discussed;these include, but are not limited to, the necessity for accurate and strong computer vision algorithms, the robot's need to have a solid awareness of its own body and how it moves, and the need to maintain balance while walking. This paper provides a detailed review of the research on humanoid robots that use vision to direct their gait. Deep learning for vision-guided walking, tactile and proprioceptive sensing combined with vision, and reinforcement learning for adaptive walking are only a few of the issues covered in this overview. Future directions in vision-guided walking research are discussed in this paper. These include the development of more advanced and reliable computer vision algorithms, the incorporation of other modalities like tactile and auditory sensing, and the investigation of new applications for vision-guided walking in fields like healthcare and rehabilitation. © 2023 IEEE.

关键词： Anthropomorphic robots

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A Data-Driven Approach to Soil Fertility Assessment Using Extreme Learning Machine 1

A Data-Driven Approach to Soil Fertility Assessment Using Ex...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Pant, Janmejay Kotlia, Priyanshi Singh, Devendra Pant, Hitesh Kumar Gangola, Saurabh Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Amrapali Group of Institutes Department of Computer Applications Uttarakhand Haldwani India Kumaun University Department of Management Studies Uttarakhand Nainital India Graphic Era Deemed to be University Department of Microbiology Dehradun248002 India

ISBN: (纸本)9798350376517

Soil fertility is a critical factor in agricultural productivity and sustainability. Accurate soil fertility assessment can lead to better crop management and yield optimization. This paper presents a data-driven approach to soil fertility assessment using an Extreme Learning Machine (ELM), an advanced machine learning algorithm known for its fast-learning speed and high generalization capability. The study involves comprehensive exploratory data analysis (EDA) and feature engineering to identify the most relevant soil properties influencing fertility. A dataset comprising various soil attributes was preprocessed, standardized, and subjected to feature selection techniques such as Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA). The ELM model was trained on this refined dataset and evaluated for its performance in predicting soil fertility ranks. This research highlights the potential of using ELM for soil fertility assessment, offering a robust and efficient tool for agricultural stakeholders. The findings can contribute to more informed decision-making in soil management and crop planning, ultimately enhancing agricultural productivity. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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