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A Prediction and Classification Process for DDoS Attacks Using Machine Learning 7

A Prediction and Classification Process for DDoS Attacks Usi...

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2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Salunke, Mangesh D. Rathod, Vijay U. Mali, Yogesh Kisan Tambe, Ravindra S. Dange, Anup A. Kothavle, Suhas Ramdas Marathwada Mitra Mandal's Institute of Technology Lohgaon Pune India G H Raisoni College of Engineering and Management Wagholi Pune India Dr.Vithalrao Vikhe Patil College of Engineering Ahmednagar India

ISBN: (纸本)9798350304268

The popularity of computer networks in our daily life has grown recently as a result of the extensive use of the Internet. Due to server flaws, hackers can access computers using not only well-known attack types but also newer, more advanced, and harder to spot ones. One of the maximum popular defenses is the Intrusion Detection System (IDS), which uses machine learning techniques to train on a pre-collected dataset to defend computers from them. In this paper, the authors examine the present practice of Distributed Denial of Service (DDoS) attacks. DDoS attacks make use of restrictions that are applicable to any arrangement asset, such as the website's framework for an authorized organization. However, it is crucial to use the most recent dataset in order to identify the most recent DDoS activities. The authors employed an old-fashioned KDD dataset. In this research, a machine learning technique was used to identify and predict the sorts of DDoS attacks. The K Nearest Neighbor, Random Forest, and Decision Tree are three machine-learning-based IDSs approaches that we propose in this paper. © 2023 IEEE.

关键词： Classification D-DoS Attacks KNN Prediction Random Forest

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IoT-enabled Student Attendance System using RISC-V based Embedded System 22

IoT-enabled Student Attendance System using RISC-V based Emb...

引用

22nd international Learning and Technology conference, L and T 2025

作者： Khan, Khadija Alzahrani, Nada Abed, Reham Razi, Iman Sofia Dhaou, Imed Ben Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah22246-4872 Saudi Arabia

ISBN: (纸本)9798331532215

Inefficiencies, cumbersome processes, and a significant risk of errors, delays, and misuse often plague conventional methods for tracking student attendance. To address these challenges, this paper introduces a smart monitoring system driven by the Artificial Intelligence of Things (AIoT), specifically designed for resource-limited embedded processors. This research examines a real-time facial recognition-based student attendance tracking system implemented on an edge device. Additionally, it assesses the effectiveness of ARM and RISC-V microprocessors as the foundation for a reliable edge computing platform. The system employs Python's Dlib library and the ResNet model for effective facial recognition. Comparisons between the Raspberry Pi Model 4 B and the RISC-V StarFive VisionFive2 are made to determine the optimal platform for extensive deployments. The proposed edge system emphasizes security, automation, and reliability, facilitating real-time, precise attendance tracking and significantly improving operational efficiency for educators and administrators. The solution using the RISC-V processor is at least 10 times more energy-efficient than the competing solution based on the RISC processor instruction set architecture. © 2025 IEEE.

关键词： Students

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A Comprehensive Analysis of UAV Collision Avoidance Techniques for Enhanced Aerial Safety 4

A Comprehensive Analysis of UAV Collision Avoidance Techniqu...

引用

4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Jaiswal, Kartikey Vashisth, Anshu School of Computer Science & Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350393248

Drones, otherwise called Unmanned Aerial Vehicles (UAVs), are as of now normal in a wide range of enterprises and have changed jobs like bundle package delivery and surveillance. In any case, given the immense number of UAVs in the skies today, keeping up their safe coexistence with different drones is of the best concern. This paper explains this significant topic by offering a definite investigation of generally applied UAV collision avoidance techniques. Collision avoidance, which immensely affects all parts of the course and flight execution, is a pivotal piece of UAV path planning. Five primary techniques are covered in this paper: Proximity sensors, GPS-based systems, Communication and Tracking, Sense and Avoid Techniques, and Autonomous Decision-Making Algorithms. To reveal insight into every technique's efficiency, the review looks at its advantages and downsides. Understanding and further developing collision and Obstacle Avoidance Techniques is significant for the continued protected and productive activity of these aerial vehicles, and they play a significant part in UAV path planning as UAVs become more coordinated in our airspace. © 2023 IEEE.

关键词： Object detection

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Unsupervised Human Action Recognition for Quality Control in Industrial Environments 6

Unsupervised Human Action Recognition for Quality Control in...

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6th IEEE international conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Vexo, Wetu Jeenanunta, Chawalit Chanyachatchawan, Sapa Tunpan, Apinun Sirimarnkit, Nisit School of Management Technology Sirindhorn International of Technology Thammasat University Phatum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Phatum Thani Thailand Smart Sense Industrial Design Co. Ltd Bangkok Thailand

ISBN: (纸本)9798350389692

Ensuring robust and efficient quality control in industrial environments is critical as automation and artificial intelligence continue to advance. Human action recognition (HAR) plays a vital role in monitoring and maintaining the accuracy and safety of industrial processes. Traditional HAR methods rely on supervised learning, requiring extensive labeled datasets, and are often impractical due to high costs and time constraints. This research addresses these gaps by proposing a hybrid model integrating Beta Variational Autoencoders AE) with various clustering algorithms to enhance unsupervised human action recognition in industrial environments. The method begins with extracting 2D keypoints from video data using MediaPipe, a powerful tool for keypoint detection. These keypoints are then processed through a β-VAE to extract a latent space representation, capturing the essential features of the actions. Various clustering algorithms, including K-means, DBSCAN, spectral clustering, hierarchical clustering, and mean shift, are applied to the latent space to group similar actions. Evaluation of these clustering methods shows that hierarchical clustering performs the best, achieving an accuracy of 91.67%, indicating that the approach effectively groups similar actions, enhancing the reliability of HAR systems in industrial environments. © 2024 IEEE.

关键词： Unsupervised learning

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Evolutionary Swarming Particles To Speedup Neural Network Parametric Weights Updates 9

Evolutionary Swarming Particles To Speedup Neural Network Pa...

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9th international conference on Smart Computing and Communications, ICSCC 2023

作者： Dutta, Subhayu Adhikary, Subhrangshu Dr. B. C. Roy Engineering College Department of Computer Science and Engineering West Bengal Durgapur713206 India National Institute of Technology Department of Computer Science and Engineering West Bengal Durgapur713213 India

ISBN: (纸本)9798350314090

Gradient-based neural networks weight optimizers such as stochastic gradient descent, adam, adagrad or adadelta provides robust and reliable results, and they are very slow to train as they slowly and steadily iterate over the solution search space. These days deep learning models are getting very large, having billions or even trillions of parameters. Updating these many parameters with gradient-based methods would require large computational resources and time. Therefore, an alternative, reliable and fast parametric update rule is required. Particle swarm optimizer is a popular algorithm used for solving various optimization problems like design control systems, feature selection, signal denoising, compression techniques, etc. In the state of the art, the closest application of particle swarm in a neural network is to tune hyperparameters to find the best combinations for a given set of problems. But no study has explicitly used evolutionary swarming particles to directly update weights and biases of a neural network. With some modifications on both the general batch weight update mechanism of a simple neural network and the particle swarm, we successfully combined the two together. The tests were performed on two datasets, one for kidney stone prediction and one for breast cancer classification showing that the classification accuracy with the proposed modification is better than the state of the art in multiple cases. But the most important observation was that the proposed model was found to be up to 147 times faster than AdaDelta and 110 times faster than stochastic gradient descent. © 2023 IEEE.

关键词： Swarm intelligence

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A Systematic Review based on the Detection of PCOS using Machine Learning Techniques 2

A Systematic Review based on the Detection of PCOS using Mac...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Oviya Graselin, S. Arunprasath, T. Pallikonda Rajasekaran, M. Kottaimalai, R. Ramalakshmi Department of Electronics andcommunication Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Biommedical Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer science Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India

ISBN: (纸本)9798350340235

Women lacking appropriate nutrients in their meals are having the most rising non-communicable illness called Polycystic Ovary Syndrome (PCOS). The major reason behind this disease is unhealthy diet. At their reproductive age, 8–10% of women influenced with endocrine disorder is PCOS it leads to lot of health issues especially miscarriage and infertility with ovarian dysfunction. The male hormones abnormality production and female hormone levels variation characterizes an endocrine disorder. The PCOS symptoms are hirsutism, acne, male hormone excessive production, irregular menstrual cycle and obesity. Because of symptoms associated with heterogeneity, PCOS diagnose is extremely difficult. The PCOS patients met the major challenge is ovary scanning and innumerous clinical tests with cost as well as time. By addressing these challenges, the existing authors suggested different kinds of machine learning (ML) algorithms with respect to artificial intelligence models. This research study reviews the ML scheme to analyze PCOS diagnosis with its advantages, limitations and experimental analysis and datasets. By this investigation, the tables and figures are summarized for readers’ convenience. Still, there are several problem to tackle and possibility to pursue growing popularity of ML models in the future. © 2023 IEEE.

关键词： ANN challenges Graphical analysis ML model PCOS SVM

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A Federated Learning Framework for Early Risk Prediction of Acute Kidney Injury using Knowledge Distillation 5

A Federated Learning Framework for Early Risk Prediction of ...

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5th international conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Runxian Guo, Shengwen South China University of Technology School of Automation Science and Engineering Guangzhou China

ISBN: (纸本)9798350373820

This study aimed to develop a predictive model for early detection of acute kidney injury (AKI) in a multicenter environment, while ensuring data safety and privacy. The dataset consisted of clinical information from 16 hospitals affiliated with the China Cooperative Research Center for Acute Renal Injury (CCS-AKI), which included demographics, laboratory test results, critical illness scores, and medication information from enrolled AKI inpatients (46,395) and non-AKI inpatients (534,066). The dataset was randomly divided into a training set and an independent testing set, with a ratio of 4:1. In order to ensure data safety and privacy, we proposed a novel algorithmic framework, federated averaging-knowledge distillation (FedAvg-KD), incorporating the federated averaging (FedAvg) with knowledge distillation. In this framework, the user data set is replaced with a distilled data set. Experimental results demonstrated that the FedAvg-KD model, with knowledge distillation, achieved an average area under the curve (AUC) of 0.89 pm 0.02 for AKI prediction within the next 24 hours, and 0.88 pm 0.02 for AKI prediction within the next 48 hours. Additionally, the study investigated the impact of serum creatinine (SCr)-related variables in prediction, the proposed model still exhibited promising performance without SCr-related variables. It was verified that the proposed method could not only obtain promising ability in AKI prediction, but also ensure data safety and privacy in a multicenter environment. © 2024 IEEE.

关键词： Data privacy

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Hybrid attention network for Semantic segmentation 4

Hybrid attention network for Semantic segmentation

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4th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2023

作者： Yang, Yin Yang, Juan Wang, Ronggui School of Computer Science Hefei University of Technology Hefei China

ISBN: (纸本)9781510666412

Deep neural networks have made significant improvements in pixel -level semantic segmentation. However, the existing semantic segmentation algorithm still faces the problem of weighing between the accuracy and calculation cost of the segmentation. In response to this issue, this article proposes a hybrid network structure (HAM), looking for a balance point in the calculation accuracy and calculation speed. In this method, we construct a dual -attention module. The role of this module is to guide high -level characteristics through underlying characteristics to obtain more context information. Among them, the shape flow branch retains low -level space details, and semantic flow branches capture senior context information. These two branches are fused to strengthen information dissemination between different levels, thereby achieving higher segmentation accuracy. Experiments on the dataset show that this method achieves higher accuracy and speed under relatively small parameters. Compared with other real-time semantic segmentation methods, our network has achieved good compromise between parameters, speed, and accuracy. © 2023 SPIE.

关键词： Semantic Segmentation

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A Semi-Supervised Learning Approach for Fault Detection and Diagnosis in Complex Mechanical Systems 19

A Semi-Supervised Learning Approach for Fault Detection and ...

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19th IEEE international conference on automation Science and engineering, CASE 2023

作者： Askari, Bahman Cavone, Graziana Carli, Raffaele Grall, Antoine Dotoli, Mariagrazia The Department of Electrical and Information Engineering The Polytechnic of Bari Italy The Department of Civil Computer Science and Aeronautical Technologies Engineering The University Roma Tre Italy The LIST3N-LM2S University of Technology of Troyes France

ISBN: (纸本)9798350320695

The integration of artificial intelligence in mechanical fault detection and diagnosis (FDD) helps to increase reliability, reduce costs, and improve the overall performance of mechanical systems in Industry 4.0 applications. Most interesting industrial applications nowadays come from dynamic environments where data are generated continuously over time and where the labeled data are scarce and expensive. Therefore, semi-supervised learning (SSL) can be particularly useful in FDD because faults may be rare or difficult to identify, and may not be fully represented in the labeled data. By using a combination of labeled and unlabeled data, SSL can help to identify these rare or difficult-to-detect faults, leading to more effective FDD. In this paper, graph-based SSL relying on label propagation is combined with conventional classification algorithms to detect potential failures in complex mechanical systems. Experimental results on realistic pneumatic and hydraulic systems from the related literature show that the proposed method can effectively enlarge the labeled datasets and interestingly identify different types of non-nominal conditions with higher accuracy compared to baseline methodologies. © 2023 IEEE.

关键词： Graphic methods

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Comparison in Detection of Shilling Attackers Using KNN and SVM

Comparison in Detection of Shilling Attackers Using KNN and ...

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international conference on Systems, Control and automation, ICSCA 2023

作者： Gambhir, Sarika Dhawan, Sanjeev Singh, Kulvinder Department of Computer Engineering University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra136119 India

ISBN: (纸本)9789819773831

Recommender systems based on collaborative filtering are vulnerable to shilling assaults owing to its openness. Shiller’s introduce fictitious profiles into the database of the system with the goal of modifying the recommendations to their advantage. To make product recommendations more reliable and robust, it is essential to find potential attackers concealed in recommender systems. Several shilling attack detection techniques have been proposed thus far, and they all have advantages and disadvantages that are complementary to different attacks. We present a thorough experimental evaluation of detection of attackers using K-nearest neighbour and Support Vector machine in this paper. Amazon electronic dataset is used to simulate several attack models. In the meantime, for investigation attackers are produced in our dataset using average-filling, random-filling and bandwagon attack models. For detection of shilling attack two models i.e., K Nearest neighbour and Support Vector Machine are also compared. Precision, recall, F1-score and accuracy are used to analysis the performance of these two algorithms. The result of our experiments shows that SVM is more accurate than KNN in detection of shilling attackers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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