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Applying Data science and Machine Learning for Predictive Analytics in Organizational Decision-Making 10th

Applying Data Science and Machine Learning for Predictive An...

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10th International Conference on Technologies and Innovation

作者： Vergara-Lozano, Vanessa Lagos-Ortiz, Katty Sinche, Andrea Medina-Moreira, Jose Rochina Garcia, Christian Univ Agraria Ecuador Fac Ciencias Agrarias Ave 25 Julio Guayaquil Ecuador Univ Bolivariana Ecuador Duran Ecuador Univ Internac Ecuador Fac Ciencias Empresari Ave Las Aguas Guayaquil Ecuador

ISBN: (纸本)9783031757013;9783031757020

This article examines current and future trends in data science and machine learning (ML) within organizational settings using data from the 2020 Kaggle Data science and Machine Learning Survey, which collected over 20,000 responses. The survey provides a detailed view of the field, addressing demographics, employment trends, preferred tools and methods, and relevant educational backgrounds. Through in-depth descriptive analysis, the study reveals key patterns and concepts illustrating how organizations are adopting and integrating these technologies into their operations. Additionally, the implications of these findings for the future of data science andML are explored, emphasizing the anticipated evolution in terms of business practices and technological advancements. This approach provides a crucial strategic vision for those seeking to understand and anticipate the changing dynamics in the field of data science and machine learning in the corporate context. The study employs advanced predictive modeling techniques to analyze the dataset, using generative neural networks and optimization methods such as Adam. These tools allow for the exploration of complex relationships and hidden patterns in the data, providing deep insights that go beyond traditional descriptive analyses.

关键词： Data science Machine learning Demographics Labor trends Tools and methods Educational environments

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Performance Analysis of Queuing-Inventory System with Catastrophes Under (s, Q) Policy 2nd

Performance Analysis of Queuing-Inventory System with Catast...

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2nd International Conference on information Technologies and Their Applications

作者： Melikov, . Gassi Ozkar, Serife Poladova, Laman Baku Engn Univ AZ-0101 Absheron Azerbaijan Balikesir Univ TR-10145 Balikesir Turkiye Inst Control Syst AZ-1141 Baku Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

We discuss the single-server queuing-inventory system with possible catastrophes in warehouse and negative customers. Consumer customers arrive in the system according to Poisson process and their service times follow exponential distribution. The customers can be queued in an infinite buffer. Upon arrival of catastrophe all inventory of the system is destroyed but consumer customers in the system (on server or in buffer) continue to waiting for replenishment of stocks. One consumer customer is pushed out from the system, if a negative customer arrives to the system. In the system (s, Q) inventory policy is used. When the inventory level drops to s, an order quantity of Q = S - s is placed and upon replenishment the level becomes sum of the current items and order quantity. If upon arrival of the consumer customer, the inventory level is zero, this customer is either lost (lost sale) or joins to the queue (backorder sale). Mathematical model of the system is constructed via two-dimensional Markov chain. Steady-state probabilities are obtained using the matrix-geometric method and ergodicity condition is established in closed-form. Numerical results are presented about the behavior of performance measures and the optimum inventory policy to minimize the expected total cost.

关键词： queuing-inventory system catastrophes negative customers hybrid sale schema (s, Q) policy matrix-analytic method

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Challenges the Members of Bidding and Negotiating Committees Face During the Public Procurement Processes 5th

Challenges the Members of Bidding and Negotiating Committees...

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5th 5th Olympus - International Conference on Supply Chain

作者： Koligiannis, Georgios Drakaki, Maria Tzionas, Panagiotis Int Hellen Univ Thermi Greece

ISBN: (纸本)9783031693434;9783031693441

Prior research has explored procurement performance in public entities. However, there seems to be a gap in the challenges members of the procurement committees face during the bidding process. During the bidding processes, procurement committees have to cope with several challenges to evaluate the offers submitted by the economic operators. In order to explore the challenges these committees face, a survey questionnaire was designed and sent to personnel who have been members of negotiation and procurement committees in the public sector. The survey questionnaire gathered data from experts in the field in Greece to provide solutions to public entities that could help the members of procurement committees become more flexible and effective in their processes. Greece has recently modified the procurement rules that public organisations have to abide by. The study found that the negotiation and procurement committee members feel less stress when they have more field experience and knowledge of the procurement laws. The study also revealed that the members of the committees face more challenges when the bidding documents are not precise enough for the potential economic operators, creating risks in the performance of the process.

关键词： Bidding challenges public procurement suppliers

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Asynchronously or Synchronously?: Key Insights of Doing Co-Design of Technology with a Vulnerable Population Online 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Maestre, Juan F. Shih, Patrick C. Swansea Univ Dept Comp Sci Swansea W Glam Wales Indiana Univ Luddy Sch Informat Comp & Engn Bloomington IN USA

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Co-design is a collaborative and participatory design (PD) method frequently used in face-to-face settings. However, in-person studies can be difficult or even impossible to conduct in certain situations due to different constraints. Thus, an increasing line of work has been relying on methods that allow researchers to conduct PD studies remotely. In this paper, we cover findings of a case study with 25 participants being part of asynchronous and synchronous remote co-design workshops. It was found that there are important differences between the two modalities regarding participation and engagement, activity design, as well as issues with privacy and confidentiality. We also provide methodological implications and actionable steps to conduct remote co-design studies both synchronously and asynchronously more effectively.

关键词： Collaborative design Co-design Remote study Asynchronous Synchronous Vulnerable populations Online co-design

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Hierarchical Feature Selection Method Based on Sequential Backward Selection Algorithm for Fasting Blood Glucose Prediction 42nd

Hierarchical Feature Selection Method Based on Sequential Ba...

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42nd National Conference on Theoretical computer science

作者： Sun, Wencheng Chen, Xiaoyong Natl Univ Def Technol Nanjing 210039 Peoples R China

ISBN: (纸本)9789819614899;9789819614905

It is important to identify people at high risk of diabetes, such as correlating them from physical examination data, and to intervene in advance in health management and medical treatment. In this paper, we employ both bagging and boosting methods to predict the patient's fasting blood glucose (FBG) based on physical examination data. A hierarchical feature selection method based on sequential backward selection (SBS) algorithm is presented to select an optimal feature subset. The results of extensive experiments on account of physical examination database suggest that the presented feature selection method has better performance.

关键词： FBG physical examination data SBS feature selection

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iWMS: A Warehouse Management System for a Goods-to-Person Warehouse Automation 5th

iWMS: A Warehouse Management System for a Goods-to-Person Wa...

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5th 5th Olympus - International Conference on Supply Chain

作者： Zotou, Dimitra Papadam, Stefanos Kargakos, Andreas Kostavelis, Ioannis Bliantidou, Maria Giakoumis, Dimitrios Ioannidis, Dimosthenis Tzovaras, Dimitrios Informat Technol Inst CERTH ITI Ctr Res & Technol Hellas 6 Km Charilaou Thermi Thessaloniki Greece Int Hellen Univ P Kanellopoulou 2 Katerini Greece TETTIX NMORENOS SINGLE MEMBER PC Zone COt 48A Thessaloniki 57400 Greece

ISBN: (纸本)9783031693434;9783031693441

Recently, there has been a surge in the development of advanced warehouse management systems that seamlessly incorporate robotized technologies. These robots have the capability to operate independently or collaborate with human staff to enhance the efficiency of items picking and storage. The paper at hand, introduces an intelligent Warehouse Management System, denoted as iWMS, designed for a goods-to-person warehouse environment. The system is configured to retrieve data of standard format compatible with commercial WMS through an application programming interface (API), while maintaining control over the robotic processes. This work aims to address the development of a software that will analyze incoming and outgoing orders and allocate the task of collecting and distributing bins with an Autonomous Mobile Robot (AMR). The proposed software performs a systematic analysis of incoming orders, linking them with the respective fiducial markers that contain detailed information about the precise location of the relevant bin within the warehouse. Subsequently, the system assigns the task of orders collection to one or more AMRs, taking into account their availability and current positions within the warehouse. This is achieved using an algorithm that maximizes efficiency by optimizing both execution time and travel distances. Additionally, the application consistently updates the stock in the database based on the products identified within the bins, ensuring accurate and real-time stock counting in the warehouse database. The ultimate goal of the iWMS is to provide an easy-to-use tool, allowing the seamless co-existence between the workers and the robotic platforms in a common workplace. This iWMS is part of a robotic warehouse solution currently undergoing testing in simulation.

关键词： Warehouse Management System Order picking Robotics goods-to-person

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Interpreting CNN for Brain Tumor Classification Using XGrad-Cam

Interpreting CNN for Brain Tumor Classification Using XGrad-...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Tehsin, Sara Nasir, Inzamam Mashood Damasevicius, Robertas Kaunas Univ Technol Ctr Real Time Comp Syst Kaunas Lithuania

ISBN: (纸本)9783031834349;9783031834356

One of the worst diseases in recent years is thought to be brain tumors. If it is not identified in its early stages, it also results in death. Numerous deep learning and machine learning methods have been put out to accurately identify the presence of a brain tumor at any stage. A customized convolutional neural network (CNN) is proposed in this paper which has minimum layers as compared to the standalone networks. It has 11 layers which takes less training time and generates the correct class. The proposed model results are supported by the Explainable AI algorithm which gives practitioners a better visualization regarding the predicted class. The Axioms-based Gradient-weighted Class Activation Mapping (Xgrad Cam) is an extended version of Grad-Cam which dominates the areas that contribute to predicting the corresponding class. The proposed model has given us promising results not only on multi-class publicly available brain tumor datasets but also on binary-class brain tumor datasets. Two multi-class brain tumor datasets and one binary-class brain tumor dataset are used for testing the presented technique. These datasets have an imbalanced dataset and the customized model outperforms in those applications. The results are briefly discussed in the experimental results section.

关键词： Brain tumor detection customized CNN Explainable artificial intelligence Xgrad Cam Deep learning

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A Comprehensive Comparison of Lattice-Based Password Authenticated Key Exchange Protocols Defined on Modules 2nd

A Comprehensive Comparison of Lattice-Based Password Authent...

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2nd International Conference on information Technologies and Their Applications

作者： Seyhan, Kubra Akleylek, Sedat Ondokuz Mayis Univ Dept Comp Engn Samsun Turkiye Univ Tartu Tartu Estonia

ISBN: (纸本)9783031734168;9783031734175

With the proposal of Shor algorithm, a new era has been started for the security of public-key cryptography. In the presence of large-scale quantum computers, it was proved that some computationally hard problems, such as discrete logarithm and factorization, will be solved, so public-key primitives will be insecure. Password-authenticated key exchange (PAKE) schemes whose security is generally defined under discrete logarithm problem have also been affected due to this ongoing process. To obtain a post-quantum secure PAKE scheme, lattice-based structures come to the fore due to the strong security guarantees and efficient solutions. In this paper, we compare three currently proposed Diffie-Hellman (DH)-like lattice-based PAKE schemes that were constructed based on module structure to provide guidance about efficient password-based authentication with lattice assumptions. By giving the algorithmic explanation of selected PAKEs, we deeply analyze proposed ideas according to the component-wise comparison in terms of reconciliation structure, hard lattice problem, and design idea. The implementation results of selected PAKEs are analyzed in terms of running time and computational costs. According to the experimental results, *** gives the best performance in high security levels by considering execution time.

关键词： Post-Quantum Cryptography Lattice-Based Cryptography Password Authenticated Key Exchange Reconciliation Module Learning With Rounding Module Learning With Errors

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Refining U-Net Architecture Through Genetic Algorithms for Improved Skin Lesion Image Segmentation 4th

Refining U-Net Architecture Through Genetic Algorithms for I...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： El Abassi, Fouzia Darouichi, Aziz Ouaarab, Aziz Cadi Ayyad Univ Comp & Syst Engn Lab L2IS FSTG Marrakech 40000 Morocco

ISBN: (纸本)9783031821554;9783031821561

Recently, various approaches using manually designed convolutional neural networks (CNNs) and U-shaped (encoder-decoder) models have yielded encouraging results in the automatic segmentation of medical images. Nevertheless, the manual construction of these models is laborious, requires a high level of expertise, is time-consuming, error-prone and can result in the loss of crucial details. To improve segmentation performance, these models often incorporate numerous parameters, which can lead to problems such as overfitting, vanishing and exploding gradient and increased computational complexity. In this study, we implement a genetic algorithm (GA) specifically designed for a U-Net architecture to segment images of skin lesions, using two datasets ISIC-SMALL1 and ISIC-SMALL2 constructed from ISIC-2017 dataset. The main objective is to optimize internal block topologies within the U-Net, in order to identify a high-performance deep neural network with a minimum of parameters. Compared to the U-Net baseline, our proposed methodology demonstrates favorable outcomes in terms of both the number of trainable parameters and Intersection over Union (IoU). For the ISIC-SMALL1 dataset, the leading model surpasses U-Net with a reduced parameter count (0.352001M) and an elevated IoU score (0.826052 vs. 0.736352). Similarly, the second-best model outperforms U-Net with fewer parameters (0.258781M vs. 31.043586M) and a higher IoU score (0.821956 vs. 0.736352). In the ISIC-SMALL2 dataset, both top models outshine U-Net in IoU (0.753122 and 0.722079 vs. 0.725363, respectively) while maintaining lower parameter counts.

关键词： Medical Image Segmentation Convolutional Neural Networks (CNNs) U-Net architecture Genetic Algorithms (GA) Skin Lesion

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Real-Time Automated Pothole Detection and Localization with Deep Learning and Geolocation Integration for Improved Road Safety and Maintenance 4th

Real-Time Automated Pothole Detection and Localization with ...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Rao, Abhishek S. Karthik, Pai B. H. Shyvalanai, B. S. Nayak, Trupthi D. Vaishnavi, R. Thanisha NITTE NMAM Inst Technol NMAMIT Dept Informat Sci & Engn Nitte 574110 Karnataka India

ISBN: (纸本)9783031837920;9783031837937

Real-time identification of road damage, particularly potholes, is essential for enhancing road safety and minimizing vehicle damage. Traditional road inspection methods are often labor-intensive and inefficient. To overcome these limitations, we propose an automated system that leverages deep learning to detect potholes in real-time from video input. This system can detect potholes under varying lighting conditions, specifically the YOLO (You Only Look Once) model, utilizing advanced computer vision techniques, including daylight, darkness, and vehicle headlights. The system aims to improve road monitoring by automatically identifying potholes and notifying authorities of their locations. By integrating geolocation services, the system pinpoints pothole locations using latitude and longitude coordinates and sends real-time alerts via a Telegram bot. Additionally, image enhancement techniques are employed to optimize detection performance in low-light conditions. The system has demonstrated high accuracy in detecting large potholes and identifying multiple potholes within a single frame. Through automated pothole detection and location sharing, this solution has the potential to significantly enhance road maintenance efficiency, thereby improving road safety and reducing accidents caused by road damage.

关键词： Pothole Detection Real-time Video Processing Deep Learning computer Vision Low-light Image Enhancement

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