For applications such as weather forecasting, power grid management, and solar energy integration, accurate solar radiation prediction is essential. Using real-time data from the EU science Hub’s Climate Monitoring S...
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For applications such as weather forecasting, power grid management, and solar energy integration, accurate solar radiation prediction is essential. Using real-time data from the EU science Hub’s Climate Monitoring Satellite Application Facility (CM SAF), this research examines the efficacy of several time series models for hourly solar radiation prediction. The data are subjected to stationarity testing using the Augmented Dickey-Fuller (ADF) test prior to the models being applied. This study uses well-known time series models, including Vector Auto-regression (VAR), Linear Regression, and Autoregressive Integrated Moving Average (ARIMA). The performance of the proposed model is evaluated using Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Absolute Percentage Error (MAPE). It is found that Vector Auto-regression (VAR) is encouraging for predicting solar radiation on hourly basis with least amount of overall error. This analysis has used residual error inspection, error distribution analysis and sample accuracy evaluation using MAPE. This paper highlights the importance of VAR on using time series model for predicting solar radiation.
Traditional voting systems, both paper and electronic, suffer from major security, transparency, and fraud-prevention challenges. Paper ballots are vulnerable to manipulation and logistical concerns, whereas electroni...
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ISBN:
(数字)9798331544607
ISBN:
(纸本)9798331544614
Traditional voting systems, both paper and electronic, suffer from major security, transparency, and fraud-prevention challenges. Paper ballots are vulnerable to manipulation and logistical concerns, whereas electronic voting machines may be hacked into and manipulated. Centralized systems also create problems of voter anonymity, accessibility, and auditability. VoteBlock, an online voting system built on blockchain technology, takes advantage of the decentralized and immutable features of blockchain. VoteBlock utilizes a distributed ledger to prevent single points of failure, such that no single entity can modify or manage the voting process. Every vote is encrypted and written as a transaction on the blockchain, with real-time verifiability at no cost to voter privacy. Multi-factor authentication is used to protect access and keep unauthorized voting at bay. The open architecture makes it possible for election officials and auditors to validate results cost-effectively and reliably. VoteBlock was prototyped and tested under various conditions to measure performance metrics including transaction speed, network latency, and security scalability. The testing proved that VoteBlock successfully inhibits double voting and tampering with votes, providing an elevated solution that heightens the security, transparency, and credibility of contemporary voting systems.
Medical image classification is critical for accurate disease diagnosis, necessitating models that balance performance and interpretability. This study presents Dilated Y-Block-based Feature Summarized Pyramidal Atten...
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With the increasing integration of AGVs (Automated Guided Vehicles) and Robot Arms in manufacturing systems, traditional scheduling approaches that handle them separately often lead to inefficiencies and poor coordina...
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In recent times, the Internet of Things (IoT) has inched itself as an evolutionary technology in agriculture as it allows for managing and controlling the growth conditions of the plants in an optimal way. This paper ...
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ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
In recent times, the Internet of Things (IoT) has inched itself as an evolutionary technology in agriculture as it allows for managing and controlling the growth conditions of the plants in an optimal way. This paper demonstrates the features of an IoT based system for effective plant growth monitoring along with ideal condition detection. The set of sensors embedded in the system incorporates measuring devices that capture and send the temperature, humidity, soil moisture content and light intensity to a central processing unit. The system sets and analyzes the data cloud along with AI algorithms for efficient agricultural activities and systems. This study focuses on the system design, its data gathering procedures, and decision processes, and their potential in smart farming and urban gardening. The experiments, conducted within the framework of the given concept showed that the system was able to operate within ideal conditions and therefore when changes occur, it initiates an action that helps avoid resource losses and assist in improving the yield. It reiterates the importance of IoT in world of agriculture and the quest for sustainability in modern farming practices.
India’s reliance on fossil fuels to meet its energy generation demands has a negative impact on both the environment and public health. In the present energy transition, photovoltaic (PV) technologies have become mor...
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A business needs to work on the right problems to be able to grow. These problems can only be provided by the people who are using the product or service made by the business. Accurate customer feedback is what allows...
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The Agri Predict will enhance the farmer's ability to implement highly informative interpretative agricultural practices. The use of machine learning algorithms gives accurate information regarding the nutrients r...
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Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are l...
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Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t
The metals recycling industry has undergone a revolution thanks to the quick development of computer vision and artificial intelligence (AI), which have made it possible to sort metal trash using efficient and automat...
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