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17th international conference on Information Security Practice and Experience, ISPEC 2022

17th International Conference on Information Security Practi...

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17th international conference on Information Security Practice and Experience, ISPEC 2022

ISBN: (纸本)9783031212796

the proceedings contain 35 papers. the special focus in this conference is on Information Security Practice and Experience. the topics include: Sparse Attack on Skeleton-Based Human Action recognition for Internet of Video things Systems;LMGROUP: A Lightweight Multicast Group Key Management for IoT Networks;Using API Calls for Sequence-pattern Feature mining-Based Malware Detection;evaluating the Possibility of Evasion Attacks to machine learning-Based Models for Malicious PowerShell Detection;detect Compiler Inserted Run-time Security Checks in Binary Software;Detection of MSOffice-Embedded Malware: Feature mining and Short- vs. Long-Term Performance;priApp-Install: learning User Privacy Preferences on Mobile Apps’ Installation;BADPASS: Bots Taking ADvantage of Proxy as a Service;A CNN-Based Semi-supervised learning Approach for the Detection of SS7 Attacks;blockchain-Based Self-Sovereign Identity System with Attribute-Based Issuance;Federated Intrusion Detection on Non-IID data for IIoT Networks Using Generative Adversarial Networks and Reinforcement learning;preventing Adversarial Attacks Against Deep learning-Based Intrusion Detection System;detection of Adversarial Examples Based on the Neurons Distribution;techniques for Continuous Touch-Based Authentication;Performance Evaluation of Post-Quantum TLS 1.3 on Resource-Constrained Embedded Systems;secure Human Identification Protocol with Human-Computable Passwords;practical Backdoor Attack Against Speaker recognition System;anonymous Authentication Protocols in Vehicular Ad Hoc Network-An Survey;optimal Generic Attack Against Basic Boneh-Boyen Signatures;Differential Cryptanalysis of Salsa20 Based on Comprehensive Analysis of PNBs;blockchain-Based Confidential Payment System with Controllable Regulation;physical Zero-Knowledge Proof Protocol for Topswops;attribute Based Tracing for Securing Group Signatures Against Centralized Authorities;sherlock Holmes Zero-Knowledge Protocols;Continued Fractions Applied to a

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Identification of Atrial Septal Defects in Congenital Heart Disease Patients Using Deep learning-based Chest Radiographs

Identification of Atrial Septal Defects in Congenital Heart ...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Hongmin Shao Yunfei Ling Jiyun Pang Qijun Zhao Yongjun Qian College of Computer Science Sichuan University Chengdu China Department of Cardiovascular Surgery West China Hospital Sichuan University Chengdu China West China School of Medicine Sichuan University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Objective: Chest X-rays are a non-invasive and cost-effective diagnostic method; however, they are not commonly used for diagnosing atrial septal defects (ASD). Typically, the diagnosis of ASD requires more expensive tests. We have discovered that an enlarged heart shadow can be an initial indicator of ASD. We have developed and validated a model using deep learning techniques to determine whether a patient has an atrial septal defect based on their chest X-ray. Method: this study involved chest X-ray examinations of 1459 patients diagnosed with atrial septal defects at West China Hospital of Sichuan University from January 8, 2009, to June 25, 2022, as well as 1580 individuals who underwent general health check-ups at the West China Hospital's Examination Center from July 21, 2022, to August 13, 2022. Results: this study included chest X-ray examinations of 3,039 patients (44.3% male; median age 33 years with an interquartile range of 28–42 years). For the classification task, the deep learning model achieved an accuracy of 0.96, sensitivity of 0.97, specificity of 0.96, and an AUC (Area Under the Curve) of 0.9922. Conclusion: Current research indicates that chest X-ray analysis based on deep learning can determine the presence of atrial septal defects, a congenital heart condition. these findings suggest that deep learning methods can offer an objective diagnosis for atrial septal defects in routine health screenings, offering a low-cost and non-invasive approach.

关键词： Heart Deep learning Analytical models Sensitivity Hospitals pattern recognition Diagnostic radiography X-ray imaging Biomedical imaging Diseases

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Prosodic Parameterisation and Ensemble machine learning Techniques for Speech Emotion Detection

Prosodic Parameterisation and Ensemble Machine Learning Tech...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olaniyan Julius Ibidun. C. Obagbuwa Esiefarienrhe B. Michael Ayodele A. Adebiyi Olaniyan Deborah Sadura Priscilla Akinrinwa Department of Computer Science Bowen University Osun State Nigeria Department of Computer Science & Information Technology Sol Plaatje University Kimberley South Africa Department of Computer Science North-West University Mafikeng South Africa Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Data Science Federal University of Technology Akure Akure Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

this paper presents a robust approach to speech emotion recognition using an auto-correlated dynamic classical algorithm selection method. It employs FFT-based autocorrelation to extract fundamental frequency power spectral density for intensity, and inter-stress intervals for rhythm. machine learning models (SVM. Decision Tree Naive Bayes, KNN, and Random Forest) were trained on RAVDESS, Emo-DB, TESS. IEMOCAP. and CREMA-D datasets, with ensemble learning achieving a state-of-the- art accuracy of 99.98%. the results highlight the effectiveness of the proposed approach in accurately detecting emotions from speech.

关键词： Support vector machines Emotion recognition Adaptation models Accuracy Heuristic algorithms Speech recognition Rhythm Feature extraction Autocorrelation Context modeling

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

5th International Conference on Information and Communicatio...

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811639449

the proceedings contain 78 papers. the special focus in this conference is on Information and Communication Technology for Intelligent Systems. the topics include: Iris recognition System Based on Convolutional Neural Network;database Management Systems—An Efficient, Effective, and Augmented Approach for Organizations;parallel Computation of Hadamard Product for Dynamic dataset on Google Colab;cooperative Hypothesis Building for Computer Security Incident Response;Analysis and Prediction of COVID-19 with Image Super-Resolution Using CNN and SRCNN-Based Approach;Hierarchical Multi Label Classification of News Articles Using RNN, CNN and HAN;enhancing the Date Rate by the Management of Bandwidth and Power Using Interference Temperature Constraint in Cognitive Radio Networks;dialogue Extraction and Translation from Stories on thirukural Using Verb Cue Quote Content Source Identifiers;a Survey on Using machine learning to Counter Hardware Trojan Challenges;green Cloud Computing-Oriented Algorithmic Approach-Based Virtual machine Consolidation;an Impact of Radon Transforms and Filtering Techniques for Text Localization in Natural Scene Text Images;Algorithmic Approach to Select MOOCs Platform: An Institutional Perspective;decentralised Content Creation in Digital learning: A Blockchain Concept;data Security in Internet of things;Forecasting of COVID-19 Using Hybrid ARIMA-FB Prophet Algorithm;novel Approach to Improve Security Systems;Review of Forthcoming ICT-Enabled Applications Promoting learning in Higher Education;a Comprehensive Travel Recommendation System;manufacturing Information Systems for Forestry Companies Based on Integrated Modular Approach «Many Companies—One Tool»;ICT-Based Enhancement of Employment Schemes: A Case Study in Rural Uttarakhand, India;sustainable Technologies for Improved Income Generation: A Case Study of Women Villagers in Himachal Pradesh;named Entity Recognizer for Konkani Text;preface.

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

5th International Conference on Information and Communicatio...

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811641763

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Medical Image Analysis Using Deep learning Technnique 23

Medical Image Analysis Using Deep Learning Technnique

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Surabhi Saxena Nikhat Parveen Devendra Agarwal Department of Artificial Intelligence and Data Science Poornima Institute of Engineering & Technology India Department of Computer Science and Engineering Konneru Lakshmaiah Education Foundation India Computer Science Goel Instiute of Technology and Management India

ISBN: (纸本)9798400709418

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. this review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonias, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. this research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. the primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications.

关键词： KEYWORDS

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Research on EEG Acquisition System of Smart Internet of things with Enhanced Mamba 5

Research on EEG Acquisition System of Smart Internet of Thin...

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5th international conference on Signal Processing and machine learning, CONF SPML 2025

作者： Li, Zhe Wuhan University of Engineering Science Wuchang District Hubei Province Wuhan City China

ISBN: (纸本)9781837243235

this paper explores an advanced system for the acquisition of brain electrical signals within the framework of intelligent systems. By integrating a sensor network, cloud platform, brain electrical signal acquisition system, data processing algorithms, and deep learning models, the system facilitates real-time monitoring, data transmission, storage, and intelligent recognition of brain electrical signals. In addition, we investigate the application of the Mamba deep learning model in the intelligent medical domain. To enhance the performance of the Mamba model, this paper proposes an Multi-branch Attention module and an multiscale Convolution module. On the UCI dataset, the Mamba model strengthened with these two modules significantly outperformed other traditional models, achieving an accuracy of 98.3%, with a model loss below 10e-7 and an AUC of 0.995. On the eegmmidb dataset, the enhanced new model improved the accuracy by 2.4% compared to the baseline model. © the Institution of Engineering & Technology 2025.

关键词： Deep learning

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5th international conference on Smart Computing and Informatics, SCI 2021

5th International Conference on Smart Computing and Informat...

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5th international conference on Smart Computing and Informatics, SCI 2021

ISBN: (纸本)9789811697043

the proceedings contain 58 papers. the special focus in this conference is on Smart Computing and Informatics. the topics include: QCA-Based Error Detection Circuit for Nanocommunication Network;evaluating Performance on Covid-19 Tweet Sentiment Analysis Outbreak Using Support Vector machine;minimum Simplex Nonlinear Nonnegative Matrix Factorization for Hyperspectral Unmixing;prediction and Analysis of Vitamin D Deficiency Using machine learning Algorithms;student Performance Prediction Using Classification Models;estimating Driver Attentiveness through Head Pose Using Hybrid Geometric-Based Method;IIWSCOA-Based DCNN: Improved Invasive Weed Sine Cosine Optimization Algorithm for Early Detection of Myocardial Infarction Using Deep Convolution Neural Network;intrusion Detection Using Support Vector machine and Artificial Neural Network;student’s Academic Performance Prediction Using Ensemble Methods through Educational data mining;A Flexible Accession on Brain Tumour Detection and Classification Using VGG16 Model;check Certificate—A Certificate Verification Platform for Students and Organizations;secure Cluster-Based Routing Using Modified Spider Monkey Optimization for Wireless Sensor Networks;ChefAI Text to Instructional Visualization Using Amazon Web Services;Identification of Predominant Genes that Causes Autism Using MLP;Detecting Impersonators in Examination Halls Using AI;telugu Text Classification Using Supervised machine learning Algorithm;Application of Hybrid MLP-GWO for Monthly Rainfall Forecasting in Cachar, Assam: A Case Study;Temperature Prediction Using Hybrid MLP-GOA Algorithm in Keonjhar, Odisha: A Case Study;development of Different Word Vectors and Testing Using Text Classification Algorithms for Telugu;addressing Longtail Problem using Adaptive Clustering for Music Recommendation System;A Proficient GK-KMA Based Segmentation and Lung Nodule Detection in CT Images Using PTRNN;framework for Diabetic Retinopathy Classification.

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Effectiveness of data Augmentation in Multi-class Face recognition 5

Effectiveness of Data Augmentation in Multi-class Face Recog...

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5th international conference on Informatics and Computational Sciences (ICICoS)

作者： Rahmatullah, Prinanda Abidin, Taufik Fuadi Misbullah, Alim Nazaruddin Univ Syiah Kuala Dept Informat Fac Math & Nat Sci Banda Aceh Indonesia

ISBN: (纸本)9781665438070

Recent research shows that classification results of using deep learning methods provide better accuracy than those of using machine learning techniques. However, these results are highly dependent on the amount of data used. this study examines the effectiveness of data augmentation in improving the performance of convolutional neural net- work models, namely ResNet-50, MobileNetV3 Small, and MobileNetV3 Large. the performance is measured using the F1-score of the three models of classifying student faces. the experimental results indicate that the F1-score of the ResNet-50 architecture increases from 0.753 to 0.928 when trained using an augmented dataset. the accuracy of the models constructed using the MobileNetV3 Small and Large architectures increased from 0.685 to 0.920 and from 0.660 to 0.930, respectively, when trained using an augmented dataset. the accuracies of ResNet-50, MobileNetV3 Small, and MobileNetV3 Large when evaluated using the testing dataset were 0.865, 0.850, and 0.870, respectively.

关键词： data Augmentation Face recognition ResNet-50 MobileNetV3

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Digital Technology Empowering Brand Management Based on Big data Research

Digital Technology Empowering Brand Management Based on Big ...

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international conference on Applied machine learning (ICAML)

作者： Bin Li Luchun Qu China Automotive Technology And Research Center Co. Ltd. Tianjing China China Auto Information Technology (Tianjin) Co. Ltd. Tianjing China BYDAutoIndustry Company Limited

the purpose of brand positioning is inseparable from brand communication. If the brand image of enterprise design is not conveyed to consumers, or consumers do not agree with the image of enterprise design, it proves that this brand positioning is a failure. this paper attempts to further study the brand management of digital technology empowerment based on big data. Among the two variables at the government level, the maximum value of each variable measurement item is no more than 7, and the minimum value is 1, and the option values are widely distributed. Judging from the average value of the variable measurement items, it is mainly concentrated in the range of 4.44-5.62, indicating that the respondents have a high degree of recognition of each item. According to the characteristics of consumers' consumption psychology, the attributes of products are designed to strengthen consumers' understanding of brands. Successful brand positioning can attract a large number of consumers and improve their loyalty, which is the key to the survival and sustainable development of enterprises. therefore, how to adapt to the trend of the times, use big data and digital technology to improve the efficiency of corporate brand communication.

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