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2023 Winter Simulation Conference, WSC 2023

作者： Mittal, Vikram Evangelista, Paul F. United States Military Academy Department of Systems Engineering Mahan Hall West PointNY10996 United States United States Military Academy Office of Data and Analytics Taylor Hall West PointNY10996 United States

ISBN: (纸本)9798350369663

There is significant variation in shooting ability among U.S. Army soldiers, which is often overlooked in combat simulations. This study introduces a Monte-Carlo model to estimate the dispersion of a soldier's shot group based on their marksmanship score. This model is used to assess the impact of marksmanship on a squad's performance through two analyses. The first analysis employs a dueling model to examine various marksmanship skills between dueling teams, offering insights into overmatch requirements. The second analysis uses an agent-based combat simulation to investigate the influence of marksmanship on squad performance in a dueling scenario in addition to tactical rural and urban missions. The results reveal that marksmanship becomes increasingly crucial in enhancing lethality and survivability as the distance between combatants grows. Notably, superior marksmanship skills are particularly vital in offensive, rural operations. These findings emphasize the significance of marksmanship and its implications for military requirements and tactical decision-making. © 2023 IEEE.

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Benchmarking Graph Learning for Drug-Drug Interaction Prediction

arXiv

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arXiv 2024年

作者： Shen, Zhenqian Zhou, Mingyang Zhang, Yongqi Yao, Quanming Department of Electronic Engineering Tsinghua University Beijing China Thrust of Data Science and Analytics The Hong Kong University of Science and Technology Guangzhou China

Predicting drug-drug interaction (DDI) plays an important role in pharmacology and healthcare for identifying potential adverse interactions and beneficial combination therapies between drug pairs. Recently, a flurry of graph learning methods have been introduced to predict drug-drug interactions. However, evaluating existing methods has several limitations, such as the absence of a unified comparison framework for DDI prediction methods, lack of assessments in meaningful real-world scenarios, and insufficient exploration of side information usage. In order to address these unresolved limitations in the literature, we propose a DDI prediction benchmark on graph learning. We first conduct unified evaluation comparison among existing methods. To meet realistic scenarios, we further evaluate the performance of different methods in settings with new drugs involved and examine the performance across different DDI types. Component analysis is conducted on the biomedical network to better utilize side information. Through this work, we hope to provide more insights for the problem of DDI prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Low Complexity Fall Detection Using 2D Skeletal data

Low Complexity Fall Detection Using 2D Skeletal Data

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Praveen Jesudhas Raghuveera Tripuraribhatla Data Science Tiger Analytics Chennai India Department of Computer Science and Engineering Anna University Chennai India

Falling remains one of the events that can potentially cause irreversible damage, especially among elderly people if not treated early. Fall detection systems help to identify if a person has fallen and promptly alert the people involved to ensure timely medical care. Existing fall detection systems are found to utilize specialized sensors or require heavy computation requirements to identify the occurrence of falls. An easy-to-adopt, fall detection system is presented which can identify falls from a commodity-grade 2D CCTV camera. 2D skeletal data are extracted from the video frames and representative features are computed. The features specific to fall detection are grouped and analyzed in terms of their discriminative ability to distinguish falling from day-to-day actions. LSTM-based classifiers are trained on the filtered fall features to identify a person falling. The results are validated on the NTU (RGB) skeletal dataset. The precision and recall of the Fall detection classifier are found to be 98% and 100% respectively. The number of parameters in the developed system is found to be several magnitudes lesser than in existing systems, thereby validating low complexity with high accuracy.

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Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems

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Wiley Interdisciplinary Reviews: data Mining and Knowledge Discovery 2025年第2期15卷

作者： Sachdeva, Tamanna Goyal, Lalit Mohan Mittal, Mamta Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Faridabad India Department of Data Analytics Delhi Skill and Entrepreneurship University New Delhi India

Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommendations. E-commerce giants such as Amazon, Netflix, and YouTube are offering recommendations to users based on their interests, past experiences, demographic information, etc. hence, increasing the user's engagement on these applications. This study offers a comprehensive review of recommendation systems, covering their types, fundamental techniques, and emerging trends, with a focus on the predictive models and algorithms that power personalization. This study shows how in comparison to traditional collaborative and content-based recommendation systems-building techniques, the novel approaches of deep learning, graph-based techniques, meta-learning, few-shot learning, exploration, and federated learning offer promising prospects to improve recommendation systems' scalability, privacy-preserving abilities, and accuracy. These advanced methods deliver more diverse, context-aware, and personalized recommendations by leveraging large-scale data and complex predictive algorithms. Furthermore, this paper depicts forthcoming trajectories in the field of recommendation systems, including the adoption of graph-based approaches, federated learning, and the exploration of ethical considerations. By mapping the current landscape of prediction-driven personalization and identifying emerging trends, this review serves as a valuable resource for scholars and practitioners seeking to deepen their understanding of the field and drive innovation in recommendation systems. Readers can expect to gain insights into both foundational and cutting-edge techniques and how these can shape the future of personalized recommendations. © 2025 Wiley Periodicals LLC.

关键词： Federated learning

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A Study on Object Detection using Faster R-CNN, Retina net, RPN_R_50_FPN, RPN_R_50_C4

A Study on Object Detection using Faster R-CNN, Retina net, ...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Aswin Tirumala Someswar Kadiyam Chinmaya Ranjan Padhan Sibarama Panigrahi Computer Science and Engineering National Institute of Technology Andhra Pradesh India Intelligent Data Analytics Laboratory Computer Science and Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

The computer vision concept that involves the detection and characterization of objects in images is known as object detection. This research mainly performs this function using four popular models faster R-CNN, RetinaNet, RPN_R_50_FPN, and RPN_R_50_C4 across multiple datasets namely udacity self-driving car dataset, tomatOD dataset, and chess piece detection dataset. Faster R-CNN is famous for its outstanding accuracy although RetinaNet employs a unique focal loss function to balance between speed and accuracy. The purpose of the models RPN_R_50_FPN and RPN_R_50_C4 is to generate region proposals with high quality using different stages in the ResNet-50 backbone. For normalization techniques, the datasets are preprocessed in this research. Instead of training the models from scratch, transfer learning is used whereby starting with weights that were pre-trained on the COCO dataset and after that fine-tuning the required datasets. Accuracy was quantified by evaluating two efficiency measures which are minimum loss and total training duration. Across the different datasets, various performances were demonstrated by different models.

关键词： Training Technological innovation Accuracy Computational modeling Transfer learning Object detection Loss measurement Time measurement Autonomous automobiles Tuning

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Customer Profiling Based on Mobile Apps GPS data : A Case Study on Westfield Shopping Malls

Customer Profiling Based on Mobile Apps GPS Data : A Case St...

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International Conference on Geoinformatics

作者： Fangzhou Zhou Xianghui Zhang Xinglei Wang Tao Cheng Department of Civil Environmental and Geomatic Engineering SpaceTimeLab for Big Data Analytics University College London London UK

In order to provide a personalised experience to customers, it’s essential for shopping centers to understand its customer base and their shopping behaviors. Building a well-developed customer profile is critical for improving marketing efficiency, expanding market share, and building long-term, stable business ties with trading partners. Currently most shopping malls or retail business use footfall or customer surveys to grasp the customer behaviors, which are insufficient to obtain accurate and representative information about the customers. This study aims to provide a detailed customer profile for shopping centers using GPS datasets. We choose the two Westfield shopping malls in London as the case study area. In order to uncover additional customer information, this study focuses four research questions:(1) Origin places of customers; (2) Their transportation mode to the mall; (3) The average dwell time of customers; (4) The pattern of return visitors. According to the results, malls can develop a range of marketing initiatives to provide a better shopping experience for customers and attract more of them.

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Multimodal Supported Chatbot Framework for People with Aphasia

Multimodal Supported Chatbot Framework for People with Aphas...

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Moratuwa engineering Research Conference (MERCon)

作者： R.L.A. Sanduni Ayesha L.B. Hasini M. Vijayarathna N.P.A. Vithanage Uthayasanker Thayasivam Damminda Alahakoon Achini Adikari Nuwan Pallewela Department of Computer Science and Engineering University of Moratuwa Katubedda Sri Lanka Research Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Chatbots have become a trending topic with emerging platforms like ChatGPT, Gemini, and Copilot, for conversation assistance. Current chatbots mainly focus on the general public assuming a natural flow of conversation. However, there is a need for a chatbot that supports people with various communication disabilities. This research fills this gap by offering a novel technique for a chatbot that assists people with Aphasia, a condition characterised by difficulties with language. We propose a multi-modal chatbot that is customised and designed to assist users with communication disabilities in navigating a website. Unlike typical chatbots, which rely on one form of communication, our architecture combines multiple modalities to improve comprehension and promote effective communication for people with Aphasia. We focus on gathering multimodal inputs by recognising and combining user intents from diverse sources. The use of Txtai, an all-in-one embeddings database for semantic search improves our chatbot’s capacity to process various inputs efficiently. We leverage specialised models like Whisper for audio transcription and MediaPipe Gesture Recognizer for gesture detection to enhance user interactions. Additionally, Rasa Core integration improves conversational experiences for users. We propose that this new approach will make communication more accessible and inclusive for individuals with Aphasia.

关键词： Navigation Semantic search Intent recognition Diversity reception Oral communication Gesture recognition Machine learning Aphasia Chatbots User experience

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REVERSE engineering THE REPRODUCTION NUMBER: A FRAMEWORK FOR data-DRIVEN COUNTERFACTUAL ANALYSIS, STRATEGY EVALUATION, AND FEEDBACK CONTROL OF EPIDEMICS

arXiv

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arXiv 2023年

作者： She, Baike Smith, Rebecca Lee Pytlarz, Ian Sundaram, Shreyas Paré, Philip E. Department of Mechanical and Aerospace Engineering University of Florida GainesvilleFL United States Department of Pathobiology University of Illinois Urbana-Champaign ChampaignIL United States Institutional Data Analytics + Assessment Purdue University West LafayetteIN United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

During the COVID-19 pandemic, different countries, regions, and communities constructed various epidemic models to evaluate spreading behaviors and assist in making mitigation policies. Model uncertainties, introduced by complex transmission behaviors, contact-tracing networks, time-varying spreading parameters, and human factors, as well as insufficient data, have posed arduous challenges for model-based approaches. To address these challenges, we propose a novel framework for data-driven counterfactual analysis, strategy evaluation, and feedback control of epidemics, which leverages statistical information from epidemic testing data instead of constructing a specific model. Through reverse engineering the reproduction number by quantifying the impact of the intervention strategy, this framework tackles three primary problems: 1) How severe would an outbreak have been without the implemented intervention strategies? 2) What impact would varying the intervention strength have had on an outbreak? 3) How can we adjust the intervention intensity based on the current state of an outbreak? Specifically, we consider the epidemic intervention policies such as the testing-for-isolation strategy as an example, which was successfully implemented by the University of Illinois Urbana-Champaign (UIUC) and Purdue University (Purdue) during the COVID-19 pandemic. By leveraging data collected by UIUC and Purdue, we validate the effectiveness of the proposed data-driven framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Feedback control

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Speech Emotion Recognition Using Mel Spectrogram HPCA and Variational Mode Decomposition

Speech Emotion Recognition Using Mel Spectrogram HPCA and Va...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： David Hason Rudd Xingyi Gao Md Rafiqul Islam Huan Huo Guandong Xu Faculty of Engineering and IT The University of Technology Sydney Sydney Australia Information Systems (Data Analytics) Australian Institute of Higher Education (AIH) Sydney Australia

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

The rapid evolution of affective computing demands sophisticated methodologies to enhance the reliability and effectiveness of speech emotion recognition (SER). This study integrates harmonic-percussive component analysis (HPCA) with variational mode decomposition (VMD) to overcome various drawbacks for conventional speech emotion recognition (SER) methodologies that primarily rely on stand-alone feature extraction techniques. This implementation refines acoustic feature extraction and optimizes VMD decomposition to prevent information loss from mode duplication and mixing problems. We propose a feature map generator that channels the enhanced feature vectors into a convolutional neural network, specifically the VGG16 model, and the model is further enriched by incorporating diverse acoustic features including HP and log Mel spectro-grams into two-dimensional spaces to intensify data augmentation and enrich emotional feature representation. Extensive testing on Berlin EMO-DB and RAVDESS databases confirmed positive impacts for the proposed HP-VMD model performance, achieving robust classification accuracy of 96.67%. Thus, the proposed integrated approach to developing SER systems significantly enhances empathetic human computer interactions.

关键词： Emotion recognition databases Heuristic algorithms Speech recognition Speech enhancement Feature extraction Acoustics Vectors Classification algorithms Spectrogram

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