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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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Parameterized Clustering Cleaning Approach for High-Dimensional Datasets with Class Overlap and Imbalance

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SN computer science 2023年第5期4卷 464页

作者： Goel, Navansh Singaravelu, Mohanapriya Gupta, Shivani Namana, Sriram Singh, Richa Kumar, Ranjeet School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India

Software Defect Prediction (SDP) uses machine learning algorithms to detect faulty and defective modules inside software projects. Like any machine learning model, the model’s performance depends on the training data. The ongoing research in the field explores various options for improving the quality, but class overlap and imbalance hinder the models’ effectiveness. Unwarranted instances grouped with relevant data act as noise, hindering the classification process. The removal of such instances can lead to improvement in the performance of standard machine learning classification models. This paper proposes a novel parameterized clustering approach, ‘CLARANSCA‘, for cleaning high dimensional datasets suffering from class overlap and imbalance. The parameterized clustering approach ‘CLARANS’ serves as the base for the proposed method. A multi-fold approach to calculating the evaluation metrics is used, ensuring consistency in results. The aggregate results show significant improvements, especially for Recall, AUC, and G-mean values. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC CLARANS CLARANSCA Class imbalance Class overlap G-mean Parameterized clustering Recall Software defect prediction

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Implementation of Hector SLAM Algorithm for Mapping Indoor Environments with Obstacles 3

Implementation of Hector SLAM Algorithm for Mapping Indoor E...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Amreen Chowdhury, Raian Haider Brac University Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Mechanical Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

Autonomous navigation in obstacle-rich indoor environments is crucial for both industrial and domestic robotic applications. A central aspect of this process is Simultaneous Localization and Mapping (SLAM). This paper presents a comprehensive methodology for implementing Hector SLAM, a widely used SLAM algorithm that operates without relying on external odometry and pose data from wheel encoders or Inertial Measurement Unit (IMU) sensors. The experimental setup involves a custom-built two-wheeled mobile robotic platform, equipped with essential components including a 2D Light Detection and Ranging (LiDAR) sensor, wheel encoders, IMU, motor drivers, an Arduino UNO, and a Raspberry Pi 4B, with Robot Operating System (ROS) serving as the primary software framework. The robot navigates within a predefined rectangular indoor environment, where real-time mapping results are captured at different time intervals to assess mapping accuracy. Additionally, a randomly placed object is introduced to test the obstacle detection capability during operation. Results indicate that Hector SLAM demonstrates high accuracy, achieving precision within a few centimeters, particularly in environments with distinct structural features. © 2024 IEEE.

关键词： Robot Operating System

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