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Game-Based Discovery: Harnessing Mini-Games within Primary Games for Scientific Data Collection and Problem Solving

Game-Based Discovery: Harnessing Mini-Games within Primary G...

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international Computer Sciences and Information Technologies (CSIT)

作者： Abhishek Phadke Mamta Yadav Stanislav Ustymenko School of Engineering and Computing Christopher Newport University Newport News Virginia USA Department of Computer Science Texas A&M University-Corpus Christi Corpus Christi Texas USA School of CARDS Saint Leo University Saint Leo FL USA

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

In the popular video game Batman: Arkham Knight, produced by Rocksteady Studios and released in 2015, the primary protagonist of the game is Batman, a vigilante dressed as a bat, fighting crime from the shadows in the fictitious city of Gotham. The game involves a real-world player who takes up the role of Batman to solve a peculiar side mission wherein they have to reconstruct the clean DNA sequence of a human and separate it from mutant DNA to manufacture an antidote to cure the villain. Although this is undoubtedly a fascinating part of the game, one that was absent in previous Batman games, it showcases an interesting notion of using mini-games embedded within primary games to achieve a particular real-world research objective. Although the DNA data used in this case was not real, there are multiple such instances in video games where mini-games have been used for an underlying motive besides entertainment. Based on popular case studies incorporating a similar method, this study characterizes the methodology of designing mini-games within primary games for research purposes into a descriptive framework, highlighting the process's advantages and limitations. It is concluded that these mini-games not only facilitate a deeper understanding of complex scientific concepts but also accelerate data processing and analysis by leveraging crowd-sourced human intuition and pattern recognition capabilities. This paper argues for strategically incorporating miniaturized, gamified elements into established video games that are mainly intended for recreational purposes.

关键词： Computer science Video games Urban areas Entertainment industry Games Data collection pattern recognition Problem-solving Information technology

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Diabetes Foot Ulcer Detection Using Inception V3 Deep Learning Technique

Diabetes Foot Ulcer Detection Using Inception V3 Deep Learni...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： S. Geeitha Aravinth. S Rishikesh. K Nishanth. J P. Renuka Department of Information Technology M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Diabetes problems like Diabetic Foot Ulcers (DFU) can lead to frequent hospitalizations and costly treatments. Ulcers in the feet caused by diabetes are extremely prevalent. Mellitus adverse effect, and if left untreated, they can lead to amputations. Diabetic feet impact 15-25% of diabetic individuals worldwide. People who suffer from diabetic foot ulcers (DFU) have a more than twice higher death risk than non-ulcerated diabetic individuals. The present investigation offers a new particle swarm optimization approach that combines deep learning to identify and classify diabetic foot ulcers. The described technique's purpose is to recognize and categorize DFU. To do this, the suggested approach generates feature vectors using the Inception V3, Squeeze net, VGG-16, and VGG-19 model. Among these four techniques Inception v3 technique attains highest accuracy. The particle swarm optimization approach optimizes hyper parameter selection for the Inception-v3 model, improving DFU classification results by eliminating the need for human trial-and-error tweaking. The Inception v3 model works well, and our approach outperforms it with an accuracy of 93.3%. DFU is classified utilizing the Deep stacked sparse auto encoder method. This method outperforms other deep learning algorithms, according to thorough experimental results.

关键词： Deep learning Representation learning Accuracy Systematics Medical services Vectors Diabetes pattern recognition Particle swarm optimization Optimization

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1st international conference on Artificial Intelligence and Sustainable computing for Smart City, AIS2C2 2021

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and Sustainable computing for Smart City, AIS2C2 2021

ISBN: (纸本)9783030823214

The proceedings contain 21 papers. The special focus in this conference is on Artificial Intelligence and Sustainable computing for Smart City. The topics include: Implementation of Touch-Less Input recognition Using Convex Hull Segmentation and Bitwise AND Approach;virtually Interactive User Manual for Command and Control Systems Using Rule-Based Chatbot;wrapper-Based Best Feature Selection Approach for Lung Cancer Detection;application of Ensemble Techniques Based Sentiment Analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners;A Hybrid Mathematical Model Using DWT and SVM for Epileptic Seizure Classification;multimodal Cyberbullying Detection Using Ensemble Learning;frequent Route pattern Mining Technique for Route Prediction in Transportation Network;student Clickstreams Activity Based Performance of Online Course;feature Selection with Random Forests Predicting Metagenome-Based Disease;Emotion recognition Using Portable EEG Device;predicting the Default Borrowers in P2P Platform Using Machine Learning Models;human Activity recognition for Multi-label Classification in Smart Homes Using Ensemble Methods;classification of Extraversion and Introversion Personality Trait Using Electroencephalogram Signals;the Productivity Forecasting in the Sector of Non-market Services (Education and Health Care Sectors): A Case Study of Ukraine;An Emergent Role of Knowledge Graph and Summarization Methodology to Simplify Recruitment for the Indian IT Industry;knowledge and Innovation Management for Transforming the Field of Renewable Energy;a Research Perspective on Security in Fog computing Through Blockchain Technology;ZKPAUTH: An Authentication Scheme Based Zero-Knowledge Proof for software Defined Network;Efficiency Evaluation of Handover Management Techniques in LTE Heterogeneous Networks.

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Indoor Elderly Fall recognition System Based on Target Detection and Self-attention Model 2

Indoor Elderly Fall Recognition System Based on Target Detec...

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2nd international conference on Electronics, Communications and Information Technology, CECIT 2021

作者： Liu, Min Ling, Xufeng Yang, Jie School of Health Tianhua College Shanghai Normal University Shanghai China AI School Tianhua College Shanghai Normal University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781665437578

With the increasing aging of the society, a series of consequences caused by falls of the elderly have become a serious medical problem and a real social problem. Aiming at the fall behavior of the elderly, the research and development of human fall recognition technology has practical application value. This paper proposes a method to recognize human targets in indoor images using target detection technology, and then use the trained neural network based on Self-attention technique named Vision Transformer (ViT) to recognize the falling posture of the elderly. Firstly, the target detection model is optimized and trained for indoor scene, so that the model can accurately detect human targets in indoor scene;Next, the indoor scene human target image is preprocessed, and the indoor human pose image data set is constructed;Then, the data set is used to train the ViT depth network, and enable it to correctly classify five postures (standing, sitting, lying, bending and crawling). The experimental results show that ViT network achieves high accuracy and excellent generalization capacity for the above 5 human posture and can realize the recognition of elderly falls. © 2021 IEEE.

关键词： Deep learning

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Comprehensive Review of Smart Healthcare Recommendation Systems Using Deep Learning

Comprehensive Review of Smart Healthcare Recommendation Syst...

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Innovative and Intelligent Information Technologies (IC3IT), international conference on

作者： Soumeya Bouslah Saber Benharzallah Moumen Hamouma Laid Kahloul Departement of Computer Science LAMIE laboratory University of Batna 2 Batna Algeria Departement of Computer Science LINFI laboratory University of Biskra Biskra Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

As smart cities advance and increasingly use data from Internet of Things (IoT) sources to enhance urban environments, smart healthcare has become a key focus area. This paper investigates the integration of Deep Learning (DL) techniques in Smart Healthcare Recommendation Systems (SHRSs), presenting a foundational taxonomy, that categorizes these systems into four distinct types: Content-Based Filtering (CBF), Collaborative Filtering (CF), Context-Aware Filtering (CAF), and Hybrid Filtering (HF). These approaches, implemented through DL, enable advanced data processing, pattern recognition, and personalization. Additionally, the paper introduces a new classification based on the nature of intervention, dividing SHRSs into preventive, diagnostic, treatment-focused, and hybrid categories. This dual classification fills a critical gap in the comparative analysis of DL-driven healthcare, which demonstrates the efficacy of these systems in providing personalized health advice, leveraging real-time sensor data and optimizing the handling of complex health datasets. Furthermore, this research outlines future work and potential directions for advancing SHRSs, offering insights into the application and impact of sophisticated recommendation techniques within the context of urban health management.

关键词： Deep learning Technological innovation Smart cities Filtering Reviews soft sensors Taxonomy Medical services Internet of Things Recommender systems

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Handwritten mathematical expression recognition with self-attention 4

Handwritten mathematical expression recognition with self-at...

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4th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2021

作者： Chi, Xueke Wang, Da-Han Wu, Yuefeng Wu, Yun Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450385053

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN, the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. The experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of 51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. The improvements demonstrate the effectiveness of the self-attention module. © 2021 ACM.

关键词： Signal encoding

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Cloud Burst Prediction System using Machine Learning

Cloud Burst Prediction System using Machine Learning

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Herin Shani. S G. Nagappan Department of CSE Saveetha engineering college (Autonomus) Chennai India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This abstract presents an innovative approach that leverages the Gramian Angular Field (GAF) in conjunction with Convolutional Neural Networks (CNN) to improve the accuracy and reliability of cloudburst prediction systems. The utilization of GAF and CNN represents a breakthrough in modeling the complexities inherent in meteorological data. GAF, with its generative capabilities, constructs synthetic data closely resembling the underlying meteorological distributions. CNN, renowned for its proficiency in spatial data analysis, is adept at recognizing intricate patterns within meteorological images. The application of GAF and CNN in cloudburst prediction systems signifies a significant advancement in the field. By effectively recognizing and generating synthetic data representative of meteorological complexities, this approach has the potential to significantly improve the precision and lead time of cloudburst predictions, thereby enhancing early warning systems and disaster preparedness.

关键词： Technological innovation Accuracy Clouds Weather forecasting Spatial databases pattern recognition Complexity theory

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Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets

Transferring Quantified Emotion Knowledge for the Detection ...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： P. A. Pérez-Toro D. Rodríguez-Salas T. Arias-Vergara S. P. Bayerl P. Klumpp K. Riedhammer M. Schuster E. Nöth A. Maier J. R. Orozco-Arroyave Pattern Recognition Lab Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany GITA Lab Facultad de Ingeniería Universidad de Antioquia UdeA Medellín Colombia Department of Otorhinolaryngology Head and Neck Surgery Ludwig-Maximilians University Munich Germany Technische Hochschule Georg Simon Ohm Nürnberg Germany

Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model’s decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition; then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD.

关键词： Emotion recognition Analytical models Computational modeling Transfer learning Forestry Artificial neural networks Depression

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Autonomous Drone Swarm Navigation in Complex Environments 19

Autonomous Drone Swarm Navigation in Complex Environments

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19th international Bhurban conference on Applied Sciences and Technology, IBCAST 2022

作者： Qamar, Suleman Qamar, Maryam Shahbaz, Muhammad Arif Arshad, Muhammad Saher Shah, Najmus Khan, Asifullah Pattern Recognition Lab DCIS Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan CS & IT University of Azad Jammu and Kashmir Pakistan Department of Computer Science and Engineering Kyung Hee University Korea Republic of

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Swarm intelligence is the behavior exhibited by a group of organisms performing simple actions in unison which leads to complex global behavior. This learnt behavior promotes formation of artificial swarm systems for accomplishing complex tasks and in this paper, the behavior learnt by a swarm of drones is used to accomplish multiple tasks related to complex environment navigation, obstacle avoidance and single-target tracking. In the past, manual formulation of artificial swarm systems has been attempted. The swarms usually have to be flexible entities that need to adapt to changing environments but manually crafted swarms can't adapt to newer operating conditions thus they are not feasible. To tackle these challenges, an autonomous drone swarm navigation (ADSN) system is presented by introducing a customized architecture based on Truly Proximal Policy Optimization (TPPO) with the addition of memory cells. Furthermore, suitable 3D environments are designed for conductive learning with the addition of stability factors for drones to mimic real-life environments. Measures like Mean Cumulative Reward (MCR), Value Loss (VL), and Entropy are used to measure the performance of the presented ADSN system and other models with and without LSTM and found that incorporating memory does enhance the performance of the models although in some cases increase might not be that significant. TPPO with memory seems to have least loss and almost always performs best. Significant improvements were achieved compared to existing methodologies in convergence speed and enhanced stability of the model by customizing the architecture and hyper- parameter optimization. It is observed that soft Actor Critic (SAC) is highly dependent on the values of the hyper-parameters and its results vary greatly with different values of hyper-parameters. The presented model has applications in maze navigation, target tracking, hover drones and other real-time scenarios. © 2022 IEEE.

关键词： Drones

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8th international conference on computing and pattern recognition, ICCPR 2019

8th International Conference on Computing and Pattern Recogn...

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8th international conference on computing and pattern recognition, ICCPR 2019

ISBN: (纸本)9781450376570

The proceedings contain 82 papers. The topics discussed include: a reparameterized construction in L2-norm weight decay for rectifier neural networks;velocity-driven particle swarm optimization;neural network hyperparameter tuning based on improved genetic algorithm;the impact of online word-of-mouth on movie box office based on fine-grained sentiment analysis;understanding the structure of streaming documents based on neural network;optimization of recurrent neural networks on natural language processing;few-shot knowledge reasoning method based on attention mechanism;data repair method based on timeliness and conditional function dependency rules;and data acquisition system of weather sounding air pressure sensor.

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