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Breast Cancer Classification using Machine Learning Algorithm

Breast Cancer Classification using Machine Learning Algorith...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Anu, D. Vidhya, K. Nitte Meenakshi Institute of Technology Artificial Intelligence & Data Science Bangalore India East West Institute of Technology Information Science & Engg Bangalore India

ISBN: (纸本)9798350359688

Around the globe in this era breast cancer is the most common disease among women, previously detection of breast cancer was a long and tedious process. In the proposed system the patient's data set will be collected. The collected data set will be manipulated and cleaned using feature extraction process [1]. The data set attributes consist of the nuclei measurements information. Nuclei measurements include radius, texture, perimeter etc. Also, the data set has the information whether the women breast does have the cancer or not by means of already calculated data which is indicated as benign or malignant. Benign are the non-cancerous tumor cell and that does not invade the neighboring cell, but malignant are the cancercausing tumor and it invades the neighbor cell after the manipulation and feature extraction of the data, it is further processed by machine learning algorithms under neural network. The dataset will be split into dependent and independent data. Dependent data are the data expect id and diagnosis, where the independent are diagnosis data [2]. The split data is 75% of training data and 25 % is testing data. Then scale the data (feature scaling) to bring the training data to a particular range of 0 to 1. The logistic regression is a part of machine learning algorithm which is a subset of random forest classifier, confusion matrix, SVM classifier, KNN classifiers are made used for both training and testing the data set. With the result the accuracy will be calculated [3]. The highest accuracy method will be chosen and the result data and original data will be compare to find the accurate result that whether the women does have the cancer or not. © 2024 IEEE.

关键词： Lung cancer

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Collaborative Electromechanical engineering: A Concept to Overcome Domain- and IT-System Borders 14

Collaborative Electromechanical Engineering: A Concept to Ov...

引用

14th international Electric Drives Production conference, EDPC 2024

作者： Urban, Nikolaus Vogel, Constantin Brunner, Thomas Gansauge, Ludwig Deggendorf Institute of Technology Faculty for Applied Natural Sciences and Industrial Engineering Deggendorf Germany ti4f GmbH Regensburg Germany

ISBN: (纸本)9798350356885

The product development process for new electric motors necessitates close collaboration between the domains of electrical and mechanical engineering. Starting from the customer specification, requirements must be translated into a three-dimensional design that aligns with manufacturing capabilities while considering the economic aspects of the overall project. As a multidimensional optimization problem involving multiple engineering disciplines, a systematic and efficient collaboration focused on customer value becomes the critical success factor. Current practices involve spatially close collaboration among project stakeholders, including electrical designers, mechanical engineers, manufacturing experts, procurement, logistics, sales, project- and product-management. Information exchange predominantly occurs synchronously through meetings or asynchronously via drawings or text-based documents. However, due to specialized IT applications and data formats in each domain, like electro-magnetic finite-element simulations or 3D-CAD-design, achieving systematic data and information continuity remains challenging. Consequently, information loss at domain boundaries becomes nearly unavoidable. We present a concept that systematically utilizes color coding within 3D-CAD environment to achieve both visually accessible and technically precise representation of product-and manufacturing-related information across domain borders. By uniquely associating motor components or component regions with relevant features, standardization of product and manufacturing properties becomes feasible. These standardized features can then be automatically processed throughout the subsequent process chain. The RGB color space provides ample combinations suitable for the design and manufacturing of electric motors, associated suppliers and even beyond. The concept derived can be seen as a starting point for knowledge management within organizations, inter-company collaboration based on standard

关键词： Computer aided logic design

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare 28th

Generating synthetic mixed-type tabular data by decoding sam...

引用

28th international conference on knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Drapala, Jaroslaw Swiatek, Jerzy Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types-some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented. © 2024 The Authors.

关键词： data privacy

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knowledge based Organizational Personal Assistant 2

Knowledge based Organizational Personal Assistant

引用

2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mohan, Vasanthakumar Paramesh, Anandh Sivaguru Panimalar Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350328202

In the modern era, most activities are being digitized. One of the main objectives of AI is to enable human machine interaction. The dialogue systems technology has been utilized by numerous organizations to create a variety of types of personal assistants depending on their applications and domains. Here we introduce the development of a versatile knowledge Based Personal Assistant for College that leverages advanced technologies, including natural language processing, voice recognition, and text to speech synthesis, to address the diverse and evolving needs of the college community. This AI driven system is designed to facilitate a seamless and efficient user experience, allowing users to interact with the assistant through both voice and text inputs. Alongside core functionalities encompassing academic schedule management, emergency contact assistance, email integration, location based services, and academic inquiries, the assistant provides responses in both voice and text formats. Technical aspects include the integration of state of the art NLP algorithms for query understanding and context aware responses. Users can easily switch between input mode and receive responses, accordingly, offering a flexible and inclusive user experience. The assistant adaptability and expandability are central to its design, featuring modular architecture that facilitates the integration of additional features and services. © 2024 IEEE.

关键词： knowledge based systems

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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data Acquisition to Handle Complexity in Matrix Production systems 56

Data Acquisition to Handle Complexity in Matrix Production S...

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56th CIRP international conference on manufacturing systems, CIRP CMS 2023

作者： Berkhan, Patricia Kärcher, Susann Bauernhansl, Thomas Fraunhofer Institute of Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Stuttgart Germany

Dynamically changing markets, the ongoing trend towards individualized products and the reduction of product life cycles confront manufacturing companies with new challenges and require an adaption of their production systems. Compared to rigidly coupled production lines or single assembly stations, so-called matrix production systems, combine high flexibility and high productivity to face these challenges. However, such manufacturing systems may be more complex concerning production planning and control. This paper proposes a generic model of a matrix production system to outline its main properties. In addition, the complexity of a matrix production system is characterized based on selected complexity drivers. As a result, data acquisition is identified as an opportunity to cope with complexity during production operations in matrix production systems. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP international conference on manufacturing systems 2023.

关键词： assembly complexity driver Matrix production model resilient

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A Robust system for disaster detection and management model

A Robust system for disaster detection and management model

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Karthikeyan, M.P. Mani, A. Indhumathi, S. Jothika, B. Deepika, Kongara Department of CSE R.M.K.Engineering College India Department of CSE S.A.Engineering College India

ISBN: (纸本)9798350305708

Numerous advancements are produced as a result of the development of embedded computer systems and the science of sensors. In this research, a unique sensing network framework for the disaster gathering and analysis, comprising data on the actual surroundings and prospective surviving' messages, is discussed. A centralized information server and a large number of sensor devices make up the overall structure. The main database server supervises the sensor devices' internal ZigBee system, which is coordinated by them. For the purpose of being able to deliver comprehensive catastrophic event data globally, the server must be linked to the internet. An automated configuration that can travel to catastrophe sites and assess the viability of an individual's involvement was built into the framework. © 2023 IEEE.

关键词： Communication systems Embedded systems Neural networks Sensors Wireless communications

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Smart Cloud Equipment Tracking and Control System

Smart Cloud Equipment Tracking and Control System

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2024 international conference on Industrial engineering, Applications and manufacturing, ICIEAM 2024

作者： Krutskikh, V.V. Ushkov, A.N. Chukashov, D.S. Trofimov, A.Yu. National Research University 'Moscow Power Engineering Institute' Moscow Russia Moscow Polytechnic University Moscow Russia

ISBN: (纸本)9798350395013

The extensive development of technology for various industrial production processes in the 21st century resulted in the occurrence of numerous specialized companies and research centers. Using a large number of devices and instruments for quality task implementation requires the consideration of the individual features of the employed devices and their proper maintenance. This can be achieved through timely instrument calibration and adjustment based on automated tracking systems. These operations largely depend on maintenance staff, thus, cloud platforms and network resources can solve the problem and promote the development of autonomous Industry 4.0 systems. As a solution, this article proposes a smart system structure with a cloud processor for a database of instrument data. The system is linked to physical objects with QR codes, and the database control and monitoring are implemented through a chatbot program. The authors analyzed the key integration areas of the smart equipment tracking and control system. The suggested system structure was used to test the prototype of a control and monitoring program for the state of industrial and laboratory equipment. The authors reviewed error control coding techniques for the recovery of facility information based on the Reed-Solomon code algorithm. The data obtained from live experiments confirmed the efficiency of cloud platform application and the economic feasibility of the proposed solution to the equipment control parameter monitoring problem under various situations. © 2024 IEEE.

关键词： Internet of things

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Ethical and Legal Implications of AI on Business and Employment: Privacy, Bias, and Accountability

Ethical and Legal Implications of AI on Business and Employm...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Reddy, K. Saketh Kethan, Manyam Mahabub Basha, S. Singh, Arti Kumar, Praveen Ashalatha, D. International Institute of Business Studies Department of Management Bengaluru India International Institute of Business Studies Department of Commerce Bengaluru India Kristu Jayanti College Autonomous Department of Commerce Bangalore India Chandigarh University India Telangana Medchal India

ISBN: (纸本)9798350359688

The proliferation of Artificial Intelligence (AI) in business and employment contexts necessitates a critical examination of the ethical and legal implications surrounding privacy, bias, and accountability. As AI systems become integral to decision-making processes, concerns about data privacy violations and algorithmic biases have heightened. This paper delves into these challenges, presenting a comprehensive framework to address the ethical and legal intricacies associated with AI deployment. Drawing on a thorough literature survey, we identify the gaps in current practices and propose a multifaceted approach to mitigate privacy infringements, combat bias, and establish accountability mechanisms. Our methodology combines quantitative and qualitative analyses, examining existing AI systems to gauge their impact on privacy and bias. The proposed implementation model integrates advanced encryption for privacy preservation, bias-detection algorithms for algorithmic fairness, and transparent decision-making processes to enhance accountability. The results showcase significant advancements in each domain, providing a foundation for responsible AI deployment in business and employment. This study contributes to the ongoing discourse on ethical AI by offering practical solutions to the evolving challenges, ultimately promoting a harmonious integration of AI technologies that align with societal values and legal standards. © 2024 IEEE.

关键词： Differential privacy

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IOT and developed deep learning based road accident detection system and societal knowledge management 2nd

IOT and developed deep learning based road accident detectio...

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

作者： Ananth, K.R. Fayaz Khan, Sarfraz Agarwal, Abhishek Wasim Bhatt, Mohammed Murtaza Alvi, Ahmad Degadwala, Sheshang Nandha Arts and Science College Tamil Nadu Erode India SAT – Algonquin College Ottawa Canada Vermont State University United States Model Institute of Engineering and Technology J&K Jammu India College of Finance Department of Business Administration Saudi Electronic University Riyadh Saudi Arabia Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

Business organizations and the research community try to precisely detect occurrences and assist in the case of a disaster. Most development systems are hardware-based, making them pricey and unavailable in every vehicle. A vehicle’s sensors can be destroyed in various ways, including minor accidents or fixed interactions. In instances, the sensors are incapable of detecting an accident. Intelligent phone sensors are a great alternative because of their dependability and availability. The study involves creating a smartphone application that continually reads sensor data and sends it to the cloud. The crash is discovered by threshold analysis. The critical novelty of this research is creating a scheme that alerts nearby hospitals and ambulances when an accident occurs. The system will have more minor inaccuracies, precisely identify accidents, and perform better than earlier techniques using four sensory inputs. This paper introduced novel types of deep learning for accident detection. © 2025 The Author(s).

关键词： knowledge management

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