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Scorigami: Simulating the Distribution and Assessing the Rarity of National Football League Scores 24

Scorigami: Simulating the Distribution and Assessing the Rar...

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Proceedings of the Winter Simulation Conference

作者： Liam Moyer Jameson Railey Andrew Daw Samuel C. Gutekunst Dept. of Computer Science Bucknell University Lewisburg PA USA Dept. of Computer Science Stevens Institute of Technology Hoboken NJ USA Dept. of Data Sciences and Operations University of Southern California Los Angeles CA USA Depts. of Computer Science and Mathematics Bucknell University Lewisburg PA USA

ISBN: (纸本)9798331534202

NFL Scorigamis have a cult-like following, and occur whenever a game ends in a new, never-before-seen score. While substantial research has gone into simulating and predicting NFL game scores, most work has been in relation to winner prediction and betting spreads. We analyze a Poisson random variable model for the distribution of NFL game scores and show that it fails to incorporate important game dynamics. Through an analysis of extensive play-by-play data, we extend this to a non-stationary, state-dependent Poisson process model. This latter model more closely fits real NFL score data, and we use it in NFL score simulations to forecast likely future Scorigamis.

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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation 17

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Raha, Avi Deb Gain, Mrityunjoy Saha, Sudip Kumar Rahman, Md Shohanur Adhikary, Apurba Debnath, Rameswar Bairagi, Anupam Kumar Biswas, Sujit Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh KUET Dept. of Mechatronics Engineering Khulna Bangladesh Noakhali Science and Technology University Dept. of ICE Noakhali Bangladesh City University of London Computer Science Department EC1V 0HB United Kingdom

ISBN: (纸本)9798331531195

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes. © 2025 IEEE.

关键词： Distillation equipment

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Forecasting Nutrient Flows using Terrain Elevation-aware Spatial-Temporal Graph Neural Networks

Forecasting Nutrient Flows using Terrain Elevation-aware Spa...

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IEEE International Conference on Big data

作者： Jiaqi Li Yinghui Wu Alexandar Harding Bradley Olatunde Akanbi Erika I. Barcelos Laura S. Bruckman Roger H. French Dept. Computer and Data Sciences Case Western Reserve University Cleveland USA Dept. Material Science and Engineering Case Western Reserve University Cleveland USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Spatiotemporal graph neural networks (STGNNs) have been adopted for predictive analysis in various scientific domains. Despite their promising performance, the dominance of big (geo)spatiotemporal data with large and heterogeneous dimensions raise computational challenges to effective adoption, generalization and fine-tuning of graph models. Moreover, continuous and high quality historical data may not always exist for such generalization. This paper proposes a framework that can co-evolve historical geospatial temporal datasets and an STGNN model by (1) incorporating elevation features optimized for water systems, and (2) integrating and interacting geospatial data discovery and graph learning with a "rehearsal" mechanism, that automatically generalize STGNNs to broader areas. The process divides spatiotemporal data into regional fragments with inferrable features, and iteratively (1) augment sparse training data in terms of feature similarity, (2) explore the augmented data by a trial "rehearsing" of the current model to decide a fraction of data to be adopted, over which a consistently good accuracy is observed, and (3) generalize STGNNs with promising regional data, ensured by rehearsal performance. This exploratory process hence learns to decide when and where to generalize STGNNs, for cost-effective generalization. Using real-world datasets, we experimentally verify the effectiveness and efficiency of our rehearsal framework.

关键词： Adaptation models Accuracy Computational modeling Transfer learning Training data data models Graph neural networks Geospatial analysis Spatiotemporal phenomena Predictive analytics

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CardioPredict:Advancing Cardiovascular Diagnostics with Deep Learning 2

CardioPredict:Advancing Cardiovascular Diagnostics with Deep...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Chris Collin, J. Jenefa, A. Edward Naveen, V. Santhiya, P. Kuriakose, Bessy M. Lincy, A. Karunya Institute Of Tech. And Sci School Of Computer Science And Tech Coimbatore India Sri Shakthi Institute Of Engg. And Tech Dept. Of Cse Coimbatore India Malla Reddy Engineering College For Women Dept Of Cse India National Engineering College Dept. Of Cse Kovilpatti India

ISBN: (纸本)9798350381665

Cardiovascular diseases (CVDs) stand as the foremost cause of death globally, highlighting the paramount importance of precise diagnostic techniques. Traditional diagnostic approaches, foundational yet challenged by interpretive variability and extensive time requirements, depend on the manual analysis of of electrocardiogram (ECG) images by medical professionals, introducing inconsistency in results. This study introduces 'CardioPredict,' a deep learning framework aimed at automating and refining the accuracy of CVD diagnoses through sophisticated ECG image analysis. 'CardioPredict' harnesses an extensive dataset derived from the MIT-BIH Arrhythmia and PTB Diagnostic ECG databases, encompassing more than 48 hours of ECG recordings meticulously annotated with a diverse spectrum of cardiac conditions. Experimental outcomes reveal 'CardioPredict' markedly surpasses traditional machine learning techniques, achieving an accuracy of 91.2%, precision of 91.8%, and recall of 90.5%. These outcomes demonstrate the model's exceptional performance in identifying diverse cardiovascular abnormalities, establishing a new benchmark for deep learning in medical imaging analysis. By automating ECG image interpretation, this project offers a pathway towards more dependable, accessible, and swift diagnoses of cardiovascular diseases, significantly enhancing patient care and management. © 2024 IEEE.

关键词： Electrocardiograms

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Improved Sampling Methods for Evaluation of Classification Performance

Improved Sampling Methods for Evaluation of Classification P...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yue-Shi Lee Show-Jane Yen Yi-Jie Tang Dept. of Computer Science & Information Engineering Ming Chuan University Taoyuan Taiwan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

To evaluate the classification accuracy of a classification algorithm on a certain dataset, it is necessary to sample training and test data from the original dataset. After building the classification model from the training data, the test data can be used to evaluate the classification accuracy. However, in order to evaluate the classification accuracy of a classification algorithm on an original dataset, it is usually necessary to sample the training/test data many times, build the classification model many times, and then average the accuracies of all the evaluation, which will consume a lot of time and resources. To address this, we propose a sampling method so that the sampled training/test data can replace the results of building multiple models and averaging multiple classification accuracy, that is close to the evaluation results of the original data. Our approach introduces different methods for calculating the similarity of data distributions and incorporates feature weights in the similarity calculation process to select training/test sets that are close to the distribution of the original dataset.

关键词： Training Earth Accuracy Buildings Training data Sampling methods data models Classification algorithms Artificial intelligence Testing

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Utilization of Non-Orthogonal Multiple Access (NOMA) for Spectral Efficiency in Mixed Fading Environments 2

Utilization of Non-Orthogonal Multiple Access (NOMA) for Spe...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Rashmi Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Symbiosis Institute of Technology Department of CSE Nagpur Campus India Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798350366570

We explore in this paper a potential application of Non-Orthogonal Multiple Access (NOMA) to improve spectral efficiency in multi-fading environments that are ubiquitous in wireless communication systems. This is enabled by NOMA, which distributes the transmit power over different users according to their channel conditions and effectively utilizes the spectrum resources. The performance of NOMA is evaluated by extensive simulations and analytical results are provided to analyze the gains of NOMA over Orthogonal Multiple Access (OMA) in various fading models such as Rayleigh, Rician, and Nakagami-m. The following key metrics including Spectral Efficiency, Bit Error Rate (BER), Capacity, Outage Probability, Throughput and Cumulative Distribution Function (CDF) of User Rates are analyzed to measure the performance gains provided by NOMA for system throughput improvement, reduced errors, support for additional users as well as a fair resource allocation. Simulation results show the superiority of NOMA technique in comparison to OMA, especially for high density users and different channel models, making NOMA an important candidate as a future radio access scheme. © 2024 IEEE.

关键词： Distribution functions

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics 10

Towards the Utilization of Parallel Programming to Speed Up ...

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10th International Conference on Software Defined Systems, SDS 2023

作者： Al-Sharif, Ziad A. Alhamouri, Rahaf Jararweh, Yaser Lewis University Dept. of Engineering Computing and Mathematical Sciences Romeoville United States Jordan University of Sci. and Tech. Dept. of Software Engineering Irbid Jordan Jordan University of Sci. and Tech. Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides 3X to 5X speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers. © 2023 IEEE.

关键词： Parallel programming

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Technology and Community Interventions for food Security and Waste Reduction

Technology and Community Interventions for food Security and...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Arunkumar K Muthupandi G S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Food waste and insecurity using a multifaceted approach that includes nutrition-focused interventions in supportive housing, culturally appropriate food programs for Indigenous communities, and mobile applications to cut down on food waste. Geographical barriers, cultural significance, and logistical inefficiency are among the major barriers. This study assesses the impacts of digital food-waste apps, targeted nutritional interventions, and Indigenous food sovereignty initiatives using a mixed-methods approach. The integration of community-based and technology-driven solutions is unprecedented. The results highlight the worth of cooperative, sustainable approaches toward improving community well-being, environmental resilience, and food access.

关键词： Food waste Waste reduction Economics Food security Collaboration Medical services Mobile applications Global communication Cultural differences Resilience

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GOSPA-Driven Gaussian Bernoulli Sensor Management 26

GOSPA-Driven Gaussian Bernoulli Sensor Management

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26th International Conference on Information Fusion, FUSION 2023

作者： Jones, George García-Fernández, Ángel F. Wong, Prudence W.H. Dept. of Electrical Engineering and Electronics University of Liverpool United Kingdom Dept. of Computer Science University of Liverpool United Kingdom Aries Research Centre Universidad Antonio de Nebrija Madrid Spain

ISBN: (纸本)9798890344854

This paper presents a multi-target metric driven approach to sensor management for Bernoulli filtering, in which at most one target of interest is present. The metric used is the generalised optimal sub pattern assignment (GOSPA) metric. We consider the problem of having an agile sensor operating in a surveillance area, tracking objects as they appear from a target birth distribution. Only one target of interest can exist at any given time-step and its single-target density is Gaussian. In this scenario, we have a grid of sensors that we can select from, one at a time using myopic planning. We evaluate the proposed sensor management algorithm via simulations. © 2023 International Society of Information Fusion.

关键词： Target tracking

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Autism Spectrum Disorder Detection Based on Optimal Feature Selection Technique

Autism Spectrum Disorder Detection Based on Optimal Feature ...

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Smart Technologies, Communication and Robotics (STCR)

作者： Lakshaya Kaushik Ranjit Singh Manjit Singh Dept. of Computer Science & Engineering Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

A neurodevelopmental disorder autism spectrum disorder (ASD) still remains a challenge among research fields, and the current diagnostic procedures still need to be improvised for more precise detections. Although several investigations by involving the usage of different machine learning (ML) algorithms toward the identification of ASD among individuals, there is still a large gap in optimizing feature selection methods toward better accuracy and efficiency. Systematic comparisons among various feature selection methods are rather neglected in prior studies, so there is less potential for discovering the best diagnostic markers. In this light, our work brings forth a comprehensive framework that investigates three different kinds of feature selection methods-mutual information, wrapper-based selection by Random Forest, and LASSO regularization by XGBoost. Our methodology implements structured evaluations toward the assessment of the impact of each tool for the identification of detecting accuracy and computation. Also, comparative evaluation results that though wrapper-based selection increased the first model’s accuracy up to 90%, the use of XGBoost with LASSO regularization outperformed it with an accuracy of 99.8%. Results establish the importance of our approach in streamlining clinical assessment processes and identifying features about ASD that are crucial towards advancing data-driven detection methodologies.

关键词： Autism Accuracy Systematics Machine learning algorithms Computational modeling Pipelines Feature extraction Robots Random forests Mutual information

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