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Rollick Games: A Formal Language Based Platform for Location-Based Pervasive Games 1

15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Anestis, George Gioldasis, Nektarios Karasavvidis, Stefanos Palioudakis, Tzanis Moumoutzis, Nektarios Christodoulakis, Stavros Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Greece School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

In this paper, we present the Rollick Games platform and its Pervasive Game Modeling Language that enables the description of location-based pervasive games using a GUI provided by the Game Studio web app. The runtime environment (Pervasive Game Execution Environment) is responsible for generating the Pervasive Game Execution Language from a game model and loading it to an instance of the game's Execution Engine. The Execution Environment includes a set of agents responsible for monitoring the players’ context and interaction with other game elements and among each other. The Engine follows an event-driven paradigm and uses an event loop waiting for events to be dispatched from the agents to carry out its computations based on its current state, context, and event data. The proposed approach allows for both sharing and reusing game models, significantly reducing time to market and development costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Modeling languages

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Rumour influence minimization and topic modelling for twitter dataset using machine learning schemes

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Materials Today: Proceedings 2022年 58卷 535-539页

作者： Saravanan, T.M. Mohammed Ajmal, M. Manoranjith, M. Sanjaay, B.G. Prakash Mishra, Jay Department of Computer Applications Kongu Engineering College Tamil Nadu Perundurai 638 060 India

We advocate a joint combined trend sentiment categorization method to guide sentiment classifiers for a couple of tweets at the same time. Distinctively, putrefy the sentiment classifier of all trends linked to two additives, namely the specific trend and the global trend. Our method gives a green way to as it should sort out trendy subjects devoid of the lack of outside facts making news channels to find out the infringement news in a particular time or to rush out viral memes to enhance marketing decisions with competitors. The analysis of social functions also reveals styles associated with all kinds of trends, such as tweets with approximately ongoing activities of trendsetters. The unique version of the Trends Greedy and Dynamic Blocking Algorithms can capture the precise expressions of sentiment in each Trend. In addition, we extract trends unique sentimental knowledge from each of the classified and unmarked samples in each Trend and use it to embellish the mastery of Trends precise sentiment classifiers. In addition, we are introducing green algorithms to resolve the version of our technique. These schemes can effectively boost the performance of the combined trends group and outperform traditional methods with experimental effects on benchmark datasets. © 2022

关键词： Sentiment analysis

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Detection of Leading CNN Models for AI Image Accuracy and Efficiency

Detection of Leading CNN Models for AI Image Accuracy and Ef...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nayim, Meshal Mohan, Vishnu Pandey, Trilok Nath Dash, Bibhuti Bhusan Dash, Bibek Bikram Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India

ISBN: (纸本)9798350360660

This research explores the challenge of distinguishing between authentic and AI-generated images using advanced convolutional neural network (CNN) architectures. Leveraging the CIFAKE dataset, which includes a diverse array of AI-generated and real images, we evaluated the performance of three pre-trained models: ResNet50, EfficientNetB0, and DenseNet121. Our study involved training these models with images of 32x32 and resized 64x64 pixel resolutions to assess their classification accuracy, precision, recall, and computational efficiency. DenseNet121 emerged as the most effective model, achieving an accuracy of 98.49% on the test dataset, significantly outperforming the other models. This work highlights the potential of deep learning methodologies in addressing the growing need for reliable AI image detection, providing a robust foundation for future advancements and applications in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Extractive Summarization on Twi Language Using Text-Rank and Fuzzy Clustering Algorithm 1

Extractive Summarization on Twi Language Using Text-Rank and...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Dhole, Sayak Sagnika, Santwana Dominic, Ampofo Mishra, Bhabani Shankar Prasad Fernando, Aloka Dash, Satya Ranjan School of Computer Engineering Kiit Deemed to Be University India School of Computer Applications Kiit Deemed to Be University India University of Moratuwa Department of Computer Science and Engineering Sri Lanka

ISBN: (纸本)9798350315769

Twi is a dialect of the Akan language spoken in southern and central Ghana by several million people. About 80% of the Ghanaian population speaks Twi as a first or second language. Summarization is a basic natural language processing task, that is a step to further more advance tasks as well as an aid in reviewing information. This paper tackles the problem of summarizing Twi texts. Three extractive summarization techniques - TF-IDF, Text-Rank and Fuzzy C-means Clustering are implemented. We find that Text Rank performs best, while TF-IDF despite being fairly simple performs comparably well to Text Rank. Our work suggests that embedding based approaches are potent and further work should employ their use. © 2023 IEEE.

关键词： Fuzzy clustering

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A Generalized Framework for Brain Tumor and Pneumonia Detection Using Streamlite Application 4

A Generalized Framework for Brain Tumor and Pneumonia Detect...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kishore, S. Nair, Rekha R Mehra, Vishal Babu, Tina Dayananda Sagar University Department of Computer Applications School of Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering School of Engineering Bengaluru India

ISBN: (纸本)9798350335750

One of the dangerous disease pneumonia, which can affect one or both lungs, is frequently brought on by viruses, fungi or bacteria. Radiotherapists with advanced training are required to evaluate chest X-rays used to diagnose pneumonia. Brain tumours are another fatal disease that can have a profound impact on a patient's quality of life and alter everything for them and their loved ones. Therefore, creating an automatic system for diagnosing pneumonia would help treat the disease quickly, especially in remote areas. The work proposes a general framework for MRI and CT images that can automatically detect diseases such as brain tumours and pneumonia with single framework using VGG16. The proposed work focusing on developing streamlit web application for deployment. The proposed work gained an accuracy of 97.87% compared to Inception, Xception and ResNet for brain tumor detection and 94.3% for Pnemonia detection model. The training and testing data are compared using a CNN-based classifier to determine the optimal outcome. © 2023 IEEE.

关键词： Tumors

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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm with Niching Technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Computer School Beijing Information Science and Technology University Beijing China School of Mathematics and Computer Science Tongling University Tongling China National Engineering Laboratory for Internet Medical Systems and Applications the First Affiliated Hospital of Zhengzhou University Zhengzhou China

In this paper, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multi-modality, we designed a hybrid metaheuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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Monaural speech separation using WT-Conv-TasNet for hearing aids

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International Journal of Speech Technology 2023年第3期26卷 707-720页

作者： Agrawal, Jharna Gupta, Manish Garg, Hitendra Department of Electronics & Communication Engineering GLA University U.P. Mathura281406 India Department of Computer Engineering & Applications GLA University U.P. Mathura281406 India

Resilient speech separation in multi-talker situations remains a challenge, particularly in real-time and low latency applications. Modern speech separation systems strive to separate loud, reverberant voice mixes, especially for hearing-impaired individuals. Several single channel and speaker-independent speech separation algorithms have recently been developed using deep learning. To overcome the limitation of a lengthy time window to attain appropriate frequency resolution, and long latency in processing the spectrograms, this paper addresses single-channel speech separation to extract and improve desired speech quality from mixed audio using state-of-the-art deep neural networks which is convolutional- time-domain audio separation network (Conv-TasNet). A novel monaural speech separation model by modifying an encoder-decoder pair of Conv-TasNet using wavelet transform (WT-Conv-TasNet) is presented here, which can be applied to hearing aids. From the perspective of isolating two and three speakers’ combinations using the LibriSpeech dataset, the proposed WT-Conv-TasNet system surpasses earlier time–frequency masking systems. Additionally, it has been shown that the proposed model lowers the computing cost of speech separation, and minimizes the output's minimum delay, as it is smaller in size. Therefore, it is well suited to real-time implementation requiring low power, low computing cost of speech separation, and minimum output delay, for example, hearing devices, telecommunication systems automatic speech recognition, and acoustic-based control. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep neural networks

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An Automated Machine Learning NLP Model for Enhancing the Translation of English Causative Sentences into Malayalam Language

An Automated Machine Learning NLP Model for Enhancing the Tr...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Tom, Rony Mathew, Juby Amal Jyothi College of Engineering Department of Computer Applications Kerala Kanjirappally India Amal Jyothi College of Engineering Department of Computer Science & Engineering Kerala Kanjirappally India

ISBN: (纸本)9798350303452

This research paper introduces an innovative Neural Machine Translation (NMT) model designed to accurately translate causative English sentences into Malayalam. The study comprehensively explores the various forms of causative sentences utilized in both English and Malayalam, focusing on syntactic and semantic differences. Additionally, it delves into the limitations of current translation models such as Google Translator in accurately translating causative constructs. To address these challenges and enhance translation accuracy, a collaborative effort with professional linguists in both languages led to the creation of a rich dataset. The dataset comprises a diverse collection of causative sentences, covering tense variations, singular and plural forms, noun and verb classes, as well as sentence forms involving non-human and animate-human subjects. This model integrates essential components including tokenization, Part of Speech (POS) tagging, tense identification, suffix identification and addition, and sentence reordering for improved translation. Comparative evaluations between the Google Translator and the newly developed system were conducted based on translation accuracy and fluency. The evaluation revealed that the novel NMT model consistently outperforms existing translation systems, demonstrating superior translation results. The findings underscore the effectiveness and potential of the proposed NMT model in advancing causal English to Malayalam translation, offering valuable insights for the improvement of machine translation systems. © 2023 IEEE.

关键词： Computational linguistics

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Face Mask detection using Mask R-CNN to control the spread of Covid-19

Face Mask detection using Mask R-CNN to control the spread o...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Mohan, V.S. Abhay Gowda, G. Nair, Rekha R Kishore, S. Babu, Tina School of Engineering Dayananda Sagar University Department of Computer Applications Banglore India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350396195

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

关键词： Access control

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