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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning

Predictive Design for Quality Assessment Employing Cloud Com...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Gaurav Jindal Vidhika Tiwari Riyaz Mahomad Anita Gehlot Mimoh Jindal Dibyhash Bordoloi Department of Master of Computer Applications G L Bajaj College of Technology and Management Gr. Noida Mechanical Engineering Department DPGITM Gurugram School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India National Informatics Centre Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.

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Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

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Evaluating shallow capsule networks on complex images

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International Journal of Information Technology (Singapore) 2021年第3期13卷 1047-1057页

作者： Mensah, Patrick Kwabena Weyori, Benjamin Asubam Ayidzoe, Mighty Abra Department of Computer Science and Informatics University of Energy and Natural Resources P.O. Box 214 Sunyani Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

Useful real-life images meant for computer vision applications appear in complex forms with varied backgrounds. Convolutional neural networks with their invariance outperforms capsule networks (CapsNets) on such images. Recent research to improve the accuracy of CapsNets on complex images such as CIFAR10 involves the increase in depth of the networks. However, smaller architectures with efficient feature extractors can perform comparatively well with additional advantages of having less computational cost, reduced risk of overfitting, increased suitability for deployment on devices with smaller memories, and enhanced feasibility to perform distributed training across servers and in the cloud. This paper, therefore, proposes a shallow CapsNet based on dynamic routing with a custom squashing function and normalizer to gain all of these advantages. The model is evaluated on three publicly available datasets;fashion-MNIST, CIFAR-10, and tomato datasets. Experimental results show that the proposed model outperforms the original CapsNet by generating fewer parameters and performing comparably well in terms of accuracy with some state-of-the-art multi-lane and ensemble capsule network models. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Capsule network Complex images Convolutional neural networks Dynamic routing Local binary pattern Plant diseases

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Extracting Temporal-Based Spatial Features in Imbalanced Data for Predicting Dengue Virus Transmission

Extracting Temporal-Based Spatial Features in Imbalanced Dat...

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International Conference on computer, Communication and Computational Sciences, IC4S 2019

作者： Setiyoutami, Arfinda Anggraeni, Wiwik Purwitasari, Diana Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9789811544088

Since the movements of mosquito or human can potentially influence dengue virus transmission, recognizing location characteristics defined as spatial factors is necessary for predicting patient status. We proposed feature extraction that considers location characteristics through previous dengue cases and the high possibility of encounters between people with different backgrounds. The number of incoming populations, school buildings and population density was included as the location characteristics. Besides the information of the spatial factors, the number of dengue cases set within a particular time window was specified for virus transmission period. Our experiments obtained two datasets of dengue fever which were patient registry and location characteristics of Malang Regency. Manually recorded Registry Data only contained positive group data and not the negative group when the patients were healthy. Thus, the proposed extraction method also included the process of generating negative data from the existing positive data. Then, we preprocessed the data by cleaning, imputing, encoding, and merging, such that there were four features representing previous dengue cases and eight features describing location characteristics. The experiments demonstrated that by using some ranked features the prediction had a better accuracy of 78.7% compared to using all features. Temporal-based features displayed better performances, but the result was improved in the wider location where people met. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Location

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Machine Learning-based Ransomware Detection Using Low-level Memory Access Patterns Obtained From Live-forensic Hypervisor

arXiv

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arXiv 2022年

作者： Hirano, Manabu Kobayashi, Ryotaro Department of Information and Computer Engineering National Institute of Technology Toyota College Toyota Japan Faculty of Informatics Kogakuin University Tokyo Japan

Since modern anti-virus software mainly depends on a signature-based static analysis, they are not suitable for coping with the rapid increase in malware variants. Moreover, even worse, many vulnerabilities of operating systems enable attackers to evade such protection mechanisms. We, therefore, developed a thin and lightweight live-forensic hypervisor to create an additional protection layer under a conventional protection layer of operating systems with supporting ransomware detection using dynamic behavioral features. The developed live-forensic hypervisor collects low-level memory access patterns instead of high-level information such as process IDs and API calls that modern Virtual Machine Introspection techniques have employed. We then created the low-level memory access patterns dataset of three ransomware samples, one wiper malware sample, and four benign applications. We confirmed that our best machine learning classifier using only low-level memory access patterns achieved an F1 score of 0.95 in detecting ransomware and wiper malware. © 2022, CC BY-NC-ND.

关键词： Malware

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Adversarial-aware Machine Learning for Enhancing Smart Grid Security

Adversarial-aware Machine Learning for Enhancing Smart Grid ...

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International Middle-East Power System Conference, MEPCON

作者： Yusuf Ademola Sodiq Tarek Gaber Reham Amin Angel Jimenez-Aranda Mohamed Galeela School of Science Engineering & Environment The University of Salford Manchester United Kingdom Computer Sciences Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Centre for Sustainable Innovation Salford Business School The University of Salford Manchester United Kingdom TNEI Service LTD Manchester United Kingdom Electrical Power Engineering Department Faculty of Engineering Cairo University

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Smart grid systems are essential components of modern power infrastructures, where machine learning has found wide-ranging applications, particularly in the development of intrusion detection systems. However, such systems remain vulnerable to adversarial attacks, including data poisoning techniques like label flipping attack. This paper proposes a novel, adversarial-aware machine learning-based intrusion detection system that is robust against label flipping attacks, thereby enhancing the integrity and reliability of the energy distribution network. The proposed solution was evaluated using the ICS cyber-attack dataset from the University of Queensland, with multiple models undergoing progressive testing. Initially, the base model employed three key algorithms: Random Forest, K-Nearest Neighbors, and XGBoost. These models were subjected to simulated label flipping attacks with varying intensities (10%-50%), which caused a significant decline in performance. To counter this, a countermeasure technique was integrated into the system, successfully restoring model accuracy and achieving a consistent accuracy of 75.96% across all attack intensities. Among the algorithms, Random Forest demonstrated the greatest resilience, showing a notable recovery after mitigation. This study underscores the critical role of preventive defense strategies and robust machine learning algorithms in safeguarding smart grids against cyber threats.

关键词： Accuracy Machine learning algorithms Prevention and mitigation Intrusion detection Smart grids Security Reliability Integrated circuit modeling Random forests Resilience

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Random Load Balancing in Mini-NDN Using Modified Inherent Topology

Random Load Balancing in Mini-NDN Using Modified Inherent To...

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International Conference on Wireless and Telematics (ICWT)

作者： Cendika Sahid Pratama Muhammad Iqbal Arrachman Muhammad Shafly Nurrasyid Eki Ahmad Zaki Hamidi Ridha Muldina Negara Syaiful Ahdan Ratna Mayasari Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia

Named Data Networking (NDN) is a newly developing networking method that focuses on information, compared to TCP/IP, which focuses on hosts. This study proposed a solution to the growing complexity of computer networks. The fundamental problem will still be able to be found, one of them being difficulty in accessing a node as data traffic is piling up. One of the solutions is load balancing, which is implementing a system that will redirect packets to different service providers to ease up the load and reduce the number of packets that are dropped due to timeouts. This paper analyses the impact of load balancing in an extreme case emulated environment using Mini-NDN and random load balancing strategy. From the results gathered, implementing random load balancing to the consumer nodes decreases-and in some cases eliminates-dropped packets. In addition, implementing load balancing stabilizes packets' RTT (Round Trip Time) but increases the average RTT compared to using best route forwarding in an unloaded environment.

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Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification

arXiv

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arXiv 2024年

作者： Gheorghiu, Adrian Tăiatu, Iulian-Marius Cercel, Dumitru-Clementin Marin, Iuliana Pop, Florin Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Engineering in Foreign Languages National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania Academy of Romanian Scientists Bucharest Romania

The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification. © 2024, CC BY-NC-SA.

关键词： Image classification

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CARA: A Hybrid Framework Integrating Swarm AI Agents and Knowledge Graphs for Advanced LLM Reasoning

CARA: A Hybrid Framework Integrating Swarm AI Agents and Kno...

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Advancements in Computing (ICAC), International Conference on

作者： Nirodya Pussadeniya Ruchira Wijesinghe Udaya Wijenayake Bhagya Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments.

关键词： Adaptation models Adaptive systems Decision making Training data Knowledge graphs Linguistics Cognition Artificial intelligence Standards Context modeling

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