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Multiclass Classification for Cyber Threats Detection on Twitter

computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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An Efficient Technique for the Better Recognition of Oral Cancer using Support Vector Machine 7

An Efficient Technique for the Better Recognition of Oral Ca...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Manikandan, J. Devakadacham, Sterlin Rani Shanthalakshmi, M. Arockia Raj, Y. Vijay, K. Rajalakshmi Engineering College Thandalam Computer Science and Engineering Chennai India R. M. D. Engineering College Computer Science and Engineering Kavaraipettai India Sri Venkateswara College of Engineering Computer Science and Engineering Chennai India PSNA College of Engineering and Technology Computer Science and Engineering Dindugal India

ISBN: (纸本)9798350397253

Accuracy is among the most important factors in a disease diagnosis. It is essential to select the characteristics that you find most pertinent for the highest accuracy. This study aims to more accurately predict the presence of the primary stage of squamous cell carcinoma using fewer indicators. Stages of oral cancer were first demonstrated to be predicted by 25 features. The variety of features that are obtained from various patient records is indirectly decreased in this study through the combination of the unified medical system using hybrid features selection techniques to identify the characteristics that are most useful for the identification of oral cancer. Hybrid feature selection has been used to condense 25 qualities into 14 features. The diagnosis of patients with oral cancer is then predicted using four classifiers: Updatable Naive Bayes, Multilayer Perceptrons, K-Nearest Neighbor, and Support Vector Machines. Also, the data show that, after adding feature development decisions with SMOTE during the preprocessing phases, the support vector machine's performance surpasses other machine learning techniques. © 2023 IEEE.

关键词： Support vector machines

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AI-Driven Health: A Web App for Enhanced Healthcare Queries and Nutrition Analysis 5

AI-Driven Health: A Web App for Enhanced Healthcare Queries ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ushashree, P. Naik, Archana Sri, P. Amrutha Sai Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331504403

The AI-Driven Health chat assistant is an innovative healthcare solution that seamlessly integrates technology and care, enabling users to have natural language conversations about symptoms, treatments, and general health inquiries. The chatbot functions as a virtual assistant, enabling users to inquire about medical concerns and obtain clear explanations. The nutrition bot utilizes the advanced capabilities of Google Gemini to analyze food images, offering users comprehensive nutritional information. The proposed system undergoes constant refinement through regular testing and user feedback, guaranteeing a smooth and dependable platform for accessible healthcare interactions. The AI-Driven Health chat assistant strives to enhance the accessibility and comprehensibility of healthcare information, ultimately enhancing a healthier and more informed society. © 2024 IEEE.

关键词： Bot (Internet)

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An Autoencoder-BiLSTM framework for classifying multiple types of lung diseases from CXR images

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Multimedia Tools and Applications 2024年第38期83卷 86091-86120页

作者： Ankayarkanni, B. Sangeetha, P. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai600 119 India Rajiv Gandhi College of Engineering Research and Technology Maharashtra Chandrapur442403 India

Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better

关键词： Equalizers

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Retraction Note: Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network (Wireless Personal Communications, (2022), 127, 1, (749-765), 10.1007/s11277-021-08403-5)

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Wireless Personal Communications 2025年第1期140卷 781-782页

作者： Setiawan, Roy Ganga, Ramakoteswara Rao Velayutham, Priya Thangavel, Kumaravel Sharma, Dilip Kumar Rajan, Regin Krishnamoorthy, Sujatha Sengan, Sudhakar Department Management Universitas Kristen Petra Surabaya Indonesia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Information Technology Adhiyamaan College of Engineering Tamil Nadu Hosur635109 India Department of Computer Science Wenzhou-Kean University Zhejiang Wenzhou325060 China Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

The Editor-in-Chief has retracted this article because of concerns regarding the substance and overall soundness of this work. An investigation conducted after its publication discovered instances of nonsensical statements, raising questions on both the authorship and validity of the content presented. Sujatha Krishnamoorthy disagrees with this retraction. Dilip Kumar Sharma and Sudhakar Sengan have not stated whether they agree or disagree with this retraction. The remaining authors have not replied to correspondence from the Publisher. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

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Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks

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IEEE Access 2025年 13卷 87640-87653页

作者： Tamotia, Aryaman Karmokar, Dhruv Shantu Komal, Rethi Nawas, K. Khadar Shahina, A. Khan, A. Nayeemulla Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India Sri Sivasubramaniya Nadar College of Engineering Department of Information Technology Tamil Nadu Kalavakkam603110 India

Speaker identification using audio data is quite challenging because of inherent differences between people, ambient noise, and variable recording conditions. Although the classical deep learning methods are effective, they have rather high computational cost, which leads to usually cumbersome parameter tuning processes and hence reduce their applicability to real-world deployments. In this paper, we discuss an efficient version of Kolmogorov-Arnold Network for audio-based speaker identification using three different types of audio inputs: bone conduction, air conduction, and throat microphone recordings, alongside evaluation on the publicly available LibriSpeech dataset to enhance reproducibility. To optimize the performance of these modalities, we investigate data-level, feature-level, and decision-level fusion techniques. An exhaustive comparison is performed against some of the prominent deep learning architectures, namely VGG-19, ResNet-152, EfficientNetV2, and DenseNet-121, both as standalone architectures and within multimodal combinations for the purpose of evaluating the proposed framework. We demonstrate that our model achieves a significant increase in accuracy up to 99.375% through the use of MFCC and GFCC feature inputs with data-level fusion through MFCC features and achieves this level of accuracy using substantially fewer parameters than those of conventional deep learning models. This makes the KAN framework a highly efficient choice for audio-based speaker identification. One of the critical innovations within this design is a class of trainable activation functions that eliminate the need for traditional weight parameters, thereby providing scalable and computationally efficient solutions. This work contributes to the field of biometrics by providing a refined, resource-efficient approach to speaker identification based on multimodal audio data. © 2025 IEEE.

关键词： Deep learning

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Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid Deep Learning Perspective 3

Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Prakash, S. Jaya Devi, D. Muthukumaran, N. Romila, R. Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Chinnasalem India Maha Barathi Engineering College Department of Computer Science and Engineering Tamilnadu Chinnasalem India A.R. Engineering College Department of Computer Science and Engineering Tamilnadu Villupuram India Sri Muthukumaran Institute of Technology Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331509675

Several vital resources are increasingly being protected by cyber-physical systems (CPSs), makes the detection of incidents on these systems critical. CPSs along with other domains, such as the Internet of Things (IoT), frequently use machine learning (ML) and deep learning (DL) approaches to combat intrusion detection. Existing approaches, however, frequently favor the deployment of advanced detection models over their usefulness in actual operations. This research work develops a hybrid intrusion detection technique by combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). This work analyzes the experimental results to LSTM and CNN to develop an effective comparative approach. The experiments are conducted on the two datasets. The efficiency indicators examined are intrusion detection results such as accuracy, precision, recall, and f1score. This comparison indicates that the proposed hybrid algorithm is the preferable approach. © 2025 IEEE.

关键词： Network intrusion

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Customer Feedback and Sentiment Analysis for Hotel Services 2

Customer Feedback and Sentiment Analysis for Hotel Services

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Abhyudhay, S.R. Aditya, G.M. Upadya, Achyuta K. Naik, Archana Ushashree, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India

ISBN: (纸本)9798350382952

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry. © 2024 IEEE.

关键词： Sentiment analysis

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A Novel Intelligent Task Offloading Scheme for Multicontroller Environment in Software Defined Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 20018-20029页

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J. F. Javvaji, Pavan Kumar Alliance University Department of Computer Science and Engineering Alliance College of Engineering and Design Anekal562106 India National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Yunlin Douliu64002 Taiwan Sunway University School of Engineering and Technology Subang Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Mohali140413 India VIT-AP University School of Computer Science and Engineering Amaravati522237 India National Institute of Technology Department of Computer Science and Engineering Shillong793003 India King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing (CC) platform meets these stringent computation requirements, but it has a significant latency that fog computing (FC) effectively handles. Software defined network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network prerequisites in an agile manner. Task offloading (TO) is a crucial issue in software defined-IoV (SD-IoV), particularly when the vehicle’s resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this article introduces two proactive intelligent TO (ITO’ and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO’ and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7–13.9)% more than the existing scheme. They also reduce average packet loss, delay, round trip time (RTT), and offloaded node time selection by (45.35–21.3)%, (50–33.3)%, (53.9–9.9)% and (43.2–26.06)% which demonstrates the efficacy of the proposed scheme. © 2014 IEEE.

关键词： Fog computing

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