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Proactive Threat Detection in Network Security: An Ensemble Machine Learning Approach

Proactive Threat Detection in Network Security: An Ensemble ...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Akash, Pritidhrita Paul Fahim, F.M. Shafiullah Nahian, Naila Islam, Muhammad Nazrul Al Faisal, Faiz Bangladesh University of Professionals Department of Information Communication Technology Dhaka1216 Bangladesh Bangladesh University of Professionals Dept. of Ict Dhaka1216 Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh Green University of Bangladesh Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9798331535476

With the continuous advancement of technology, ensuring network security has become increasingly paramount, especially in safeguarding data and privacy. This study aims to introduce a machine learning (ML) based Intrusion Detection system (IDS) for improving the proactive threat-hunting capabilities. The proposed approach employs ensemble learning by integrating Random Forest and Gradient Boosting algorithms to improve detection accuracy and minimize false alarm rates. The system's performance was rigorously evaluated using the NSL-KDD dataset and the outcome exhibits a remarkable accuracy rate of 99.86%. These results highlight how well the ensemble models operate to enhance IDS performance and reinforcing robust network security. By applying advanced ML techniques, the proposed IDS demonstrates its potential in safeguarding organizational networks against a broad spectrum of emerging cyber threats. As such, this study offers insightful information to develop more reliable and efficient I DS solutions for t he future of cybersecurity. © 2024 IEEE.

关键词： Adversarial machine learning

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Hardware Implementation to Detect Brain Tumor using AI ML based Convolution Neural Networks CNN in the Field of Bio Medical engineering 15

Hardware Implementation to Detect Brain Tumor using AI ML ba...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ravindranath, C. Gopalaiah Adithya, T.G. Pavithra, G. Manjunath, T.C. Department of Civil Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Electronics & Instrumentation Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Dept. of Computer Science & Engineering PES University Hoskerehalli 100 Feet Ring Road Karnataka Bangalore560085 India Dept. of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331300579

A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. We are using a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) Transfer learning for detecting brain tumors. The performance of the model is predicting image tumor is present or not in image. If the tumor is present, it returns yes otherwise return no. Here the location of the tumor is identified by K-means clustering using Raspberry pi. The Arduino controller is used for the movement of the robotic wheels to move in the exact location and determines the position of the tumor. © Grenze Scientific Society, 2024.

关键词： Convolution

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A Novel Approach for Segmentation and Classification of Skin Diseases based on Active Contour Model and Multiclass SVM Classifier 10

A Novel Approach for Segmentation and Classification of Skin...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Manonmani, V. Ganesan, P. Sajiv, G. Akila, D. Hajira, A.B. Jency, S. Dr.M.G.R Educational and Research Institute Dept. of ECE Chennai India Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Simats Saveetha School of Engineering Chennai India Saveetha College of Liberal Arts and Sciences Simtas Dept of Computer Applications Chennai India Karpaga Vinayaga College of Engineering and Technology Dept. of Mca Chennai India

ISBN: (纸本)9798350353068

Skin disorders are a major worldwide health concern that requires quick intervention and effective diagnostic methods. For efficient medical assessment and therapy, skin diseases must be correctly identified and classified. This work presents a novel approach for the accurate segmentation as well as categorization of different skin conditions, using the Active Contour Model (ACM), Region Growing (RG), and a Multiclass Support Vector Machine (SVM) Classifier. The suggested method begins with a strong region-growing (RG) technique that makes it easier to find starting regions of interest (ROI) in skin imagery. Then, to improve segmentation preciseness, the ACM is used to fine-tune and define the borders of these regions. A MSVM Classifier is then employed with the features that were retrieved from the partitioned regions as input, enabling a thorough and precise categorization of various skin conditions. To validate the suggested strategy, comprehensive experiments were carried out on a broad dataset that included a wide spectrum of skin conditions. The effectiveness of proposed approach is evidenced by the results, which show excellent performance in both the categorization and segmentation assessments. © 2024 IEEE.

关键词： Classification (of information)

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Hybrid Beamforming-based Beam Tracking using UKF in Massive MIMO systems

Hybrid Beamforming-based Beam Tracking using UKF in Massive ...

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2023 International Conference on Electronics, Information, and communication, ICEIC 2023

作者： Sim, Yuna Sin, Seungseok Cho, Jihun Kim, Kyunam Moon, Sangmi Hwang, Intae Chonnam National University Dept. of Ict Convergences System Engineering Gwangju Korea Republic of Chonnam National University Dept. of Electronics and Computer Engineering Gwangju Korea Republic of Chonnam National University Deptartment of Ict Convergences System Engineering Gwangju Korea Republic of Alps Electric Korea Co. Ltd. Gwangju Korea Republic of Korea Nazarene University Department of It Artificial Intelligence Cheonan Korea Republic of Chonnam National University Dept. of Electronic Engineering Dept. of Ict Convergence System Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350320213

Millimeter wave frequencies are one of the promising technologies to support high data rates in 5G wireless communication systems, but short wavelengths due to high frequencies cause signal attenuation and path loss. To compensate for this problem, in this paper, we apply the hybrid beamforming technique and try to track the exact beam angle using the Unscented Kalman filter to obtain the complete beamforming gain. As a result of the simulation, it can be seen that the proposed algorithm exhibits good Mean Square Error (MSE) performance with a moderate level of implementation complexity. However, the computational complexity that arises when hybrid beamforming is applied still remains a problem to be solved. Therefore, in the course of future research, if a deep neural network is applied in terms of beamformer and combiner, it will be able to show a higher level of performance with a lower level of complexity. © 2023 IEEE.

关键词： beam tracking hybrid beamforming unscented Kalman filter

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APLASE: Compression using Adaptive Piecewise Linear Approximation and Sparse Encoding

APLASE: Compression using Adaptive Piecewise Linear Approxim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ravi Raj Saxena T.V. Prabhakar Joy Kuri Chandra R. Murthy Dept. of Electronic System Engineering Indian Institute of Science Bengaluru India Dept. of Electrical Communication Engineering Indian Institute of Science Bengaluru India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σ d , which is used to derive error bounds for SE using σ d , the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios.

关键词： Measurement Dictionaries Time series analysis Piecewise linear approximation NASA Signal processing algorithms Encoding Sensors Speech processing Standards

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation system Using Smartphone 6

An Investigation of Running Load Comparisons of ARCore on Na...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Brata, Komang Candra Funabiki, Nobuo Sukaridhoto, Sritrusta Fajrianti, Evianita Dewi Mentari, Mustika Okayama University Dept. of Information and Communication Systems Okayama Japan Universitas Brawijaya Dept. of Informatics Engineering Malang Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Informatic and Computer Surabaya Indonesia State Polytechnic of Malang Dept. of Information Technology Malang Indonesia

ISBN: (纸本)9798350326642

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-Time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements. © 2023 IEEE.

关键词： Augmented reality

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment system “Stationary 3-Axis Fire-Emergency Response Base”

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Shyantan Kundu Swarajit Dey Md. Mustafa Abdullha Gobinda Sen Malay Gangopadhyay Dept. of Computer Science & Engineering Institute of Engineering & Management Kolkata India Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named “Stationary 3-Axis Fire-Emergency Response Base” (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management.

关键词： Industries Projectiles Forestry Flame retardants Hazards Real-time systems Production facilities Time factors Reliability Internet of Things

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Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning

Key Vertex Pairs Extraction for Graph Classification Based o...

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International Conference on Artificial Intelligence in Information and communication (ICAIIC)

作者： Jianming Huang Hiroyuki Kasai Dept. of Computer Science and Communication Engineering WASEDA University Tokyo Japan

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets.

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Machine Learning Techniques for Detecting DDOS Attacks 3

Machine Learning Techniques for Detecting DDOS Attacks

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Saeed, Mamoon M. Rashad Mohammed, Husam Nedhal Hazaea Gazem, Othman Abdulkafi Saeed, Rashid A. Ahmed Morei, Hossam Mohammed Thabet Eidah, Aimen Ebdulkreem Gaid, AbdulGuddoos S.A. Ameen Saif Al-Uosfi, Ameen Sami Qasem Al-Madhagi, Mohamed Galal Faculty of Engineering Department of Communications and Electronics Engineering Yemen College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen

ISBN: (纸本)9798350305333

The development witnessed by the world of science and technology and the emergence of the Internet, where cybersecurity has become one of the most important areas that are growing rapidly every day, and one of its goals is to maintain access to users at any time. Where it is noted that the biggest enemy for the availability is Distributed Denial of Service (DDoS) attack, which works to prevent access to the service and slow down the service. Through this paper, machine learning will be used to discover the DDoS attack and know its type to be aware of it and take the necessary measures for that. Where the CICDDoS2019 dataset will be used. After it is studied, analyzed, cleaned and the best algorithm was selected. Five classification algorithms (Random Forest, Decision Tree, SVM, Naive Bayes, and xgboost) were used to train and test the data from the datasets, and it can be said that of the five, Random Forest algorithms had the highest level of accuracy (99.95426%). © 2023 IEEE.

关键词： Denial-of-service attack

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Non-intrusive Emotional Development Monitoring of Pre-Primary Children Using Image Processing and Machine Learning Model

Non-intrusive Emotional Development Monitoring of Pre-Primar...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Anilloy Frank Mrinal Raj Dept. of Electronics and Communication Engineering Presidency University Bangalore Dept. of Computer Science Engineering Presidency University Bangalore

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The development of emotions begins in a child from infancy and continues into adulthood. The pre-primary experiences are unique to each child and the learning environments in early life often serve as the source for behavioral outcomes as they grow. The study is mainly based on detecting the emotions of children to have an insight of their behavior with respect to the external environment. The approach aligns with the Goal-3 Good health and well-being of the United Nations Sustainable Development Goals. It provides with the behavior pattern in better understanding of the child emotions both by parents and teachers of the student. Emotion recognition model using image processing enable understand how the emotions of children change and affects their lives with the human interaction. The model uses Transfer Learning model. Many students sit in front of their computers and can easily analyze their interaction with the system. The technology is evolving and using this model, can analyze human interaction and their behavior patterns in a given particular situation.

关键词： Training Pediatrics Analytical models Technological innovation Accuracy Computational modeling Image processing Transfer learning Sustainable development Monitoring

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