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Advancements in Diabetes Prediction: A Comprehensive Study Utilizing Advanced machine learning Techniques 4

Advancements in Diabetes Prediction: A Comprehensive Study U...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Al Jaziri, Maryam Bonny, Talal Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

In the landscape of healthcare, diabetes remains an urgent concern, demanding effective detection methods due to its widespread impact. In response, our research takes up the mantle, harnessing cutting-edge machine learning algorithms to advance diabetes prediction. Our core methodology revolves around pioneering deep learning algorithms, which unravel intricate patterns within complex medical datasets. through rigorous evaluation, our study demonstrates the proficiency of this approach in accurately distinguishing between positive and negative diabetes cases. Notably, our investigations reveal that Deep learning - Scenario 1 emerges as the most promising methodology, achieving an accuracy of 84.24%, surpassing the accuracy attained by Logistic Regression (82.46%), Deep learning - Scenario 2 (69.48%), and Deep learning - Scenario 3 (80.21%). this remarkable outcome underscores the potential of Deep learning - Scenario 1 in enhancing diabetes prediction accuracy. the fusion of innovative methodology and empirical outcomes marks a significant leap forward in diabetes management, highlighting the transformative power of machine learning applications. through this synergy of inventive techniques and promising results, our work resonates with the evolving realm of diabetes research and healthcare practices. © the Institution of Engineering & Technology 2023.

关键词： Deep learning Diabetes Diabetes Cases machine learning Algorithms

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Empowering Healthcare with Deep learning: An application for Early Detection of Skin Cancer 4th

Empowering Healthcare with Deep Learning: An Application for...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Oluwatobi, Komolafe Joseph Asghar, Ikram Diyan, Muhammad Nawaz, Rab Saleem, Sidra Ullah, Rahmat Ahmad, Jawad Univ Teesside Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Agr Faisalabad Faisalabad Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031821523;9783031821530

Skin cancer is a hazardous ailment and a leading contributor to mortality. Early diagnosis of skin cancer can significantly minimize or prevent these fatalities. the diagnostic process can be both lengthy and costly. this study aims to establish a basis for creating an exact and effective convolutional neural network (CNN) model for detecting skin cancer. this model aims to enhance the early and accurate diagnosis of skin cancers, potentially leading to a decrease in the number of associated fatalities. the model utilizes a convolutional neural network with Keras Tensorflow as the backend to categorize seven distinct categories of skin cancer. Subsequently, the results are analyzed to determine the practical applications of the model. the MobileNet optimizer is used for classification. A web application has been developed that offers dermatologists the three most likely diagnostics for a specific blister. It will aid in swiftly recognizing patients with high priorities and accelerating their workflow. the application generates a result within a time frame of 5-10 seconds.

关键词： Skin Cancer CNN machine learning image classification medical data security

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4th international conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th international conference on Human-computer Interaction, HCII 2023

4th International Conference on Artificial Intelligence in H...

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4th international conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th international conference on Human-computer Interaction, HCII 2023

ISBN: (纸本)9783031358906

the proceedings contain 85 papers. the special focus in this conference is on Artificial Intelligence in HCI. the topics include: Unvoiced Vowel Recognition Using Active Bio-Acoustic Sensing for Silent Speech Interaction;Is It Possible to Measure Real-Time VR Motion Sickness with EDA Wavelet Features?;How Do AI Explanations Affect Human-AI Trust?;the thousand Faces of Explainable AI Along the machine learning Life Cycle: Industrial Reality and Current State of Research;how to Explain It to a Model Manager?: A Qualitative User Study About Understandability, Trustworthiness, Actionability, and Action Efficacy;application of Reinforcement learning for Intelligent Support Decision System: A Paradigm Towards Safety and Explainability;INFEATURE: An Interactive Feature-Based-Explanation Framework for Non-technical Users;A Quantitative Comparison of Causality and Feature Relevance via Explainable AI (XAI) for Robust, and Trustworthy Artificial Reasoning Systems;Evaluating Explainable AI (XAI) in Terms of User Gender and Educational Background;AI Explainability, Interpretability, Fairness, and Privacy: An Integrative Review of Reviews;examining Simulated Agricultural Tasks Using an Arm-Support Exoskeleton;Emotional Debiasing Explanations for Decisions in HCI;exploring Mental Models for Explainable Artificial Intelligence: Engaging Cross-disciplinary Teams Using a Design thinking Approach;requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work;Exploring the Effect of Visual-Based Subliminal Persuasion in Public Speeches Using Explainable AI Techniques;Assessing the Impact of Cognitive Biases in AI Project Development;How is the AI Perceived When It Behaves (Un)Fairly?;Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks;through a Fair Looking-Glass: Mitigating Bias in Image Datasets;Ethical AI Based Decision Making to Reduce Tax Related Debts for Governments;improving Fairness via Deep Ensemble Fra

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machine learning And Blockchain: A Promising Future 4

Machine Learning And Blockchain: A Promising Future

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Maheshwari, Himani Chandra, Umesh Yadav, Dharminder Gupta, Ashulekha Kaur, Rasmeet School of Computing Graphic Era Hill University Uttarakhand Dehradun India Banda University of Agriculture and Technology Department of Statistics and Computer Science UP Banda India Glocal University Department of Computer Science and Technology UP Saharanpur India Graphic Era University Department of Management Studies Uttarakhand Dehradun India

ISBN: (纸本)9798350341126

Currently, the two developments that are most obvious to the public are machine learning (ML) and blockchain technology. the first is the development of AI and big data, and the financial sector has been substantially impacted by the second. Recently, the advancement of blockchain technology has converted a spectacular, frequently challenging, and moving innovation. the blockchain's decentralized database emphasizes information security and protection. the agreement tool also makes sure that data is reliable and true. In any event, it brings up a few security concerns;to address these concerns, data analytics on blockchain-based safe information is required. Analysis of this data highlights the importance of the newly-emerging invention, machine learning (ML). the substantial amount of data used by machine learning technology allows it to provide precise conclusions. In ML, reliability and data exchange are crucial to enhancing the accuracy of results. ML and blockchain technology combined can produce incredibly precise results. Since the two innovations are information-driven, there is a growing interest in implementing them for more secure and useful information analysis and sharing. In this article, research is done to demonstrate the viability and productivity of merging blockchain with machine learning advances. First, examine the fundamental language and structure of blockchain and machine learning. Different ML techniques that can be applied to blockchain framework are presented in the next section. Additionally, consider how the fusion of these two concepts advances data security and cybersecurity. the study is ended in the final section, which discusses future prospects for research and challenges brought on by the ongoing and possible integration of ML and blockchain. © 2023 IEEE.

关键词： Blockchain

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A Review of Research on Instance Segmentation Based on Deep learning 1

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13th international conference on computer Engineering and Networks (CENet)

作者： Yang, Qing Peng, Jiansheng Chen, Dunhua Guangxi Univ Sci & Technol Coll Automat Liuzhou 545000 Peoples R China Hechi Univ Dept Artificial Intelligence & Mfg Hechi 547000 Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

the field of machine vision has witnessed a significant surge in the application of deep learning technology, as researchers increasingly leverage its capabilities in their work. While deep learning has been extensively used in object detection and semantic segmentation, research on deep learning-based instance segmentation has gained significant traction only in recent years. Instance segmentation is a computer vision task that is closest to the real human visual experience and provides a deep understanding of image scenes. Instance segmentation encompasses more than just pixel-level segmentation of various object categories;it also involves the ability to distinguish and separate individual instances within each category. It can be widely applied in fields such as autonomous driving, assisted medical treatment, and remote sensing imaging. this article systematically summarizes some typical instance segmentation models in two parts: two-stage and single-stage, analyzes and compares the advantages and disadvantages of different algorithms, and conducts performance tests on the COCO dataset. this article also provides a brief introduction to the COCO dataset and instance segmentation evaluation indicators. Finally, the possible future development directions and challenges faced by instance segmentation are discussed.

关键词： Deep learning Instance Segmentation computer Vision

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Predicting Z Boson Decay Modes: Evaluating the Performance of machine learning and Deep learning Techniques in Particle Physics 8

Predicting Z Boson Decay Modes: Evaluating the Performance o...

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8th international conference on computer Science and Engineering, UBMK 2023

作者： Sag, Canda Deniz Sahin, Onur Izmir Turk College Science İzmir Turkey Izmir Turk College Anatolian İzmir Turkey

ISBN: (纸本)9798350340815

this study presents an application of machine learning and deep learning techniques to the problem of classifying the decay modes of the Z boson, a fundamental particle in the Standard Model of particle physics. We focus on two specific decay modes: Zee and Zmumu. Our dataset comprises simulated events with variables such as transverse momentum, pseudorapidity, and phi angle of the decay leptons. the data is preprocessed via a mapping method for categorical data and a MinMaxScaler for numerical data. We compare the performance of several classification models, including K-Nearest Neighbors (KNN), Weighted KNN, Random Forest Classifier, KDTree KNN, XGBoost, Multi Layer Perceptron, Support Vector machine, Decision Tree Classifier, and a Deep learning model based on a sequential neural network architecture. Our results indicate that the XGBoost provides the highest accuracy (81.42%) among the tested models. However, the Deep learning model exhibits superior precision (84.14%), which is crucial in reducing false positives in the classification task. the study emphasizes the potential of machine learning and deep learning in high-energy physics and suggests directions for future research to further improve the classification performance. © 2023 IEEE.

关键词： learning systems

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Detecting Diseases in Potato Leaves using Deep learning and machine learning Approaches: A Review 4

Detecting Diseases in Potato Leaves using Deep Learning and ...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Bommala, Harikrishna Babu, N Junnu Srikanth, Pothuganti Mallidi, S. Kumar Reddy Sai, thota Siva Ratna Mounika, Rudrapati Kg Reddy College of Engineering and Technology Dept. of Cse Telangana Hyderabad India Dept. of Computer Science and Technology Andhra Pradesh Madanapalle517325 India Dept. of Cse Maisammaguda Telangana 500100 India Sri Vasavi Engineering College Dept. of Cse Andhra Pradesh Tadepalligudem534101 India Dept. of Cse Telangana India

ISBN: (纸本)9798350300888

As a mostly agricultural country, India is deeply concerned about its crop production rate. When crop yields drop, food prices rise, and people who can't afford even potatoes go hungry. Physical examination is a component of conventional approaches for spotting diseases in plants. this approach is exceedingly costly, laborious, and doesn't yield good outcomes. A unique approach for diagnosing potato leaf ailments should be developed to detect sick leaves at the start of their developmental period, which will assist to boost the output and consequently reduce the financial losses suffered by agriculturalists. Fortunately, new Deep learning (DL) and machine learning (ML) models have flourished as a method of reducing plant disease and increasing crop yields, alleviating some of the burdens on farmers. Deep Neural Networks (DNN), a kind of artificial intelligence technology, may be used to identify disease-infected plants so that they can be treated early on, in the bud stage, before the sickness has spread too far. Based on a comprehensive literature assessment, the authors of this research concluded that Convolution Neural Networks (CNN) are superior to other methods for detecting leaf diseases and it is determined that CNN provides the highest achievable accuracy of 91% to 100%. © 2023 IEEE.

关键词： Diagnosis

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Trust-Based Geo-Social Access Control Framework: Employee Management through machine learning Techniques 4

Trust-Based Geo-Social Access Control Framework: Employee Ma...

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4th international conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Hiremath, Priyanka C. Raju, G.T. Department of Computer Science and Engineering SJC Institute of Technology Chickballapur India

ISBN: (纸本)9798350369694

In the modern corporate sphere, organizational trustworthiness is crucial, particularly in understanding and predicting employee behavior through data science. Managing employee access levels is vital for security and privacy. Our study introduces a novel approach, utilizing Geo-Social and Trust data. Various machine learning techniques, including Linear Regression, K-Nearest Neighbours, Decision Tree, Random Forest, XGBoost, and Multi-Layered Perceptron, are employed to extract insights from geographical and social sources. this extensive dataset contains detailed geographic employee information, navigation choices, spatial exploration patterns, and choice set formations. Grounded in this blend, we establish an access control data modeling system to evaluate employee access based on trustworthiness. this model categorizes employees into low, moderate, high, and very high access levels, adapting to changing behavior. Our method fosters a cohesive, trustworthy employee network and provides a precise mechanism for assessing trustworthiness. It promises augmented organizational coherence, heightened commitment, and reduced attrition. Additionally, this approach safeguards against data breaches and privacy violations by restricting access for less trustworthy employees. In summary, our innovative access control framework, merging Geo-Social and Trust data with cutting-edge machine learning, heralds a new phase of trust, precision, and security in organizational dynamics. © 2023 IEEE.

关键词： Access control

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Method and application of Analyzing Multi-class Data by Artificial Intelligence Model Group Based on machine learning

Method and Application of Analyzing Multi-class Data by Arti...

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2023 international conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Zhuo, Bihua Officers College of Pap Department of Information and Communication Sichuan Chengdu610213 China

ISBN: (纸本)9798350313314

With the continuous in-depth development of science and technology in China, the field of artificial intelligence has also made great progress [1]. As a comprehensive cross-cutting research field of computer science, information science and mathematics, artificial intelligence mainly refers to the development of intelligent technology realized by computer technology. Firstly, this paper analyzes the basic concepts of artificial intelligence and machine learning, and then studies the influencing factors and construction methods of machine learning based on artificial intelligence technology [2]. the purpose is to expand our thinking for the development of artificial intelligence technology in China, and make contributions to the field of artificial intelligence for the research of machine learning in China, so as to better serve the economic development of China [3]. In order to bring the value of artificial intelligence into full play, it is necessary to study the intrinsic meaning, research purpose and machine learning methods of machine learning [4]. Based on big data technology, this paper expounds the accurate judgment of educational data in different regions, the problems of artificial intelligence integration into the educational process, and the fact that machine learning meets people's demand for education in the process of modernization. It also discusses the basic concept and application of machine learning, so as to promote the development of artificial intelligence technology [5]. © 2023 IEEE.

关键词： Economic and social effects

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4th international conference on Design Tools and Methods in Industrial Engineering, ADM 2024

4th International Conference on Design Tools and Methods in ...

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4th international conference on Design Tools and Methods in Industrial Engineering, ADM 2024

ISBN: (纸本)9783031765933

the proceedings contain 157 papers. the special focus in this conference is on Design Tools and Methods in Industrial Engineering. the topics include: the Simultaneous Effect of FFF Build Orientation on Tensile Strength, Flatness and Roughness;reasons Behind Selecting a Manufacturing Process: A Semi-structured Interview Study;2D Frequency-Based Topological Optimization: machine learning to Aid Tuning Simulation Parameters;A Short Overview of Optimization Methods for BCC, FCC and Octet Lattice Structure;advancements in Design of Personalized Bone Scaffolds and Fabrication through 3D Printing;lightweighting of a Kart Connecting Rod in Additive Manufacturing;Design of a Local Heating System for Cost-Effective Fused Filament Fabrication of PEEK;a Multi-Criteria Analysis Method in Algorithm-Driven Design;a Design Method for Graded Density Components by Polylactic Acid Foaming via Material Extrusion;A New BCC Lattice Modelling Approach through a Neural Network Optimisation;design and Assessment of Functionally Graded Lattices;A Strategy Towards In Situ Monitoring and Quality Assessment of 3D Components Manufactured by FDM: Preliminary Features;Integration Between AI and TRIZ for Technical Innovation;engineering Design and Prototyping Methodology in Complex Environments: the In-Bore Welding Tool;machine learning for Costing Sheet Metals;application of a Simplified Value Analysis Methodology (VAM-2P) and CAD/CAE Tools to Agro-Industrial Product Redesign: Harvesting Trolley Case Study;Preliminary Concept Design of an Innovative Under-Actuated Gripper for the Remote Handling Operations in the RFX-Mod2 Fusion machine;Preliminary Design of a General Control Architecture for the DTT Remote Handling Facility;study and application of a New Index to Support Design for Disassembly;digital Product Passports: A Framework for Developing Adaptable and Compliant Data Models.

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