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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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Analysis of Ensemble Machine Learning Models for Fraud Detection

Analysis of Ensemble Machine Learning Models for Fraud Detec...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chaurasia, Siddharth Kesharwani, Satyam Sharma, Shanu Sharma, Swati Chugh, Bharti Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

Recent developments in electronic payment and e-commerce have led to a rise in digital fraud cases, including credit card fraud. Within the financial services industry, identifying credit card fraud is still a difficult task that can have significant effects on consumers' and financial institutions' reputations. Therefore, it is crucial to implement systems capable of detecting credit card fraud. This research utilizes the European Cardholder Data Collection to assess the efficacy of ensemble machine learning techniques in identifying credit card fraud. The European cardholder data set is subjected to a rigorous assessment and evaluation of series of machine learning models, such as Gradient Boosting, Random Forest and CAT Boost, to detect fraudulent transactions. It has been noted that the dataset is unbalanced, which may indicate that the models' performance is not very ideal. The study suggests using data sampling strategies to achieve a balanced distribution of data across various algorithms that yield optimal outcomes. The main goal is to identify the model that performs best for machine learning classification problems by comparing the performance of different models. The results of this study will support further initiatives to improve credit card transaction security and lower financial losses brought on by fraud. © 2024 IEEE.

关键词： Losses

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PassChain: A Blockchain-Based Decentralized Password Manager 3

PassChain: A Blockchain-Based Decentralized Password Manager

引用

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

作者： Anik, Nur Adnan Chowdhury Hossain, Md. Sabbir Rahman, Habibur Rumpa, Ummay Khadiza Datta, Joy Nawaz, Rifat Karmoker, Sagar Khan, Md. Adib Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

A Nobel approach to the password management system is introduced in this paper, which is through a decentralized system named blockchain. Our goal is to secure people's passwords by providing them with a secure and distributed password management system named "PassChain."In today's digital world, a secure password management system is crucial, but it is still challenging. The traditional system lacks security and is a centralized system, so trust issues also arise. Our proposed blockchain-based solution offers full control over users' passwords. Because of the decentralized method, they are not stored on a single server. Decentralized servers are also more secure than databases. This approach will ensure user security and privacy and will also reduce dependency on third-party services that are prone to vulnerabilities. © 2024 IEEE.

关键词： Decentralized systems

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GNN-based Disease Detection and Classification in Cassava Leaf 5

GNN-based Disease Detection and Classification in Cassava Le...

引用

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

作者： Meenalochini, M. Amalroshini, A. Nandhini, K L Megha Murugan, C R Sogil Kongu Engineering College Dept. of Computer Science and Design Erode India

ISBN: (纸本)9798331518097

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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ImportChainBD: Blockchain Solutions for Transparent and Accountable Imported Cosmetic Products 3

ImportChainBD: Blockchain Solutions for Transparent and Acco...

引用

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

作者： Khan, Md. Adib Karmoker, Sagar Rumpa, Ummay Khadiza Rahman, Habibur Hossain, Md. Sabbir Anik, Nur Adnan Chowdhury Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

To improve the supply-chain management system of Bangladesh for imported cosmetic products this paper proposes a blockchain-based solution. One of the major goals of this project is to increase the tax compliance of the government by stopping counterfeit and illegally imported cosmetic products. Which will enhance the trust of consumers and the brand's reputation. Using this system is quite easy and convenient, as All the information will be stored in the blockchain, so it will become a secure and immutable product tracking system for importers, distributors, and retailers to maintain their ledgers just by printing and scanning a QR code through an IoT device, and consumers can check the authenticity of these cosmetic products just by scanning them. If the product is verified to be suspicious, The Mobile Court Management System has the power to check its origin. Finally, we can say that it will avoid the sale of counterfeit items in the market and increase government revenue on tax. © 2024 IEEE.

关键词： Cosmetics

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Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

Estimating Post-Synaptic Effects for Online Training of Feed...

引用

2024 International Conference on Neuromorphic Systems, ICONS 2024

作者： Summe, Thomas M. Schaefer, Clemens JS Joshi, Siddharth University of Notre Dame Dept. of Computer Science and Engineering United States

ISBN: (纸本)9798350368659

Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements restrict scalability. This is typically addressed through approximations that limit learning in deep models. In this study, we propose Online Training with Postsynaptic Estimates (OTPE) for training feed-forward SNNs, which approximates Real-Time Recurrent Learning (RTRL) by incorporating temporal dynamics not captured by current approximations, such as Online Training Through Time (OTTT) and Online Spatio-Temporal Learning (OSTL). We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity. This approximation incurs minimal overhead in the time and space complexity compared to similar algorithms, and the calculation of temporal effects remains local to each layer. We characterize the learning performance of our proposed algorithms on multiple SNN model configurations for rate-based and time-based encoding. OTPE exhibits the highest directional alignment to exact gradients, calculated with backpropagation through time (BPTT), in deep networks and, on time-based encoding, outperforms other approximate methods. We also observe sizeable gains in average performance over similar algorithms in offline training of Spiking Heidelberg Digits with equivalent hyper-parameters (OTTT/OSTL - 70.5%;OTPE - 75.2%;BPTT - 78.1%). © 2024 IEEE.

关键词： Encoding (symbols)

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Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson’s Disease Through Drawn Image Patterns 2

Exploring the Efficacy of Modified Transfer Learning in Iden...

引用

2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Daiyan, Nabil Haque, Md Rakibul Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331508227

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes. ©2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Indoor Navigation Efficiency with WiFi Signal Mapping 15

Enhancing Indoor Navigation Efficiency with WiFi Signal Mapp...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, N. Sai Pranavdhara Narayanan, Gayathri Singh, Aman Kumar Cheeramvelil, Geo Jolly Sethulekshmi, U.S. Kumar, R. Adithya Tripathi, Akshat Amrita Vishwa Vidyapeetham Dept. Of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

In the contemporary digital landscape, efficient indoor navigation has become imperative for large, intricate spaces like shopping malls, airports, hospitals, and universities. Traditional GPS-based methods often prove inadequate in these indoor settings due to poor reception and a lack of fine-grained location accuracy. To address these challenges, the work employs WiFi signals, pervasive in modern buildings, and harnesses their ubiquity and reliability for sophisticated indoor navigation. The team developed a Python package for collecting WiFi signal details, implementing robust preprocessing to filter out private networks, and storing the data in a database. The core of the system lies in a machine learning ensemble consisting of Gaussian Bayes classifier, decision tree, and K-nearest neighbors (KNN). This ensemble utilizes WiFi signal strengths to predict users' real-time positions within large indoor spaces. The system aims to enhance user experiences and simplify wayfinding in complex indoor environments. © 2024 IEEE.

关键词： Decision Tree Gaussian Bayes Classifier Indoor Navigation K-nearest Neighbors Machine Learning Ensemble WiFi Signals

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Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning 6

Key Vertex Pairs Extraction for Graph Classification Based o...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Huang, Jianming Kasai, Hiroyuki WASEDA University Dept. of Computer Science and Communication Engineering Tokyo Japan

ISBN: (纸本)9798350344349

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets. © 2024 IEEE.

关键词： Graph neural networks

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'RNN-Driven Prognosis of Heart Disease Based on Health Parameters' 2

'RNN-Driven Prognosis of Heart Disease Based on Health Param...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Singh, Inderjeet Singh, Ajay Pal Kumar, Alok Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification. © 2024 IEEE.

关键词： Recurrent neural networks

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