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检索条件"机构=Dept Computer Engineering and Technology"

共 19722 条记录，以下是21-30 订阅

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Towards Automated Lip Reading: Developing Marathi Lip Reading Datasets and Neural Network Frameworks 4

Towards Automated Lip Reading: Developing Marathi Lip Readin...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kulkarni, Apurva Kirange, Dnyaneshwar Khemachandra Ssbt College of Engineering and Technology Dept. of Computer Engineering Jalgaon India

ISBN: (纸本)9798350349900

This paper introduces an innovative method for automating lip-reading, with a specific focus on the Marathi language. Lip-reading plays a crucial role in aiding those with hearing impairments, but automating it presents significant challenges, especially for languages like Marathi lacking sufficient datasets. To tackle this, we propose a novel approach to automatic lip-reading, accompanied by the development of a specialized Marathi dataset. Leveraging advancements in computer vision and deep learning, our model deciphers linguistic content from lip movements, trained on this dataset. We employ various neural network architectures, including feed-forward, recurrent, and convolutional networks, to extract vital visual features crucial for accurate language interpretation Our primary goal is to provide a robust solution for automated lip-reading tailored specifically to regional languages like Marathi, aiming to enhance accessibility for individuals with hearing impairments, particularly in linguistically diverse contexts. This paper primarily discusses detailed exploration of dataset creation, neural network architectures for lip-reading system, we demonstrate the feasibility and potential impact of our approach. Our study underscores the significance of giving priority to regional languages in technological advancements to promote inclusivity for all individuals. © 2024 IEEE.

关键词： Convolutional neural networks

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Segmentation and Classification of Skin Lesions Using Ensemble of Convolutional Neural Networks

Segmentation and Classification of Skin Lesions Using Ensemb...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Rahman, Md. Mahfuzur Chandra Shill, Pintu Khulna University of Engineering and Technology Dept of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

This research proposes an advanced ensemble model based on Convolutional Neural Networks (CNNs) designed to enhance the segmentation and classification of skin lesions, a critical step in the early detection of melanoma and other skin cancers. The study explores ensemble strategies, including bagging, boosting, and stacking, to identify the most effective approach for combining multiple CNN models. A novel ensemble method incorporating pre-trained dual encoders is introduced to improve segmentation accuracy by extracting critical features and precisely delineating lesion boundaries. The dual encoders are configured with different encoder-decoder architectures, activation functions, and optimization algorithms to optimize feature extraction and segmentation performance. The suggested model was tested on the ISIC 2019 and HAM10000 datasets, attaining an accuracy rate of 95.3%, with sensitivity at 93.7%, specificity at 96.8%, and an F1-score of 94.5%. The findings demonstrate a significant improvement over current leading techniques, confirming the robustness and reliability of the proposed ensemble strategy. The superior performance of the model suggests its potential for integration into clinical practice, providing dermatologists with a valuable tool for early skin cancer detection and improving patient outcomes through timely intervention. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Street Lighting for Sustainable Urban Environments: Sensor-Based Automation and Efficiency

Smart Street Lighting for Sustainable Urban Environments: Se...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Afrin, Sumaiya Abdi, Khalid Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

In recent times, many streetlights have been wasting energy by staying on unnecessarily during the evenings and nights. This not only harms the environment but also costs a lot of money. The goal of Smart Street Lighting is to stop this wasteful energy use and make better use of resources. While other researchers have worked on this issue, we have a specific aim: to create a smart street lighting system that only turns on when it's needed and turns off when it's not. To achieve this, we're using laser sensors, ZigBee wireless technology, and a powerful microprocessor. Our system identifies when there are vehicles on the road using sensors. It then turns on the streetlights only where they are needed. This saves energy by not lighting empty areas. We can control and monitor each streetlight remotely through ZigBee technology, and everything is connected to a computer that makes the streetlights work smarter. Our sensors are good at detecting objects and people with an accuracy of 89.87 percent. This innovation can reduce electricity consumption by 38.1 percent and cut lighting maintenance costs by 48 percent. © 2024 IEEE.

关键词： Zigbee

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Face Generation and Recognition in Forensic Science 11

Face Generation and Recognition in Forensic Science

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ravi, Gayathri Joy, Heynes Jitto, Jeffin Joshy, Jocelyn Jose, Jisha Mary Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350379136

Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic Science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic Science is a promising step in st

关键词： Convolutional neural networks

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Masked Face Detection and Recognition Using a Unified Feature Extractor 5

Masked Face Detection and Recognition Using a Unified Featur...

引用

5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Iftikhar, Aroobah Shaukat, Arslan Tariq, Rimsha College of Eme Dept of Computer and Software Engineering Islamabad Pakistan Information Technology University Dept of Computer Engineering Lahore Pakistan

ISBN: (纸本)9798350394764

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area. © 2024 IEEE.

关键词： Face recognition

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Classification and Featurization of Motherboard Components Using YOLO Through Machine Learning

Classification and Featurization of Motherboard Components U...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 IEEE.

关键词： Robot vision

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Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Classification Models of Firewall Log Data Attacked by Data Poisoning and Evasion Attacks 9

Analysis of Classification Models of Firewall Log Data Attac...

引用

9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Permpoon, Nayotsakorn Thanamitsomboon, Titichaya Kittipeerachon, Kanticha Thai-Nichi Institute of Technology Dept of Computer Engineering Bangkok Thailand Computer Engineering Robotic and Technology Laboratory Thai-Nichi Institute of Technology Bangkok Thailand

ISBN: (纸本)9798350383010

This research explores vulnerabilities in machine learning classification models applied to firewall log data, specifically examining susceptibility to data poisoning and evasion attacks. The main objective is to evaluate the robustness of different algorithms when classifying firewall logs under adversarial conditions. The study assesses each model's accuracy in classifying log actions and introduces deliberate data poisoning and evasion attacks. The results highlight significant variations in model resilience. Notably, the Artificial Neural Network (ANN) demonstrates exceptional performance, maintaining a comparatively accuracy even after being subjected to data poisoning. In contrast, the rest model experiences a substantial drop in accuracy, indicating its vulnerability to such attacks. Additionally, evasion attacks impact model performance differently. Despite the evasion attack, the ANN consistently maintains the same accuracy, showcasing its steadfast nature. Conversely, the rest model witnesses a notable decrease in accuracy after evasion attack especially the decision tree model. These findings provide valuable insights into the comparative resilience of machine learning models against data poisoning and evasion attacks in the context of firewall log classification. The research underscores the importance of selecting robust algorithms to enhance the security of network systems amidst evolving cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Building Resilient CICD Pipelines: A DevOps Security-First Framework

Building Resilient CICD Pipelines: A DevOps Security-First F...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Ashtagi, Rashmi Belani, Chirag Bhalerao, Prasanna Bhalerao, Yash Chawle, Atharva Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331512965

Continuous Integration and Continuous Deployment are now integrated in automated systems that enhance code stability, and accelerate the deployment process as well as improving monitoring of the whole system in the software development field. This document deals with setting up of a strong CI/CD process involving AWS cloud services and DevOps. The infrastructure is built with the help of Terraform which ensures the scalability and repeatability applying the Infrastructure as Code (IaC) approach. A safe Virtual Private Cloud (VPC) is designed consisting of public and private subnets, load balancing for traffic distribution, and CloudFront CDN to improve geographical distribution. Jenkins is used in CI process to perform disk analysis, OWASP and Trivy vulnerability scanning, and making Docker images. The CD phase use ArgoCD for updates on Kubernetes and integrates Monitoring and observability using Prometheus and Grafana. Notifications are produced to JIRA and email communication for improvement. With the help of the offered cloud services and DevOps tool it is possible to consolidate the SDLC and bring effects in code safety, testing and utilization. This document also provides an overview of methods, instruments, and outcomes, demonstrating how the CI/CD automation influences the development and operation positively. © 2025 IEEE.

关键词： Observability

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Integrated Approach for Car Price Prediction Utilizing Random Forest Regression and Image Processing 5

Integrated Approach for Car Price Prediction Utilizing Rando...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Agarkar, Aarti Amod Kumbhare, Krrish Piyush Kshirsagar, Ashutosh Santosh Kokate, Shravan Shrikrushna Khandelwal, Saloni Vishwakarma Institute of Technology Dept of Computer Engineering Pune India

ISBN: (纸本)9798331518097

Since the automobile industry has had quick growth in the last few years, it should have the most accurate and efficient systems related to forecasting the prices of cars so that the business can both cope with the market and meet the demands of the customers. As a result, this paper proposes an integrated approach based on car price prediction. It combines Random Forest Regression with an image processing approach. The approach is built on a database that has in it the numeric features of the vehicle and the visual features of the vehicle. Random Forest Regression is made use of as it is a very strong algorithm that can easily manage complex, high-dimensional data and image processing is used to tune the visual interfacing of cars. This combination is likely to increase the accuracy of the forecast and, at the same time, solves the problems that are often encountered in the models. The experimental data demonstrate the fact that the suggested strategy outperforms the stand-alone methods in terms of both the mean squared error and R-squared metrics. The model of hybrid concept presented in this article is a very innovative and practical solution for automobile industries as it is a mixture of machine learning as well as computer vision technology to produce a trustworthy tool for pricing estimation © 2024 IEEE.

关键词： Automotive industry

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