Traditional farming procedures are time-consuming and expensive asbased on manual labor. Farmers haveno proper knowledge to select which crop issuitable to grow according to the environmental factors and soilcharact...
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Traditional farming procedures are time-consuming and expensive asbased on manual labor. Farmers haveno proper knowledge to select which crop issuitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologiessuch as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factorssuch assoil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. Thissystem proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.
Image semantic segmentation is an important branch of computer vision of a wide variety of practical applicationssuch as medical image analysis,autonomous driving,virtual or augmented reality,*** recent years,due to ...
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Image semantic segmentation is an important branch of computer vision of a wide variety of practical applicationssuch as medical image analysis,autonomous driving,virtual or augmented reality,*** recent years,due to the remarkable performance of transformer and multilayer perceptron(MLP)in computer vision,which is equivalent to convolutional neural network(CNN),there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning *** survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic ***,the commonly used image segmentation datasets are ***,extensive pioneering works are deeply studied from multiple perspectives(e.g.,network structures,feature fusion methods,attention mechanisms),and are divided into four categories according to different network architectures:CNN-based architectures,transformer-based architectures,MLP-based architectures,and ***,this paper presentssome common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used ***,possible future research directions and challenges are discussed for the reference of other researchers.
Playing games is an important way to promote the integration, inclusion, and socialization of participants. This is especially the case of persons with disabilities, such as visually impaired people. Unfortunately, ve...
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Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation signi...
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Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental resultsshow the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.
This paper presentssuperCyberKids, a project funded by the European Union that focuses on integrating digital game-basedlearning into cybersecurity education for children. The project aims to equip educators with ef...
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This paper introducescomputer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves huma...
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Air temperature (Ta) measurements are crucial for characterizing phenomena like the urban heat island (UHI), which can create critical conditions in cities during summer. Thisstudy aims to develop a machine learning-...
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Air temperature (Ta) measurements are crucial for characterizing phenomena like the urban heat island (UHI), which can create critical conditions in cities during summer. Thisstudy aims to develop a machine learning-based model, namely gradient boosting, to estimate Ta from geostationary satellite LsT data and to apply these estimates to investigate UHI dynamics. Using Rome, Italy, as a case study, the model was trained with Ta data from 15 weather stations, taking multi-temporal LsT values (instantaneous and lagged up to 4 h) and additional predictors. The model achieved an overall RMsE of 0.9 degrees C. The resulting Ta fields, with a 3 km spatial and hourly temporal resolution, enabled a detailed analysis of UHI intensity and dynamics during the summers of 2019-2020, significantly enhancing the spatial and temporal detail compared to previousstudiesbasedsolely on in situ data. The results also revealed a slightly higher nocturnal UHI intensity than previously reported, attributed to the inclusion of rural areas with near-zero imperviousness, thanks to the complete mapping of Ta across the domain now accessible.
The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessi...
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The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networksshowing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combinessimulated Annealing(sA)with a reinforcement learning algorithm,specifically simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(sA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and sliding Mode Control(sMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control wassuccessfully implemented on a Parrot Mambo mini *** resultsshowcase the potential of the proposed sA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.
Recent advancements in machine learningbased energy management approaches,specifically reinforcement learning with a safety layer(OptLayerPolicy)and a metaheuristic algorithm generating a decision tree control policy...
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Recent advancements in machine learningbased energy management approaches,specifically reinforcement learning with a safety layer(OptLayerPolicy)and a metaheuristic algorithm generating a decision tree control policy(TreeC),have shown ***,their effectiveness has only been demonstrated in computer *** paper presents the real-world validation of these methods,comparing them against model predictive control and simple rule-based control *** experiments were conducted on the electrical installation of four reproductions of residential houses,each with its own battery,photovoltaic,and dynamic load system emulating a non-controllable electrical load and a controllable electric vehicle *** resultsshow that the simple rules,TreeC,and model predictive control-based methods achieved similar costs,with a difference of only 0.6%.The reinforcement learningbased method,still in its training phase,obtained a cost 25.5%higher to the other *** simulationsshow that the costs can be further reduced by using a more representative training dataset for TreeC and addressing errors in the model predictive control implementation caused by its reliance on accurate data from various *** OptLayerPolicy safety layer allowssafe online training of a reinforcement learning agent in the real world,given an accurate constraint function *** proposed safety layer method remains error-prone;nonetheless,it has been found beneficial for all investigated *** TreeC method,which does require building a realistic simulation for training,exhibits the safest operational performance,exceeding the grid limit by only 27.1 Wh compared to 593.9 Wh for reinforcement learning.
Prompt-basedlearning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount sig...
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