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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Vats, Satvik Mehta, Shiva Kukreja, Vinay Manwal, Manika Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350304268

the unique approach introduced in this research categorizes Java Plum Leaf illnesses into five severity categories using an FL-CNN. the study tackles the primary difficulty of accurate diagnosis and early response to these plant diseases by leveraging four local clients (Cl-X, Cl-Y, Cl-Z, and Cl-W). Operating across local clients, the federated learning network showed a surprising performance. For instance, in Cl-X, F1-Scores were close to 90% across all severity classes, and the model's performance varied from an accuracy of 95% to 97%. Class E highlights the model's ability to accurately identify genuine positive cases in Cl-X, which had the highest recall value of 93.50%. Like customer Cl-Y, who showed improved outcomes, Class A displayed an enhanced 94.92%, 92.57% and 98%. the values for all classes in Cl-Y varied from 90.33% to 94.57%., supporting the model's effectiveness even further. Results from the client datasets Cl-Z and Cl-W were similar. Class A achieved excellent precision values (94.34% in Cl-Z and 94.92% in Cl-W) in both datasets, resulting in respectable F1-Scores of 91.75% and 92.57%, respectively and an impressive accuracy of 98%. Additionally, the global performance of the federated learning CNN supported the regional analyses. For example, Cl-X had a vital accuracy of 96% and an F1 score of 90.99%. the F1-Scores for Cl-Y, Cl-Z, and Cl-W ranged from 92.33% to 92.73%., and all three had constant accuracy values of 97%. In its conclusion, this research emphasizes the reliability of the federated learning CNN in correctly identifying Java Plum Leaf illnesses across a range of severity levels. Its outstanding performance on a local and worldwide scale demonstrates its ability to impact plant pathology substantially. © 2023 IEEE.

关键词： CNN Architectures Cooperative machine learning Java Plum Foliage Pathologies Severity Tiers Severity Tiers

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AI-Powered Detection and Prevention Tool to Secure APIs from Malicious Bot Attacks 7th

AI-Powered Detection and Prevention Tool to Secure APIs from...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Dinuwan, Chanuka Amandakoon, Hiruni Aberathne, Iroshan Wimalarathna, tharindu Ratnayake, Rashmi University of Sri Jayewardenepura Nugegoda Gangodawila Sri Lanka

ISBN: (纸本)9789819908370

the Application Programming Interface (API) has become the primary method for integrating different software systems. Malicious bots have recently become the main cyber security threat and have been used as an infrastructure to carry out almost any form of cyberattack. the existing methods, however, have been insufficient for solving this issue, specifically in APIs. this study introduces an automated malicious bot attack detection tool for APIs based on artificial intelligence and machine learning. the time series forecasting, and neural network were used to develop a model on the API log data. Without human interaction, the trained model recognizes malicious bot attempts in APIs and prevents them in real time. Moreover, the performance measures of the model are a key indicator to using this application as a real time bot detector and preventer in API. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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A Novel machine learning-Based Model for Reentrant Vulnerabilities Detection 7th

A Novel Machine Learning-Based Model for Reentrant Vulnerab...

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Zhao, Hui Su, Peng Qiu, Meikang Educational Information Technology Laboratory Henan University Kaifeng China Software School Henan University Kaifeng China Dakota State University MidisonSD United States

ISBN: (纸本)9783031281235

machine learning-based models are one of the main methods for detecting reentrant vulnerabilities. However, these models extract smart contract features only from a single form, resulting in incompleteness and inaccuracy of features. To address this problem, we propose a novel machine learning-based model for reentrant vulnerabilities detection. We extract and fuse features from abstract syntax trees, opcodes, control flow graph basic blocks, and combine machine learning algorithms for reentrant vulnerabilities detection. Additionally, to address the time-consuming problem of manual labeling, we also propose an approach for automatically adding dataset labels. We perform experiments on Smartbugs and SolidiFi-benchmark datasets and results show that our model outperforms existing models. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Comparative Predictive Analysis of machine learning Algorithms for SIW Bandpass Filter 7th

Comparative Predictive Analysis of Machine Learning Algorith...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Praveena, N. Gunavathi, N. National Institute of Technology Tiruchirappalli Tamilnadu India

ISBN: (纸本)9789819908370

this article presents a dual-mode Substrate Integrated Waveguide (SIW) filter by perturbing the square cavity for C band applications. the TE110 and TE120 perturbed modes are formed by introducing the center metallic via in the SIW square cavity, which has distinct field distributions. the proposed filter is designed by combining the solution of CST supported by machine learning algorithms. the four regression algorithms (XGboost, random forest, decision tree, and K-Nearest Neighbor (KNN)) are compared and evaluated based on the accuracy score. Here KNN algorithm provides better accurate results of 89 % for S11 and 99.96 % for S21. the RTduroid 5880 substrate is used for the fabricated filter of size 28.4 x 28.4 x 0.51 mm3. the fabricated and simulated SIW filter results are validated with slight discrepancies. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bandpass filters

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Development of Punjabi Automatic Speech Recognition Application for Mobile Devices 7

Development of Punjabi Automatic Speech Recognition Applicat...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mittal, Puneet Sharma, Sukhwinder Manipal University Jaipur Department of Artificial Intelligence & Machine Learning Jaipur India Manipal University Jaipur Department of Data Science & Engineering Jaipur India

ISBN: (纸本)9798350304268

Speech recognition based mobile device applications are gaining popularity due to their ease of use, flexibility as well as ability to provide hands-free access to device features and functions for persons without hands or with disabilities like blindness. the increased accuracy of Automatic Speech Recognition (ASR) systems and support to multiple languages is further motivating the researchers to develop such applications. the development of an application for mobile device helps to validate the applicability of an ASR system in real world environment. An android based speech recognition application for calling and messaging features has been designed and developed in this paper. User can send message or call to any phone number or contact stored in the mobile phone using Punjabi speech. Punjabi based ASR system having pronunciation dictionary, acoustic model and language model is used to implement the underlying speech recognition for Punjabi speakers. Speech user interface has been designed using Android, while AndroidPocketSphinx has been used as speech decoder. the successfully running application has been tested for multiple male and female native Punjabi speakers, followed by thorough performance analysis. the results shown are quite optimistic with a very low combined error of 1.6% for all female speakers and 2.1% for all male speakers. the application gives 98.4% success rate or recognition accuracy for female speakers and 97.9% for male speakers for the underlying Punjabi based ASR system. © 2023 IEEE.

关键词： Acoustic model AndroidPocketSphinx ASR Language Model Pronunciation Dictionary Speech Recognition

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machine learning Framework for Prediction of Parkinson’s Disease in Cloud Environment 7th

Machine Learning Framework for Prediction of Parkinson’s Di...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Shastry, K. Aditya Sushma, V. Bansal, Naman Saxena, Ujjwal Srivastava, Shrey Samal, Suvang Nitte Meenakshi Institute of Technology Bengaluru560064 India

ISBN: (纸本)9789811954818

Parkinson’s disease signifies a neurological illness caused by a gradual neural system problem. the signs appear progressively, occasionally beginning with a scarcely noticeable tremor in a single hand. the speech of a person with "Parkinson’s disease" is harmed. Over the last two decades, the rate of innovation in the field of machine learning (ML) has accelerated dramatically. Over the last two decades, the rate of innovation in the field of ML has accelerated dramatically. the indicators of advancement may be found in numerous sectors, from machine learning models that can classify every object in an image to diagnosing diseases. this research focuses on a relatively new challenge in the current context, namely, detecting Parkinson’s disease at an early stage employing only the voice data from the participants. Experiments revealed that the random forest method outperformed decision tree classifier with an accuracy improvement of 31% for detecting Parkinson. the application was deployed on cloud for better accessibility. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Echocardiographic Assessment of Heart Disease Using CNN and Image Processing 7

Echocardiographic Assessment of Heart Disease Using CNN and ...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bokade, Aditya Nitin, Zaware Sarika Kakade, Arjun Deshpande, Yash Kridutta, Janhvi Aissms Ioit Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Heart diseases are leading causes of premature death in adults over the age of 30, particularly for individuals with high cholesterol and diabetes. the diagnosis of these diseases often requires an echocardiogram, which provides a detailed view of the heart. However, the proper functioning of the echocardiogram machine and the expertise of a competent radiologist are essential for accurate analysis, which is not always feasible and possible. Manual annotation of echocardiograms to determine the presence of heart diseases is a laborious task that is prone to human errors like misdiagnosis and can be automated by using image processing algorithms like CNN. the probability of misdiagnosis increases in remote areas, where the training of radiologists may be inadequate compared to those in metropolitan cities. Additionally, faulty or defective equipment can contribute to inaccurate diagnoses in these remote areas. To address these challenges, we propose a system that automates the prediction of heart diseases based on echocardiograms. By reducing human error, our goal is to ensure a consistent standard of healthcare across both urban and rural medical institutions. For the initial analysis of this project, we implemented Convolutional Neural Network (CNN), ResNet50, and VGG 16 algorithms. After testing their accuracies we choose CNN as the optimal algorithm due to accuracy concerns. Specifically, the system focuses on detecting Angina Pectoris, Cardiovascular disease, Coronary Artery Disease, and Left Ventricular Hypertrophy(LVH). During preliminary testing, our CNN model achieved an accuracy score of 95.27%. © 2023 IEEE.

关键词： CNN (Convolutional Neural Network) Deep learning Echocardiograms Feature Extraction Image Segmentation Resnet50 VGG-16

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (soft computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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soft computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

the author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) the extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) the Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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Autism Spectrum Disorder Prediction Using machine learning 7th

Autism Spectrum Disorder Prediction Using Machine Learning

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Ramachandra, A.C. Rajesh, N. Harshitha, G. Sai Prashanth, C.R. Nitte Meenakshi Institute of Technology Bengaluru560064 India Dr. Ambedkar Institute of Technology Bangalore560056 India

ISBN: (纸本)9789811954818

Autism is a neural disorder that is characterized by impaired social communication verbal and nonverbal interaction, restrictive, or repetitive behaviour. Autism is most noticeable in children younger than or equal to two years of age years. Autism is characterized by high levels of autism awareness. Both environmental and heritable factors are important. Genetic susceptibility: It is therefore important to have these data. this contains information about patients, including symptoms and lab tests data, history, and vaccination details that provide specific details about patients. this paper proposes an effective prediction model based on the ML technique and to create a user interface ASD prediction for all ages. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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machine learning in Human Resources Management for Higher Education Institutes 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Karthikeyan, M. Mariyappan, M.S.R. Sridevi, J. Kumar, S. Vishnu School of Management Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

this research article explores the intersection of machine learning (ML) and the Internet within the domain of Human Resource Management (HRM) in Higher Education Institutes (HEIs). It investigates how these technologies can reshape Human Resource (HR) practices, specifically focusing of faculty retention. the ML model is designed to act upon the collected data to provide immediate support in aiding HR professionals in predicting and managing employee retention. the research also highlights the importance of data collection using Internet, ensuring speeding and ethical data handling practices. the article encompasses a comprehensive review of existing literature, detailed methodology, practical implementation, and rigorous assessment of the proposed model, aiming to provide a holistic view of this transformative landscape in HRM. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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