This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-...
This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-support to facilitate the transition process. The system will consider people's interests and consider different languages and cultures and budgets of people. This is useful in war or disaster environment.
Soil monitoring is important aspect of agriculture to grow good quality food. It is crucial aspect for famers and the environmental scientists as well. If soil is monitored properly then chances of good quality crop i...
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ISBN:
(数字)9798350394412
ISBN:
(纸本)9798350394429
Soil monitoring is important aspect of agriculture to grow good quality food. It is crucial aspect for famers and the environmental scientists as well. If soil is monitored properly then chances of good quality crop increased which reduces farmer’s tension and increase their income. This research paper is a step towards applying machine learning algorithms for soil monitoring. Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor and Stacking Regressor are such algorithms. Comparison analysis of these techniques are also done in this paper. This work will be helpful for farmers and land managers to accurately predict about the soil fertility which will further help them to take decision which crop should be grow as per the soil condition.
The minimum completion (fill-in) problem is defined as follows: Given a graph family F (more generally, a property Π) and a graph G, the completion problem asks for the minimum number of non-edges needed to be added ...
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Dealing with missing data is necessary to develop a solid predictive medical research model. This work presents a hybrid dynamic imputation method devoted to addressing the challenge of missing data, specifically focu...
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ISBN:
(数字)9798350391183
ISBN:
(纸本)9798350391190
Dealing with missing data is necessary to develop a solid predictive medical research model. This work presents a hybrid dynamic imputation method devoted to addressing the challenge of missing data, specifically focusing on medical datasets. In this work, we investigate the incompleteness of the dataset using a sophisticated measure called the Incompleteness Factor. This metric is used to guide a hybrid dynamic approach. A novel Transformer-based Self-Supervised imputation method is employed to learn complex data dependencies to impute missing data. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and preprocess the data to enhance the credibility and clinical relevance of the experimental results.
In this paper, we propose an improved way low light image-enhancing model for Smartphone devices by training a U-Net based fully convolutional neural network (CNN) with raw sensor images. We collected a new dataset ca...
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Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collecti...
This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conven...
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ISBN:
(数字)9798331509675
ISBN:
(纸本)9798331509682
This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques.
This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguar...
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This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%).
Land cover classification research contributes significantly to sustainable development by enhancing the baseline for urban planning, natural resource management, and environmental monitoring at the local, regional, n...
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For many developing nations, agriculture is the fundamental source of economic engine. Without a significant increase in food production, the rise in global population during the 21st century would not have been conce...
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