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Comparative Performance Analysis of Feature Selection for Mortality Prediction in ICU with Explainable Artificial intelligence 3

Comparative Performance Analysis of Feature Selection for Mo...

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3rd international conference on Electrical, Computer and Communication Engineering, ECCE 2023

作者： Tasnim, Nusrat Mamun, Shamim Al Jahangirnagar University Daffodil International University Iit Savar Dhaka Bangladesh Jahangirnagar University Iit Savar Dhaka Bangladesh

ISBN: (纸本)9798350345360

The mortality prediction model in the Intensive Care Unit (ICU) can be a great tool for assisting physicians in decision-making for the optimal allocation of ICU according to the patient's health conditions. Traditional scoring-based systems for mortality prediction don't provide good predictive performance in the case of a large dataset. Moreover, machine learning models can also provide poor performance for the lack of proper feature selection. A comparison of the performance of machine learning models with and without feature selection was explored in this study. Principal Component Analysis (PCA) was used to choose features for this investigation. For the classification job, the most widely used and diversified classifiers from the literature were used, including Logistic Regression(LR), Decision Tree (DT), K Nearest Neighbours (KNN), and Support Vector machine (SVM). The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described machine learning models. Without feature selection, the accuracy of LR, DT, KNN, and SVM models was 86.66%, 80.12%, 85.13%, and 86.49%, respectively, however with PCA, the accuracy was improved to 88.0%, 80.46%, 86.83%, and 87.34%, respectively with only 5 principal components. Finally, the model's decision-making process was analyzed with explainable artificial intelligence using Local Interpretable Model-agnostic Explanations (LIME). This analysis can help to understand the feature's contribution to the model's prediction process. It was also observed that the features involved in the prediction process were mostly common with the first 15 features found in feature importance hierarchy. © 2023 IEEE.

关键词： Feature Selection

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Breast cancer diagnosis using machine learning Techniques 3

Breast cancer diagnosis using machine learning Techniques

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3rd international conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Muhamediyeva, D.K. Shaazizova, M.E. Doshanova, M. Yu. Tashkent University of Information Technologies Named after Muhammad Al-Kharezmy Tashkent Uzbekistan Digital Technologies and Artificial Intelligence Development Research Institute Tashkent Uzbekistan

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and na39;ve Bayesian classifiers. The properties of cell nuclei taken f... 详细信息

ISBN: (纸本)9781510674486

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and naïve Bayesian classifiers. The properties of cell nuclei taken from breast biopsies are included in the Breast Cancer Wisconsin dataset, which is used in this study. Three different machine learning algorithms-naive Bayesian classifier, SVM and decision tree-are used to develop predictive models aimed at classifying samples as malignant or benign tumours. The study involves training the models on evaluation data and then evaluating their performance on test data. In order to compare the effectiveness of each method, many metrics like accuracy, sensitivity, and specificity are used in the evaluation of the results. The outcomes demonstrate the efficacy of every technique examined in this research. This work can be used as a springboard for future research into refining and customizing these techniques to intricate clinical situations, in addition to offering a useful comparison of the efficacy of three distinct machine learning approaches for breast cancer diagnosis. The results may facilitate the application of machine learning techniques in clinical settings, hence facilitating early detection and bettering the prognosis of patients with breast cancer. © 2024 SPIE. All rights reserved.

关键词： Computer vision

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Improving machine Reading Comprehension through A Simple Masked-Training Scheme 13

Improving Machine Reading Comprehension through A Simple Mas...

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13th international Joint conference on Natural Language Processing / 3rd conference of the Asia-Pacific-Chapter of the Association-for-Computational-Linguistics (IJCNLP-AACL)

作者： Yao, Xun Ma, Junlong Hu, Xinrong Yang, Jie Li, Yuan-Fang Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9798891760189

Extractive Question Answering (EQA) is a fundamental problem in Natural Language Understanding, aiming at answering given questions via extracting a contiguous sequence or span of words from a passage. Recent work on EQA has achieved promising performance with the help of pre-trained language models, for which Masked Language Modeling (MLM) is usually adopted as a pre-training task to predict masked tokens. This paper revisits MLM and proposes a simple yet effective method to improve the EQA performance, termed the [Mask]-for-Answering method (M4A). Specifically, three masking strategies are first introduced, which produce masked copies of the original passages. Instead of predicting masked tokens as in MLM, both original samples and masked copies are utilized simultaneously for training the EQA model. Importantly, a discrepancy loss is further incorporated to ensure that masked copies remain semantically close to the originals. As such, M4A is able to produce robust embeddings for both original and masked samples and infer correct answers even with masked context. Experimental study on several highly-competitive benchmarks consistently demonstrates the superiority of our proposed method over existing methods. M4A also achieves strong performance in low-resource settings and out-of-domain generalization.

关键词： Modeling languages

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The Impact of Blockchain and AI in the Healthcare 3

The Impact of Blockchain and AI in the Healthcare

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3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350390728

Integrating blockchain and artificial intelligence (AI) in healthcare presents a transformative opportunity. Therefore, this can revolutionize patient care by improving data management and medical research. Decentralization and invulnerability granted by blockchain technology foster improved data consistency and interoperability. AI enhances blockchain by streamlining data examination, automatic tasks, and decision-making processes, resulting in more precise medical diagnoses and tailored therapy programs. The intersection of blockchain and AI is explored, along with their potential benefits and practical applications in healthcare. The document examines the hurdles encountered when integrating innovations, including scalability, interoperability, and regulatory adherence issues. Additionally, the study investigates moral questions surrounding data ownership and patient consent, thereby protecting confidentiality and trust. The synergy between blockchain, AI, and healthcare will lead to transformative advancements. It pledges to fortify data safety and expedite information sharing. Consequently, this will result in an improved, price-effective, and patient-focused healthcare environment. Recommendations were made for additional research and practical implementation in healthcare technology. The study should concentrate on creating scalable and compatible blockchain systems. A vital factor for extensive use is tackling regulatory and moral issues. Hybrid blockchain designs and federated learning strategies will bolster information privacy and utility. © 2024 IEEE.

关键词： Data consistency

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Research on garment classification and detection based on artificial intelligence and machine vision 3

Research on garment classification and detection based on ar...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Liu, Ye Ren, Fei Ren, LiQun Qian, JiTian Wang, Yong Li, HongSheng Nanjing Institute of Technology School of Automation Nanjing China Mapua University School of Information Technology Manila Philippines Nanjing Yangtuo Automation Co. Ltd Nanjing China Yulin University School of Information Engineering Yulin China

ISBN: (纸本)9781665459112

With the continuous development of computer and artificial intelligence technology, image recognition technology, as an important branch of artificial intelligence, has been widely used in various fields of daily production and life. The demand of e-commerce platform for a large number of clothing classification is also increasing. In this paper, LSTM and AlexNet deep learning network are used to construct the clothing image classification model to realize the classification and detection of clothing images;At the same time, based on the yolov5 deep learning detection model, combined with shufflenet, improve its backbone network to further improve the accuracy of garment image classification and detection. Experiments show that on the fashionmnist dataset, the accuracy and precision of LSTM and AlexNet are about 87%, while the improved yolov5 accuracy and precision are about 98%. © 2022 IEEE.

关键词： Image classification

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3rd Workshop on Ethical Artificial intelligence: Methods and Applications (EAI) 24

3rd Workshop on Ethical Artificial Intelligence: Methods and...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Zhao, Chen Chen, Feng Wu, Xintao Li, Jundong Chen, Haifeng Baylor Univ Waco TX 76706 USA Univ Texas Dallas Richardson TX USA Univ Arkansas Fayetteville AR USA Univ Virginia Charlottesville VA USA NEC Labs Princeton NJ USA

ISBN: (纸本)9798400704901

Ethical AI has become increasingly important, and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science. Despite these successes, ethical AI still faces many challenges, such as a lack of interpretable and explainable methods for fairness-aware deep learning models, etc. Consequently, there is an urgent need to bring experts and researchers together at prestigious venues to discuss ethical AI, which has been rarely seen in previous KDD conferences. This workshop will provide a premium platform for both research and industry from different backgrounds to exchange ideas on opportunities, challenges, and cutting-edge techniques in ethical AI.

关键词： Ethical artificial intelligence Fairness-aware machine Learning

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Short-time multi-target SSVEP recognition using perceiver and large kernel convolution network 3

Short-time multi-target SSVEP recognition using perceiver an...

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3rd international conference on Intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Zeng, Pu Huang, Dingyong Yi, Yang Wang, Kunqing Shen, Hui College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510683105

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have garnered significant interest from researchers because of their high information transfer rate (ITR). Nevertheless, the efficacy of conventional recognition techniques is constrained by the length of the experimental data set and the number of stimulus targets. Although some studies have made progress in improving the recognition performance of SSVEP by employing deep learning algorithms, there is still significant room for performance enhancement. To address the challenges of SSVEP in short-time multi-target recognition, Our proposal involves developing a neural network based on deep learning for SSVEP that combines the Perceiver architecture and Large Kernel Depth-wise separable convolution, called PLKDNet. The network consists of an input convolutional layer targeting multiple electrode channels, a series of large kernel depth-wise separable convolutional layers, a cross-attention and self-attention mechanism that incorporates the Perceiver layer and an output layer. We evaluate the performance of PLKDNet on different datasets and compare it to two mainstream deep learning models under different training sample conditions. The comprehensive experimental results demonstrate that the PLKDNet model performs excellently in handling the short-time multi-target sample recognition task, and its classification accuracy outperforms that of the comparison algorithms, showing the potential to advance the recognition performance of SSVEP-based BCI systems. © 2024 SPIE.

关键词： Interface states

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The Impact of Innovative Artificial intelligence Technologies in Talent Acquisition 3

The Impact of Innovative Artificial Intelligence Technologie...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Umamaheswari, M. George, Ginu Mol Amsaveni, N. Divya, D. Anitha, B. Merceline Kumar, S. Ananda Department of Business Administration Dr. SNS Rajalakshmi College of Arts and Science Tamilnadu Coimbatore India Department of Management Dr. SNS Rajalakshmi College of Arts and Science Tamilnadu Coimbatore India Department of MBA Sri Ramakrishna College of Arts and Science Tamilnadu Coimbatore India

ISBN: (纸本)9798331532420

Human resources management plays a crucial role in the success of an organization by acting as a link between employees and employers. Its policies and decisions shape the dynamics of the workforce, aiming to optimize their utilization and productivity. This includes strategic planning for recruitment, retention, and training and continuous assessment of employee performance. The study on performance prediction in human resources management begins by acknowledging the crucial role HRM plays in the success of an organization, acting as a vital link between employees and employers. It involves a wide range of strategic decisions and policies aimed at optimizing the utilization and productivity of the workforce. Workforce planning, a key HRM component, entails developing recruitment, retention, and training strategies while continuously assessing employee performance to ensure alignment with organizational goals and objectives. The quality of the workforce is a crucial determinant of organizational achievement, as employees are the most asset for development. Accurately evaluating employee performance is essential for fostering growth and progress, although traditional methods may be subjective and inefficient. However, introducing machine learning algorithms offers promising opportunities to enhance accuracy and objectivity in this process. At the core of this research is the understanding that the quality of the workforce significantly impacts organizational outcomes, with employees being the most valuable asset for organizational growth. However, accurately evaluating employee performance can be challenging due to subjective biases and inefficiencies inherent in traditional evaluation methods. In this context, machine learning algorithms offer a promising approach to enhance performance evaluation processes' accuracy, objectivity, and efficiency. This research focuses on evaluating and improving existing machine learning classifiers for predicting performance in hum

关键词： Personnel rating

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Detection of cyber terrorist communities and topic mining of tweets 3

Detection of cyber terrorist communities and topic mining of...

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Pan, Xiaohui Sheng, Hongsheng School of Artificial Intelligence and Law Shanghai University of Political Science and Law Shanghai201701 China

ISBN: (纸本)9781510681880

Social media, while flourishing, has also become a vital tool exploited by various terrorist forces. To analyze and study information related to terrorism on social networks, we propose a model framework that combines complex networks with natural language processing. This framework aims to explore influential key users, community structures, and communication topics within terrorism-related communities. Using a dataset of ISIS jihadist-related tweets from Twitter between 2014 and 2016, we construct an extremist network and employ centrality measures in the framework to identify important influential members. Additionally, the framework employs the ASOCCA community detection algorithm to reveal community structures and utilizes NMF topic modeling to extract topics from tweets and determine the focus of each important member. This approach enhances the capability for monitoring, identifying, disrupting, and tracking online terrorist activities. © 2024 SPIE ·

关键词： Tweets

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Detailed review on Integrated Healthcare Prediction System Using Artificial intelligence and machine Learning 3

Detailed review on Integrated Healthcare Prediction System U...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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