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A Conceptual Framework to Predict Disease Progressions in Patients with Chronic Kidney Disease, Using Machine learning and Process Mining

A Conceptual Framework to Predict Disease Progressions in Pa...

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21st international conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Kandasamy, Nichalini Chaussalet, Thierry Basukoski, Artie School of Computer Science & Engineering University of Westminster London United Kingdom

ISBN: (纸本)9781643684000

Process Mining is a technique looking into the analysis and mining of existing process flow. On the other hand, Machine learning is a data science field and a sub-branch of Artificial Intelligence with the main purpose of replicating human behavior through algorithms. The separate application of Process Mining and Machine learning for healthcare purposes has been widely explored with a various number of published works discussing their use. However, the simultaneous application of Process Mining and Machine learning algorithms is still a growing field with ongoing studies on its application. This paper proposes a feasible framework where Process Mining and Machine learning can be used in combination within the healthcare environment. © 2023 The authors and IOS Press.

关键词： Machine learning

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A Multi-Objective Optimization Model for Single-Product and Multi-Warehouse Based On NSGA-II 21

A Multi-Objective Optimization Model for Single-Product and ...

引用

21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Hao, Li Huajian, Xie Yalin, Liu School of Artificial Intelligence and Big Data Chongqing Industry Polytechnic College Chongqing401120 China Chongqing Academy of Education Science Chongqing4500015 China

ISBN: (纸本)9798331519254

The increasing complexity of modern supply chains has amplified the challenges of warehouse allocation in e-commerce, where decision-makers must balance trade-offs between rental costs, warehouse utilization, and operational efficiency. This paper proposes a multi-objective optimization model based on the NSGA-II algorithm, which incorporates key factors such as product category correlations, rental costs, warehouse utilization, and category distribution. Unlike traditional models, this approach allows each product category to be stored in up to three warehouses, providing greater flexibility and adaptability. Experimental results using real-world data demonstrate the model's capability to generate Pareto-optimal solutions, effectively addressing conflicting objectives and offering practical, balanced, and flexible strategies for warehouse allocation. This study provides decision-makers with an advanced tool to optimize warehouse management in diverse and dynamic scenarios.. © 2024 IEEE.

关键词： Supply chains

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Teaching data Science to Non-Computer Science students: A Learner-Centered Approach 53

Teaching Data Science to Non-Computer Science Students: A Le...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Velaj, Yllka Dolezal, Dominik Ambros, Roland Plant, Claudia Motschnig, Renate University of Vienna Faculty of Computer Science Vienna Austria Ds:UniVie United States

ISBN: (纸本)9798350336429

The aim of our paper is to provide good practices for teaching data science to non-computer science students and find out how we can motivate female students to take MINT (Mathematics, Computer science, Natural science and Technology) classes. The analyzed data science course is offered at University of Vienna in the Business Analytics, data Science, and Digital Humanities faculties as part of the masters' programs. In this work, we outline the course structure and present findings gained through teaching it. Moreover, we highlight the main differences between the editions of the course. Anonymous surveys, grade analysis, and student interviews, show that female students achieved the same learning outcomes as their male peers in all the terms as measured by the total score and all three sub-scores, namely the project submission, the mid-term quiz, and the final quiz. In 2020 and 2021, no significant differences could be found in student performance by their attended study program. In 2022, a difference could be found in the midterm exam;however, no significant difference could be found in the final exam and the project work, indicating that the course was able to harmonize the learners' diverse background. This suggests that the diverse learning opportunities offered in this course fit the individual needs of students of different backgrounds, which increases the accessibility of the data science field among non-computer science students. We propose the course as described in this paper as a good practice of teaching 21st century digital skills in a studentcentered way and conclude the paper with five recommendations. © 2023 IEEE.

关键词： 21st century skills computer science education student-centered learning Terms-data science

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Motor Loosened Screws Diagnosing System Based on Vibration Detection and Neural Network 21

Motor Loosened Screws Diagnosing System Based on Vibration D...

引用

21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Dantong, Feng Ying, Zhong Yijie, Ai School of Automation and Electrical Engineering Chengdu Technological University Chengdu611730 China

ISBN: (纸本)9798331519254

Motor screw loosening is a common problem in motor operation, which, if not dealt with in time, may lead to part damage and even motor failure. However, few studies have focused on the diagnosis and warning of this problem. This study is based on a monitoring system for motor vibration detection, which uses three-axis vibration sensors to collect motor vibration displacement. Through algorithm processing and calculus, vibration velocity and acceleration can be obtained. The collected vibration signals are sent to the computer by serial port communication, and a deep learning model is trained with the abnormal vibration data to achieve fault diagnosis. The experiment results show that the motor vibration data based neural network can complete the health diagnosis and early warning of the motor in the early stage of motor end cap screw loosening and bracket screw loosening, reducing the loss caused by subsequent faults, and providing a new reference scheme for the motor fault diagnosis field. © 2024 IEEE.

关键词： Screws

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ALF: Adaptive Label Finetuning for Scene Graph Generation 21

ALF: Adaptive Label Finetuning for Scene Graph Generation

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qisheng, Chen School of Computer Science and Engineering UESTC Center For Future Media Chengdu611730 China

ISBN: (纸本)9798331519254

Scene Graph Generation (SGG) is a task to identify relationships between subjects and objects in images. However, the task is significantly hindered by the long-tail distribution of relational labels, which often biases predictions toward generic or coarse categories. To resolve this, researchers have explored data transfer techniques that refine coarse-grained predicates into more detailed fine-grained labels. These approaches, however, face two key challenges: 1) the failure to account for the contextual constraints dictated by subject-object pairs, which can result in inaccurate label refinements, and 2) the reliance on additional retraining phases, which increases computational complexity. To address these issues, a novel plug-and-play one-stage data transfer framework, termed Adaptive Label Finetuning (ALF), has been proposed. ALF integrates two main modules: Adaptive Label Construction (ALC) and Adaptive Iterative learning (AIL). Experiments demonstrate that ALF significantly outperforms conventional methods like Motif, achieving a 16% improvement in the mean Recall metrics. © 2024 IEEE.

关键词： Coarse-grained modeling

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Pulmonary Nodule Detection in Computed Tomography Images Using Deep learning 1

Pulmonary Nodule Detection in Computed Tomography Images Usi...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and intelligent Information Systems, ICAECIS 2023

作者： Longkumer, Akumlong Walling, Supongmen Kalita, Shobhanjana Icfai University Department of Computer Science Dimapur India National Institute of Technology Department of Computer Science and Engineering Dimapur India Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350348057

the well-being of individuals is seriously threatened by lung cancer, which has one of the highest fatality rates of all cancers. Therefore, to increase patient life expectancies, early diagnosis of lung nodules is essential. For many years, the science of machine vision and medical diagnostics has used convolution neural networks. Early diagnosis can significantly lower the patient's mortality, and computer-aided diagnosis has emerged as a vital tool for medical professionals. Computer-aided diagnosis depends on the ability to identify pulmonary nodules in computed tomography images. In the Computed Tomography scans, it is difficult to find the lump inside the lungs. This project uses U-Net to implement automated lung nodule identification. Furthermore, the data sets used for pulmonary nodule recognition from CT scan images are from the freely accessible LIDC/IDRI database. © 2023 IEEE.

关键词： Computer aided diagnosis

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YOLO-Based Detector for Enhancing Workplace Safety: Real-Time PPE Detection and Monitoring 21

YOLO-Based Detector for Enhancing Workplace Safety: Real-Tim...

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2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Amri, Souha Ouannes, Laila Sahbani, Anis Ben Amara, Najoua Essoukri Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Enova Robotics S.A. Paris France

ISBN: (纸本)9798331518240

Ensuring the safety of workers in hazardous environments is crucial, given the potential severity of accidents. Indeed, Personal Protective Equipment (PPE), including safety helmets, vests, and boots, is vital in mitigating risks. However, adherence to PPE usage often falls short due to inadequate safety awareness among workers. To address this challenge, we aim to develop a robust safety monitoring system by leveraging advancements in deep learning and object detection technology. Our proposed method uses YOLOv7 and YOLOv8 algorithms for real-time detection and classification of safety equipment, including helmets, vests, and boots. For this aim, we carefully collect and annotate datasets from diverse sources like Roboflow and Kaggle. Then, we accurately evaluate performance metrics such as precision, recall, and speed through rigorous training and testing of various YOLOv7 and YOLOv8 configurations on annotated data batches. By conducting thorough comparative analyses, we aim to identify the most effective model for each PPE category, considering both detection accuracy and computational efficiency. Finally, the object detection system is deployed through a web interface to determine whether a person is protected or not. © 2024 IEEE.

关键词： Deep learning

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Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Model 3

Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Mo...

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3rd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Singh, Soni Mittal, Sonam Singh, Sunaina Lovely Professional University School of Computer Science and Engineering Department of Computer Science and Engineering Punjab Phagwara144411 India Chitkara University Institute of Engineering Technology Chitkara University Punjab India Department of Electrical & Electronics School of Engineering Dehradun Bidholi India

ISBN: (纸本)9798350346169

The global community is now seriously threatened by the COVID-19 pandemic. The government of every nation must pay close attention to the analysis of this disease to take the required actions to lessen the impact of this worldwide epidemic. This research focused on the disease outbreak in the Indian region through July 21st, 2021, and evaluated the incidence and mortality. Machine learning techniques, such as the ARIMA model, are applied to perform the prediction analysis on collected data from the World Health Organization (WHO) official portal for India between January 20, 2020, and July 21, 2021. Mean Square Error (MSE), a measure of model performance, was used to assess performance, and it came in between 2170.636098 and 46.839689. In the four weeks of test data, the Expected instances are estimated to be between 192K and 230K, which is fairly similar to the actual figures. The government and physicians will be able to make future strategies with the aid of this study. © 2023 IEEE.

关键词： COVID-19

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JumpCrypt: Based on Precomputing Federated learning with Homomorphic Encryption 25

JumpCrypt: Based on Precomputing Federated Learning with Hom...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Yun Wang, Chong Xu, Chao Wang, Jing School of Computer Science and Engineering Southeast University Nanjing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

ISBN: (纸本)9798350330014

Federated learning (FL) enables organizations or individuals to train a machine learning model collaboratively by aggregating local gradient updates from each client without sharing privacy-sensitive data. To ensure the security of gradient updates, the FL framework employs additively homomorphic encryption (AHE) to prevent revealing the participants' identities during aggregation. However, strategy imposes heavy communication and computation costs. Thus, this paper proposes a system solution for cross-silo FL named JumpCrypt. Our scheme uses pre-computation to build a plaintext and noise pool for faster encryption. Meanwhile, gradient sparsity is employed to reduce communication overhead. Experimental results show that our scheme achieves a 3x speedup in training compared with BatchCrypt while reducing communication overhead by 30%. © 2023 IEEE.

关键词： Sensitive data

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GWO-CNN: Trust and intelligent Recommendation system based model to improve marketing strategies on e-commerce 2

GWO-CNN: Trust and Intelligent Recommendation system based m...

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2nd IEEE international conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sethi, Vikas Kumar, Rajneesh Mehla, stuti Department of Computer Science and Engineering Mullana Ambala India Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798350383287

In 21st century, AI-based intelligent recommendation system uses rating predictions, which are frequently utilized and helps users swiftly filter down their options and make informed judgements from an abundance of material. Nevertheless, as recommender systems only contain a small number of explicit data and are subject to cold start and sparsity issues, the majority of existing recommendation models have poor accuracy. The majority of conventional algorithms used to make recommendations simply take into account the straight-forward connection among consumers and products, largely ignoring the interests, prospective employment factors, or other social aspects of the products being *** this study focus is on user preferences, as they are key to tailoring recommendations to individual needs. By combining trust values with user preferences, the system can recommend items that not only have a high trust value but also align with a user's individual tastes or needs. For this proposed model used feature matrix, Grey Wolf Optimizer (GWO) and Convolution Neural Network (CNN). GWO is an optimization algorithm used to find optimal weights or parameters for the recommendation system. Once the GWO has found optimal or near-optimal weights, they are integrated into the feature matrix. These weights can help prioritize or scale certain features to improve the recommendation quality. Then CNN will extract patterns and relationships from the feature matrix. Based on the processed feature matrix, the system predicts the top 'K' items to recommend to the user. © 2024 IEEE.

关键词： Recommender systems

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