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Background: The rising global population and decreasing agricultural land challenge the food supply, worsened by land degradation and water scarcity. Using IoT solutions, precision farming offers promise for addressing these issues by enhancing productivity, profitability, and environmental sustainability. The paper proposes an IoT-based automated monitoring and irrigation system with predictive analysis as a solution. Aims: To develop an IoT-based irrigation model with cloud computing, a mobile app for remote monitoring and irrigation control, and a predictive analysis method for forecasting weather conditions. Methods: IoT sensors are connected to a node MCU, and they monitor real-time temperature, humidity, and soil moisture. A water pump adjusts irrigation accordingly. Data is sent to ThingSpeak for visualization and Firebase for storage and is accessible via a mobile app. Predictive analysis combines sensor and historical weather data using the Random Forest algorithm to optimize irrigation. This determines the ideal irrigation frequency, duration, and timing based on predicted temperature and soil moisture levels, ensuring optimal soil moisture for plant growth. Results: The system exhibited encouraging outcomes. It displayed transformative potential by harnessing IoT, cloud computing, and predictive analytics. Offering precise soil moisture monitoring, instant data access, and tailored advice, the system can elevate crop management, streamline water consumption, and boost productivity among Indian farmers. Discussion: The automated crop monitoring and irrigation system has transformative potential in farming. Some future scope and enhancements may include broadening crop compatibility, integrating satellite data, enhancing pest monitoring, improving connectivity, enriching the mobile app, scaling up through partnerships, and refining the predictive models. Conclusions: The IoT-driven crop monitoring system revolutionizes Indian agriculture by optimizing irrigation and integrating weather forecasts. With mobile app enhancements and future improvements, it promises sustainable farming and empowerment for farmers. [ABSTRACT FROM AUTHOR]

Copyright of Revista Tamoios is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study addresses a Biomass data classification problem. One of the objectives is to identify the most relevant variables for the classification of the pyrolysis type of Biomass. Also, to evaluate if the pyrolysis type classes are sufficient to characterize this chemical process. The Random Forest algorithm was applied to identify which variables are relevant in the pyrolysis type classification process, obtaining an accuracy around 97%. It was identified that the most important variables are: Average residence time in the reactor for the gas and carrier, Percentage of ash-free dry basis carbon in the feedstock, Average particle size in the reactor and Percentage of ash-free dry basis hydrogen in the feedstock. With the most relevant variables, the following clustering methods were used: k-means, pam, clear, diana, fanny, hierarchical, sound, sota, model. To evaluate the clustering methods, internal validation measures with Dunn's index metrics and silhouette were used. The validation measures indicated the hierarchical clustering and k-means with better results for number of groups greater than the number of existing pyrolysis classes. Thus, the dataset should be divided into a larger number of pyrolysis type groups, as considering only the available classes is too limited to characterize the pyrolysis type, since the unsupervised classification algorithms indicate the number of clusters as greater than or equal to five. [ABSTRACT FROM AUTHOR]

Alternatively of pixel-based, object-based classification have reached satisfactory results when applied to high resolution images, for landcover mapping. However, at semi-detailed level (1:50,000-scale) the best way to go remains unknown. Few studies compared those approaches using medium resolution images (~10 m). The effect of quantity and frequency distribution of training observations at each landcover classes are poorly investigated, as well as the efficiency of different kind of predictive variables. The objective of this study was to evaluate different sampling strategies for model training and the efficiency of predictive variables, comparing the results at pixel-based and object-based approaches. The mapping area was the southwest of metropolitan region of São Paulo, and the supervised classification was performed by Random Forest. Six databases were tested by changing quantity and frequency distribution of training observations at each landcover class. We used spectral information from Sentinel-2 mission, topographic information from SRTM, vegetation and water indexes, beyond of information about spatial-temporal variation of indexes. Maps performance was better when vegetation and water indexes, spatial variation and topographic information were added to training set data in pixel-based approach, reaching 82.44 % of accuracy and Kappa index of 0.80. At object-based approach, adding temporal variation of indexes and information about segments geometries were important for map improvement, reaching accuracy and Kappa index of 78.15 % e 0.75, respectively. The results of the maps depend of amount and frequency distribution of observation by classes used for training dataset. The both of approaches tested show similar quality. Toward to choose the better fit among classification results it is main to know the study region characteristics. [ABSTRACT FROM AUTHOR]

Land use and land cover maps are basic tools for a detailed understanding of spatial organization patterns, an essential tool for agro-environmental management. This article aimed to compare and evaluate the accuracy of the digital classification of land use and land cover for the Cerrado of Maranhão, using OLI (Operational Terra Imager) images from Landsat-8 and MSI (Multispectral Instrument) from Sentinel2. The analyzed images are from 2021, which were pre-processed, segmented and classified using the Random Forest algorithm. The analyzes showed that both classifications were qualified as "very good", obtaining Kappa Index (0.706) and Global Accuracy (76%) for the OLI images and Kappa Index (0.775) and Global Accuracy (84%) for the images MSI. The results obtained can support the planning and execution of new agricultural mapping and monitoring in the Cerrado, positively influencing the decision-making of research groups. [ABSTRACT FROM AUTHOR]

Copyright of Revista Tamoios is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research presented in this article analyzed the performance of the random forest algorithm in predicting the future return of the main indices of the largest stock exchanges in the world, through historical trading prices. A sample composed of the daily quotes of 35 indices of the largest stock exchanges in the world from 2001 to 2019 was used. In addition to the random forest algorithm, models were estimated, based on the decision tree algorithm and using the logistic regression technique. The models were estimated considering maximum and closing prices, as well as the complete period and its division into sub-periods. The results indicated that the performances of the estimated models were superior to the market average, and the random forest presented the best results. All models trained on the maximum prices of the indices performed better than those trained on closing prices. In addition, the subperiod models performed better for the random forest. The efficiency of markets in the weak form has been questioned in the contemporary context of the rise of the use of artificial intelligence (AI) algorithms for forecasting in finance. The study is relevant as it contributes to the literature on the use of AI algorithms in forecasting asset prices in the financial market. The main indices of the largest stock exchanges in the world were analyzed, generating general subsidies that can help guide future research in the area. [ABSTRACT FROM AUTHOR]

Copyright of Ensaio: Avaliação e Políticas Públicas em Educação is the property of Ensaio: Avaliacao e Politicas Publicas em Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Technological advances allow new content to be created and be available via Web every minute, providing great progress in several areas. However, this availability also brings drawbacks in the Educational field. It is noteworthy that the excess of materials/content makes teaching-learning process difficult due to the high time spent in searching for content that meets the needs of the users. In this sense, new methods to identify educational content, in videos, for example, need to be developed. From this perspective, it can be seen that significant differences are identified in the comments provided by users on educational videos, thus indicating the potential for using them in the process of selecting these types of videos. In this context, the present work analyzes and collects comments from 500 videos of the Youtube platform, being 250 educational and 250 non-educational, and uses Text Mining and Machine Learning techniques to develop a classification model that, based on the most frequent words of comments on videos, categorize them as educational or non-educational. Thus, we provide a mechanism that filters videos according to their class and returns to the user only videos with educational content. Results demonstrate that it is possible to classify educational and non-educational videos with an accuracy rate of 91.30%, when using the Random Forest classifier. Furthermore, due to the promising results, we developed SysVidEduc, an API that uses the comments from Youtube videos and automatically classifies them as educational or non-educational. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)