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Book Genre Classification System Through Supervised Learning Technique 1

3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Rout, Ajit Kumar Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Odisha Gunupur India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

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computer Systems science & engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

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Influence of Opinion Formation on Polarization in Social Networks 14

Influence of Opinion Formation on Polarization in Social Net...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Das, Susmita Dutta, Sangita Chakraborty, Susanta Biswas, Samit Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Shibpur711103 India

ISBN: (纸本)9798350335095

Online social networks have introduced diverse discussions regarding different topics. At the same time, these platforms have also garnered the idea of shaping opinions. The increase in user engagement in case of arguments has led to the issue of polarization in online social media platforms. In this paper, we have studied the influence of opinion change on homophily in social networks and the variation in polarization. Our own new method has been proposed for analysing the modification of user opinions dynamically in different timestamps. The probability of information sharing varies depending on the opinion similarity and homophily in social networks alters accordingly. Our approach portrays the opinion modification dynamics for different network features using complex networks concepts. We have observed that on social interaction networks like Twitter, opinion, information sharing and homophily perform a significant role in the determination of online polarization. Our approach has been compared with an established approach over external datasets and the results obtained are promising. © 2023 IEEE.

关键词： Polarization

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Security model to identify block withholding attack in blockchain

Security model to identify block withholding attack in block...

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

作者： Keshta, Ismail Reegu, Faheem Ahmad Ahmad, Adeel Saxena, Archana Chandan, Radha Raman Mahalakshmi, V. Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Applications Invertis University UP Bareilly India Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology College Jhalwa Prayagraj India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781032669663

Block interception attack, also known as block withholding attack, is an attack method in the blockchain. The attacker penetrates the target mining pool for passive mining to destroy the target mining pool. This paper briefly introduces the mining mechanism and the working principle of block interception attack, summarizes several models of block interception attack, studies the attack methods and benefits of the existing block interception attack models, and analyses their attack effects. A block interception attack model is constructed to increase the rate of return while increasing the rate of return. The completed model is verified through simulated mining experiments. The typical and made models are compared and analyzed based on the rate of return. Finally, the applicable environments of different attack models are given based on the experimental results. © 2024 The Author(s).

关键词： Blockchain

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Comparison of Deep Learning Models for Monkeypox Disease Detection 1

Comparison of Deep Learning Models for Monkeypox Disease Det...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Kottath, Anu V Malarvel, Muthukumaran Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350342796

The Monkeypox virus is a species of double stranded DNA virus that causes Monkeypox disease in humans and other mammals. The emergence of the Monkeypox virus poses a fresh threat to global health at a time, when the world continues to try to heal from the damage brought in by the widespread of COVID-19. Although the Monkeypox diseases are not that harmful or contagious as COVID-19, many new cases are reported from various nations across the world. As a result, it won't be shocking if there is another pandemic, because of insufficient precautions taken. Due to its resemblance to both chickenpox and measles, Monkeypox can be challenging to clinically diagnose in its initial stages. computer aided Monkeypox lesion detection may be useful for surveillance and the quick identification of suspected cases in circumstances where there is a difficulty in performing confirmatory Polymerase Chain Reaction (PCR) tests. Deep learning (DL) methods has shown enormous promise for image-based diagnosis including lung cancer detection, skin diseases detection, COVID-19 etc. Hence, the same application may be used to detect the Monkeypox diseases too. This paper evaluates four pre trained deep learning models (Squeeznet, DenseNet201, EfficientNetB0, ResNet50v2) for Monkeypox disease detection and six established metrics (Precision, Recall, Sensitivity, Specificity, F1-score and Accuracy) are used to analyze the results. This paper helps to identify the best pretrained model for Monkeypox diseases detection. A publicly available dataset is used in this work. © 2023 IEEE.

关键词： Chickenpox Deep Learning Machine learning Monkeypox Polymerase Chain Reaction (PCR)

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ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba 16

ColorMamba: Towards High-quality NIR-to-RGB Spectral Transla...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Zhai, Huiyu Jin, Guang Yang, Xingxing Kang, Guosheng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science Hong Kong Baptist University Hong Kong

Translating NIR to the visible spectrum is challenging due to cross-domain complexities. Current models struggle to balance a broad receptive field with computational efficiency, limiting practical use. Although the Selective Structured State Space Model, especially the improved version, Mamba, excels in generative tasks by capturing long-range dependencies with linear complexity, its default approach of converting 2D images into 1D sequences neglects local context. In this work, we propose a simple but effective backbone, dubbed ColorMamba, which first introduces Mamba into spectral translation tasks. To explore global long-range dependencies and local context for efficient spectral translation, we introduce learnable padding tokens to enhance the distinction of image boundaries and prevent potential confusion within the sequence model. Furthermore, local convolutional enhancement and agent attention are designed to improve the vanilla Mamba. Moreover, we exploit the HSV color to provide multi-scale guidance in the reconstruction process for more accurate spectral translation. Extensive experiments show that our ColorMamba achieves a 1.02 improvement in terms of PSNR compared with the state-of-the-art method. Our code is available at https://***/AlexYangxx/ColorMamba/. © 2024 H. Zhai1, G. Jin1, X. Yang2 & G. Kang1.

关键词： RGB color model

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An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder 1

An Optimized Hybrid Soil Texture Classification Model using ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Prabavathi, R. Chelliah, Balika J Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

With effective soil management, which lowers the costs associated with soil erosion, improves water utilisation, and other factors, soil data analysis can offer vital information to raise agricultural yield and nutrient utilisation efficiency. An unsupervised method of data analysis called clustering finds similar groups of objects by analysing attribute values. The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field. In light of this, the study presents a novel clustering and classification-based hybrid soil texture classification model (HSTC). Soil data will be clustered and categorised into multiple classes by the proposed (HSTC) model. Hydrological soil groupings (HSG) of various kinds are used as classes to accomplish this. Based on characteristics like the proportion of clay, silt, and sand, the HGS can be categorised. The density-based clustering (DBSCAN) algorithm is used to group the soil texture data. The stacked sparse auto encoder (SSAE) model is used to categorise the clustered data. In addition, the parameter values of the SSAE technique are determined using a new barnacle mating optimizer (BMO). The HSTC model has achieved the best results, with accuracy of 95.66%. The experimental validation of the proposed HSTC model on soil texture dataset revealed that the suggested model outperformed previous techniques. © 2023 IEEE.

关键词： Deep learning

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A Delaunay triangulation-based improved energy-aware clustering algorithm for WSNs

A Delaunay triangulation-based improved energy-aware cluster...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sivadasan, Sreeram Nagarajan, Govindan Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

Energy efficiency is an important constraint in Wireless Sensor Networks (WSNs). In this paper, we propose a Delaunay triangulation-based approach for the efficient participation of nodes. Following a Delaunay triangulation-based mobile node placement, we introduce an Equi-Quadrant Division-based Clustering for shortest path routing to the base station (BS). The proposed method not only allows us to address issues like coverage holes and sensing range identification of a node in a random distribution but also improves node participation. Our experiments on a simulated environment show that the proposed simple algorithm is capable of giving competitive results compared to traditional clustering algorithms like FL-LEACH [11] and GA-APTEEN [12]. © 2023 IEEE.

关键词： Clustering Coverage hole Delaunay triangulation Wireless sensor network

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Fish Species Classifier for Allergic People using CNN Algorithm 7

Fish Species Classifier for Allergic People using CNN Algori...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Reddy, Puchalapalli Surya Vradhan Krishna, Mannepalli Vamsi Aishwarya, R. Yogitha, R. Kumar, K. Ashok Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9781665464086

Some people may be allergic to a variety of crustaceans, including prawns, crab, and lobster, or they may be sensitive to some types of fish. Cross-reactivity is the term for this kind of condition. This approach is useful because it is challenging to predict which fish will cause an allergic reaction in you. It is challenging to determine which fish may cause an allergic reaction. Deep learning has been a huge help in recent years in solving numerous issues. Real-time datasets are not implemented in the current systems, and they additionally the current systems are not very accurate. The proposed system utilizes CNN algorithm for detecting and classifying fish that cause allergies. The implemented system aims to help people with seafood allergies, clinical immunology, or allergies. specialist in image analysis of caught fish, detection, and species classification of fish from images. © 2023 IEEE.

关键词： Deep learning

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Bengali License Plate Recognition: Unveiling Clarity with CNN and GFP-GAN 26

Bengali License Plate Recognition: Unveiling Clarity with CN...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Afrin, Noushin Hasan, Md Mahamudul Fazlay Elahi Safin, Mohammed Amin, Khondakar Rifat Zahidul Haque, Md Ahmed, Farzad Rouf Shawon, Md. Tanvir Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Automated License Plate Recognition(ALPR) is a system that automatically reads and extracts data from vehicle license plates using image processing and computer vision techniques. The Goal of LPR is to identify and read the license plate number accurately and quickly, even under challenging, conditions such as poor lighting, angled or obscured plates, and different plate fonts and layouts. The proposed method consists of processing the Bengali low-resolution blurred license plates and identifying the plate's characters. The processes include image restoration using GFPGAN, Maximizing contrast, Morphological image processing like dilation, feature extraction and Using Convolutional Neural Networks (CNN), character segmentation and recognition are accomplished. A dataset of 1292 images of Bengali digits and characters was prepared for this project. © 2023 IEEE.

关键词： ALPR CNN GFPGAN ResNet-50

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