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2nd international Workshop on Industrial Recommendation systems (IRS) 21

2nd International Workshop on Industrial Recommendation Syst...

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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Xu, Jianpeng Wu, Lingfei Pang, Xiaolin Sharma, Mohit Yin, Dawei Karypis, George Basilico, Justin Yu, Philip S. WalmartLabs San Bruno CA 94066 USA JD Com Beijing Peoples R China Google Mountain View CA 94043 USA Baidu Beijing Peoples R China Univ Minnesota Twin Cities Minneapolis MN USA Netflix Los Gatos CA USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781450383325

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms and social networks, to provide suggestions that a user will most likely consume or connect;thus, improving the user experience [1]. this motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers [2, 3]. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. these key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. the gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions for industrial recommendation systems. this workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing the practical insights, approaches, and frameworks as well.

关键词： Industrial Recommendation systems

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Benchmarking Classifiers for Loan Default Prediction using Archetypal Analysis 23

Benchmarking Classifiers for Loan Default Prediction using A...

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proceedings of the 7th international conference on algorithms, computing and systems

作者： Anastasios Antoniadis Konstantinos Georgiou Konstantinos Charmanas Nikolaos Mittas Lefteris Angelis School of Informatics Aristotle University Greece Department of Chemistry International Hellenic University Greece

ISBN: (纸本)9798400709098

the prediction of loan default is a critical process for the successful development of financial institutions. To effectively manage credit risk, numerous machine learning models have been employed to distinguish creditworthy from high-risk applicants. However, determining an optimal model remains a challenge. To address this, in the current study, we explore an alternative approach for model benchmarking. the main concept involves the usage of a pipeline that constructs different classifiers for loan prediction and compares their performance across several evaluation metrics. To achieve this goal, we deploy an approach based on a multivariate statistical method, known as Archetypal Analysis (AA). the proposed methodology is applied to four datasets with diverse structural characteristics. the findings demonstrate that advanced classifiers like Random Forests (RF) and Artificial Neural Networks (ANN), with oversampling, simple parameter tuning, and feature selection consistently outperform traditional classifiers across most evaluation criteria. In conclusion, the results showcase the ability of AA to intuitively identify the best and worst models for each unique scenario.

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Performance Analysis of Machine Learning-Based Traditional and Ensemble Techniques for Smart Grid Stability Prediction

Performance Analysis of Machine Learning-Based Traditional a...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Md. Shakib Hassan MD. Adnan Siddique Nur Mohammad Arafat Ibne Ikram Institute of Energy Technology Chittagong University of Engineering & Technology Chattogram Bangladesh Department of Electrical and Electronic Engineering Chittagong University of Engineering & Technology Chattogram Bangladesh Department of Electrical and Electronic Engineering International Islamic University Chittagong Chattogram Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

A smart grid is integral to the digitalized transformation of the electricity sector, employing self-sufficient systems that integrate information, telecommunication, and advanced power technologies. Artificial Intelligence (AI), particularly Machine Learning (ML), plays a crucial role in overcoming the limitations of traditional modeling techniques. ML enables intelligent decision-making in response to dynamic factors like changing customer energy demands or disruptions in power supply within smart grids. this paper compares Machine Learning-Based Traditional and Ensemble techniques for predicting smart grid stability, utilizing an augmented dataset from Kaggle. For optimization, various classifiers and Ensemble Techniques, including Bagging, Boosting, Stacking, and Voting, were implemented with hyperparameter tuning. the experimental study highlights that ML-based Ensemble Techniques, with optimized parameters, outperform individual traditional techniques, showcasing higher accuracy and overall superior performance. A comprehensive comparative analysis based on evaluation metrics such as accuracy, precision, recall, F1-score, and ROC emphasizes the potential of these techniques to enhance the efficiency of predicting smart grid stability.

关键词： Power supplies Stacking Power system stability Prediction algorithms Stability analysis Real-time systems Communications technology

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Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance

Aggregating Statistically Correlated Metabolic Pathways Into...

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13th international conference on Bioinformatics Models, Methods and algorithms (BIOINFORMATICS) held as part of 15th international Joint conference on Biomedical Engineering systems and Technologies (BIOSTEC)

作者： Basher, Abdur Rahman M. A. Hallam, Steven J. Univ British Columbia Grad Program Bioinformat Vancouver BC V5Z 4S6 Canada Univ British Columbia Dept Microbiol & Immunol Vancouver BC V6T 1Z3 Canada

ISBN: (纸本)9789897585524

Metabolic pathway prediction from genomic sequence information is an essential step in determining the capacity of living things to transform matter and energy at different levels of biological organization. A detailed and accurate pathway map enables researchers to interpret and engineer the flow of biological information from genotype to phenotype in both organismal and multi-organismal contexts. In this paper, we propose two novel hierarchical mixture models, SOAP (sparse correlated pathway group) and SPREAT (distributed sparse correlated pathway group), to improve pathway prediction outcomes. Both models leverage pathway abundance to represent an organismal genome as a mixed distribution of groups, and each group, in turn, is a mixture of pathways. Moreover, both models deal with missing potential pathways in the training set by provisioning supplementary pathways into the learning framework as part of noise reduction efforts. Because the introduction of supplementary pathways may lead to overestimation of some pathways, dual sparseness is applied. the resulting pathway group dataset is then used to train multi-label learning algorithms. Model effectiveness was evaluated on metabolic pathway prediction where correlated models, in particular, SOAP was able to equal or exceed the performance of previous pathway prediction algorithms on organismal genomes.

关键词： Pathway Group Relabeling Data Augmentation Correlated Models Metabolic Pathway Prediction MetaCyc

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Low-Latency Immersive 6D Televisualization with Spherical Rendering 20

Low-Latency Immersive 6D Televisualization with Spherical Re...

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20th IEEE-RAS international conference on Humanoid Robots (HUMANOIDS)

作者： Schwarz, Max Behnke, Sven Univ Bonn Autonomous Intelligent Syst Grp Bonn Germany

ISBN: (纸本)9781728193724

We present a method for real-time stereo scene capture and remote VR visualization that allows a human operator to freely move their head and thus intuitively control their perspective during teleoperation. the stereo camera is mounted on a 6D robotic arm, which follows the operator's head pose. Existing VR teleoperation systems either induce high latencies on head movements, leading to motion sickness, or use scene reconstruction methods to allow re-rendering of the scene from different perspectives, which cannot handle dynamic scenes effectively. Instead, we present a decoupled approach which renders captured camera images as spheres, assuming constant distance. this allows very fast re-rendering on head pose changes while keeping the resulting temporary distortions during head translations small. We present qualitative examples, quantitative results in the form of lab experiments and a small user study, showing that our method outperforms other visualization methods.

关键词： Visualization Head Robot vision systems Humanoid robots Reconstruction algorithms Cameras Rendering (computer graphics)

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Balancing Security, Scalability and Decentralization of Blockchain using SHBF-Based Consensus

Balancing Security, Scalability and Decentralization of Bloc...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Koli Rani Pal Jannateen Tajree Faiza Golam Ali G. M. Abdullah Al-Kafi Saha Reno Department of CSE Bangladesh Army International University of Science and Technology Cumilla Bangladesh Department of CSE Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

In the dynamic realm of blockchain consensus algorithms, existing methodologies face challenges concerning security, efficiency, and adaptability. this research addresses these challenges by introducing a new consensus algorithm aimed at resolving the blockchain trilemma, achieving a balanced interplay between decentralization, security, and scalability. this consensus, which is implemented as a federated Byzantine agreement system, integrates the most effective characteristics of several consensus models, such as Proof of Authority, Honey Badger BFT, Stellar Consensus, and Practical Byzantine Fault Tolerance. By integrating adaptive validation mechanisms, the algorithm incorporates the demands of both private and public blockchain ecosystems, enhancing its versatility. through a robust fault tolerance and identity management system, this consensus ensures resilience against malicious actors, fostering a secure and dependable consensus mechanism. Furthermore, the proposed consensus algorithm optimizes time efficiency by streamlining the consensus process, drawing insights from the nomination and ballot protocol of Stellar consensus. the evaluation results indicate the proficiency of this consensus mechanism in processing transactions, achieving a rate of 1627 TPS. It notably diminishes the necessary block confirmation time to secure a 90.9% defense against double-spending while keeping the likelihood of forking at a minimal 10%. Furthermore, it attains a decentralization metric of 8.094 on a scale of 10.

关键词： Measurement Fault tolerance Scalability Heuristic algorithms Fault tolerant systems Consensus algorithm Consensus protocol

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Implementation of ERP sub-module account payables system on purchase transaction of heavy equipment spare parts 6

Implementation of ERP sub-module account payables system on ...

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6th international conference on Information Management and Technology, ICIMTech 2021

作者： Agung, Daniel Nurlina, Nurlina Mursitama, Tirta Nugraha Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities International Relations Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

the research goal was to identify, analyze the user requirements, and implement information systems, especially on sub-module accounts payables in the invoice collecting process at Company. the implementation process using Accelerated SAP (ASAP) methodology includes 5 stages: project preparation, business blueprint, realization, final preparation, go-live support. the analysis is done on the ongoing business process, which is described with the activity diagram, then the analysis result is the documentation of the implementation process of account payables system that can assist the invoice collecting process activities and generate reports according to the needs of the company. © 2021 IEEE.

关键词： Information systems

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TeraHertz Communication and Associated Challenges in 6G Cellular Networks 4

TeraHertz Communication and Associated Challenges in 6G Cell...

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4th IEEE international conference on computing and Information Sciences, ICCIS 2021

作者： Aslam, Tahniyat Ahmed, Irfan Ali, Sundus Aslam, Muhammad Imran Institute of Business Management Department of Electrical Engineering Karachi Pakistan University of Engineering and Technology NED Department of Physics Karachi Pakistan

ISBN: (纸本)9781665494410

Due to the rapid growth of connected devices, the demand of high data rate services have also increased, which has been a major enabler for the evolution of wireless technologies from the past decade. Wireless communication at the TeraHertz (thz) frequency band is considered as one of the key technologies of 6th Generation cellular networks (6G). Owing to the availability of the wider bandwidth, the thz frequencies can provide significant increase in wireless capacity to support multi Gigabit-per-second (Gbps) data rates and maintaining highly secure transmission. Despite of numerous advantages of using thz communications, there are some challenges associated with thz communications that degrade the performance of wireless system. these challenges include increased interference, co-tier interference, effect of blockages, fading, molecular absorption loss and higher path loss etc. In this paper, we have investigated the significance of a thz band enabled 6G cellular network and have also highlighted some critical issues and challenges that should be taken into consideration when deploying thz enabled 6G cellular network. Our contribution, is presentation of a consolidated and summarized overview on various use cases, applications and challenges of thz communication enabled in 6G Cellular Networks. © 2021 IEEE.

关键词： Wireless networks

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Pattern Recognition for Uterine Fibroid Detection

Pattern Recognition for Uterine Fibroid Detection

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IEEE Eurasia conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Wai Yie Leong Yuan Zhi Leong Wai San Leong INTI International University Persiaran Perdana BBN Putra Nilai Nilai Malaysia Schneider Electric Singapore Pte. Ltd. Singapore

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

Pattern recognition plays a significant role in the detection of uterine fibroids through various medical imaging modalities such as ultrasound, Magnetic Resonance Imaging (MRI), and computed tomography (CT) scans. Ultrasound imaging helps identify hypoechoic masses within the uterine muscle layer, while MRI provides detailed images showing well-defined masses with characteristic signal intensities on Tl-weighted and T2-weighted sequences. CT imaging, though less common, is used to detect fibroids based on hypodense masses within the uterus or pelvic cavity. Computer-aided diagnosis (CAD) systems and advanced machine-learning algorithms aid in automating the detection and characterization of fibroids by analyzing image features such as shape, texture, and intensity. these pattern recognition techniques facilitate early diagnosis and accurate assessment of uterine fibroids, enabling timely management and treatment of this prevalent gynecological condition.

关键词： Ultrasonic imaging Accuracy Shape Magnetic resonance imaging Computed tomography Uterus Streaming media Pattern recognition Reliability Sustainable development

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Batteries Maintenance Schedule Optimization for Profit Maximization 28

Batteries Maintenance Schedule Optimization for Profit Maxim...

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28th IEEE international conference on Electronics, Electrical Engineering and computing, INTERCON 2021

作者： Abdeltawab, Hussein Mohamed, Yasser A.-R.I. School of Engineering Penn State Behrend EriePA16601 United States University of Alberta Department of Electrical and Computer Engineering EdmontonT6G 2V4 Canada

ISBN: (纸本)9781665412216

this paper proposes an addition to the traditional energy management system (EMS) of battery energy storage systems (BESSs). the addition includes optimizing the maintenance hours of the BESS to achieve maximum profit and has minimal interruption to the service hours. the proposed algorithm is compatible with different BESS applications, such as energy arbitrage, peak shaving, load shifting, and renewable resources integration. A case study validates the proposed algorithm and its ability to obtain the optimal maintenance hours. © 2021 IEEE.

关键词： Maintenance

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