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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Jawad, Israa Abed Kaylan Hamid, Noor Hassan, Sharmeen Izzat Al-Qaisy, Shams A. Daham Wassif, Sattar Jabar Al-Nisour University College Baghdad Iraq Al Kitab University Department of Computer Education Kirkuk Iraq College of Engineering Knowledge University Department of Computer Engineering Erbil Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq

ISBN: (纸本)9798331534950

Instructors must have a strong understanding of technological education in order to teach prospective teachers whom additionally are substantially computationally knowledgeable. Data sources include written responses to questions posed by teacher service about the use of digital tools for pedagogical documentation, particularly video and photographic recording, as well as their perspectives on childhood education procedures and goals. Examine language instructors' perspectives on the usage of online platforms for educational purposes. To have a better understanding of the expertise of internet-based language teachers. The current research examines teachers' perspectives on ICT (information and communication technologies) to establish whether they implement ICTs in their teaching spaces and whether they believe this influences their pedagogy, hence influencing the outcome. The findings indicate that three types of participation support are acknowledged in the educational process: to enable the setting and atmosphere, to build educational abilities, to encourage children's perspectives, and to encourage ongoing participation practices. Instructors advocate for Internet of Things (IoT-AICT) assisted information and communication technology (IoT) in smart education systems. Nonetheless, they often advocate notions that pretend to be related to the application of IoT in an administrative context. As a result, this study emphasizes these benefits while also addressing issues, particularly those related to digital devices and Internet connectivity. It is looking for a brief summary of current research focused on IoT procedures within educational settings. The experimental study compares the educational outcomes score of the proposed system to that of a manual student performance assessment method. The findings suggest that the proposed approach accurately assesses the student's performance. © 2024 IEEE.

关键词： Students

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Unified Security Framework for Cloud Applications with IoT Fusion via Decentralized Blockchain 3

Unified Security Framework for Cloud Applications with IoT F...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Mohanraj, B. Kalpana A, M. Priya, M. Abdul Jaleel, D. Karthikeyan A, N. Velusamy, A. Sona College of Technology Department of Information Technology Salem India Government College of Engineering Department of Computer Science Engineering Salem India Knowledge Institute of Technology Department of Information Technology Salem India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Sri Venkateshwara College of Engineering Department of Cse - Artificial Intelligence Vidyanagar India

ISBN: (纸本)9798350375190

Recent advancements in blockchain-based cloud computing highlight its potential in providing robust data security, integrity, and confidentiality. While cloud computing is increasingly utilized for remote resource access, it presents significant security challenges that necessitate innovative solutions. This research proposes a novel SecureCloudChain IoT (SCC-IoT) architecture, which integrates blockchain technology into cloud-supportive platforms to enhance security measures. The SCC-IoT architecture includes a distributed blockchain approach to ensure data security while maintaining privacy and integrity. This system is designed to be adaptable and scalable, meeting real-world requirements effectively. By promoting a decentralized and efficient environment, SCC-IoT improves the reliability, stability, and load balancing of cloud services. Performance assessments through various experimental conditions have been conducted to evaluate the proposed system. The results demonstrate SCC-IoT's effectiveness in mitigating potential attacks and ensuring secure and reliable cloud computing. This research highlights the significance of integrating blockchain technology into cloud platforms, paving way for more secure and efficient cloud computing solutions. © 2024 IEEE.

关键词： Blockchain

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A Survey on Machine Learning Techniques for Heart Disease Prediction

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SN computer Science 2025年第4期6卷 1-17页

作者： Shinde, Priti Sanghavi, Mahesh Tran, Tien Anh Department of Computer Engineering MET’s IOE Bhujbal Knowledge City Adgaon Nashik India SNJBs Late Sau KBJ CoE Chandwad India Vietnam Maritime University Haiphong 180000 Viet Nam

Machine learning is the field of data science that makes decisions according to the data. To process huge amounts of data, discover patterns, and find co-relations among data, this field has achieved remarkable success. It is very useful in the field of medicine, especially in cardiology. This application in the cardiology field has provided a great help to medical practitioners. Heart disease is now becoming a dangerous disease throughout the globe. Hence the main objective of this paper is to provide a review of the most frequently used ML techniques in cardiology, which ML techniques have gained maximum success in cardiology, and the overall performance of these techniques. We performed a systematic review from Jan 2018 to June 2023. We have selected 68 studies focusing on the prediction and classification of heart diseases, and heart failure too. Due to the limitations of image and signal data such as storing capacity, and noise, we have considered only text datasets for study. We have summarized why especially ML techniques are needed and their performance. The results obtained from this study show that RF, SVM, KNN, DT, LR, and NB have shown great performance in the prediction and classification of heart disease. Moreover, RF and LR have achieved great accuracy and proved more efficient than others. © The Author(s) 2025.

关键词： Cardiology Classification Heart disease Machine learning Prediction

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Investigation of LSSVM and RBFNN-based Techniques for Intrusion Detection Systems for IoT Networks

Investigation of LSSVM and RBFNN-based Techniques for Intrus...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Jayapal, Medida Subhashree, K. Ashwini, B. Satheesh Kumar, D. Dimri, Sushil Chandra Nishant, Neerav Department of Cse Telangana Hyderabad India Karp Agam College of Engineering Department of Computer Science and Engineering Coimbatore India Knowledge Institute of Technology Department of Cse Tamilnadu Salem India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Engineering Babu Banarasi das University Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350300857

As more and more devices become internet-enabled, it's no longer possible to imagine modern life without them. In particular, Internet of Things (IoT) devices have become widespread and indispensable. But there are issues that appear to be worsening and for which there are currently no good solutions. The safety of Internet of Things (IoT) devices and other connected gadgets is a growing concern. There has been a lot of development in the area of safeguarding IoT networks, but there is always room for improvement. The proposed methodology consists of three stages: preprocessing the data, selecting the features to use, and training the model. In data preprocessing, normalization and standardization techniques are utilized. The use of particle swarm optimization helps improve feature selection. Models are trained with SVM-RBFNN after information gain is used to choose relevant features. The proposed method achieves better results than both SVM and LSSVM, two widely usedalternatives. © 2023 IEEE.

关键词： Internet of things

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Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students 9th

Ensemble Learning Based Employment Recommendation Under Inte...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhu, Haiping Zhao, Yifei Wu, Yuchen Chen, Yan Li, Wenhao Zheng, Qinghua Tian, Feng Department of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9783031466632

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Thamaraimanalan, T. Venkatesan, C. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hkbk College of Engineering Department of Electronics and Communication Engineering Bangalore India Knowledge Institute of Technology Department of Computer Science and Business System Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350337426

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection. © 2023 IEEE.

关键词： Feature extraction

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ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection 2

ECG Signal Feature Extraction and SVM Classifier Based Cardi...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Venkatesan, C. Thamaraimanalan, T. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%. © 2023 IEEE.

关键词： Electrocardiograms

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Academic Evaluation System Using Adaptive Categorized and Authenticated Recommendation 21

Academic Evaluation System Using Adaptive Categorized and Au...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Haddad, Haider Abdula Abdullah, Abdulqadir Ismail Mahdy, Qaysar Salih Kahwachi, Wasfi College of Science Salahaddin University Erbil Department of Computer Science and Information Technology Kurdistan Region Iraq College of Engineering Knowledge University Erbil Department of Computer Engineering Kurdistan Region Iraq College of Engineering Tishk International University Erbil Department of Mechatronics Engineering Kurdistan Region Iraq Research Center Tishk International University Erbil Kurdistan Region Iraq

ISBN: (纸本)9798350374131

Teacher performance is an important issue for any academic institution, so these institutions try to develop methodologies and systems to evaluate the performance of its academic staff based on measurable and scientific metrics. These metrics include collecting data from students about classes and how the teacher is performing in the classroom and measuring the learning objectives from students. Major objective of this research work is to develop a web-based multi-user system for academic performance and a recommendation system that depends on the performance and skills of the teacher. The system includes multiple entry points for best factor such as the feedback unit, student attendance unit, teacher classification unit, and teacher recommendation unit, which can be strictly accessed by the administrative authority for academic teachers. The attendance module and the student feedback module are required to achieve better results in the teacher rating module and the teacher recommendation module. A reliability factor has been incorporated to achieve the accuracy of the proposed system. The performance of the system was improved due to the integration of the paperless approach and resulted in less data loss. The developed system was tested for a large university in Kurdistan region, Iraq. For data analysis an adaptive unsupervised learning method was used to categorize teachers according to their performance in the classroom. The system proved very successful in the academic evaluation and recommendation process in the university. This research proved that using student feedback data is the best factor in determining academic performance. © 2024 IEEE.

关键词： Students

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： knowledge graph

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital Communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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