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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Implementation of Blockchain Technology to Enhance Data Security for Cloud Computing 1

Implementation of Blockchain Technology to Enhance Data Secu...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumari, Poonam Singh, Meeta Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350356816

Cloud computing, the fastest-growing IT technology, offers flexibility and scalability with pay-per-use models, but it raises concerns about data security due to third-party storage and online accessibility. Effective cloud security measures are crucial for protecting user data. The Internet of Things (IoT) generates enormous amounts of data that must be efficiently processed and stored in the cloud. Blockchain technology enhances data security through its tamper-resistant distributed ledger and peer-to-peer networks, which allow any node with internet access to join or create networks. To protect sensitive data and maintain its integrity, it is crucial to implement robust encryption, privacy safeguards, access controls, identity management and confidentiality in both cloud and decentralized systems. © 2024 IEEE.

关键词： Cloud security

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Innovative Hosting with Hoster: Integrating AWS Services, Docker, and Real-Time Data for Scalable Deployment 2

Innovative Hosting with Hoster: Integrating AWS Services, Do...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sharma, Yash Vaibhav Baghel, Umesh Chawla, Pronika Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India

ISBN: (纸本)9798331541217

The increasing complexity of deploying modern web applications necessitates automated solutions to reduce manual intervention and ensure seamless deployment. Hoster is an automated hosting solution designed with the objective of simplifying the process of launching web applications directly from GitHub repositories. Motivated by the challenges developers face in manual deployment, such as error-prone processes and inefficiencies, Hoster utilizes a robust backend architecture that combines Redis, Docker, AWS services, and *** to streamline cloning, building, and hosting projects. To effectively handle and route client requests, the platform's architecture includes a RESTful API server, a Dockerized build server, and a unique reverse proxy. Redis and *** facilitate real-time communication and log tracking, guaranteeing openness and timely feedback throughout deployment. This study examines the strength of the proposed solution, focusing on its scalability, security, and dependability in contemporary web development. Additionally, it delves into Hoster's design, implementation, and performance. The findings demonstrate Hoster's significant contributions to deployment automation, scalability, and resource optimization, offering substantial implications for real-world applications, particularly in DevOps practices. © 2024 IEEE.

关键词： Scalability

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Data Visualization for the Dataset Collected from Education Sector using Python 1

Data Visualization for the Dataset Collected from Education ...

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1st IEEE International Conference on Communications and computer science, InCCCS 2024

作者： Dol, Sunita M. Jawandhiya, P.M. Computer Science and Engineering Department Walchand Institute of Technology Maharashtra Solapur413006 India Computer Science and Engineering Department Pankaj Laddhad Institute of Technology and Management Studies Yelgaon Maharashtra Buldana443002 India

ISBN: (纸本)9798350358858

Visualization is the representation of information in the form of various charts or images. Data visualization is used to identify useful patterns, to understand trends, and to find out outliers in dataset. Data visualization is applied in all sectors such as medical, engineering, etc. for analyzing the data. Data visualization is also useful to instructor as well as students in education sector. In this research article, we have used the Python Programming Language features to visualize the data collected from university related to Four Years computer science and Engineering Programme related to result analysis ledgers of students for the Academic Year 2014-15 and 2015-16. Python features such as streamlit, pandas, altair, and random are considered to create the visualization of data. Various charts such as Pie chart, Scatter plot, Box plot, Density chart, Heatmap chart, Distribution Box Plot, Distribution Histogram, Distribution Line, and Distribution Swarm are used to visualize the data Teacher point of view, Students point of view, and Course-wise. From teacher/ instructors point of view, it is used to analyze the data semester-wise, and Yearwise for grade distribution, marks distribution, and percentile distribution while from students/ learner’s point of view, data visualization examine the data semester-wise, and Year-wise for marks and grade analysis of particular student. In case of course-wise part of visualization, we can see students-wise grade and marks of particular course, course-wise distribution, course-wise Distribution Histogram, course-wise Distribution Line, and course-wise Distribution Swarm. © 2024 IEEE.

关键词： Data visualization

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UCSYSpoof: A Myanmar Language Dataset for Voice Spoofing Detection 27

UCSYSpoof: A Myanmar Language Dataset for Voice Spoofing Det...

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Naing, Hay Mar Soe Pa, Win Pa Hlaing, Aye Mya Aung, Myat Aye Aye Galajit, Kasorn Mawalim, Candy Olivia University of Computer Studies Yangon Faculty of Computer Science Yangon Myanmar NECTEC National Science and Technology Development Agency Pathum Thani Thailand Japan Advanced Institute of Science and Technology Ishikawa Nomi923-1292 Japan

ISBN: (纸本)9798331506032

Automatic Speaker Verification (ASV) is widely used in voice-based security mechanisms. It involves accepting or rejecting a person identification based on the individual's voice, a unique biometric feature. However, it faces many challenges and is vulnerable to direct or indirect attacks. Spoof voice detection is also an important component in secure voice authentication systems. Unfortunately, there is no spoof detection system using Myanmar language dataset. Spoof detection systems are important for many languages, including Myanmar, as they prevent fraud and misinformation, maintain trust, cultural and linguistic relevance, etc. Therefore, this paper proposes a Myanmar spoof voice dataset called UCSYSpoof, which contains both real and spoofed speech signals. End - to-end speech synthesis, vocoder-based speech reconstruction, and voice conversion techniques were used to generate the spoof speech based on 12,000 genuine speech signals. To demonstrate the impact of proposed dataset, a simple spoof detection model is implemented using long short-term memory (LSTM) and convolutional neural network (CNN) classifiers with linear frequency cepstral coefficients (LFCC) and Mel frequency cepstral coefficients (MFCC) features. Based on the empirical results, using CNN with LFCC and MFCC features achieves the comparable results on proposed dataset. The results show that the detection model has Fl-score of 0.99 and an equal error rate (EER) of 0.004, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Pathological Features for Spoof Detection 27

Analysis of Pathological Features for Spoof Detection

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Aye Aung, Myat Aye Soe Naing, Hay Mar Hlaing, Aye Mya Pa Pa, Win Galajit, Kasorn Mawalim, Candy Olivia University of Computer Studies Yangon Faculty of Computer Science Yangon Myanmar NECTEC National Science and Technology Development Agency Pathum Thani Thailand Japan Advanced Institute of Science and Technology Ishikawa Nomi923-1292 Japan

ISBN: (纸本)9798331506032

Deepfake speech, an advanced use of speech synthesis technology, presents a considerable challenge due to its highly realistic sound and the complexities involved in detecting it. The selection and analysis of effective features are essential for enhancing spoof detection capabilities. This study focuses on analyzing pathological features within the Myanmar Spoof Dataset. Spoofed speech in the dataset is created using five distinct techniques: vocoder methods with HiFiGAN and Parallel WaveGAN, pre-trained voice conversion via FreeVC, and GMM-based and Differential GMM-based voice conversion (GMMVC-DIFF) methods. In this paper, we perform a comparative analysis of pathological features, including Harmonics-to-Noise Ratio (HNR), six types of jitter, and seven shimmer features. Additionally, Cepstral Peak Prominence (CPP) features were assessed under both voiced and unvoiced speech conditions. The analysis demonstrates that these features show substantial variations across different spoofing techniques. Specifically, the voice conversion methods GMMVC-DIFF show notable differences in these features. These results highlight the pivotal role of these pathological features in enhancing the precision of future spoof detection systems. © 2024 IEEE.

关键词： Speech synthesis

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Early and Automated Diagnosis of Dysgraphia Using Machine Learning Approach

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SN computer science 2023年第5期4卷 523页

作者： Agarwal, Basant Jain, Sonal Beladiya, Kishan Gupta, Yash Yadav, Avijit Singh Ahuja, Neelu Jyothi Department of Computer Science and Engineering Central University of Rajasthan Ajmer India PG Department of Computer Science and Technology Sardar Patel University Vallabh Vidhyanagar India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Kota India Department of Computer Science School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India

Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this issue and offered several solutions. Motivation to work on this problem did arise after meeting with a few students struggling in achieving performance despite putting in sincere efforts. This paper also discusses the various forms of dysgraphia and its associated symptoms and proposes machine-learning models to detect dysgraphia. Unsupervised machine learning techniques are used to detect dysgraphia-related handwriting impairment. To accomplish the goal, a fresh handwriting dataset is created by conducting handwriting exercises and a wide variety of features are extracted to represent various handwriting characteristics. Results indicate that Random forest returns the best accuracy but scores less while detecting dysgraphic samples correctly. One class SVM has been tried to deal with the issue of the availability of dysgraphic samples required to train machines. Results indicate good hope in identification with a scope of improvement with increase in sample size for machine training. This paper also seeks to raise awareness of the dysgraphia issue and its effects on society. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Dysgraphia Learning difficulties Machine Motor ability OCC One class SVM Random forest

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Predictive Analytics on Covid-19 Prediction using ResNets 6

Predictive Analytics on Covid-19 Prediction using ResNets

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Vadivel, S Jayakarthik, R. Vels institute of Science Technology and Advanced Studies Department of Computer Science Tamilnadu Chennai India

ISBN: (纸本)9781665410281

Corona virus acute disease, a life-threatening condition, emerged in 2019. In December 2019, the virus was discovered for the first time in Wuhan, China, and has since spread throughout the world. This paper proposes using Residual Neural Networks (ResNets) to predict COVID-19, where the input is collected from Internet of Things (IoT) network. Using a system designed to combat a newly emerging infection in its early stages, this paper tackles the problem. In addition to tracking confirmed and reported cases, the system also keeps tabs on cures and deaths daily. This was done so that all parties involved could see the devastation that the lethal virus would cause as soon as possible. Using RNN and GRU in an ensemble, the RMSE value has been computed for various cases such as infected, cured, and dead. The results of simulation shows that the proposed ResNets for classification is effective in predicting the covid-19 cases than the other existing deep learning models. © 2022 IEEE.

关键词： Viruses

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Survey on Sentiment Analysis using Deep Learning 11

Survey on Sentiment Analysis using Deep Learning

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Sharmila, K. Devi, N. Shyamala Devi, R. Vels Institute of Science Technology and Advanced Studies Department. of Computer Science Chennai India

ISBN: (纸本)9781665487320

The text mining is technique that extract data from the semi structured or unstructured text and finds pattern it thought as knowledge discovery from the text. Deep learning is most powerful machine learning technique that is used to extract the feature of the text and from the feature which is identified gives the prediction for the opinion from the various text. The opinion which is mined that helps to decide the social people opinion and sentiments that will be used as an input to various business decision. This article provides overview of sentiment analysis and deep learning approach. © 2022 IEEE.

关键词： Sentiment analysis

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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