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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

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Herbal Species Identification using Deep Learning with Image Processing 5

Herbal Species Identification using Deep Learning with Image...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Anila, M. Kumar, G. Kiran Rakhee Irigela, Likith Reddy Buchala, Raghu Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Gandipet Telangana Hyderabad India The University of West Indies Faculty of Engineering Mona campus Kingston Jamaica

ISBN: (纸本)9798350389609

Sorting medicinal plant leaves according to their classification can make it easier to link those leaves to certain illnesses and, in turn, help find possible treatments. This paper examines both the front and rear aspects of leaf images along with addition to structural traits in an effort to find the unique, optimal feature combination that increases the recognition rate. To accurately identify medicinal plants, a dataset of scanned pictures of leaves collected from various angles is created. The leaves are categorized based on sizes and shapes. Identification of medicinal plant leaves will have a significant positive impact on the health care pharmaceutical production business. Though there were many methods and techniques developed by various researches using different Machine learning algorithms like KNN and SVM, there is still a need for enhancement of such developed methods. This paper introduces an automated plant detection system utilizing deep convolutional neural network (DCCN), which comprises of image pre-processing, feature extraction, and recognition stages. The classifier built upon our model employs layers such as dropout, convolutional, fully connected layers and max pooling to learn intricate leaf features, that achieved 98.1% accuracy with DCNN and Advanced Xception model, which is hyperparameter tuned, observed with 99.25% accuracy on the Indian medicinal leaf's dataset. The results are compared to models like Mobilenet, MobilenetV2 and RESNET50 on the dataset comprising of around 2000 different medicinal leaf images by leveraging knowledge from various plant species. The proposed Advanced Xception system achieved remarkable accuracy with minimal losses in predicting the category of leaf images. © 2024 IEEE.

关键词： Medicinal chemistry

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Enhancing Loan Default Prediction with Human-in-the-Loop and XGBoost Ensemble Learning 5

Enhancing Loan Default Prediction with Human-in-the-Loop and...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Khan, Shamim Ahmad Salman, Rahama Majid, Al-Hussein Maysir Mythili, M. Alazzam, Malik Bader Ponni Valavan, M. Glocal School of Science & Technology Glocal University Department of Ece U.P India College of Engineering and Computer Science Jazan University Department of Computer Science Jazan Saudi Arabia Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Department of English Erode India Jadara University Faculty of Information Technology Irbid Jordan K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

Human-in-the-Loop (HITL) Machine Learning with Ensemble Learning uses the best of both automated algorithms and human input in making predictions and decision making when it comes to Machine Learning (ML). In HITL frameworks they include human inputs in model training and validation, allowing the resultant models to be built based on actual needs rather than white algorithms, which may be insensitive to some details otherwise. It is more relevant in such areas or applications that involve higher risk such as credit risk applications in today's financial world where predictions form the basis for major loans' approval. Although previous studies that employed fully automated ML models have some shortcomings that include being prone to giving biased predictions, being less interpretable and scalability issues and failure to handle edge cases hence reducing reliability and trustworthiness. To overcome these problems, this study introduces a new approach of combining the HITL with the XGBoost, which is one of the most potent ensemble learning algorithms to develop a balanced model of loan default prediction. Through the analysis of loan data from Lending Club, this method has attained an accuracy of 99.4% along with a high precision and a high recall, thus pointing to well-rounded performance of the model. Expert knowledge in the form of the HITL loop improves the regularization and optimization of the XGBoost model leading to a better, efficient, reliable, and more interpretable model for credit scoring. It was in python the model was developed taking advantage of the powerful ml and data analysis libraries which make it easy to develop, scalable and reusable for real life organizations especially in the financial arena where precision and accuracy are paramount. This approach lays some basis for subsequent innovations in the ICT applications in HITL ML systems in different fields. © 2025 IEEE.

关键词： Personnel training

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Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

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IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

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A Review of Fintech Management and Security Development

A Review of Fintech Management and Security Development

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information technology International Seminar (ITIS)

作者： Ali Akbar Ritonga Syaiful Zuhri Harahap Adri Gabriel Sooai Aryo Nugroho Samsir Puput Dani Prasetyo Adi Department of Informatics Management Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Information System Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Katolik Widya Mandira Kupang NTT Indonesia Department of Information System Faculty of Computer Science Universitas Narotama Surabaya Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026.

关键词： Seminars Reviews computer hacking Fintech Bitcoin Blockchains Internet of Things Security Servers Protection

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Securing the Health Care Data Using Hybrid Rabin and Routing Algorithm 5

Securing the Health Care Data Using Hybrid Rabin and Routing...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Kaliyaperumal, Karthikeyan Raja, N. Kannaiya Velmurugan, L. Woyessa, Bacha Uma Ambo University Department of Information Technology HH Campus Ambo Ethiopia Ambo University Department of Computer Science HH Campus Ambo Ethiopia Ambo University Faculty of IT HH Campus Ambo Ethiopia

ISBN: (纸本)9781665493604

Chronic illnesses are becoming more common as a result of the current environmental conditions;most people prefer IoT to take care of their own health. The (IoT) plays a significant role in each and every industry that simplifies life. IoT also plays an important part in the medical field by routinely monitoring the health condition of patients in remote areas. While IoT methods for solving for a smarter living, it also has some problems while transmitting data, which is similar to the type of threat that occurs during transmitting data in WSN. When compared to other types of networks, the data transmitted in smart health monitoring is extremely sensitive. Various sorts of attacks occur during data transfer, such as sinkholes, worm holes, and grey holes, among others. To counteract these assaults, a hybrid Rabin algorithm and a modified routing method are proposed for data security. These algorithms provide 99.0% accuracy in data delivery while detecting attacks and increasing efficiency. © 2023 IEEE.

关键词： Wireless sensor networks

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Improving the Quality of Diabetic Data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach Data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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Student learning effect using flipped classroom with WPSA learning mode - An Example of Programming Design Course

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Innovations in Education and Teaching International 2023年第6期60卷 824-835页

作者： Kuo, Yu-Chen Lin, Yu-Hsuan Wang, Tao-Hua Lin, Hao-Chiang Koong Chen, Ju-I Huang, Yueh-Min Department of Computer Science & Information Management Soochow University Taipei City Taiwan General Research Service Centre National Pingtung University Science and Technology Pingtung City Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan Department of Information and Learning Technology National University of Tainan Tainan Taiwan

Flipped classroom is one of the important teaching modes among many novel teaching methods in recent years, students watch the video in the pre-class. However, if students cannot focus on the pre-class video learning or have problems with the learning content, the learning effect will be less than expected. Therefore, this research proposes a flipped classroom combined with the WPAQ learning model extended by the WSQ learning model to carry out programming courses, allowing students to perform flipped classroom learning with peers online before class and jointly complete the tasks of flipped classroom learning lists, and share with them a summary of learning content to enhance learning effectiveness. The research results show that the use of flipped classrooms combined with the WPAQ learning model of the learning system can help students improve learning effectiveness, learning motivation, self-efficacy, reflective ability, programming learning attitudes, and reduce students’ cognitive load. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： flipped learning programming learning WSQ learning mode

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An Enhanced Evolutionary Algorithm for Optimized Real-Time Scheduling in Cloud Computing

An Enhanced Evolutionary Algorithm for Optimized Real-Time S...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Hamzah Alqudah Mohammad A. Alsharaiah Waleed Awwad Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The exponential growth of cloud computing applications has posed significant challenges in achieving efficient resource management, reduced processing time, high through-put, and minimized energy consumption. Despite advancements, existing evolutionary algorithms often suffer from premature convergence, inefficient exploration-exploitation balance, and high energy overhead, particularly under heavy workloads. This paper proposes an enhanced hybrid Whale Optimization Algorithm (WOA) integrated with the Lévy Flight Mechanism for optimized resource management in cloud computing environments to address these challenges. The primary motivation for this work stems from the limitations observed in state-of-the-art algorithms. The research aims to bridge this gap by designing an evolutionary-based approach that improves resource utilization, reduces task execution delays, and optimizes energy consumption under varying workloads. The proposed model employs the WOA, enhanced with the Lévy Flight Mechanism, to ensure a more effective balance between exploration and exploitation during optimization. Extensive simulations were conducted on workloads ranging from 100 to 700 tasks, comparing the proposed method against leading algorithms. The results demonstrate that the proposed algorithm consistently outperforms the state-of-the-art techniques. Specifically, it achieves higher resource utilization (1.1 vs. 0.9), lower processing time, improved throughput (up to 500), and reduced energy consumption (50 joules compared to 70+ joules) under heavy workloads. These findings validate the enhanced WOA's effectiveness in addressing critical cloud computing optimization problems. The proposed approach provides a robust solution for dynamic resource allocation, ensuring energy efficiency and performance scalability, making it suitable for modern cloud-based environments with increasing computational demands.

关键词： Cloud computing Energy consumption Processor scheduling Scalability Evolutionary computation Throughput Whale optimization algorithms Real-time systems Resource management Optimization

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Sentiment Analysis on Tourism Place using Naive Bayes

Sentiment Analysis on Tourism Place using Naive Bayes

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International Conference on Telecommunication systems, Services, and Applications (TSSA)

作者： Aldy Rialdy Atmadja Aprilia Rahmawati Cecep Nurul Alam Popon Dauni Yogi Saputra Informatics Department Faculty of Science and Technology UIN Sunan Gunung Djati Bandung Indonesia Information System Department Faculty of Computer Science and Information System Universitas Kebangsaan RI Bandung Indonesia

The method of extracting information from a text is called text mining. In contrast, sentiment analysis involves examining text data to determine whether a statement expresses good or negative conversations. In this study, a system was developed that categorizes tweets about West Bandung Regency tourism spots that contain both favorable and negative remarks. The Cross Industry Standard Process for Data Mining (CRISP-DM) technique is used to carry out the classification process utilizing the Naive Bayes algorithm. You will learn how locals feel about West Bandung Regency’s tourist attractions through this method. Six scenarios were developed using the categorization findings from 250 pieces of data that included both positive and negative sentiment. The scenario has got the greatest f1-score of 82.8%. These findings demonstrate how effectively the Naive Bayes algorithm performs sentiment analysis.

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