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VisualBlend - A Mixed Approach on Forensic Image Generation and Recognition 6

VisualBlend - A Mixed Approach on Forensic Image Generation ...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Unnikrishna, Pillai Midhun Pulkuzhiyil, Vinayak Krishna, Jyothika Wilson, Naveen Kumar, Veena A. Saintgits College Of Engineering Dept. of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350381689

An extensive use case of Generative Adversarial Networks (GANs) that not only combines artistic creativity with technological innovation but also significantly improves identification precision, filling in critical gaps in the smooth integration of textual descriptions with visual representations. The motivation is rooted on the difficulties that currently exist in accurately identifying human face sketches by directly transforming them into images, an area where prior approaches were inadequate. Our special dual-function technology is designed to get beyond these obstacles and provides a fresh approach for forensic investigators, artists, and sculptors. It has the ability to convert text to image as well as sketch to image. This easy transformation of textual and drawing data represents an integrated combination of creativity and technology that is set to have an impact on a number of disciplines, such as suspect identification and crime scene reconstruction. © 2024 IEEE.

关键词： Generative adversarial networks

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Controlling the Accuracy and Efficiency of Collision Detection in 2d Games using Hitboxes 1

Controlling the Accuracy and Efficiency of Collision Detecti...

引用

1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sajo, Paul Gokulapriya, R. Mithun, B.N. University Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350335774

Collision detection is a process in game development that involves checking if two or more objects have intersected or collided with each other. It is a fundamental aspect of game mechanics that cannot be overlooked. Games invloves assets/sprites, which tend to be drawn digitally with the help of a computer program. This paper discusses controlling and detecting collisions in games that make use of PNG images as game assets. The conventional way to detect collision in a game is to check if the object is within the bounding box of another object or asset. However, such a method lacks realism and doesn't work well with much complex shapes as the game might register a hit when another object collides with the transparent part of the object being checked for collision. In order to overcome these limitations, the proposed algorithm divides the entire image into smaller rectangles and stores its coordinates in an array. The array is then pruned by removing coordinates with no translucent or opaque elements. Collision is detected by checking if any of the points of the collision object is inside the image array. © 2023 IEEE.

关键词： Efficiency

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Hybrid Model of Fireworks Algorithm and Deep Learning for Drought Prediction using Satellite data 9

Hybrid Model of Fireworks Algorithm and Deep Learning for Dr...

引用

9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Paudel, Ankit Bhavana, B.N. Anchalia, Aniketh Kakati, Anirudh Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

Many meta heuristic optimization techniques have been developed to address exceedingly difficult optimization issues in numerous disciplines. Fireworks Algorithm (FWA) was proposed as a Swarm Intelligence Optimization technique, mimicking fire work explosions. This paper proposes using FWA in the field of Agricultural Drought Assessment. Droughts are complex natural disasters that need efficient mitigation techniques. Latest research in existing drought assessment tools suggest utilizing Convolutional Neural Network (CNN) models, often hybridized with other optimization strategies. Considering the four vegetation indices Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI) and Soil Adjusted Vegetation Index (SAVI), a hybridized model that integrates FWA into CNN is proposed. This enhanced model can give up to 94% accuracy with very minimal loss for prediction of future drought. This model improves efficiency significantly. © 2023 IEEE.

关键词： Drought

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A Study of Various Optimization Algorithms and Entropy Measures for Image Segmentation 18

A Study of Various Optimization Algorithms and Entropy Measu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Gonthina, Nagamani Prasad, L. V. Narasimha Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Previous studies have addressed various techniques for image segmentation, like clustering, thresholding, watershed, neural networks, etc. However, a segmentation technique alone cannot achieve better-segmented results. Much research is being carried out by combining the segmentation technique with either an optimization algorithm or entropy measures to enhance the efficiency of segmented results in the segmentation process. So, there is a need to study various segmentation techniques combined with optimization algorithms or entropy measures. Recent studies focused only on segmentation techniques, entropy measures, or optimization algorithms rather than on these combinations. This paper presents various works based on optimization techniques and entropy measures combined with clustering and thresholding techniques of image segmentation. From the study it is observed that most of the work is carried out in combining thresholding-based image segmentation techniques with optimization algorithms and entropy measures. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Entropy measures Image segmentation Optimization algorithms Segmentation techniques

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Automated characterization of coal image samples using pretrained deep convolutional neural networks and transfer learning: A comparative Study 3

Automated characterization of coal image samples using pretr...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Samanta, Kalyan Firdaus, Shama Mohapatra, Subrajeet Bit Mesra Dept. of Computer Science & Engg Jharkhand Ranchi India

ISBN: (纸本)9798331528201

Quality of coal are usually determined by manually examining samples under an optical microscope, and then conducting complex chemical tests for further grading. However, such methods are expensive, time-consuming, and dependent on the competency and expertise of the concerned petrologist. Preliminary quality assessment of coal can be performed with the help of image analysis and artificial intelligence-based techniques identifying the presence of coal maceral constituents. The usage of image processing-based methods for intelligent coal gradation is simpler, faster, and less expensive, and it eliminates the subjectivity of petrologists. However, the performance of traditional image processing-based coal gradation methods, on the other hand, is dependent on the accuracy of segmentation and feature extraction methods. Deep learning models, which do not require image segmentation or manual feature engineering, are an alternative solution to the above classification problem. Pretrained deep neural networks eliminates the time-consuming process of determining the best combination of layer architecture, which would be required by standard Convolutional Neural Network models. As a result, the proposed research presents an automated coal gradation system that utilizes pretrained deep convolutional neural networks to classify coal microscopic images. Six different types of pretrained neural networks viz. VGG19, VGG16, GoogLeNet, AlexNet, ResNet50, ResNet18 are simulated over 1700 Australian coal optical microscopic images of three grades. For the considered coal data set, all of the pretrained models achieve classification accuracy of over 80%. © 2024 IEEE.

关键词： Convolutional neural networks

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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An Advanced computer Vision Model for Aiding Automobile in Traffic Sign Classification 1

An Advanced Computer Vision Model for Aiding Automobile in T...

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1st IEEE International Conference on Computing and data science, ICCDS 2024

作者： Vignesh, Veera Satya Pavan Prajith, Kukkadapu Sai Gokul, Goddati Parthav Adarsh, Cherukuri Rajareddy, Goluguri N. V. GITAM Deemed to be University Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350365337

Accurate traffic sign recognition is crucial for autonomous vehicles. This paper proposes a novel deep learning approach for sign classification that addresses limitations in training data. Our method employs a three-step process: strategic data augmentation with rotation limitations for realistic variations, feature extraction using parallel atrous convolution layers with varying dilation rates to capture multi-scale information, and robust feature map generation through output concatenation. This approach achieves a remarkable accuracy of 94.65%, surpassing the performance of established pre-trained models like VGG-16, ResNet-50, and AlexNet. This research contributes a significant advancement in real-world traffic sign classification in autonomous vehicles. © 2024 IEEE.

关键词： computer vision

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

引用

2024 IEEE International Conference on Future Machine Learning and data science, FMLDS 2024

作者： Ehsanu Hoque, Md. Habib Mahbub, Mrittika Bin Md Tomari, Mohd Razali Bashar, Rezaul Rashed, Md. Golam Das, Dipankar University of Rajshahi Dept. of Information and Communication Engineering Bangladesh Pundra University of Science and Technology Dept. of Computer Science and Engineering Bangladesh Dept. of Electronic Engineering Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (纸本)9798350391213

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification. © 2024 IEEE.

关键词： Traffic signs

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(In)Correct Smart Contract Specifications 6

(In)Correct Smart Contract Specifications

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Sorensen, Derek University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom

ISBN: (纸本)9798350316742

Poorly specified smart contracts can be vulnerable to attacks on faulty design. Formal methods are currently unable to address these vulnerabilities because they are not vulnerabilities of incorrect code, but of incorrect specification. We are thus in need of a paradigm shift in how we specify and verify smart contracts which allows for a rigorous and accurate notion of a contract specification's correctness. We submit that correctness includes appropriate cryptoeconomic behaviors, which are generally out of scope of a contract's specification. We advocate for an approach to contract specification consisting of contract axiomatization and metaspecification, and illustrate with an example. © 2024 IEEE.

关键词： Smart contract

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Auction based Resource Allocation in Cloud Computing using Blockchain Techniques 14

Auction based Resource Allocation in Cloud Computing using B...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kamaleshwar, T. Jayaraj, R. Vijayaraj, N. Sundaranarayana, D. Dr.Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Computer Science & Engineering Chennai India SRM Institute of Science and technology Dept. of Data Science and Business systems Kattankulathur Chennai India

As the spine innovation of decentralized cryptocurrencies, blockchain has additionally proclaimed numerous applications in different fields, for example, resource allocation in cloud computing, Internet of Things (IoT) and resource offloading. The database state is decentrally kept up, and any part hub in the overlay blockchain organize is allowed to partake in the state support without character confirmation. The exchanges among part hubs are recorded in cryptographic hash-connected information structures known as squares. A progression of affirmed blocksis orchestrated in ordered request to shape a consecutive chain, henceforth named blockchain. All part hubs in the system are required to pursue consensus protocol on the value-based information, cryptographic hashes and computerized marks put away in the block to ensure trustworthiness of the blockchain. © Grenze Scientific Society, 2023.

关键词： Blockchain

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