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Esports Earning Analysis Using Regression Model

Esports Earning Analysis Using Regression Model

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICaiQSA 2024

作者： Al Siddik Shammo, Basid Acharya, Biswaranjan Marwadi University Department of Computer Engineering - AI & BDA Gujarat Rajkot India

ISBN: (纸本)9798331517953

This analysis finds the possible earnings in competitive gaming, based on the 'Strategy' genre. In the esports industry, it is very important to know which type of games can generate more revenue for players, and also investors should know the teams and games for investment. This analysis aims to give insights into the financial outcomes for players and give proper decisions to the investors to invest in a worthwhile team or game. We have used exploratory data analysis (eda), data visualization techniques, linear regression analysis, data manipulation techniques, and correlation analysis. The important result of our research is how the earnings are correlating to tournaments. Through the research, a player can choose the right games for a growing career, and an investor can invest in a proper team and game. Through the research, we can expect more earnings from tournaments, and esports teams can get more investment to improve their skills. The research is innovative and unique compared to prior studies for its linear regression modeling and correlation analysis. © 2024 IEEE.

关键词： Linear regression

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Text-to-Sketch Synthesis via Adversarial Network

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computers, Materials & Continua 2023年第7期76卷 915-938页

作者： Jason Elroy Martis Sannidhan Manjaya Shetty Manas Ranjan Pradhan Usha Desai Biswaranjan Acharya Department of Information Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia Department of Computer Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia School of Computing Skyline University CollegeUniversity CitySharjahUAE Department of Electronics and Communication Engineering SR UniversityWarangalTelanganaIndia Department of Computer Engineering–AI&BDA Marwadi UniversityRajkotGujaratIndia

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security ***,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch *** the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch *** this study,we propose an adversarial network that generates human facial sketches using such cues provided by an ***,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated *** evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance *** proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

关键词： Generative adversarial networks inverse gamma correction sketch attributes text-to-sketch synthesis deep learning techniques

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A bio-inspired metaheuristic approach for cloud task scheduling using lateral hyena based particle swarm optimization

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Multimedia Tools and Applications 2025年第18期84卷 20023-20046页

作者： Malik, Meena Nandan, Durgesh Prabha, Chander Uddin, Mueen Acharya, Biswaranjan Hu, Yu-Chen Chandigarh University Mohali India School of CS & AI SR University Warangal506371 India Chitkara University Institute of Engineering and Technology Chitakara University Rajpura India College of Computing and IT University of Doha for Science & Technology Doha Qatar Department of Computer Engineering AI & BDA Marwadi University Gujarat Rajkot India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Being a rapidly emerging field, cloud computing encourages the rapid growth of all the essential internet services. One major challenge in a cloud computing environment is balancing the load among various machines to optimize the appropriate workload, thereby providing efficient task scheduling. Various studies have been proposed and implemented for task scheduling to provide a fair load balance among all the available virtual machines on the cloud. This research work targets the optimization of virtual machines by Lateral Hyena -Particle Swarm Optimization (LH-PSO) algorithm. Our main motivation is to push parallel task scheduling proficiently, producing low response time and rapid results for all the assigned tasks. This work attempts to optimize virtual machines by proposing a Lateral Hyena-based solution and parallel task scheduling. The proposed solution provides a flexible system where virtual machines will be load-balanced by serving equal tasks. The proposed solution attempts to optimize a fitness function based on the velocity and position value of particles mapped to the characteristics of incoming requests. The practice aims to find optimized virtual machines solving tasks with an optimal solution. The performance is analyzed using the Cloudsim simulator to assess all parameters such as load distribution, average response time, memory utilization, processor utilization, and average turnaround time. The analytical conclusions represent an enhancement over the existing schemes compared to the above-mentioned parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtual machine

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Securing IoT: Emphasizing Requirements for Enhanced Device Security

Securing IoT: Emphasizing Requirements for Enhanced Device S...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICaiQSA 2024

作者： Tiwari, Rajni Mishra, Debasmita Sinha, Nidhi Acharya, Biswaranjan CMR Institute of Technology Bangalore India Presidency University Bangalore India Cambridge Institute of Technology Bangalore India Marwadi University Department of Computer Engineering - AI & BDA Gujarat Rajkot India

ISBN: (纸本)9798331517953

The proliferation of Internet of Things (IoT) devices in various sectors presents an array of potential security vulnerabilities. A requirements-driven approach to IoT security is pivotal to ensure robust defense mechanisms from the inception of device design. This paper emphasizes the significance of incorporating security requirements during the initial phases, outlining the unique security challenges associated with IoT and the potential strategies to address them. Through a requirement- driven strategy, we can anticipate threats, streamline updates, reduce post-production costs, and ultimately achieve a higher level of security assurance across all IoT devices. © 2024 IEEE.

关键词： Authentication Protocol

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Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

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International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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Esports Earning Analysis Using Regression Model

Esports Earning Analysis Using Regression Model

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICaiQSA), International Conference on

作者： Basid Al Siddik Shammo Biswaranjan Acharya Department of Computer Engineering - AI & BDA Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

This analysis finds the possible earnings in competitive gaming, based on the ‘Strategy’ genre. In the esports industry, it is very important to know which type of games can generate more revenue for players, and also investors should know the teams and games for investment. This analysis aims to give insights into the financial outcomes for players and give proper decisions to the investors to invest in a worthwhile team or game. We have used exploratory data analysis (eda), data visualization techniques, linear regression analysis, data manipulation techniques, and correlation analysis. The important result of our research is how the earnings are correlating to tournaments. Through the research, a player can choose the right games for a growing career, and an investor can invest in a proper team and game. Through the research, we can expect more earnings from tournaments, and esports teams can get more investment to improve their skills. The research is innovative and unique compared to prior studies for its linear regression modeling and correlation analysis.

关键词： Industries Analytical models Correlation Quantum computing engineering profession Computational modeling Linear regression Data visualization Games Investment

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Enhancing Sentiment Classification in Twitter Data Through Context-Driven Text Processing and Tweet Embeddings 11

Enhancing Sentiment Classification in Twitter Data Through C...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Gerogiannis, Vassilis C. Kanavos, Andreas Antonopoulos, Nikos Bhola, Amrita Acharya, Biswaranjan University of Thessaly Department of Digital Systems Larissa Greece Ionian University Department of Informatics Corfu Greece Ionian University Department of Digital Media and Communication Argostoli Greece Indira Gandhi Institute of Technology Odisha Sarang India Marwadi University Department of Computer Engineering- AI and BDA Rajkot India

ISBN: (纸本)9798350326147

Sentiment analysis and text classification tasks heav-ily rely on text processing techniques. However, existing approaches often neglect domain-specific factors and rely on generic routines and pre-built dictionaries. In this paper, we investigate the impact of text processing steps on sentiment classification using Twitter data. Our approach introduces skip gram-based word embeddings that effectively capture Twitter-specific fea-tures, such as informal language and emojis. Through rigorous experimentation, we identify the detrimental consequences of conventional text processing steps like stop word removal and simple averaging of term vectors for tweet representation. To optimize sentiment classification, we propose new effective steps, including the inclusion of emoji characters, measuring word importance from embeddings, aggregating term vectors into tweet embeddings, and creating a linearly separable feature space. Our results demonstrate the superiority of context-driven word embeddings in selecting important words for tweet clas-sification, outperforming pre-built word dictionaries. Moreover, the proposed tweet embedding reduces reliance on multiple text processing steps, resulting in more accurate sentiment analysis on Twitter data. © 2023 IEEE.

关键词： Embeddings

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Securing IoT: Emphasizing Requirements for Enhanced Device Security

Securing IoT: Emphasizing Requirements for Enhanced Device S...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICaiQSA), International Conference on

作者： Rajni Tiwari Debasmita Mishra Nidhi Sinha Biswaranjan Acharya CMR institute of technology Bangalore India Presidency University Bangalore India Cambridge institute of technology Bangalore India Department of Computer Engineering – AI & BDA Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

关键词： Symbiosis Technological innovation Quantum computing Wireless networks Ecosystems Smart homes Real-time systems Security Internet of Things Microprogramming

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Enhancing Sentiment Classification in Twitter Data Through Context-Driven Text Processing and Tweet Embeddings

Enhancing Sentiment Classification in Twitter Data Through C...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Vassilis C. Gerogiannis Andreas Kanavos Nikos Antonopoulos Amrita Bhola Biswaranjan Acharya Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece Department of Digital Media and Communication Ionian University Argostoli Greece Indira Gandhi Institute of Technology Sarang Odisha India Department of Computer Engineering- AI & BDA Marwadi University Rajkot India

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