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Improving Transparency in Romanian Public Procurement: machine learning to Classify Bidders and Validate Decisions 28th

Improving Transparency in Romanian Public Procurement: Machi...

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28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Silaschi, Iasmina Oana Pop, Ioan Daniel Coroiu, Adriana Mihaela Automation and Computing Faculty Politehnica University Timisoara Timis300223 Romania Computer Science Department Babes-Bolyai University Cluj-Napoca40084 Romania

The primary objective of this research paper is to conduct a real-life case study, centred around a substantial dataset procured from numerous Romanian public procurement tenders. The study aims to classify the bidders according to their compatibility and suitability, an assessment determined by the accuracy of different machine learning models. The dataset used, is a vast body of information. More precisely, it includes data from a total of 289,472 enterprises, 47,974 contracting authorities and 42,474 tenders. The use of this extensive data set ensures a robust and comprehensive analysis, thus enabling the extraction of meaningful insights and facilitating the drawing of reliable conclusions. In the quest to rank bidders effectively, the performance of nine intelligent algorithms is evaluated and compared. The top-performing algorithms in this context appear to be Decision Trees, along with ensemble methods derived from them, namely the Random Forest Classifier and the Extra Trees Classifier. The outcomes of this study are satisfying, outperforming the metrics of a similar study conducted in the same domain. Given the potential concerns about Romania's integrity in dealing with contracting companies for public procurement auctions, this study holds significant value in its ability to validate the decision-making process employed in previous auctions. By casting light on the effectiveness of past procurement decisions and offering strategic guidance for future acquisitions, this research holds substantial implications for improving transparency and streamlining the bidder selection process within the public procurement area. Therefore, with potential for future work upon it, it promises to become a valuable addition in enhancing the integrity and efficiency of public procurement in Romania. © 2024 The Authors.

关键词： extra trees classifier labelled data precision public procurement random forest classifier

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A Design for gamified platform for Children with ASD

A Design for gamified platform for Children with ASD

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Tuli, Harshita Kumar, Bibek Department of CSE Vishveshwarya Group of Institutions India Department of CSE LIET Greater Noida India School of Data Science Symbiosis University of Applied Sciences India

ISBN: (纸本)9798350355338

Autism spectrum Disorder (ASD) is a problem that affects a child’s nervous system, growth and development. It is called a spectrum because kids under ASD can restrict a child from developing social skills, facial expression or emotion in other people . A child with ASD may hesitate to be touched, may enjoy being isolated and are often rigid to routines being changed. The medical guidelines say that all children should be screened for ASD and other development disorders before age 2. These children may have delayed milestones. However, such delays can be overcome by the use of engineering approaches. In today’s digital era, kids with ASD are also exposed to gamified platforms. And as per the studies available the exposure to a gamified platform can provide a solution to associated delays for ASD children. With such a goal in mind, we have tried to work on engineering-based solution to train such kids so that they can overcome the learning delays. © 2024 IEEE.

关键词： Gamification

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Technologies Integration With Semantic Web Techniques For Business Valuation: Identifying Equities' Intrinsic Value Without Market Bias 2

Technologies Integration With Semantic Web Techniques For Bu...

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2nd international conference on Cyber Resilience, ICCR 2024

作者： Faccia, Alessio Itani, Rania Eltweri, Ahmed Ae Wanganoo, Leena Pio, Leonardo Luigi University Of Birmingham Dubai United Arab Emirates Murdoch University Dubai United Arab Emirates Applied Science Research Center Applied Science Private University Amman Jordan Liverpool John Moores University United Kingdom Universita Di Foggia Italy

ISBN: (纸本)9798350394962

This text sets out to present an innovative approach to intrinsic equity value estimation, deploying state-of-the-art mechanisms and new technologies, but in particular, focusing on the integration of Semantic Web techniques. Its focus is on the problem of market sentiment, which commonly causes the value of equities to be far from their real worth. The proposed solution is to use in a mixed way sophisticated computational techniques and the capability of the Semantic Web's structured data handling to keep the valuations free of bias. The methodology employs advanced analytics, machine learning models, and Natural Language Processing (NLP) for the discovery of patterns and insights that may not easily be visible from traditional valuation methodologies. The holistic approach also involves qualitative analysis to enhance the understanding of the company's prospects. This approach integrates these technologies into the framework of the Semantic Web to provide a transparent and replicable way of determining the intrinsic value of equities, with minimum distortion by market sentiment. The paper argues that this method is quite opposite to traditional business valuation models that use a framework that emphasizes upon empirical evidence and methodological rigor. It postulates that, in the future, if these Semantic Web tools can be put into use for the systematic gathering of varied datasets for contextual analysis, the way valuation functions are handled will undergo a revolution. It would mean a shift in business valuation towards one that is more empirical and technology-enabled towards potentially changing investment analysis by methodological stringency and empirical data. CCS CONCEPTS. computing methodologies Modeling and simulation Model development and analysis Modeling methodologies © 2024 IEEE.

关键词： data handling

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Interdisciplinary Synergy: Resources for Embedding Plugged and Unplugged Computer and data science Activities into the K-12 Curriculum 14

Interdisciplinary Synergy: Resources for Embedding Plugged a...

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14th IEEE Integrated STEM Education conference, ISEC 2024

作者： Samaras, Ted Marlowe, Thomas J. Herbert, Katherine G. Anu, Vaibhav K. Hagiwara, Sumi Robila, Stefan A. Instructional Technology Coach Franklin Township Public Schools United States Seton Hall University Department of Mathematics and Computer Science United States School of Computing Montclair State University United States College for Education and Engaged Learning Montclair State University United States

ISBN: (纸本)9798350352801

In the ever changing environment of education, the skills learned and applied in computer and data science (CADS) have expanded beyond the reach of siloed, single-subject teaching in the classroom. The need for interdisciplinary learning and constructs have increased as real-world experiences have become a necessity and time has become a scarcity. The progression toward this reality has been accelerated in a post-pandemic world. This paper reviews literature regarding this topic, looks at barriers impacting student learning, and provides resources to assist educators in weaving plugged, unplugged, and hybrid components into their interdisciplinary lessons to amplify the impact of CADS. It also provides a scenario for and a vision of learning if educators can utilize an interdisciplinary approach to their lesson and unit plans. The sample template provided focuses on a middle and high school education model where multiple teachers are responsible for teaching the same students in a team. The curriculum discussed in the template is based on topics covered in the New Jersey State Standards for eighth grade students, but is flexible enough to be molded for the needs of various grade levels and different state and local standards. By using multimodal techniques and generating greater synergy through interdisciplinary lesson constructs, more robust learning environments are created for both students and educators. © 2024 IEEE.

关键词： data science

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Synthetic data Generation Using GANs for Improved machine learning Models 12th

Synthetic Data Generation Using GANs for Improved Machine Le...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Vinoth Kumar, S. Kumar, Kavuri Santosh Kotteeswaran, C. Manoj, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819789450

Explores the utilization of Generative Adversarial Networks (GANs) for generating synthetic images to address challenges related to limited data availability and privacy concerns. GANs, trained on diverse datasets, are capable of producing high-fidelity images from random noise. For example, training a GAN on the MNIST dataset allows for the creation of digit images that closely resemble those in MNIST, which can then be used to train other neural networks. Our evaluations demonstrate that these synthetic images maintain high fidelity and are effective in various tasks, including classification and object detection. This highlights the potential of GANs for dataset augmentation and enhancement of model performance, providing a viable solution for expanding training datasets while preserving data privacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Using machine learning to Unveil Early Signs of Parkinson’s Disease: A Review 5th

Using Machine Learning to Unveil Early Signs of Parkinson’s...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Manoj, Rudraksh Sharma, Ankush Sharma, Sanjay School of Computer Science and Engineering Shri Mata Vaishno Devi University Katra India Neurology Shri Mata Vaishno Devi Narayana Super Speciality Hospital Katra India

ISBN: (纸本)9789819725496

Parkinson’s is a brain degenerative disease striking mostly over 60 s, which gradually steals movement. Tremors, stiffness, slowness, and walking troubles mark its motor symptoms. Later, fatigue, sleep issues, and thinking changes creep in. Degeneration in a brain region called Substantia Nigra, a midbrain region responsible for dopamine production, is the culprit. The cell death is caused by abnormal accumulation of proteins into Lewy bodies. This lack of dopamine disrupts both movement and other brain functions. Unfortunately, no cure has been found for this ailment till date, but early detection of the disease can help doctors to act at the right time in stimulating the deeper parts of the brain with L-DOPA and MAO inhibitors and the patient can return to a near normal lifestyle. Due to the deficit of dopamine in the brain, the communication between synapses is hindered and functions like speech, tremor, bradykinesia, rigidity, and stooped gait are the common symptoms. This is where machine learning comes in. With the help of the machine learning models like Support Vector machine (SVM) and Random Forest (RF) and voice (tremor) dataset of patients with Parkinson’s, high accuracy results in early detection can be reached. Precise application of steps like data preprocessing and feature extraction helped in improving the performance metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brain

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Accelerate K-Mode Algorithms Using The Triangle Inequality 16th

Accelerate K-Mode Algorithms Using The Triangle Inequality

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16th international conference on Scalable Uncertainty Management, SUM 2024

作者： Nguyen, Vu-Linh Nguyen-Mau, Toan Huynh, Van-Nam Heudiasyc Laboratory University of Technology of Compiègne Compiègne France Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (纸本)9783031762345

Clustering is an unsupervised machine learning task that aims to discover natural groups in the given dataset. K-mode algorithms, which are adaptions of K-mean algorithms for continuous data, are among the most popular algorithms for discovering clusters in categorical data. In this paper, we present some results on how to accelerate them using the triangle inequality, while still always computing exactly the same result as the original K-mode algorithms. We also provide some empirical evidence to illustrate the potential gains provided by leveraging the triangle inequality. Finally, we envision future work aimed at providing a comprehensive understanding of the use of triangle inequality in accelerating other clustering algorithms for categorical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Cyberbully Detection by Using machine learning 4th

Cyberbully Detection by Using Machine Learning

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4th international conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Tamring, Norazlinda Hung, Lai Po Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789819729760

Cyberbullying is a growing problem that affects mental health and academic achievement, especially among teenagers and young people. Detecting cyberbullying is challenging due to its complex and subjective nature, as well as the rapidly evolving technology and language. Victims have difficulty avoiding attacks, which can be as simple as malicious comments online. Effective models for detecting cyberbullying are needed to prevent such cases, but data extraction from social media is difficult due to privacy concerns and limited user information. The objective of this project is to enhance manual monitoring for cyberbullying on social networks and online platforms by researching and testing the most effective feature extraction method to be implemented. The project aims to achieve the following goals: preprocess the public dataset, design machine learning algorithm models with different feature extraction methods, evaluate the accuracy and performance of each algorithm, and create a simple interface to test sentences with the designed model. The classifiers used in the research are Support Vector machine (SVM), Naive Bayes (NB) and Decision Tree (DT) that are optimized with two different feature extraction (TFIDF and Count Vectoriser). The classifier's evaluation is determined by accuracy, precision, recall, and f1-score. The classifier that obtained the highest performance is Linear SVM by using TFIDF. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phishing

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Identification of Patient Ventilator Asynchrony in Physiological data Through Integrating machine-learning

Identification of Patient Ventilator Asynchrony in Physiolog...

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17th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Stell, Anthony J. Caparo, Ernesto C. Wang, Zhe Wang, Chenyang Berlowitz, David J. Howard, Mark E. Sinnott, Richard O. Aickelin, Uwe School of Computing and Information Systems University of Melbourne 700 Swanston Street Melbourne Australia Institute of Breathing and Sleep Austin Hospital 145 Studley Road Heidelberg Australia Austin Health 145 Studley Road Heidelberg Australia

Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVAis currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labourintensive and can be error prone. This project aims to make this analysis more efficient, by using machine learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bed sideclinical monitoring system for production use in anticipation of a future clinical trial. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Sleep research

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AI -Driven data Aggregation Level Smart Contracts for Blockchain Healthcare Insurance Claims Adjudication 19

AI -Driven Data Aggregation Level Smart Contracts for Blockc...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： El-Samad, Wael Adda, Mehdi Atieh, Mirna Département de mathématiques informatique et génie Université du Québec à Rimouski 300 Allée des Ursulines QCG5L 3A1 Canada Business Computer Department Faculty of Economic Sciences Administration Lebanese University Hadat Lebanon

The current model of healthcare insurance claim adjudication faces significant challenges, in particular fraudulent claims which significantly strain the economy. This paper introduces a novel approach that integrates data Aggregation, Artificial Intelligence (AI), and Blockchain technologies to enhance the efficiency of healthcare claim adjudication. We present in this study an initial framework that integrates an AI-enhanced smart contract at the data aggregation level. A new concept that enhances the existing smart contract models commonly discussed in blockchain-related insurance literature. Our primary goal is to improve fraud detection and maximize the use of extensive data generated within the insurance ecosystem for prediction, learning, and strategic planning. The concept of an AI-data aggregation-level smart contract, introduced here, holds promise for broad application across various blockchain platforms, not limited to our discussion on healthcare claims insurance adjudication. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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