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Utilizing Deep Neural Networks to Improve Intrusion Detection System(IDS)

Utilizing Deep Neural Networks to Improve Intrusion Detectio...

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Telecommunications Conference (ITC-Egypt), International

作者： Ahmed Mahmoud Saad Mohammed Awad Meekay Mostafa Samy El Sayed Mansour Electronics and Computer and Systems Engineering Dept. Mansoura University Cairo Egypt Mansour Electronics and Communications Dept. Mansoura University Cairo Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Internet services are now accessible everywhere in the world due to the rapid advancement of networks and technology in recent decades. The development of information security solutions that can recognize novel threats is now critical due to the surge in piracy and the compromising of many modern systems. An intrusion detection system (IDS) is one of the most important information security technologies. It uses machine learning and deep learning algorithms to locate anomalies in the network. The main goal of this paper is to identify the unknown assault package by utilizing a novel intrusion detection method that offers excellent network performance. In this model, two techniques-binary classification and multiclass classification-that make use of a deep neural network algorithm are used to detect attacks. The suggested's outstanding accuracy

关键词： Deep learning Machine learning algorithms Accuracy Web and internet services Supervised learning Intrusion detection Artificial neural networks

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Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies

Multispectral Image Segmentation in Agriculture: Evaluating ...

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Iberian Robotics Conference (ROBOT)

作者： Wilgo Cardoso Tiago Barros Gil Gonçalves Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Dept. of Mathematics Institute for Systems Engineering and Computers at Coimbra University of Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In agricultural robotics, the integration of multi-spectral image processing and deep learning (DL) has become the state-of-the-art (SOTA) in crop monitoring, yield estimation, and efficient land management. This work addresses the impact of different DL-segmentation models and evaluation protocols on multispectral imagery datasets collected by a UAV over vineyards. In terms of evaluation protocols, we have considered train-test split, standard k-fold cross-validation, and group k-fold cross-validation. While the first two assume that the training and test data are drawn from the same underlying distribution, the group k-fold cross-validation protocol assumes that each fold represents distinct distributions. Most works either adopt train-test split or k-fold cross-validation under the assumption that both the training and test sets are drawn from the same distribution. However, this assumption is rarely met in real-world applications. Therefore, the objective of this study is to evaluate and compare different evaluation protocols within the context of a real-world agricultural task, highlighting their limitations and weaknesses. Two SOTA DL-based segmentation models, SegNet and DeepLabV3, are employed to perform semantic segmentation on datasets of three Vineyards. The models have been trained and tested considering single-modality representations. In addition to the RGB modality, models trained on NDVI, GNDVI and early fusion are also evaluated. The performance of the models are evaluated using the IoU metric across different dataset configurations. The results indicate that the early fusion representation achieves the highest performance across the various splitting protocols, compared to the single-input representations. The results also show that the train-test and random k-fold splitting approaches report similar results. However, when employing group k-fold the performance drops consistently across both models and the modalities. This indicates that the models

关键词： Deep learning Training Precision agriculture Measurement Protocols Data models Yield estimation Robots Monitoring Standards

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Exercise and Sedentary Activity Recognition Using Late Fusion: Building Adaptable Uncertain Models

Exercise and Sedentary Activity Recognition Using Late Fusio...

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International Conference on Information Fusion

作者： Ezequiel Juarez Garcia Victoria R. Rodrigues Mehrdad Fazli Laura E. Barnes Nicholas J. Napoli Dept. of Electrical and Computer Engineering University of Florida Gainesville FL Dept. of Systems and Information Engineering University of Virginia Charlottesville VA

Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.

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Optimizing Driver Identification Using Adaboost and Vehicular Telemetric Data Analysis

Optimizing Driver Identification Using Adaboost and Vehicula...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Mustafa Khurram Shehzad Khattak Zawar Hussain Khan Dept. of Computer Systems Engineering UET Peshawar Peshawar Pakistan Electrical Engineering Department University of Hail Hail Saudi Arabia

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In Intelligent Transportation systems (ITS), Artificial Intelligence (AI) has transformed transportation network management. This paper explores driver identification using machine learning to enhance vehicle security and efficiency. AI applications in ITS enable real-time data processing, predictive maintenance, and better traffic management, promoting a safer and more efficient ecosystem. Driver identification, or finger-printing, utilizes machine learning to identify drivers based on their unique driving behaviors, helping prevent unauthorized vehicle use, reducing theft, ensuring regulatory compliance, and optimizing resource allocation. Our research employs the AdaBoost algorithm, leveraging OBD- II data such as vehicle speed, RPM, coolant temperature, torque, accelerometer, and GPS for accurate driver identification with an accuracy of 96%. This approach capitalizes on AdaBoost's real-time processing capability, enhancing prediction accuracy, reducing overfitting, and improving generalization to new data. The results highlight the significant potential of AI -driven methods to advance ITS capabilities.

关键词： Adaptation models Accuracy Torque Biological system modeling Machine learning Real-time systems Security Resource management Driver behavior Vehicles

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Comparison of Whole Blood Coagulation Profiles in COVID-19 and Sepsis Patients Using a Handheld Dielectric Coagulometer

Comparison of Whole Blood Coagulation Profiles in COVID-19 a...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Pourang, Sina Suster, Michael A. Thomas, Asha Lapping, Stephanie D. Nayak, Lalitha V. Mohseni, Pedram Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH United States University Hospitals Cleveland Medical Center Division of Hematology/Oncology ClevelandOH United States

ISBN: (纸本)9798350300260

Rapid point-of-care (POC) assessment of thrombosis is clinically important in patients who develop significant blood coagulation abnormalities such as noted with sepsis or COVID-19. In this work, we compare the coagulation profiles of whole blood in sepsis and COVID-19 patients using a handheld dielectric coagulometer termed ClotChip®. ClotChip® is a three-dimensional, parallel-plate, capacitive sensor integrated into a single-use microfluidic channel with a total volume of © 2023 IEEE.

关键词： Blood coagulation COVID-19 dielectric spectroscopy microfluidics point-of-care testing

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Forecasting Unemployment Recession Rates: A Logistic Regression Approach Utilizing Comprehensive Economic Indicators

Forecasting Unemployment Recession Rates: A Logistic Regress...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Skandarsini R R Lakshmi Narayanaa T S. Jhansi Ida Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

This paper presents a predictive model using logistic regression to forecast unemployment recession rates, utilizing a range of variables such as GDP, inflation rates, employment rates, and trade activities. The model aims to provide accurate predictions of recession occurrence, utilizing historical data and rigorous cross-validation techniques. The model's significance lies in its potential applications in financial prudence and risk management, enabling job seekers to plan their personal finances and career trajectories, while companies can integrate these insights into strategic planning and supply chain optimization. The model's interpretability provides actionable insights by highlighting the significance and direction of each variable's impact on the predicted recession rate. The paper contributes to a deeper understanding of economic recessions and empowers stakeholders to navigate challenges with resilience and confidence. The model's deployment offers a practical tool for individuals and organizations to make informed choices during economic uncertainties, ultimately fostering prudent and sustainable economic strategies.

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Projection-Based controllers with Inherent Dissipativity Properties

Projection-Based Controllers with Inherent Dissipativity Pro...

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IEEE Conference on Decision and control

作者： Hoang Chu S.J.A.M van den Eijnden W.P.M.H. Heemels Dept. Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology The Netherlands

Projection-based controllers (PBCs) are currently gaining traction in both scientific and engineering communities. In PBCs, the input-output signals of the controller are kept in sector-bounded sets by means of projection. In this paper, we will show how this projection operation can be used to induce useful passivity or general dissipativity properties for broad classes of (unprojected) nonlinear controllers that otherwise would not have these properties. The induced dissipativity properties of PBC will be exploited to guarantee asymptotic stability of negative feedback interconnections of passive nonlinear plants and suitably designed PBC, under mild conditions. Generalizations to so-called $(q,s,r)$ -dissipativity will be presented as well. To illustrate the effectiveness of PBC control design via these passivity-based techniques, a numerical example is provided.

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Analysis of an Urban Air Taxi Inner Loop controller with Noisy IMUs in an Urban Airflow Environment

Analysis of an Urban Air Taxi Inner Loop Controller with Noi...

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IEEE Conference on Aerospace

作者： Tariq Maksoud Fidel Khouli Mohamed Atia Dept. of Mechanical and Aerospace Engineering Carleton University Ottawa Ontario Dept. of Computer and Systems Engineering Carleton University Ottawa Ontario

Interest in operating commercial Urban Air Taxis (UAT) around the world has been growing rapidly over the last few years. One of the many challenges in designing aircraft suitable for operating in a turbulent urban airflow environment is to design a robust inner loop flight controller. This study investigates the effect of filtered Angular Random Walk (ARW) error found in Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain stable, wings level, horizontal flight, while not causing noticeable discomfort to passengers and respecting the limits of authority of the aircraft's control surfaces in a representative urban airflow environment. The performance of two controller architectures were investigated: classical Proportional, Integral, Derivative (PID) control scheme as well as Linear Active Disturbance Rejection control (LADRC) control scheme. The conclusion of this study provides recommendations on a minimum threshold of IMU sensor grades and general considersations that would be useful to the controller designer. The findings are demonstrated by observing the vertical acceleration, $n_{z}$ , angular rate setpoint tracking performance, and control surface deflections.

关键词： Robust control PI control Filtering Sensor fusion Stability analysis Noise measurement PD control

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Enhancing Parking Online Reservation with a Recommendation System based on User Preferences: A hybrid Approach

Enhancing Parking Online Reservation with a Recommendation S...

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ACS/IEEE International Conference on computer systems and Applications

作者： Serine Guellab Imane Benkhelifa Dept. of Computer Science USTHB University Algiers Algeria Dept. of Computer Systems’Therories and Engineering CERIST Research Center Algiers Algeria

In response to the pressing challenges in parking online reservation platforms, the primary issue this paper addresses is the need for a user-centric parking reservation experience. To tackle this problem, the study aims to develop a recommendation system that enhances user satisfaction and streamlines the parking reservation *** provide personalized parking recommendations, a hybrid multimodal recommendation system is designed, grounded in distance-based recommendation and content-based filtering, and taking into account user preferences and feedback, history behavior and proximity to preferred tourist attractions and points of *** leveraging a rich dataset comprising 1804 parking items, results indicate a notable improvement and more user-centric user experience, as the system suggests parking lots in line with user preferences and points of interest. User feedback mechanisms are seamlessly integrated, facilitating continuous adaptation and refinement based on user convenience and past *** work shows significant potential in enhancing user satisfaction and streamlining the user experience in parking online reservation systems.

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

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Silchar Subsection Conference (SILCON), IEEE

作者： Karri Sai Sanjana Reddy Kishor Kumar Reddy C. Srinath Doss Kari Lippert Dept. of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Faculty of Engineering and Technology Botho University Botswana Dept. of Systems Engineering University of South Alabama Alabama USA

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care.

关键词： Training Accuracy Transfer learning Predictive models Prediction algorithms Genetics Classification algorithms Alzheimer's disease Random forests Medical diagnostic imaging

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