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Brain tumor detection using texture based LBP feature on MRI images using feature selection technique

Brain tumor detection using texture based LBP feature on MRI...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Guleria, Vishal Verma, Aryan Dhenkawat, Rishabh Chaurasia, Uttkarsh Singh, Nagendra Pratap Department of Computer Science and Engineering National Institute of Technology H.P. Hamirpur India

ISBN: (纸本)9781032669663

This study analyzes the utility of texture-based local binary pattern features to spot brain cancers. Local Binary Pattern is a highly effective texture feature that uses a three-bythree sliding window to label the pixels of an image by thresholding the adjacent pixel. To prepossess images, they are first converted from RGB to Lab to narrow the color space, and then this image is operated with Principal component analysis. This processed image is used to extract the LBP features. Finally, Support vector machines are trained on the extracted features from the dataset and compared against the deep neural network results. We found that the deep neural network achieves 98.6% accuracy while the SVM approach achieves an accuracy of 98.4%. Finally, a comparison has been drawn between these approaches using various comparison metrics. © 2024 The Author(s).

关键词： Local binary pattern

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Unmanned Aerial Vehicles and Low-Flying Drones: A Review of Classification 2

Unmanned Aerial Vehicles and Low-Flying Drones: A Review of ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pagrut, Shrijeet Jawandhiya, Pradip Shri Sant Gajanan Maharaj College of Engineering Department of Computer Science and Engineering Maharashtra Shegaon India Pankaj Laddhad Institute of Technology and Management Studies Department of Computer Science and Engineering Maharashtra Buldhana India

ISBN: (纸本)9798331528713

Nowadays Research and Development of Novel and Efficient drones becomes the core part of strategic practices of every nation. There is a growing need of using a variety of Unmanned Aerial Vehicles with significant capabilities in Civilian, Military and Commercial Applications. UAVs flight patterns such as Vertical Takeoff Landing and Horizontal Takeoff Landing, its maximum altitude capacity, range are discussed. Designing of Low Flying drones are classified into Small, Micro and Nano-Pico-SmartDust(NPSD) categories. Use and Impact of different materials like Titanium alloy in design is provided. Additionally, current activities in the market related to small drones and future research direction in the field of utility and applications drones are discussed. © 2024 IEEE.

关键词： Drones

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A Framework for Quantum based Software Development Process

A Framework for Quantum based Software Development Process

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nagarajan, Sathishkumar Malarvel, Muthukumaran Thangakumar, J. Hindustan Institute Of Technology And Sciences Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Quantum software is essential for fully harnessing the capabilities of quantum computing systems. Consequently, it has been garnering growing interest in recent times. This document provides a precise definition of the concept of 'quantum software engineering' and presents a comprehensive framework for the life cycle of quantum software. Given the interdisciplinary nature of quantum computing, it is essential to establish a shared understanding of the process for developing and implementing a quantum software application. At present, there isn't a complete methodology or lifecycle that covers every significant stage that could come up along the process of development and execution. That's why we provide the quantum software lifecycle in this document. With this paradigm, practitioners, developers, and scholars have a clear path forward for quantum computing. The provision of a methodical development roadmap for quantum- principles-based software encourages creativity in this quickly expanding subject. © 2024 IEEE.

关键词： Error correction

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

引用

8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rakshitha, Sai Nithyashree, H.V. Rakshitha, S.L. Shravan, Motamarri Sai Uttej, K Sai Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content. © 2024 IEEE.

关键词： Convolutional neural networks

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Drowsy Driver Detection System using computer Vision and Transfer Learning for Preventing Road Accidents 3

Drowsy Driver Detection System using Computer Vision and Tra...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Bagal, Naisargi Dingreja, Kashish Atterkar, Atharva Bachhav, Prasad Sable, Nilesh P. Yenkikar, Anuradha Alkunte, Ritesh Vishwakarma Institute of Information Technology Department of Computer Science and Engineering - Artificial Intelligence Pune India

ISBN: (纸本)9798350354348

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection - which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research. © 2024 IEEE.

关键词： Face recognition

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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Offline Mathematical Expression Evaluator using Non-Linear SVM

Offline Mathematical Expression Evaluator using Non-Linear S...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gogula, Susmitha Valli Reddy Pittala, Venkata Dheeraj Chirala, Ashrith Mantena, Krishnam Raju Pillai, Ashwin Raj Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350308167

The Offline Handwritten Expression Evaluator [OHMEE] is a Machine Learning Model used to recognize and solve the Offline Handwritten Mathematical Expressions [OHME]. The term Offline refers to documents, images, papers and transcripts. We perform Image Pre-processing steps like Image Segmentation, Character Segmentation, Normalization and Binarization to covert the Image data to appropriate training data for the model using OpenCV Library. The character images in the [OHME] are classified and recognized using Non-Linear Support Vector Machines [SVM] and they are converted to appropriate Strings. These Strings are solved using user-defined mathematical methods. These modules are used to evaluate these String expressions and the appropriate results are generated and plotted. © 2023 IEEE.

关键词： Binarization Character Segmentation Image Segmentation Normalization OHME OHMEE OpenCV SVM

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A Fuzzy Approach Towards the Measurement of Subject-Specific Cognitive Load during Mental Task Using Hemodynamic Response 4

A Fuzzy Approach Towards the Measurement of Subject-Specific...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Karmakar, Subashis Pal, Tandra Koley, Chiranjib National Institute of Technology Durgapur Department of Computer Science and Engineering West Bengal India National Institute of Technology Durgapur Department of Electrical Engineering West Bengal India

ISBN: (纸本)9798350387063

Every individual exhibits a different cognitive load for a particular task of the same level of difficulty. Measuring the cognitive load of an individual can help to develop an effective learning environment, design personalized learning materials and diagnose specific learning impairments. The primary aim of this study is to measure the subject-specific utilization of cognitive load during the mental arithmetic task using functional near-infrared spectroscopy (fNIRS) signals. For this purpose, we have collected fNIRS signals from fourteen healthy subjects while performing mental arithmetic tasks of three difficulty levels: low, medium and high. Statistical features are extracted from the collected fNIRS signal. After that, a fuzzy classifier has been developed where the fuzzy membership functions for each of the input features are generated using fuzzy c-means clustering. The experimental results show that the subjects undergoing the experiment utilize different levels of cognitive loads for a given task with the same level of difficulty. The proposed model achieves 89.92 % classification accuracy. © 2024 IEEE.

关键词： Near infrared spectroscopy

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Data Analysis on Determining False Movie Ratings

Data Analysis on Determining False Movie Ratings

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Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Sahni, Ridhika Arockiasamy, Karmel School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819955206

Online movie ratings have evolved into a serious business. Hollywood generates around $10 billion in box office revenue in the United States each year, and online ratings aggregators may have an increasing influence over where that money goes. A single film critic can no longer make or break a film, but perhaps thousands of critics, both professional and amateur, can how well can these platforms be trusted especially if they are showing the ratings and making money by selling the tickets? Do they have a bias by rating movies higher than it should be? The objective of the analysis is to provide brief research of one such online movie-ticket selling company, Fandango. The data collected from Fandango are analyzed and compared with the data collected from sites like Rotten Tomatoes, IMDB, and Metacritic. With the help of visualization, fraud and risk can be reduced offered by such online sites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Visualization

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Deep Learning-Based Intelligent GUI Tool For Skin Disease Diagnosis System 5th

Deep Learning-Based Intelligent GUI Tool For Skin Disease Di...

引用

5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Karmakar, Mithun Mondal, Subhash Nag, Amitava Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Assam Kokrajhar783370 India

ISBN: (纸本)9783031488757

Skin diseases are generally normal around the globe, as people get skin diseases because of inheritance and natural elements. Dermatologists rely heavily on visual examinations to diagnose skin diseases. However, this method can be inaccurate and time-consuming. The development of the technique in deep learning (DL) and the availability of GPUs can expand and improve the quality of computer-aided disease diagnostics systems. This paper proposes a DL-based smartphone application to aid a dermatologist in diagnosing skin disease in real-time. In the proposed work, we first used fine-tuned DenseNet201 for feature extraction and SVM to accurately classify normal and abnormal skin. A Deep Ensemble CNN (DECNN) framework with DenseNet-201, Resnet50, and MobileNet is further used for skin disease categorization if abnormal skin is detected. The experimental outcome of this study demonstrates that the framework can achieve an 84% accuracy in skin disease classification and outperform existing state-of-the-art works. The simple implementation and acceptable accuracy of the proposed method can be helpful for dermatologists in the diagnosis of skin disease. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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