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Control, Electronics and computer Technology (ICCECT), IEEE International Conference on

作者： Zhiyong Feng Yike Shi Duomu Zhou Liping Mo The School of Computer Science and Engineering Jishou University Jishou China Faculty of Computer Science and Technology Computer Science and Engineering Jishou University Jishou China

The current machine learning algorithms classify human activities with inaccurate accuracy, poor generalization ability of the model, and poor classification effect. Proposing to use Random Forest classifier to classify the samples. The classifier has the advantage of good generalization ability, high accuracy, and the ability to handle a large number of sample data. After comparing with BP neural networks, Naive Bayesian networks, and decision trees, the random forest classifier achieved an accuracy of 98% on the test set, much higher than the 55%, 87%, and 88% of the other algorithms. In addition, we also tested the generalization ability of the model using the K-Folder cross-validation method, which yielded an average accuracy of 95.5%, also much higher than the 49.9%, 85%, and 90% of the other classifiers. The experimental results show that the random forest classifier has significantly improved in accuracy and generalization ability compared with BP neural network by 78% and 91%, respectively. Therefore, the superiority of the random forest classifier in human activity classification is proved.

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Comparison between Single-Input and Multi-Input Classification with the Application of Canny Feature Extraction and Classification Algorithms on the Toraja Buffalo Dataset 19

Comparison between Single-Input and Multi-Input Classificati...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Manga, Abdul Rachman Nanda, Assyahrin Handayani, Anik Nur Herwanto, Heru Wahyu Asmara, Rosa Andrie Lantara, Dirgahayu Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Department of Electrical Engineering Universitas Negeri Malang Malang Indonesia Information Technology Department State Polytechnic of Malang Malang Indonesia Faculty of Industrial Engineering Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements. © 2025 IEEE.

关键词： Support vector machines

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Unveiling the Landscape of Machine Learning and Deep Learning Methodologies in Network Security: A Comprehensive Literature Review 2

Unveiling the Landscape of Machine Learning and Deep Learnin...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Saeed, Nouf Majid Sultan Eid Ibrahim, Amer Ali, Liaqat Al-Dmour, Nidal A. Mohammed, Abdul Salam Ghazal, Taher M. American University In The Emirates Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates College Of Engineering Mutah University Department Of Computer Engineering Jordan Skyline University College University City Sharjah Shariah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The dynamic nature of cyber threats offers a continual problem in the field of cybersecurity in the context of the expanding internet environment. This study provides an in-depth assessment of the literature on machine learning (ML) and deep learning (DL) methodologies for network analysis for intrusion detection. This review curates, assesses, and distils method-specific findings while considering temporal or thermal correlations. It provides a recognition of the importance of data in ML and DL approaches, and a comprehensive overview of frequently used network datasets in ML/DL applications, as well as the inherent challenges of adopting ML/DL in the cybersecurity field. The study concludes with well-informed recommendations for future areas of research in this critical domine. © 2024 IEEE.

关键词： Intrusion detection

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Myocardial infarction detection based on deep neural network on imbalanced data

Myocardial infarction detection based on deep neural network...

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作者： Hammad, Mohamed Alkinani, Monagi H. Gupta, B.B. Abd El-Latif, Ahmed A. Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom32511 Egypt Department of Computer Science and Artificial Intelligence College of Computer Sciences and Engineering University of Jeddah Jeddah Saudi Arabia Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented a method based on a deep convolutional neural network (CNN) for the detection of MI automatically. The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals. In the case of imbalanced data, we optimize our deep model with a new loss function named the focal loss to deal with this case by constituting the loss indirectly the focus in those difficult classes. The Physikalisch-Technische Bundesanstalt (PTB) dataset was employed in the validation to classify the signals to normal and MI. The performance of our technique alongside state-of-the-art in the area shows an increase in terms of average accuracy and F1 score. Results show that focal loss improves the detection accuracy by 9% for detecting MI signals. In summary, the proposed method achieved an overall accuracy, precision, F1 score, and recall of 98.84%, 98.31%, 97.92%, and 97.63, respectively using focal loss and overall accuracy of 89.72%, a precision of 88.52%, a recall of 81.11% and F1 score of 83.02% without using focal loss. Our method using focal loss is an effective tool to perform a fast and reliable MI diagnosis to assist the cardiologists in detecting MI early. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Convolutional neural networks

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Clickbait Detection in Indonesia Headline News Using BERT Ensemble Models

Clickbait Detection in Indonesia Headline News Using BERT En...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Muhammad Edo Syahputra Ade Putera Kemala Farrel Alexander Tjan Rudy Susanto School of Computer Science Computer Science Binus University Jakarta Indonesia Faculty of Engineering Computer Engineering Binus Universitty Jakarta Indonesia

This study explores the effectiveness of combining BERT (Bidirectional Encoder Representations from Transformers) with convolutional neural networks (CNN) and multilayer perceptrons (MLP) for a specific task. The results showcase promising performance of the BERT + CNN model, with precision, recall, and F1-score values of 0.91, 0.86, and 0.89, respectively. The BERT + MLP model exhibits consistent performance with an F1-score of 0.875. A comparative analysis against a previous study utilizing IndoBERT highlights the competitive edge of our BERT + CNN model, particularly in terms of recall and F1-score. Additionally, our proposed model demonstrates competitive performance against other state-of-the- art models such as RoBERTa and xlmRoBERTa. This study contributes valuable insights into the optimization of BERT -based models for specific tasks, emphasizing the efficacy of the BERT + CNN architecture.

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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Service Migration for Delay-Sensitive IoT Applications in Edge Networks

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IEEE Transactions on Services Computing 2025年第03期18卷 1782-1797页

作者： Li, Xiaocui Zhou, Zhangbing Wang, Yasha Deng, Shuiguang Hung, Patrick C. K. School of Information Engineering Beijing100083 China Peking University National Research & Engineering Center of Software Engineering Beijing100871 China Peking University New Generation Information Technology Research Institute Tianjin300380 China School of Information Engineering Frontiers Science Center for Deep-Time Digital Earth Beijing100083 China Institut Polytechnique de Paris Télécom SudParis Palaiseau91120 France Zhejiang University College of Computer Science and Technology Hangzhou310012 China Ontario Tech University Faculty of Business and Information Technology Oshawa Canada

The proliferation of Internet of Things (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient service Configuration (E2rC) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced Deep Reinforcement Learning (DRL) approach with a two-layer Q-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Continuous Wavelet Transform Spectral Image Based Epileptic Episode Classification 25

Continuous Wavelet Transform Spectral Image Based Epileptic ...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Nuhi-Alamin, Md. Sarkar, Ajay Krishno Talukder, Md. Simul Hasan Aljaidi, Mohammad Swapnil, Safkat Shahrier Rajshahi University of Engineering & Technology Department of Electrical and Electronic Engineering Rajshahi Bangladesh Dhaka University of Engineering & Technology Department of Electrical and Electronic Engineering Gazipur Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331540012

Numerous individuals globally, regardless of age, have the neurological condition epilepsy. Recurrent seizures compromised motor and sensory abilities, and a hindered normal lifestyle are all signs of epilepsy. By seeing or manually examining electroencephalogram (EEG) data, neurologists can make the diagnosis of epileptic seizures. In this paper, a continuous wavelet transform spectral image-based epileptic episode classification technique is proposed. The EEG signal was first converted into 227x227 RGB images using CWT. Then these images were used to create an image dataset, and classification was performed using the proposed deep ensemble epileptic seizure classifier (DE-ESC). DE-ESC ensembled Dense-net-169, Dense-net-201, and Inception-V3. This proposed method obtained 100% accuracy (Ac), precision (Pr), recall, and F1 Score, respectively. © 2024 IEEE.

关键词： Wavelet transforms

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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. Data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

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computer Systems science & engineering 2022年第12期43卷 1175-1188页

作者： Norwini Zaidi Iskandar Ishak Fatimah Sidi Lilly Suriani Affendey System Development and Engineering Center Universiti Sains Islam Malaysia71800Negeri SembilanMalaysia Faculty of Computer Science and Information Technology Universiti Putra MalaysiaUPM Serdang43400SelangorMalaysia

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

关键词： Data migration data transformation column-oriented database relational database big data

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