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Data Generation in Healthcare Using Constraint Satisfaction Parameters (CSP)

Data Generation in Healthcare Using Constraint Satisfaction ...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Hassanin M. Al-Barhamtoshy Tarik Himdi Information Technology Dept. Faculty of Computing King Abdulaziz University Jeddah Saudi Arabia Computer Science Dept. Jeddah International College Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

In this research, we examine the synthetic creation, alteration, and enhancement of data through Generative Artificial Intelligence (GAI). This inaugural study focused on generating healthy data via GAI for analysis and detection of chronic diseases (Depression, Stress, and Anxiety). Following the generation process using this deep learning approach, we analyze and visualize synthetic data through various metrics. The outcomes of this methodology are expected to yield novel insights into data generation concerning pressure-related diseases (depression, stress, and anxiety). Integrating high-quality synthetic data is anticipated to improve the production of financial transaction data. Finally, the proposed model is evaluated, and the results of the initial GAI model are generated. In this paper, we introduce a specialized generative Transformer language model that has been pre-trained on an extensive collection of medical constraint satisfaction parameters. We assess generation across medical natural language processing tasks and show that it surpasses existing models in most evaluations. Additionally, we offer a comprehensive analysis of the model's limitations by detailing numerous errors and challenges it faces. The mean scores for the produced descriptive data and shape pair trends in continuous columns were around 0.99 and 0.92, respectively. Conversely, the average scores for the categorical text generated in the initial region were notably high. This highlights the GAI model utilized in implementing each method. Our evaluation revealed that a majority of studies employ synthetic data generators to (i) reduce the costs and duration linked to clinical trials for rare diseases, (ii) enhance the predictive performance of AI models in personalized medicine, and (iii) provide researchers with access to high-quality, representative multimodal datasets while protecting sensitive patient information, among other advantages. Synthetic data generators based on deep learn

关键词： Deep learning Technological innovation Generative AI Anxiety disorders Data collection Depression Transformers Generators Synthetic data Diseases

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Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryogenic Applications

Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryo...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Zachary Cochran Maher Rizkalla John J. Lee Trond Ytterdal Linknath Surya Balasubramanian dept. of Electrical and Computer Engineering) Indiana University Purdue University at Indianapolis Indianapolis USA dept. of Electrical and Computer Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798350338782

ISBN: (纸本)9798350338799

Cryogenics amplifiers feature very low noise injection and low power consumption, and therefore, they are of highly demand in RF imaging, space communications, medical applications, and quantum computing. Unconditional stability with high gain also feature these applications. The band gaps of these devices are independent from temperature, suggesting their suitability for cryogenics applications. Furthermore, the nanometer scale lengths of these devices lead to ballistic transport with no scattering or diffusion, resulting in high-speed, low-power, and low noise capabilities within Extremely High Frequency (EHF) operating range that may extend into the THz. This study addresses these parameter issues within the range of 90K to 300K, assuming liquid nitrogen coolant medium, and further shows its stability above the room temperature as well. An appropriate model for the device that incorporates complex parasitics was investigated within this temperature range. The device with its estimated parameters, has been integrated into a simulated three-stage amplifier system. A low-noise gain between 16.9-18.9 dB with estimated noise figure of 2.97 dB and a bandwidth of 269 GHz bandwidth were predicted, with a high Stern stability factor suggesting unconditional stability over the bandwidth.

关键词： Bandwidth Power system stability Stability analysis Nanoscale devices Mathematical models Thermal stability Gain

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A Comparison of Feature Selection Approaches for Liver Disease Data

A Comparison of Feature Selection Approaches for Liver Disea...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ayushi Pillay Monika Arya Sumit Kumar Sar Bhupesh Kumar Dewangan Dept. of Computer Science & Engg. Bhilai Institute of Technology Durg CG INDIA Dept. of Computer Science & Engg OP Jindal University Raigarh Chhatisgarh India

Efficient feature selection (FS) methods are often needed to correctly classify multiple types of diseases, recognize disease symptoms, and enhance treatment modalities. The aim of this study is to compare the filter and wrapper FS approaches and to show how they enhance classification. First, on disease data, the FS approach has been employed with different classification models. We compared the effectiveness of two FS techniques: recursive feature elimination (RFE) and chi-square feature (CSF) selection. Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree are three different models (DT) that are used. Performance in classification is evaluated using K-fold crossvalidation. The methods are evaluated on datasets of diseases that are openly accessible. The findings showed that FS is important for accurate disease classification. A subset of the most prevalent characteristics is obtained after examining the relationship between particular symptoms and disease. Thus, by using FS that selects only a few disease predictors, the classification accuracy can be significantly improved.

关键词： Support vector machines Liver diseases Predictive models Feature extraction Medical diagnosis Decision trees Sustainable development

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An AI based solution for predicting the text pattern from sign language 21

An AI based solution for predicting the text pattern from si...

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13th International Conference on Contemporary Computing, IC3 2021

作者： Bhargav Dn, Abhishek Deekshitha Talanki, Skanda Aradhya, Dr. Sumalatha Thejaswini Dept. of Computer Science and Engineering Siddaganga Institute of Technology Tumkur India

ISBN: (纸本)9781450389204

A large social group get benefit from sign language detection through technology, but it is an overlooked concept. Communicating with others in society is a primary aim of learning sign language. Communication between members of this social group is rare due to limited access to technology. Hearing-impaired people are left behind. As normal people cannot make signs, they need to use texting methods to communicate with hearing-impaired people, which is less than ideal. Increasingly, deaf people must be able to communicate naturally no matter the practitioner's knowledge of sign language. An analysis of sign language is based on the patterns of movement generated by the hand or finger, commonly referred to as sign language. The aim of this paper is to recognize sign language gestures using convolutional neural networks. The proposed solution would generate the text pattern from the sign gesture. An RGB camera was used to capture static sign language gestures. Preprocessed images were used to create the cleaned input images. The dataset of sign language gestures was trained and tested on multiple convolutional neural network layers. The trained model recognizes the hand gestures and generates the speech from the text. In addition to outlining the challenges posed by such a problem, it also outlines future opportunities. © 2021 ACM.

关键词： Convolutional neural networks

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Synergistic dynamics of heart rate variability and systolic blood pressure revealed by dual Poincaré plot analysis 46

Synergistic dynamics of heart rate variability and systolic ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Agarwal, Prachi Guo, Yu Gharibani, Payam Prakash, Poorna Thakor, Nitish V. At Johns Hopkins University School of Medicine Dept. of Electrical and Computer Engineering BaltimoreMD21205 United States At Johns Hopkins University School of Medicine Dept. of Biomedical Engineering BaltimoreMD21205 United States At Johns Hopkins University School of Medicine Dept. of Neurology BaltimoreMD21205 United States Thomas Jefferson High School for Science and Technology AlexandriaVA22312 United States

ISBN: (纸本)9798350371499

Cardiac arrest (CA) survivors often suffer secondary brain injury due to impaired autonomic nervous system (reflected by heart rate HR) and hemodynamic function (reflected by blood pressure BP and baroreflex regulation). This study proposes a Dual Poincaré Plot, a novel method analyzing coupled variability in HR - BP dynamics to assess these impairments. Ten rats were categorized into good and poor neurological outcome groups in a rat model of CA. Dual Poincaré plot analysis, squared partial directed coherence, and sequence method were used to characterize the outcome. Results revealed differences in variability of HR, BP, and baroreflex, as well as coupling strength, between groups. Good outcome subjects exhibited 1) increased variability in both BP and HR, 2) enhanced baroreflex regulation, and 3) weakened cardiovascular coupling. Conversely, subjects with poor outcomes displayed 1) decreased HR-BP variability, 2) impaired baroreflex, and 3) stronger coupling. The Baroreflex Index from the Dual Poincaré plot showed a high correlation with the traditional sequence method (R2 = 0.91). These results imply that Dual Poincaré offers a real-time assessment of autonomic-hemodynamic interaction, effectively stratifying post-CA neurological recovery and potentially enhancing prognostic accuracy for timely interventions. © 2024 IEEE.

关键词： Rats

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Performance Analysis of Machine Learning Algorithms to Predict Water Potability

Performance Analysis of Machine Learning Algorithms to Predi...

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Computational Intelligence for Information, Security and Communication Applications (CIISCA), International Conference on

作者： J Parnika Shridhar B Devamane C N Ramya M G Swarna Laxmi Dept. of Computer Science & Engineering Global Academy of Technology Bengaluru Karnataka India

The survival of all living organisms, industry and agriculture is dependent on freshwater availability. A crucial step in the management of freshwater is the examination of the water's quality. According to the annual report of the World Health Organization, a lack of safe drinking water is resulting in the illness or death of many people, particularly children and pregnant women. Before the water is used for any purpose, including chemical spraying (for pesticides, etc.), or drinking water, it is essential to test its quality. Finding water that is safe to consume can be done to some extent by testing the water's quality. Therefore, water quality testing is essential. In this paper, we have carried out the implementation of 3 machine learning algorithms to check for potability of water after balancing, selecting important features using the Univariate Selection method and standardizing the data. Random Forest Classifier gave the maximum accuracy of 88.75%. The main objective of our work is to increase the accuracy and demonstrate the performance analysis of the considered algorithms' accuracy in this paper along with the existing works.

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An Innovative Approach Towards Efficient Diabetes Mellitus Prediction Using Federated Forest

An Innovative Approach Towards Efficient Diabetes Mellitus P...

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Silchar Subsection Conference (SILCON), IEEE

作者： Gifsy Madhu Anurag De Gautam Pal School of Computer Science and Engg. VIT-AP University Amaravati AP India Computer Science & Engineering Dept. Tripura Institute of Technology Narsingarh Tripura India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

In this study, we discuss a method for diagnosing diabetes mellitus that combines the Random Forest algorithm with Federated Learning. The global model preserves data privacy while guaranteeing accurate and dependable diagnostics by combining local models rather than data. The diabetes dataset, sourced from Mendeley Data, is preprocessed to address class imbalance through the Synthetic Minority Oversampling Technique (SMOTE). The dataset is further augmented using Conditional Generative Adversarial Networks (CGAN) to introduce non-lID variations while splitting the data. Our global model is evaluated and compared across different node configurations, with the optimal performance achieved using six nodes. The results show impressive performance characteristics with 98.18% accuracy, 99.09% specificity, 98.18% sensitivity, and an ROC AUC of 1.00. These findings indicate that our Federated Forest based approach significantly enhances the diagnostic capability for Diabetes Mellitus while maintaining data privacy and integrity.

关键词： Data privacy Accuracy Sensitivity Federated learning Smart healthcare Forestry Generative adversarial networks Data models Diabetes Random forests

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Machine Learning Based Potential Customer Recommendation System for E-Commerce Using Feature Selection and XAI

Machine Learning Based Potential Customer Recommendation Sys...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Zahrul Jannat Peya Md. Shymon Islam Nusrat Jahan Urme Saiful Islam Auny Dept. of Computer Science and Engineering North Western University Khulna Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In this era of digital commerce, systems that provide personalized recommendations are paramount in enhancing user experience and facilitating sales on online commerce platforms. For this reason, merchants need to know their potential customers. This work is devoted to developing a potential customer recommendation system using the well-known customer behavior and shopping habits Kaggle dataset. A customer is considered as potential customer having purchase amount greater than 70 USD and a review rating of at least 4. One of the contributions of this work is the utilization of the Select K-best with Mutual Information (MI) feature selection method in the e-commerce recommendation system. This study proposes a smart customer recommendation system using Machine Learning (ML) and Explainable AI to help merchants make data-driven decisions on which customers are the most potential ones. The proposed work makes the best use of several machine learning algorithms including Random Forest, Logistic Regression, and K-Nearest Neighbor. Among these models, Random Forest reveals the best performance of 100% accuracy. In addition, the Local Interpretable Model-agnostic Explanations (LIME) from Explainable AI are used to detect the responsible transaction records, product features and purchasing behavior of the consumers.

关键词： Visualization Accuracy Explainable AI Computational modeling Feature extraction User experience Electronic commerce Recommender systems Random forests Mutual information

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Movie Recommendation System Using Euclidean Distance

Movie Recommendation System Using Euclidean Distance

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Muneeb Sami Khan Zunnurain Hussain Muhammad Irtaza Amaad Muhammad Zulkifl Hasan Aqsa Khalid Saad Hussain Chuhan Rimsha Awan Muhammad Atif Yaqub Farhan Ashraf Department of Computer Engineering Information Technology University Lahore Pakistan Dept. of Computer Science Bahria University Lahore Campus Faculty of Information Technology University of Central Punjab Department of Computer Science National College of Business Administration and Economics Lahore Pakistan

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In this study, we pivot from the commonly used cosine similarity to the Euclidean distance as the foundation for our film recommendation algorithm. Initially, data pertaining to users, films, and ratings are loaded using the panda’s library. The algorithm then computes normalized ratings, tabulates the number of films in each genre, and visualizes this distribution. The frequency of ratings for each genre is similarly plotted. A dictionary is maintained to store movie IDs for each genre. These values are then integrated into the normalized rating matrix. For each genre, the normalized ratings of movies are cataloged in a lexicon. Using the normalized ratings matrix and movie IDs, a Data Frame is generated. Subsequently, the movie and ratings datasets are merged. A pivot table captures the number of films each user has watched per genre. The study also be happy with their purchase if these recommendations are tailored to their needs. As a result, the client would utilize this application once more. Because of the significant money gained determines the average number of films a user watches in each genre. This pivot table is refined to display movies in each genre that a user has watched, but only those exceeding the genre’s average viewership. Instead of cosine similarity, the Euclidean distance is computed to gauge the similarity between movies, offering a fresh perspective on recommendation systems.

关键词： Technological innovation Dictionaries Data visualization Euclidean distance Filtering algorithms Motion pictures Libraries Fourth Industrial Revolution Recommender systems

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Parking management system

Parking management system

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2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021

作者： Sathya, D. Roopa, S Nithya Sudha, V. Praveen, K. Arunkumar, A. Sanjeevan, G. Kumaraguru College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665428293

The main concern for vehicle owners often is to identify the proper place to park the vehicle at the earliest. In this work, a web application is created and tested, which can be used to find the availability of different parking slots in a parking lot. The web application controlled by the admin consists of various modules ranging from parking allotment to payment status. To implement this, we have used the Apache Tomcat server using the database SQL server. This system will verify the occupancy of parking slots, provide customers about the availability of parking slots in a short period and monitor occupied slots. The convenience of the person driving the vehicle is positively handled which further improves the effective time management with increased effectiveness which also paves way for less energy consumption and pollution. © 2021 IEEE.

关键词： Energy utilization

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