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ML_Diabetes: An Efficient Framework for Diabetes Predictions Using machine Learning Techniques 3

ML_Diabetes: An Efficient Framework for Diabetes Predictions...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Elhattab, Laila Hany Elsayed, Omar Refaie Mohamed, Zaid Yasser Abed, Salma Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt

ISBN: (纸本)9798350306231

Diabetes is a chronic disease that can be fatal;currently, millions suffer from it. The diagnosis process can be tedious, long, or even inaccurate;in addition, the delay in diagnosis can cause serious health problems that include amputation, kidney failure, blindness, or even death. Thus, measures should be taken to ease the process of diagnosis. In this paper, the aim is to propose a methodology to apply machine learning and neural network models to predict diabetes based on a set of features that are applied to different data sets. In addition, those models are proposed to speed up the diabetes diagnosis process and predict diabetes accurately, as this will ease the process for doctors and patients. Moreover, we shed light on other research paper's shortcomings concerning diabetic prediction. This paper utilized eight models, seven of which are machine learning models, including Support Vector machine, Decision Tree, Naive Bayes, Gradient Boosting, K nearest Neighbors, Random Forest, and Logistic Regression, while one model is a Neural Network. The following results were established: Neural Network achieved very high accuracies in the three tested data sets: 82.3%, 91.9%, and 75.5% for Pima Indian, Diabetes 2, and Mendeley diabetes data-sets, respectively. Gradient Boosting, Logistic regression, Support vector machine, Naive Bayes weren't far behind, but they sometimes underperformed. As shown in this paper, machine learning has immensely helped in the prediction of diabetes, but with even more future improvements could be closer to 100 %. © 2023 IEEE.

关键词： Forecasting

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Online credit default prediction model based on fusion of Random Forest and XGBoost algorithm 3

Online credit default prediction model based on fusion of Ra...

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3rd IEEE international conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Shen, Yanhua Bao, Han Feng, Yanhui Dai, Yonghui Shanghai Xingjian College Shanghai China Shanghai University of International Business and Economics School of Management Shanghai China Shanghai JingAn District College Shanghai China

ISBN: (纸本)9781665490788

With the development of the Internet, online credit has grown rapidly. At the same time, the low threshold for online credit has caused some problems, such as illegal fund raising and telecommunication fraud. In order to solve the above problems, machine learning algorithms have been used as an effective way to predict the credit defaults. In this paper, we use real data from Internet credit platforms as the sample set. We choose Random Forest (RF) algorithm and XGBoost algorithm to build separate default prediction models, using Blending method to fuse these two single models to construct a well-performing credit default prediction model. After evaluating the prediction results, the fused models outperformed the single models in default prediction. © 2023 IEEE.

关键词： Forecasting

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Traffic Sign Detection Using Deep Learning 15th

Traffic Sign Detection Using Deep Learning

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Galety, Muhammad Gouse Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (纸本)9783031810824

The recognition of traffic signs, also known as TSR, is an essential component of many advanced driver assistance systems (ADAS) and autonomous driving systems (ADSs). Traffic sign detection (TSD), the first critical stage of TSR, is a tough challenge because of the various types of traffic signs, their small proportions, difficult driving conditions, and obstructions. TSD methods based on machine recognition and pattern classification have increased recently. This study's primary goal is to research Traffic Sign Detection using Deep Learning Techniques, and the method used in this research is Deep Learning Models like Faster R-CNN and YOLOv5. Convolutional Neural Networks are used for identifying and classifying road signs because they begin with an input image, assign weights to various elements of that image, and then differentiate those weights' respective aspects from one another. According to the results of this research, YOLOv5 is essential, even though its accuracy rate is slightly lower than Faster RCNN. In actual TSR, a faster recognition speed is frequently needed. TSR should use YOLOv5. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Road and street markings

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U-NET and RCNN Ensembled Satellite Object Detection and Segmentation 3

U-NET and RCNN Ensembled Satellite Object Detection and Segm...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Subhashini, G. Devi, Ajantha Niroshini Infantia, C. Karthi, M. Raghul, G. Shankar, S. Aravind St.Josephs Institute of Technology Chennai India AP3 Solutions Chennai India

ISBN: (纸本)9781665462167

Humans have always been curious to learn and explore new things and have achieved great things with that curiosity. "CURIOSITY IS THE WICK IN THE CANDLE OF LEARNING,"said William Arthur Ward. With the advancing technologies and advancing human needs, automation has become a leading technology in the IT sector. In the year of 1959, the first ever man-made satellite, which could take pictures was launched for the purpose of observing our planet. This led to the widely used technology in the year 2022, The Satellite images play a very crucial role in the n day-to-day activities of humans. It's because of satellite imagery, the weather forecasting capabilities of humans have improved drastically. People can sleep without worrying about the next upcoming storm as it can be detected with the help of satellite imagery and machine learning algorithms. Thus, precautions for the same can be taken even before the storm is formed. GPS system is another widely used technology used for security and exploration purposes. Satellite imagery with GPS technology gave another revolutionary use for humans, such as live location tracking in a graphical map, detected and captured by satellites. An example of such an application is Google Maps. Google Maps use various type of satellite images and machine learning algorithms to automate the various operations for public use. With the help of these technologies, this article will give an overview that how satellite imagery helps us to detect objects from satellite images and further segment images from satellite images using machine learning algorithms like RCNN with RPN and U-NET. © 2023 IEEE.

关键词： Object detection

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AI Integrated Healthcare System for Disease Predictions and Virtual Consultations 3

AI Integrated Healthcare System for Disease Predictions and ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331509675

AI-based Disease Prediction and Online Consultation System aims to redesign the way people experience healthcare. The deep growth of machine learning opened doors for new opportunities, such as this system that carefully inputs and validates patients' symptoms, and subsequently predicts possible diseases. In addition, it fosters remote consultation in a user-friendly manner over the internet and erases the barriers of distance and mobility. It is expected that this combination of data-driven forecasting and logic will enhance the treatment accuracy in terms of increased availability of healthcare services. AI-based Disease Prediction and Online Consultation System is a practical showcase on how to transform equity in healthcare. The increasing change in technology can give us possibilities for the utmost usage of this system. In this regard, the complex predictive analytics associated with the system may in effect change how HEIs (health education institutions) practice healthcare management. Patients will be offered a practice which supports the idea of delivering the right information on time and stress the universal right of access to healthcare. Its transformational power has far more prospects than it seems at first. At the core it sets the landscape and, indeed, the stage for the most equal, efficient and inclusive healthcare ecosystem that could ever exist. © 2025 IEEE.

关键词： Electronic health record

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pattern recognition and neural networks for acoustic monitoring and conservation of the historic port of Ancona 3

Pattern recognition and neural networks for acoustic monitor...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Di Loreto, Samantha Ricciutelli, Alessandro Lori, Valter Serpilli, Fabio Montelpare, Sergio Università degli studi G. D'Annunzio Viale Pindaro 42 Pescara60123 Italy Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60124 Italy

The soundscape of Ancona's historic port is a vital aspect of its cultural heritage, capturing the essence of its maritime and urban environment. This research focuses on the application of advanced sound event recognition techniques to characterize and preserve the unique auditory landscape of the port. Utilizing convolutional neural networks (CNNs) we developed an automated system capable of identifying and classifying sound events within the port's diverse acoustic environment. Our approach involves the extraction of significant "soundmarks" - unique sounds that define the auditory identity of the port, such as ship horns, dockyard activity, and traditional marketplace sounds. By capturing and analyzing these soundmarks, our system not only characterizes the existing soundscape but also aids in the preservation of these auditory elements, ensuring that they are protected in the event of environmental changes or disasters. The findings of this study underscore the importance of soundscape preservation as a critical component of heritage conservation, particularly for historic sites where auditory elements play a significant role in cultural identity. Our methodology provides a framework for the automatic extraction and classification of soundmarks, offering a valuable tool for heritage conservationists and urban planners in safeguarding the acoustic heritage of historical buildings and sites globally. © 2024 Copyright for this paper by its authors.

关键词： Urban planning

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Development of a Graphical User Interface as a Learning Tool for Artificial intelligence 22

Development of a Graphical User Interface as a Learning Tool...

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22nd Annual international conference of the Rapid-Product-Development-Association-of-South-Africa (RAPDASA) / Robotics and Mechatronics conference (RobMech) / conference of pattern-recognition-Association-of-South-Africa (PRASA)

作者： Botha, Natasha Marais, Stephen CSIR Next Generat Enterprises & Inst Cluster Operat Intelligence Pretoria South Africa CSIR Ctr Robot & Future Prod Mfg Cluster Pretoria South Africa

ISBN: (纸本)9781665408035

This study developed a Graphical User Interface (GUI) aimed at teaching the concepts of machine Learning (ML) algorithms which form the backbone of Artificial intelligence (AI). This is done by means of tutorial videos and allowing the student to interact with example datasets through a dashboard within the GUI before completing an assessment. This GUI is referred to as a practical station and is one of ten other Industry 4.0 technology practical stations within the MerSETA funded CSIR Learning Factory. The GUI provides an interactive learning experience to introduce the user to three machine learning concepts.

关键词： artificial intelligence interactive learning tool machine learning

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Research on the Application of Artificial intelligence in Comparative Analysis of Man-machine Translation 3

Research on the Application of Artificial Intelligence in Co...

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3rd international conference on Robotics and Artificial intelligence, JCRAI 2023

作者： Xiao, Chengxiao School of Foreign Languages Harbin Normal University Harbin150025 China

ISBN: (纸本)9798400707704

Due to the inherent differences in grammar structures, cultural customs, and social contexts among various languages, the current efficacy of machine translation still falls short of meeting individuals' diverse and nuanced needs. Merely isolated training in translation skills proves inadequate in nurturing the caliber of high-quality translation professionals required today. In this regard, a comprehensive translation education must encompass practical skills and a strong foundation in theoretical knowledge. Consequently, there arises a necessity for the integration of vocational translation education with a substantial theoretical and academic framework. In light of this, the present study delves into the utilization of Artificial intelligence (AI) technology for a comparative analysis between human and machine translations. The research findings underscore the promising potential of the proposed AI-driven approach, showcasing a relatively robust accuracy rate of 73.96% across human-machine translation evaluations. These outcomes undeniably highlight the efficacy of the method elucidated in the study's framework, particularly in the realm of human-machine translation evaluations. The implications of these findings are far-reaching, potentially smoothing the transition into the field of translation, especially for individuals endowed with broad-ranging knowledge and adept oral communication skills. Such proficiencies not only constitute fundamental requisites for advanced interpretation training but also serve as pivotal prerequisites for cultivating highly proficient English-Chinese communicators. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Artificial intelligence

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2022 3rd international conference on Artificial intelligence and Data Sciences: Championing Innovations in Artificial intelligence and Data Sciences for Sustainable Future, AiDAS 2022 - Proceedings

2022 3rd International Conference on Artificial Intelligence...

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3rd international conference on Artificial intelligence and Data Sciences, AiDAS 2022

ISBN: (纸本)9781665491648

The proceedings contain 60 papers. The topics discussed include: comparative analysis of machine learning techniques for acoustic machine tracking under different signal durations;preliminary study of diabetic retinopathy classification from fundus images using deep learning model;lexicon-based non-compositional multiword augmentation enriching tweet sentiment analysis;disparity-aware pandemic response classification by fine-tuning transfer learning approach;performance of ACO-based decision tree algorithm with imbalanced class data sets - a heuristic approach;heritage properties price prediction using random forest classifier;design of a fuzzy logic controller for washing machine;PCA method in facial features extraction for attendance tracking prototype;student career recommendation system using content-based filtering method;and duplicate detection using unsupervised random forests: a preliminary analysis.

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Blockchain Technology's Role in an Electronic Voting System for Developing Countries to Produce Better Results 3

Blockchain Technology's Role in an Electronic Voting System ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Milcah Blessy, I. Manikandan, G. Robinson Joel, M. Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350343632

The centralized approach is used by traditional electronic voting systems. These systems have a central administration that controls the database and the entire system as a whole as well as the complete voting process. Accidental or deliberate, this might lead to issues including possible database tampering and duplicate voting. Many of these problems have been addressed by the application of permissionless blockchain technology in modern voting platforms;nonetheless, each voting action requires a certain amount of processing effort due to the conventional consensus process of such blockchains. The removal of the standard consensus procedures employed by public blockchains delay has a major influence on power consumption, impairs system productivity, and increases system utilization of energy. The main goal of this review was to evaluate the current status of voting systems that are electronic and powered by blockchain election research, along with any related difficulties, in order to anticipate potential future developments. This investigation led to the discovery that some of the problems now plaguing election systems may be resolved by blockchain technologies. In order to create an all-encompassing facial recognition system, the project suggests a unique Connected Neural Network noted as CNN architecture for recognising facial features. This pipeline involves the collection of real-time data, such as visages of people, pre-processing, training of models, and ultra-parameter optimisation. Additionally, an online application to post participation utilising Face recognition and the newly built innovative CNN model is being developed. Age detection AGES expanded as Ageing pattern Subspace, which is frequently done with the aid of technology or algorithms, is a method of estimating or identifying a person's age according to a variety of criteria. © 2023 IEEE.

关键词： Ageing pattern Subspace Blockchain Connected Neural Network Electronic voting system Face recognition

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