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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kiruthiga, S. Balamanigandan, R. Mahaveerakannan, R. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Department of Spatial Informatics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350379945

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P © 2024 IEEE.

关键词： k-nearest neighbors

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Stationary Feature Extract and Process Monitoring Based on Autoencoder 42

Stationary Feature Extract and Process Monitoring Based on A...

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42nd Chinese Control Conference, CCC 2023

作者： Fan, Weiwei Tang, Wanqing Zhang, Hanwen Jia, Chao Liang, Juntao He, Jie School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China China Electronics Standardization Institute Software Engineering and Appraisal Center Beijing China

ISBN: (纸本)9789887581543

To monitor non-stationary industrial processes, a stationary feature extract method is proposed, which can be regarded as a nonlinear version of traditional stationary subspace analysis. To enhance feature extraction, the linear transformation in stationary subspace analysis is replaced by an autoencoder. To extract the stationary features, an additional term is introduced into the loss function of the autoencoder, which measures the consistency of the feature distributions over different time periods. The convex hull based on the stationary features is used to construct a monitoring statistic for the alarm of abnormal working conditions. A case study based on an ironmaking process illustrates that the proposed method can effectively achieve abnormality detection. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Process monitoring

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ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 214-224页

作者： Zhao, Ying Ge, Luhao Xie, Huixuan Bai, Genghuai Zhang, Zhao Wei, Qiang Lin, Yun Liu, Yuchao Zhou, Fangfang School of Computer Science and Engineering Central South University Changsha China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China College of Information and Communication Engineering Harbin Engineering University Harbin China China Research Institute of Radiowave Propagation Qingdao China

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses. © 2022 IEEE.

关键词： Data visualization

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Empirical Analysis of software Success Rate Forecasting During Requirement engineering Processes

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computers, Materials & Continua 2023年第1期74卷 783-799页

作者： Muhammad Hasnain Imran Ghani Seung Ryul Jeong Muhammad Fermi Pasha Sardar Usman Anjum Abbas Lahore Leads University Lahore42000Pakistan Computer and Information Sciences Virginia Military InstituteLexingtonVA24450USA Graduate School of Business IT Kookmin UniversitySeoul136Korea School of Information Technology Monash UniversityBandar Sunway47500Malaysia Department of Computer Science and Software Engineering Grand Asian University Sialkot51040Pakistan Government Sarwar Shaheed Associate College Gujjar KhanRawalpindi46000Pakistan

Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development *** order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success *** discussed a potential relationship between forecasting of software success or failure and the development *** noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development *** analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success *** results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’*** has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software *** empirical study highlights a need for other software practitioners to avoid such bias while working on software *** investigation can be performed to identify the other human factors that may impact software systems’success.

关键词： Cognitive bias misinterpretation of requirements miscommunication software success and failure prediction decision making

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Triple Diseases Prediction System using Deep Learning Techniques

Triple Diseases Prediction System using Deep Learning Techni...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： R. Vijayan V. Mareeswari A. B. Jaswanth Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu Department of Software and Systems School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu MTech (Software Engineering) School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu

With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an approach to predict multiple diseases using Flask API, with a specific focus on brain tumors, COVID-19 and pneumonia. The proposed work represents a significant contribution to the field of disease prediction, harnessing the power of deep learning algorithms and modern web application development. The primary focus is on disease prediction, with a particular emphasis on ensuring accuracy and accessibility for end-users. The initial phase of this research involves data collection, where relevant datasets of various diseases are gathered. These datasets serve as the foundation for training and validating the deep learning models. Two prominent deep learning algorithms, Sequential CNN and VGG16, are employed for this purpose. These algorithms are chosen for their ability to handle complex data and recognize patterns within medical images and other health-related data. The core of the research involves training the deep learning models using the collected datasets. This step is crucial in enabling the models to learn and generalize from the provided data, ultimately enhancing their predictive capabilities. The models are modified to elevate their performance and accuracy. Following the training phase, the models are rigorously tested to evaluate their predictive accuracy. This assessment is vital in gauging the real-world applicability of the models in medical diagnosis. To make these powerful disease prediction models accessible to a wider audience, a front-end web application is developed.

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Fair Resource Allocation in 6G networks using Reinforcement Learning

Fair Resource Allocation in 6G networks using Reinforcement ...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Praghash, K. Sharma, Vikrant Yuvaraj, N. Shukla, Rishi Prakash Kumar, Divya Manwal, Manika School of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Graphic Era Hill University Computer Science and Engineering Dehradun India Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Ait Management Chandigarh University Punjab Mohali India Chandigarh University Mohali India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350309140

The development of 6G networks is necessarily ushering in a new era of communication technology. One of the key demanding situations of 6G networks is the allocation of resources on a truthful basis to meet the objectives of every user. This hassle necessitates the usage of advanced algorithms and device-getting-to-know strategies. Reinforcement mastering (RL) is a promising approach to permit fair useful resource allocation in 6G networks. RL algorithms can employ a metric to make certain equity among network users, in preference to utilizing simplistic fee capabilities along with bandwidth constraints. Furthermore, RL algorithms can pick out and optimize the green resource utilization for every network consumer to maximize the general community performance. this can be executed with the aid of maximizing the reward of each community user based on their character goals. moreover, RL algorithms can be used to mitigate conflicts between customers, even while still ensuring equitable utilization of resources. these capabilities of RL algorithms make them nicely suitable for fair aid allocation in 6G networks. © 2024 IEEE.

关键词： Queueing networks

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Syn Flood DDoS Attack Detection with Different Multilayer Perceptron Optimization Techniques Using Uncorrelated Feature Subsets Selected by Different Correlation Methods 3rd

Syn Flood DDoS Attack Detection with Different Multilayer Pe...

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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Devarakonda, Nagaraju Dasari, Kishorebabu School of Computer Science and Engineering VIT-AP University Amaravati522237 India Keshav Memorial Institute of Technology Telangana Hyderabad500029 India

ISBN: (纸本)9789819966899

Cyber attackers widely used Distributed Denial of Service (DDoS) attacks to saturate servers with network traffic, preventing authorized clients to access network resources and ensuing massive losses in all aspects of the organizations. With the use of ADAM, SGD, and LBFGS optimization techniques, this paper evaluates a Multilayer Perceptron (MLP) classification algorithm for Syn flood DDoS attack detection using various uncorrelated features chosen with Pearson, Spearman, and Kendall correlation methods. Dataset for a Syn flood DDoS attack was taken from the CIC-DDoS2019 dataset. Experiment results conclude that among optimization techniques, ADAM optimization gives better results and among uncorrelation feature sets and Pearson uncorrelated feature subset produce the best results. Multilayer Perceptron produces the best classification results with ADAM optimization and Pearson uncorrelation subset on Syn flood DDoS attack. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Correlation methods

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Ilayaraja V S.P. Rajamohana Jagadeesan S V. Aishwarya M. Rakesh School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science School of Engineering & Technology Pondicherry University Karaikal Puducherry India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability.

关键词： Deep learning Surveys Accuracy Tropical cyclones Disasters Sea measurements Estimation Transformers Satellite images Reliability

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Paloma: A Benchmark for Evaluating Language Model Fit 38

Paloma: A Benchmark for Evaluating Language Model Fit

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Magnusson, Ian Bhagia, Akshita Hofmann, Valentin Soldaini, Luca Jha, Ananya Harsh Tafjord, Oyvind Schwenk, Dustin Walsh, Evan Pete Elazar, Yanai Lo, Kyle Groeneveld, Dirk Beltagy, Iz Hajishirzi, Hannaneh Smith, Noah A. Richardson, Kyle Dodge, Jesse Allen Institute for Artificial Intelligence United States Paul G. Allen School of Computer Science & Engineering University of Washington United States

Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains-varying distributions of language. We introduce PERPLEXITY ANALYSIS FOR LANGUAGE MODEL ASSESSMENT (PALOMA)1, a benchmark to measure LM fit to 546 English and code domains, instead of assuming perplexity on one distribution extrapolates to others. We include two new datasets of the top 100 subreddits (e.g., r/depression on Reddit) and programming languages (e.g., Java on GitHub), both sources common in contemporary LMs. With our benchmark, we release 6 baseline 1B LMs carefully controlled to provide fair comparisons about which pretraining corpus is best and code for others to apply those controls to their own experiments. Our case studies demonstrate how the fine-grained results from PALOMA surface findings such as that models pretrained without data beyond Common Crawl exhibit anomalous gaps in LM fit to many domains or that loss is dominated by the most frequently occurring strings in the vocabulary. © 2024 Neural information processing systems foundation. All rights reserved.

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A Comparative Study of Decision Review System in Cricket: Residual Network Vs SVM Algorithm Poor Accuracy in Decision Review System in Cricket 2

A Comparative Study of Decision Review System in Cricket: Re...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Nagalakshmi, T.J. Premkumar, M. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Commnication Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350368413

This study evaluates and compares the performance of Support Vector Machine (SVM) and Residual Network (ResNet) algorithms within the Cricket Decision Review System (DRS) to enhance umpiring accuracy. Using a dataset of 3400 analyses, we implemented both algorithms for predicting online video quality and assessed their predictive accuracy with a sample size of 10. The results demonstrated that the Standard SVM achieved an impressive accuracy of 99%, significantly outperforming the Residual Network, which achieved only 59%. Statistical analysis confirmed that this difference in accuracy was significant, with less than 5% probability of occurrence by chance. The findings indicate that SVM offers a notable improvement in average accuracy compared to ResNet, highlighting its potential to refine umpiring decisions and positively impact the sport of cricket. © 2024 IEEE.

关键词： Adversarial machine learning

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