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Prediction of Cardiovascular Disease by Feature Selection and Machine Learning Techniques

Prediction of Cardiovascular Disease by Feature Selection an...

引用

International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Ranade, Aditya Pise, Nitin Dr Vishwanath Karad MIT World Peace Univ Pune 411038 Maharashtra India

ISBN: (纸本)9789819984787;9789819984794

Cardiovascular diseases (CVDs) are prevalent in the population and often lead to fatalities. Recent polls indicate that the death rate is increasing due to people's increased use of tobacco, high blood pressure, cholesterol, and obesity. These factors also exacerbate the severity of the disease. Therefore, it is crucial to conduct research on the variations of these factors and their impact on CVD. To prevent further disease progression and reduce mortality rates, it is essential to utilize current procedures. Various techniques, such as AI and data mining, are available to predict CVD precursors and detect their behavior patterns in large amounts of data. The results of these forecasts will assist clinical experts in decision-making and early diagnosis, reducing the likelihood of patient fatalities. This study investigates and comments on the Exhaustive Feature Selection (EFS) and Sequential Feature Selection (SFS) techniques and the results obtained using them with various classifiers. The paper also provides an overview of the current methods based on features and algorithms used.

关键词： Machine learning Cardiovascular disease Exhaustive feature selection Sequential feature selection Bagging method

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Sensory Substitution in Music: Enhancing Deaf Perception Through Vibrotactile Feedback 1

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9th International Congress on Information and Communication Technology (ICICT)

作者： Jadan, Johann Zapata, Mireya Remache, Paul Univ San Francisco Quito Quito Ecuador Univ Indoamer Ctr Invest Mecatron & Sistemas Interact MIST Av Machala & Sabanilla Quito 170103 Ecuador Univ Indoamer Fac Ingn Ind & Producc FAINPRO Ambato 180103 Ecuador

ISBN: (数字)9789819735594

ISBN: (纸本)9789819735587;9789819735594

Music experienced through vibrotactile interfaces is a method of perceiving musical elements through the sense of touch, often involving vibrations. This technology functions by converting audio signals into physical sensations that can be sensed through the skin, typically via a wearable device like a wristband. Beginning with an initial audio file devoid of tactile feedback, the procedure entails altering it through sinusoidal modeling and, if necessary, implementing a Space-Fixed Audio transformation by utilizing the Head-Related Transfer Function (HRTF). In this study, we successfully transformed sound files into tactile stereo vibrations using specialized hardware. This process was rigorously tested and validated through experimentation involving ten individuals. Our findings confirm that psychophysical sensations can indeed be perceptible. Notably, the most consistent responses were observed when applying the Vibrato and Tremolo effect, following an HRTF transformation. The Space-Fixed Audio transformation primarily introduced variations in azimuth, covering 360 degrees in a clockwise direction. Consequently, this processing led to significant spectral changes, effectively rescaling and compressing the audio's frequencies into lower equivalents. These modified spectral characteristics were subsequently transmitted through vibrotactile actuators, thereby transforming the essence of sound into a tactile experience. This innovative system creates a sensory replacement approach based on the psychophysical sensations perceived on the skin. It has proven to be exceptionally beneficial, particularly for individuals with hearing impairments who may not perceive music in the same way as individuals with typical hearing abilities.

关键词： HRTF SMS Sinusoidal modeling Vibrotactile actuator Sensory substitution

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Artificial Creativity. Perceptions and Prejudices on AI Music Production 9th

Artificial Creativity. Perceptions and Prejudices on AI Musi...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Micalizzi, Alessandra Pegaso Univ Ctr Direz ISOLA Naples Italy SAE Inst Via Trentacoste 14 Milan Italy

ISBN: (纸本)9789819733040;9789819733057

This paper aims at contributing to the ongoing discourse concerning the involvement of artificial intelligence (AI) in the realm of music production. Previous scholarly inquiries have consistently highlighted the prevalence of biases and concerns in AI-mediated creative processes, particularly among technicians, experts, and creators. However, studies demonstrate that in certain instances of blind listening evaluations, individuals exhibit a reduced predisposition toward prejudice, demonstrating genuine interest in and approval of creative musical outputs generated through AI-mediated processes. We will present the primary findings of an empirical study grounded in a tripartite experimental framework. Each of the three groups was assigned the task of evaluating musical productions, with one originating from human creativity and the other stemming from AI-based generative processes. This study represents the initial step of a broader research work aimed at comprehending how AI tools could be integrated into the creative process, whether it engenders a new aura around the creative product, to echo Benjamin, and ultimately, the implications for the broader cultural industry and its role as a producer of collective imagination.

关键词： Creativity Artificial intelligence Arts Music production Prejudice

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Students' Habits, Strategies, and Decisive Factors Around Course Selection 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Mendez, Gonzalo Gabriel Mendoza, Patricio Escuela Super Politecn Litoral Guayaquil Ecuador

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Before the beginning of a newacademic term, university students carry out a series of activities to decide which courses theywill be attending. The specific combination of chosen courses and the academic load these courses impose have a direct impact on several aspects of a student's life, including their academic performance. In this paper, we report the results of a survey that inquired about the activities students engage in when making course enrollment decisions in planning an upcoming academic term. Our survey asked about how students use some of the resources provided by their institution to inform their enrollment decisions, particularly when it comes to selecting specific instructors. We discuss the implications of our observations both for the student population and other university stakeholders. Our findings can inform the development of better tools to assist students in term planning.

关键词： Students

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Steady-State Features of DFIM in Electrical Traction systems 4th

Steady-State Features of DFIM in Electrical Traction Systems

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Zerzeri, Mouna Khedher, Adel Univ Sousse LATIS Lab Adv Technol & Intelligent Syst Ecole Natl Ingn Sousse Sousse 4023 Tunisia Univ Sousse Inst Super Transport & Logist Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia

ISBN: (纸本)9783031686498;9783031686504

This paper explores the potential integration of Doubly-Fed Induction Machines (DFIM) into a variable speed traction system by investigating the DFIM's steady-state features and performances. The equivalent circuit is an indispensable tool for analyzing the steady-state characteristics of DFIM. Nevertheless, the existing steady-state model does not accurately analyze some special operation modes. To overcome the limitation, in this article, a complete DFIM drive model is developed under MATLAB environment using d-q dynamic model to verify the strength and significance of the proposed controller. Based on the synchronized mathematical model, analytic determination of the control laws is presented and illustrated by various figures to understand the effect of the applied rotor voltage on the torque and the efficiency. The motor performances with proposed rotor voltage control are compared to conventional asynchronous motor. This study shows that by combining the synchronous and asynchronous torques, the DFIM electromagnetic torque can be increased across a wide range of speeds and overload capabilities can be improved. This is an attractive and valuable feature for electric vehicle applications.

关键词： Doubly-Fed Induction Motor high efficiency high torque characteristics of steady-state rotor voltage control parameters

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Predicting the Propensity of Customers to Pay via Mobile Applications with Machine Learning Methods

Predicting the Propensity of Customers to Pay via Mobile App...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Ozkan, Ece Ceran, Berkan Merl, Buse Eskiocak, Defne Idil Yuceoglu, Birol Migros Ticaret AS R&D Ctr TR-34758 Istanbul Turkiye

ISBN: (纸本)9783031671944;9783031671951

The retail industry is experiencing constant transformation due to increasing customer needs and the rapid growth of innovative technologies. Getting to know your customers better comes to the fore in achieving these transformations. Improving shopping experience of customers by using personalized offers and purchasing options such as online wallets is usually one of the first applications in this journey. This study is about determining propensities of customers to use MoneyPay mobile application using their past spendings and behavioural patterns. Propensity modelling is a set of techniques for building predictive models that adopt past behavioural information to predict future actions of target audiences. In this study, we create attributes to represent tendencies of customers to use the MoneyPay mobile application. Using real-life data, we estimate the probability for each customer using various classification models such as Random Forest and XGBoost. We use various metrics to determine the success of the models and consider customer similarities for more reliable results. We present the results of our computational study.

关键词： Propensity Model Machine Learning

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Sliding Mode Inventory Control with a Pregenerated Reference Trajectory Profile 28th

Sliding Mode Inventory Control with a Pregenerated Reference...

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2024 International Conference Automation

作者： Adamiak, Katarzyna Zwierzchowski, Tymoteusz Lodz Univ Technol Inst Automat Control 18 Stefanowskiego St PL-90537 Lodz Poland

ISBN: (纸本)9783031782657;9783031782664

This study tackles the problem of discrete time sliding mode control design for inventory management systems and proposes a new concept of demand structure and control. The paper considers a single product multi supplier inventory system. It is assumed that the demand divides into two sections: the unknown but bounded random demand and an a priori known contractual component. Therefore, it is proposed to firstly take into account the contractual demand only and generate the desired trajectory profile. In other words, the strategy aims to shape the chain of supplies according to the a priori known demand term. Next, the obtained trajectory profile is applied as a reference in the sliding mode control scheme, where an additional compensation of the random sales is introduced. This concept guarantees satisfaction of both contractual and random demand within their bound at any time instant. Moreover, the proposed strategy greatly simplifies the control design process.

关键词： Control Design Discrete-Time systems Inventory systems Sliding Mode Control Trajectory Generator

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Using the Swin-Transformer for Real and Fake Data Recognition in PC-Model

Using the Swin-Transformer for Real and Fake Data Recognitio...

引用

Intelligent systems Conference

作者： Park, Jiyoon Branksome Hall Asia Seogwipo South Korea

ISBN: (纸本)9783031664304;9783031664311

Recently, due to the rapid development of generative AI technologies, the use of AI-generated images has increased significantly, making the distinction between real and fake images crucial. Generative images may be used in various ways such as data training and fast image generation, but a potential for misuse, such as in deep fake or spreading false information, still exists. This study explores a novel model using the architecture of Swin-Transformer to distinguish between fake and real images generated based on CNN (Convolutional Neural networks) and GAN (Generative Adversarial networks). The Swin-Transformer, a successor model of Vision in Transformer (ViT), applies the structure of the Transformer, which has shown outstanding performance in natural language processing, to the field of images and demonstrates excellent pixel-level segmentation performance. Real and fake images require detailed pixel-level analysis, in which the Swin-Transformer exhibits higher accuracy. Improving the performance of distinguishing between real and fake images is expected to set limits on indiscreet image generation, bringing further effects such as preventing the indiscriminate use of AI images through program-based discrimination/legal sanctions.

关键词： Artificial intelligence Convolution neural network Generative adversarial network Real and fake

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Hybrid Convolutional Neural Network with Intuitionistic Fuzzy Estimations for Detection of Kidney Damage in Patients with Diabetes Mellitus 1

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Kralev, Krasimir Mirincheva, Zlatina Sotirov, Sotir Prof Dr Assen Zlatarov Univ Prof Yakimov Blvd Burgas 8010 Bulgaria Univ Hosp Act Treatment Address 73 Stefan Stambolov Burgas 8000 Bulgaria

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

The early detection of kidney damage in patients with diabetes mellitus is crucial for timely intervention and improved clinical outcomes. In this paper, we propose a novel approach for kidney damage detection by combining the power of convolutional neural networks (CNNs) with intuitionistic fuzzy estimates. The proposed hybrid CNN method leverages the hierarchical feature extraction capabilities of CNNs to automatically learn discriminative features from medical imaging data, while intuitionistic fuzzy estimates are utilized to model uncertainty and imprecision inherent in medical diagnosis. The intuitionistic fuzzy estimates enable the incorporation of expert knowledge and domain-specific information into the classification process, enhancing the interpretability and robustness of the model. We demonstrate the effectiveness of the proposed approach on a dataset of medical images obtained from patients with diabetes mellitus, achieving state-of-the-art performance in kidney damage detection. Our results highlight the potential of hybrid CNNs with intuitionistic fuzzy estimates as a promising tool for early diagnosis and management of kidney complications in diabetic patients.

关键词： Intuitionistic Fuzzy Sets Intuitionistic Fuzzy Pairs Intelligent systems kidney damage in patients with diabetes mellitus convolutional neural network

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Enhancing Diabetic Retinopathy Detection with Image Processing Techniques and Deep Learning EfficientNetB0: A Comparative Analysis 20th

Enhancing Diabetic Retinopathy Detection with Image Processi...

引用

20th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Babu, Sanjana Kumar, Gaurav Haider, Imran SRM Valliammai Engn Coll Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9783031665936;9783031665943

Diabetic retinopathy (DR) is a major global cause of blindness, underscoring the critical need for early and accurate detection. This study addresses this challenge by conducting an in-depth comparative analysis of various image processing techniques coupled with the state-of-the-art EfficientNetB0 model for DR identification. The researchers employed retinal images from the Asia-Pacific Teleophthalmology Society (APTOS) 2019 Blindness Detection competition on Kaggle, a valuable dataset for this purpose. The study considers five image processing methods: Median Subtraction, Ben Graham's Filter, Adaptive Histogram Equalization, Histogram Normalization, and Selective Gamma Correction. These techniques aim to enhance image quality and information content, which is crucial for training the model effectively. The study's primary performance metric is accuracy, recall, precision, and F1 score, which measures the model's ability to classify images correctly. The outcomes reveal that the choice of image preprocessing techniques significantly influences the model's performance. In summary, the study demonstrates that by combining advanced image pre-processing methods with a powerful deep learning model, Ben Graham's Filter can notably enhance diabetic retinopathy detection performance with 86% accuracy. Such advancements are instrumental in the early diagnosis and intervention of DR, thereby mitigating the risk of blindness in affected individuals.

关键词： Diabetic Retinopathy Image Processing Techniques Deep Learning EfficientNetB0 Model Ben Graham Filter

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