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An Embedded 3D Object Detection in Real-Time Using Deep learning Techniques 4

An Embedded 3D Object Detection in Real-Time Using Deep Lear...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Aravinda, K. New Horizon College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350300888

the field of computer vision is currently experiencing significant growth in the area of real-time 3D object identification. this research aims to efficiently and precisely identify and locate objects in a three-dimensional space. the demand for efficient detection approaches has escalated due to the growing prevalence of sensors such as LiDAR and depth cameras in various applications, including autonomous vehicles and augmented reality. this study explores the most recent developments in utilizing deep learning algorithms for real-time 3D object recognition. It is worth mentioning that algorithms such as PointRCNN, VoteN et, and 3D-CNN s have exhibited the capability to effectively handle large and sparse point cloud data. Nevertheless, there are ongoing challenges that continue to exist in this field, including the issue of limited availability of data and the high computational demands required for real-time processing. By utilizing optimal neural network designs and integrating multi-modal sensor input, the systems presented demonstrate the potential to overcome these problems. this study provides novel research insights on the prospective trends by highlighting the potential of end-to-end learning and domain adaptability. © 2023 IEEE.

关键词： Object detection

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Performance Evaluation of Intrusion Detection System Using machine learning and Deep learning Algorithms 4

Performance Evaluation of Intrusion Detection System Using M...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Hossain, Md. Sabbir Ghose, Dipayan Partho, All Masror Ahmed, Minhaz Chowdhury, Md. Tanvir Hasan, Mahamudul Ali, Md Sawkat Jabid, Taskeed Islam, Maheen East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350300192

Now that Internet access is so widely used, our society has a greater number of networked technologies. Data travels between them because of their daily activities. Due to the server's weaknesses, hackers may get access to the system through difficult-to-identify network breaches. One of the most well-known defense mechanisms against these attacks on networked devices is the Intrusion Detection System (IDS), which is built into the system. IDS has previously received extensive training in the classification of threats using traditional machine learning-based models and pre-assembled datasets. In this research, we presented two deep learning-based models, the Multilayer Perceptron Model (MLP) and Long-Short Term Memory (LSTM), along with five machine learning-based models, including Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector machine (SVM). the NSL-KDD dataset has been used to achieve 89.6% accuracy with normalization and 89.2% without normalization, 97.77% with LSTM and 96.89% with MLP. Each record in the data collection has 43 features, including two labels and 41 features that are related to traffic input. © 2023 IEEE.

关键词： Classification (of information)

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machine learning-Driven Multistage Classification for Lung Lesion Detection 4

Machine Learning-Driven Multistage Classification for Lung L...

引用

4th international conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Rahmani, Amir Shahalinezhad, Sahand Aliyu, Aliyu Sohani, Behnaz School of Engineering University of Lincoln Brayford Pool United Kingdom School of Engineering Urmia Graduate Institute Iran Wolfson School of Mechanical Electrical & Manufacturing Engineering Loughborough University Loughborough United Kingdom

ISBN: (纸本)9781510683242

Multi-stage classification for lung lesion detection on CT scan images employs a hierarchical approach, involving sequential stages for accurate identification and classification. this methodology integrates medical image processing techniques, including segmentation, feature extraction, selection, and classification, to enhance the detection performance and reliability of lung lesion diagnosis. Medical imaging, particularly medical image processing, is rapidly advancing and transforming various aspects of healthcare, including prevention, diagnosis, and treatment. In lung cancer diagnosis, computed tomography (CT) scans play a crucial role. Accurate identification of masses is essential, as misdiagnosis can lead to incorrect treatments. Detecting and delineating masses within lung tissue pose critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. this has been performed employing CT images based on multilevel thresholding. the proposed machine learning technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of the lung cancer is obtained with 96.3% accuracy and 87.25%. the purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current machine learning method to achieve more accurate resulting images including further details available to machine vision systems to recognise objects in lung CT scan images. © 2024 SPIE.

关键词： Lung cancer

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Enhanced Image-Text Retrieval Based on CLIP with YOLOv10 and Next-ViT 4

Enhanced Image-Text Retrieval Based on CLIP with YOLOv10 and...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Qian, Xue Liu, Bo Beijing Information Science and Technology University Beijing China University of Westminster London United Kingdom

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In recent years, the CLIP model has achieved remarkable success in image-text retrieval tasks through contrastive learning. However, CLIP still exhibits certain limitations when handling complex backgrounds and small objects. To address these challenges, this paper proposes two key innovations: First, during inference, the YOLOv10 model is employed to detect and crop small objects and essential background information in the image, enhancing ability of CLIP to comprehend complex scenes. Second, the Next-ViT network is utilized as the backbone for image encoding. By leveraging its more efficient multi-scale feature extraction capabilities, Next-ViT improves the retrieval accuracy of small objects while also being more adaptable for deployment in industrial scenarios. Experimental results demonstrate that these two innovations significantly enhance performance of CLIP in image-text retrieval tasks and achieve a balance between accuracy and efficiency across various vision tasks. © 2025 SPIE.

关键词： Contrastive learning

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the GANfather: Controllable generation of malicious activity to improve defence systems 23

The GANfather: Controllable generation of malicious activity...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Pereira, Ricardo Ribeiro Bono, Jacopo Ascensao, Joao Tiago Aparicio, David Ribeiro, Pedro Bizarro, Pedro Feedzai Coimbra Portugal Univ Porto Porto Portugal

ISBN: (纸本)9798400702402

machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7-4 trillion euros are laundered annually and go undetected. We propose the GANfather, a method to generate samples with properties of malicious activity, without label requirements. We propose to reward the generation of malicious samples by introducing an extra objective to the typical Generative Adversarial Networks (GANs) loss. Ultimately, our goal is to enhance the detection of illicit activity using the discriminator network as a novel and robust defence system. Optionally, we may encourage the generator to bypass pre-existing detection systems. this setup then reveals defensive weaknesses for the discriminator to correct. We evaluate our method in two real-world use cases, money laundering and recommendation systems. In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system. In the latter, we recommend the target item to a broad user base with as few as 30 synthetic attackers. In both cases, we train a new defence system to capture the synthetic attacks.

关键词： learning systems

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Adaptive IDS Concept with PRBS Multi Inputs Multi Outputs (MIMO) and Matched Filtering Algorithm 4

Adaptive IDS Concept with PRBS Multi Inputs Multi Outputs (M...

引用

4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Bin Tuan Muda, Saiyed Rasol Mohd Yusof, Mohammad Hafiz Shamsuddin, Solahuddin Universiti Teknologi Mara UiTM Faculty of Computer and Mathematical Sciences Tapah Malaysia Menara Cyber Axis Jalan Impact Cyberjaya Selangor Darul Ehsan Cyberjaya63000 Malaysia

ISBN: (纸本)9798350318432

Latest development in cyber-attacks are becoming more complex and obscure. Current Deep learning approaches in network intrusion is limited to experimental conditions, limited training on real time live traffic, manual training interventions, and highly dependent on standard dataset. Notwithstanding, adaptive system has grabbed the attention of multiple industrial players from automobiles to cybersecurity. this is due to the capability of the system to adapt to any updates, trends and changes. Hence Adaptive IDS must be studied and designed immediately to prevent further damage to cyber infrastructure and community. Adaptive IDS will automate process and contrast live traffic to spring realistic output. this work discusses the conceptual idea of Adaptive IDS in cybersecurity domain. the discussion covers three (3) parts, with initial directly related to the overview of the Adaptive IDS concept. Next part is an expanded version that discusses on the backend software that resides in the computer system and a bit of hardware outside. Finally, the third part deliberates on designing a simulated environment through PRBS. this allows synthetic composite dataset generation and detection, employing modified \mathrm{n}=8, giving (255+1) 256BitsPRBS using Embarcadero Delphi IDE VCL application development in contrast to electronic version generation using Simulink or PowerSim. © 2023 IEEE.

关键词： Deep learning

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computer Systems and Technologies - 24th international conference, CompSysTech 2023: proceedings

Computer Systems and Technologies - 24th International Confe...

引用

24th international conference on computer Systems and Technologies, CompSysTech 2023

ISBN: (纸本)9798400700477

the proceedings contain 31 papers. the topics discussed include: in session artificial intelligence & machine learning: distributed task allocation in network of agents based on ant colony foraging behavior;in session computer software, image processing & visualization: models and computation of traffic resilience;in session application aspects of computer systems and technologies: enhancing healthcare accessibility: a teleconsultation mobile application for patient-centric care in Cyprus;in session network and information security: tamper detection and localization in forensic images;in session educational aspects of computer systems and technologies: enhancing the understanding of computer engineering students in financial analysis through educational authoring software;and automated tool for assessment of spreadsheet type assignments.

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Intelligent Road Anomaly Detection and Driver Assistance System: machine learning Approach for Enhanced Road Safety 4

Intelligent Road Anomaly Detection and Driver Assistance Sys...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Mohanaprakash, T.A. Savija, J. Peter, M. John Tamilselvi, C. Hariharan, S. Dhanush Sunitha, T. Panimalar Engineeering College Department of Cse Chennai India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Panimalar Engineering College Department of Mathamatics Chennai India Dr. Mgr Educational and Research Institute Department of It Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India P.B College of Engineering Department of Cse Chennai India

ISBN: (纸本)9798350300888

Intelligent system designed to detect road anomalies and provide driver assistance for improved road safety. the system is integrated into vehicles and utilizes a combination of accelerometer sensors, vibration motion sensors, gyroscopic sensors, and GPS technology. By analyzing sensor signals using machine learning techniques, the system can identify various road irregularities based on the vehicle's current position. Additionally, the system collects and compares data from other vehicles at the same GPS position to confirm the presence of potholes or speed bumps. the gathered information is securely stored in a cloud-based storage accessible through a GPS application. the system proactively warns drivers in advance, notifying them to slow down as they approach GPS points previously identified as having road anomalies. By leveraging real-time sensor data and GPS information, this intelligent system enhances road safety and promotes a smoother driving experience. © 2023 IEEE.

关键词： Anomaly detection

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Cybersecurity Measures Using machine learning for Business applications 4

Cybersecurity Measures Using Machine Learning for Business A...

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

作者： Rajasekaran, Arun Sekar Priya, S.Shanmuga Sivakumar, Janaki Varkale, Akshay Reena, R. Sr University Dept. of Ece Telangana Warangal India Srm Institute of Science and Technology Department of Cse Tiruchirappalli India Global College of Engineering and Technology Department of Csct Oman Ies College of Technology Department of Cse Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Cse Chennai India

ISBN: (纸本)9798350393248

Cybersecurity is a significant concern for organisations in the modern, globally interconnected world, but it's also a dynamic field. Conventional security measures are often insufficient due to the growing complexity of attackers. this research investigates the potential for enhancing cybersecurity protocols in business operations via the integration of machine learning methods. In order to emphasise the urgent need for creative solutions, we first provide a high-level summary of the present state of cyber threats. In the first segment, machine learning is introduced along with its application to cybersecurity, with a focus on the need for adaptable models and data-driven insights. the various machine learning methods that may be used to detect and prevent cyberattacks are then covered. these algorithms include those for identifying patterns in data, evaluating behaviour, and deciphering natural language. the study then moves on to real-world commercial applications, such as threat intelligence, network security, and user behaviour analysis. We demonstrate how machine learning may enable businesses to proactively detect and address security breaches, resulting in cybersecurity plans that are more adaptable and durable. Finally, we discuss the difficulties and constraints associated with using machine learning to cybersecurity, along with the moral issues pertaining to data protection and openness. © 2023 IEEE.

关键词： Anomaly detection

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Empowering Farmers with AI: Federated learning of CNNs for Wheat Diseases Multi-Classification 4

Empowering Farmers with AI: Federated Learning of CNNs for W...

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4th international conference for Emerging Technology, INCET 2023

作者： Mehta, Shiva Kukreja, Vinay Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335750

Higher agricultural outputs are required due to the rising worldwide population, shifting nutritional preferences, and growing demand for food and basic materials for the industry. However, the farming sector confronts several difficulties, such as climate change and a rise in the severity of production risks, which have had a detrimental effect on food output. Crop production forecast algorithms have been created using machine learning and deep learning techniques to handle this issue. Traditional machine learning techniques, however, are less effective because many characteristics related to meteorological data, earth data, and agricultural management data are dispersed and isolated to specific organization computers or smart farming devices. Using about 9,876 pictures, a collaborative learning CNN method for wheat disease identification is suggested. the suggested method used the federated averaging technique to train the model on laterally dispersed datasets across various client devices. the suggested method beat current state-of-the-art models for detecting wheat illness, according to the findings of our experiments, obtaining high accuracy, precision, recall, and F1 score. the suggested method shows how federated learning can enhance machine learning models in a distributed environment. It can also be applied to other agricultural uses, such as crop forecast, soil analysis, and insect detection. © 2023 IEEE.

关键词： Crops

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