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Predicting DDoS Attacks: A Machine Learning Approach using ALDDoS dataset 15

Predicting DDoS Attacks: A Machine Learning Approach using A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shookdeb, Shantanu Muduli, Debendra Bhatta, Sandesh Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C V Raman Global University Dept. of computer Science and Eng. Bhubaneswar India C V Raman Global University Dept. of computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) dataset by Warda (Owner). © 2024 IEEE.

关键词： ALDDos Cyber defense DDoS RandomForest Web-proxy

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

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EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series

EBV: Electronic Bee-Veterinarian for Principled Mining and F...

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2024 SIAM International Conference on data Mining, SDM 2024

作者： Hossain, Shamima Faloutsos, Christos Baer, Boris Kim, Hyoseung Tsotras, Vassilis J. Dept. of Computer Science and Engineering University of California Riverside United States School of Computer Science Carnegie Mellon University United States Dept. of Entomology University of California Riverside United States Dept. of Electrical and Computer Engineering University of California Riverside United States

ISBN: (纸本)9781611978032

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else? We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers;(ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly coincide with domain expert's opinions, showcasing our approach's potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data. Copyright © 2024 by SIAM.

关键词： Forecasting

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Bauder, Chandler J. Fathy, Aly E. University of Tennessee Knoxville Dept. of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-dept. metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases. © 2025 IEEE.

关键词： Electrocardiography

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UHI Web Application: Empowering Healthcare with Disease Prediction for Seamless Doctor Consultations 4

UHI Web Application: Empowering Healthcare with Disease Pred...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Vardhan, Nunna Harsha Rajesh, M. Vishal Matcha, V.R.S. Reddy, Sathi Ravi Shankar Chowdary, Verelli Nivas Dept. of Computer Science Engineering Amrita School of Engineering Bangalore India

ISBN: (纸本)9798350305258

The large amount of data generated by hospitals is helping machine learning algorithms to generate accurate solutions. This data includes information about treatment, diagnosis, and medications. This valuable information can be used to create automated solutions. The Indian government is planning to introduce a technology like the United Payment Interface (UPI) in the healthcare sector. This technology is called the United Health Interface (UHI). The UHI aims to consolidate various healthcare services, such as the storage of patients' health records, management of blood banks, provision of hospitals' information, doctors' details, bed availability, medical insurance services, and more. Our goal is to develop a similar system where all these services can be accessed through a single account. The UHI is an advanced technology that has revolutionized the healthcare industry. It offers numerous benefits to both patients and healthcare providers. One of the most significant advantages of UHI is the seamless sharing of patient information across different healthcare providers and institutions. This means that doctors, nurses, and other healthcare professionals can easily access patient data, regardless of their location, and make informed decisions regarding treatment. The online doctor appointment system has also revolutionized healthcare. It offers several advantages for patients and healthcare providers alike. These advantages include convenience, time savings, improved access to healthcare, reduced costs, and enhanced patient management. Therefore, our aim is to develop a web application that consolidates all healthcare services under one platform, significantly improving our lives. Here are some additional details about the UHI and the online doctor appointment system, The UHI is a network of open protocols that enable the interoperability of health services. This means that patients' health records can be shared securely and easily between different healthcare provi

关键词： Disease prediction Gradient Boosting Classifier Random Forest Symptoms Unified health interface

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

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Investigation of Convolutional Neural Network with Motor Imagery-based Brain-computer Interface Decoding 14

Investigation of Convolutional Neural Network with Motor Ima...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Madhan, H.K. Bindushree, V. Dept. of Computer Science and Engineering Navkis College of Engineering Hassan India

In recent years, Brain-computer Interfaces (BCI) have become increasingly popular for assisting individuals with disabilities in restoring communication and mobility. The area of research focuses on utilizing convolutional neural networks (CNNs), specifically deeper architectures with high dropout, to decode brain signals and enable users to interact with the world more naturally, particularly through motor imagination. Motor imagination involves analyzing brain signals to decipher a person's intention to perform a motor task. CNNs are well-suited for BCI decoding due to their ability to learn intricate features from raw EEG data. However, the challenge lies in preventing overfitting in deeper CNN architectures, which have a larger number of parameters. To overcome this, the proposed approach involves a high truncation CNN architecture, a regularization technique that randomly turns off neurons during training to mitigate overfitting. © Grenze Scientific Society, 2023.

关键词： Brain computer interface

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Skyline Algorithm based on QoS to improve Web Services: A Review 6

Skyline Algorithm based on QoS to improve Web Services: A Re...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Barge, Yamini Patel, Tanvi Purohit, Lalit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Information Technology SGSITS Indore India

ISBN: (纸本)9798350369748

The popularity of Web Service-based systems is due to the advantage of flexibility, reusability and support for rapid system development. Web Service selection is a very important step for these web service-based systems. The skyline technique is widely used in the past for the selection of web services. In this work, an exhaustive study of Skyline-based web service selection techniques is presented. A review of the existing state-of-the-art is presented based on the approach followed for selection of web services, the advantages of using the proposed approach, the limitations of the approach and the problem addressed. © 2024 IEEE.

关键词： Web services

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Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels 1

Comparison of Various Computational Models for Accurate Stro...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Arun, Chalana B Anusha, M. Kodipalli, Ashwini Gargi, N. Harish, L. Devamane, Shridhar B Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bangalore India School of Computing Amrita Vishwavidhyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Stroke is one dangerous illness that kills people. Stroke is so called because of the way it strikes people down. Across the globe, it is among the main causes of mortality and disability. It requires early identification and precise measures to be taken in order to prevent stroke and further damages to the brain and other parts of body. Our study investigates the use of machine learning techniques that includes Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Free Classifier, Random Forest Classifier, Naive Bares and multiple Boosting Classifiers. The 'Healthcare dataset Stroke data' is the dataset used, which is preprocessed and then trained upon by Ml. models. For Random Forest Classifier, the maximum accuracy observed was 94.91%, and upon Hyper parameter Tuning- there was rise in accuracy to 95.64%, Evaluation criteria like F1-score, precision, and recall provides additional evidence for the effectiveness of these models: Further in order to explain how each feature contributes differently to each prediction we have made use of two popular techniques for XAT, LIME and SHAP. LIVE and SHIP provides local, interpretable explanations and global explanations respectively. © 2024 IEEE.

关键词： Random forests

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