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Synchronous Parallel Heuristics for Solving the Joint Order Batching and Picker Routing Problem

Synchronous Parallel Heuristics for Solving the Joint Order ...

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Congress on Evolutionary Computation

作者： Son Thai Tran Rui Jorge Almeida Christof Defryn Inneke Van Nieuwenhuyse Dept. of Quantitative Economics School of Business and Economics Maastricth University Maastricht The Netherlands Dept. of Data Analytics and Digitization School of Business and Economics Maastricth University Maastricht The Netherlands Dept. of Engineering Management Faculty of Business and Economics University of Antwerp Antwerp Belgium Faculty of Sciences FlandersMake@UHasselt and Data Science Institute Hasselt University Hasselt Belgium

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The joint order batching and picker routing problem is an important problem for improving warehouse efficiency. The goal is to minimize the total distance travel of picking customer orders. It is NP-hard, indicating that exact solutions are intractable for large instances. Many solution methods provided in the current literature use local search to solve the problem sequentially, often including a complex searching procedure for the order batching problem (OBP) followed by a simple heuristic for the picker routing problem (PRP). In this paper, we propose three heuristics that jointly solves the OBP and the PRP: two of these are combinations of variable neighborhood search, simulated annealing, and tabu search, while the third one uses guided local search. We discuss how the search procedure of the proposed algorithms can be parallelized, and benchmark them against state-of-the-art heuristics using well-studied instances. The results show a reduction of 3.06% to 7.63% in the geometric mean of the total travel distance across 64 instances. Increasing the number of threads in parallelization leads to diminishing returns in reducing running time and has no statistical effect on travel distance. In contrast, increasing the chunk size results in a linear increase in running time for all proposed algorithms, and improves the travel distance obtained when the batch size is 75 items.

关键词： Heuristic algorithms Simulated annealing Evolutionary computation Benchmark testing Routing Search problems

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Air Traffic and Usage Predictions in Avionic Communications using Attention Based VAEGAN Model

Air Traffic and Usage Predictions in Avionic Communications ...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Hind Mukhtar Raymond Schaub Melike Erol-Kantarci Satcom Direct Inc. Melbourne Florida University of Ottawa Ottawa Ontario Dept. of Data Science Satcom Direct Inc. Melbourne Florida Dept. of Electrical & Computer Engineering University of Ottawa Ottawa Ontario

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The need for uninterrupted connection is an enabler for enhanced connectivity in aircrafts. Satellite based communication in aircrafts exhibits high latency and can have limited data rates. Furthermore, the increasing demand for air travel can strain the capacity of satellite communication systems, necessitating the development of more robust traffic prediction methods. This necessity is particularly pronounced in the realm of business aviation, given the irregular traffic patterns compared to scheduled commercial flights. In this paper, we present an attention-based VAEGAN model designed to forecast the number of active tails within satellite beams. We extend the capabilities of our proposed model to predict the volume of upstream and downstream usage within these satellite beams. To validate our model, we employ real avionics data collected from the two most heavily traversed flight routes. Finally, we perform a comparative analysis, benchmarking the performance of existing machine learning-based techniques with our proposed model. The findings indicate that the proposed VAEGAN model exhibits superior performance in forecasting irregularities in the timeseries pattern, specifically in forecasting unusual highs or lows in the number of aircrafts within the satellite beam, outperforming alternative models.

关键词： Adaptation models Solid modeling Satellites Atmospheric modeling Tail Predictive models Aerospace electronics

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Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

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IEEE Southeastcon

作者： Deborsi Basu Ritu Chaudhary Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy.

关键词： data privacy Federated learning data integrity Medical services Servers Security Software defined networking Next generation networking Batch normalization Edge computing

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset

Comparative Analysis of Machine Learning Techniques for Arab...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Hani Attar Yousef Sharrab Saja Smadi Safa Jbaily Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Data Science and Artificial Intelligence Isra University Amman Jordan dept. Computer Science Yarmouk University Irbid Jordan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency; indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications.

关键词： Support vector machines Logistic regression Adaptation models Accuracy Text categorization Artificial neural networks Nearest neighbor methods Natural language processing Robots Sports

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Leveraging Reinforcement Learning and AlphaGo Algorithms for Enhanced Cancer Detection: A Novel Approach in Medical AI

Leveraging Reinforcement Learning and AlphaGo Algorithms for...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Pawan Whig Iti Batra Seema Nath Jain Pronaya Bhattacharya Shweta Redkar Satyabrata Roy VIPS-TC New Delhi India Vivekananda College Delhi University Delhi India School of Engineering and Technology Amity University Kolkata India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India Dept. of CSE Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Advancements in artificial intelligence (AI) have opened new avenues for enhancing healthcare outcomes, particularly in the field of cancer detection. This research paper investigates the application of reinforcement learning (RL) and AlphaGo algorithms to improve cancer detection accuracy and efficiency. By integrating the strategic decision-making capabilities of AlphaGo with the adaptive learning approach of RL, we propose a novel AI model designed to analyze medical imaging data for the early and precise identification of malignant cells. Our experiments, conducted on a large dataset of 10,000 labeled medical images, demonstrate significant improvements in key performance metrics, including accuracy, sensitivity, specificity, precision, and the F1-score, compared to traditional machine learning models. The proposed model achieved an accuracy of 94.7 % , a sensitivity of 92.3%, and a specificity of 96.8%. These findings highlight the potential of RL and AlphaGo algorithms to revolutionize cancer diagnostics, offering more reliable and timely detection methods. The study also addresses the challenges and ethical considerations of implementing AI in clinical settings, emphasizing the need for transparent and interpretable systems. This research suggests a promising future for AI-driven cancer detection, ultimately aiming to improve patient outcomes and healthcare efficiency.

关键词： Adaptation models Ethics Accuracy Sensitivity Decision making Reinforcement learning Cancer detection Reliability Medical diagnostic imaging Cancer

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Design and Implementation of a Mobile Application for Intelligent Attendance Tracking

Design and Implementation of a Mobile Application for Intell...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Nadhiya S Deepak Jain Anupama Verma Saurabh Kumar S. Bhavadharani Dept. of Computer Science Engineering Chandigarh University Chandigarh India Dept. of AI & Data Science Dhanalakshmi Srinivasan College Coimbatore India Dept. of Management IIM Indore Indore India Dept. of Business Management & Comm Usha Martin University Jharkhand India Dept. of Business Management & Commerce Mangalayatan University Aligarh India Dept. of Commerce Meenakshi A.H. Edu. & Research Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Taking attendance is an important task in schools, universities, and other institutions. However, traditional attendance taking methods such as paper-based attendance sheets or manual entry into an electronic system are time-consuming and prone to errors. In recent years, the emergence of facial recognition technology has opened up new possibilities for attendance management systems. This research paper discusses the development and implementation of a Smart Attendance System using Facial Recognition technology using OpenCV in educational institutions. The study explores the benefits, limitations, and ethical considerations associated with this technology and its implications for the future of attendance management systems. This model takes use of LBPH and gja, exfilaface recognition to increase accuracy over conventional models. After this, the connection of recognized faces is compared with the kdatabase containing student’s faces. This model will be an effective method for controlling student attendance.

关键词： Privacy Accuracy Face recognition Scalability Refining Organizations Real-time systems Mobile applications Security Faces

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Smart City Solutions Leveraging Internet of Things (IoT) Technology

Smart City Solutions Leveraging Internet of Things (IoT) Tec...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Bhawesh Kumawat E. Shalini Vinay Tomar Rajesh Kumar Upadhyay Divyanshu Gupta Geetha P V Dept. of CSE Madhav University Pindwara India Dept. of Computer Science Meenakshi A.H. Edu. & Research Chennai India Dept. of Chemistry Jiwaji University Gwalior India Dept. of EEE Usha Martin University Jharkhand India Dept. Electronics & Comm. Engineering J.S. University Shikohabad India Dept. of AI & Data Science Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The widespread adoption of the Internet of Things (IoT) is facilitating the development of Smart City initiatives globally. Everyday objects are increasingly equipped with electronic gadgets and connected through various protocol suites, becoming part of a vast network. According to a recent Gartner study, by 2020, smart cities are projected to deploy 50 billion connected devices. This advancement is expected to make urban environments more adaptive and resilient to future changes. This paper explores current and future trends in smart cities and IoT, highlighting the technologies and applications driving their growth. We examine how the expansion of IoT directly impacts the evolution of smart cities and discuss the interplay between the two, as well as the factors motivating their development. Smart cities have the potential to reduce manual labor, mitigate disaster risks, and streamline daily life. Lastly, we address some of the challenges associated with IoT in the context of smart cities and propose potential solutions to these issues.

关键词： Technological innovation Protocols Smart cities Scalability Strategic planning Public security Internet of Things Resource management Waste disposal Sustainable development

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Enhancing IoT Ecosystems through Effective Master data Management 11

Enhancing IoT Ecosystems through Effective Master Data Manag...

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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Pansara, Ronak Ravjibhai Kasula, Balaram Yadav Gupta, Piyush Kumar Khan, Tabrej Ahamad Alam, Naved Whig, Pawan Master Data Specialist Tesla United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Jamia Hamdard Department of Computer Science and Engineering New Delhi India Research Scholar Jamia Millia Islamia New Delhi India Whig Research Scientist Threws VIPS-TC New Delhi India

ISBN: (纸本)9798350308433

This study explores the quantitative impact of Master data Management (MDM) implementation on Internet of Things (IoT) ecosystems. Employing a comparative analysis methodology, pre-And post-MDM integration metrics were assessed within the IoT environment. Results showcased a notable enhancement in data accuracy by 20%, attributed to minimized errors and improved consistency following MDM adoption. Operational efficiency exhibited a significant 30% reduction in data integration time across diverse IoT platforms, contributing to streamlined processes. Financially, operational costs decreased by 15%, primarily due to reduced data cleansing efforts post-MDM implementation. Decision-making processes benefited from a 25% faster response time to real-Time analytics derived from IoT data insights. Additionally, the IoT ecosystem's scalability increased by 40%, indicating amplified adaptability to accommodate higher data volumes without compromising performance. These quantitative findings highlight the substantial impact of MDM on fortifying the reliability, efficiency, and scalability of IoT ecosystems, providing empirical evidence for the transformative potential of MDM in optimizing IoT applications. © 2024 IEEE.

关键词： Decision making

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

Learning on Transformers is Provable Low-Rank and Sparse: A ...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hongkang Li Meng Wang Shuai Zhang Sijia Liu Pin-Yu Chen Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. Data Science New Jersey Institute of Technology Newark NJ USA Dept. Computer Science and Engineering Michigan State University East Lansing MI USA IBM Thomas J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which de-pends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings.

关键词： Training Analytical models Adaptation models Computational modeling Neurons Stochastic processes Signal processing algorithms

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Design Objectives for Synthesis of Graphene PN Junction Circuits Based on Two-Level Representation

Design Objectives for Synthesis of Graphene PN Junction Circ...

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Euromicro Symposium on Digital System Design

作者： Subrata Das Arighna Deb Petr Fiser Debesh Kumar Das Dept of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Hooghly India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Faculty of Information Technology Czech Technical University Prague Czech Republic Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350380385

ISBN: (纸本)9798350380392

The development of electrostatically doped graphene PN-junctions shows promise for creating efficient low-power, high-speed circuits. In recent years, there has been a considerable interest in the synthesis of graphene PN junction logic circuits. However, existing synthesis methods lack assessment based on technology-specific cost metrics (e.g., the number of graphene PN junction gates, constant inputs), leading to insufficiently addressed design objectives. In this paper, we introduce synthesis approaches for graphene PN-junction circuits based on Sum-of-Products (SoP) and Exclusive Sum-of-Products (ESoP) function representations. Experimental results indicate that ESoP-based synthesis significantly reduces the number of graphene PN junction gates, constant inputs, and switching activity compared to SoP-based approaches. Overall, ESoP-based synthesis is deemed more suitable than SoP-based methods for designing graphene PN-junction logic circuits.

关键词： Measurement Costs Digital systems Logic circuits Graphene Switches Logic gates Circuit synthesis Logic Junctions

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