The accuracy of wind and solar renewable energy power forecasting not only impacts the assessment of “dual guidelines” for each wind and solar energy site, but also affects the electricity market-based transactions ...
The accuracy of wind and solar renewable energy power forecasting not only impacts the assessment of “dual guidelines” for each wind and solar energy site, but also affects the electricity market-based transactions and power generation revenue of these facilities. Employing deep learning models, we amalgamate meteorological grid characteristics, temporal features, weather pattern scenarios, and refined wind speed and power observation data. Utilizing machinelearning techniques, we integrate diverse numerical forecasting outcomes and refine wind speed predictions. Furthermore, transitioning from wind to electricity models, we directly forecast wind power outcomes, either through wind speed adjustments or directly generating wind power predictions. In summation, based on the geographical location, meteorological attributes, node consumption conditions, frequency, and types of extreme weather events in the current subsidiary's renewable energy site distribution area, we undertake the research and development of a high-precision power prediction and electricity generation estimation system rooted in a semi-supervised strategic machinelearning framework.
The Internet of Things (IoT) is the connection of smart devices and objects to the internet, allowing them to share and analyze data, communicate with each other, and be controlled remotely. Several IoT devices are de...
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Nowadays, the target recognition and tracking technology in Unmanned Aerial Vehicles (UAV) vision systems has gained considerable interest due to its promising implications for various applications. However, the exist...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
Nowadays, the target recognition and tracking technology in Unmanned Aerial Vehicles (UAV) vision systems has gained considerable interest due to its promising implications for various applications. However, the existing Open Flow based Tracking (OFT) system faced challenges by factors such as varying lighting conditions and occlusions. Hence, this research proposes a You Only Look Once version 5 with Deep learning based Simple Online Real-Time Tracking (YOLOv5-DeepSORT) is proposed to handle the occlusions. The proposed YOLOv5-DeepSORT detect and track objects across frames for accurate target detection and tracking in UAV vision systems. Initially, the input data is collected from Vision-based Drone (VisDrone) dataset and then, the data augmentation is performed to expand the dataset variability. After that, YOLOv5 is employed for object detection by incorporating Transformer based Multi-Head Attention (T-MHA) as backbone to extract features. Then, GhostNet is used as feature fusion technique to minimize the computational overhead of the network. Finally, DeepSORT is introduced to track the objects by predicting object motion and associating object across frames. From the results, the proposed YOLOv5-DeepSORT achieved better results in terms of recognition accuracy (99.5%), recognition speed (33.7 vehicles/sec), and manual time (1.4s) when compared with existing UAV-Vision Based Moving Target Detection (UAV-MTD).
In this era, with the technology used in every field, the agriculture market also generates a huge amount of revenue every day. Computer technology plays a vital role in managing and finding meaningful information abo...
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In the contemporary digital landscape, the security of large datasets is paramount, given the increasing volume, variety, and velocity of data generation and collection. This paper provides an intricate examination of...
In the contemporary digital landscape, the security of large datasets is paramount, given the increasing volume, variety, and velocity of data generation and collection. This paper provides an intricate examination of the application of various datamining techniques to bolster security measures in handling expansive datasets. Through a systematic exploration of methods including clustering, classification, and association, we aim to elucidate the efficacy and adaptability of these techniques in identifying and mitigating potential security threats. We critically analyze real-world scenarios and case studies, drawing comparative insights to underscore the nuanced applications and challenges inherent in diverse data environments. A detailed discussion is presented on the role of machinelearning algorithms and artificial intelligence in augmenting datamining processes, enhancing predictive accuracy, and real-time responsiveness to security breaches. The findings indicate a significant positive correlation between advanced datamining applications and enhanced data security. However, they also highlight existing vulnerabilities and areas necessitating further research and innovation. We conclude with recommendations for integrating adaptive, scalable, and robust datamining frameworks to ensure comprehensive security in managing large datasets, paving the way for future research intersections between datamining and cybersecurity.
Student's proficiency in a subject may be gauged through the use of learning Management Systems (LMS) (LMSs). Using a scoring algorithm to calculate the percentage of each student's attentiveness in the specif...
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ISBN:
(数字)9781665479714
ISBN:
(纸本)9781665479714
Student's proficiency in a subject may be gauged through the use of learning Management Systems (LMS) (LMSs). Using a scoring algorithm to calculate the percentage of each student's attentiveness in the specific class, this system aims to increase the use of artificial intelligence approaches. The graph provided by this tool may be used to measure how well a student understands the material being taught. datamining tools are increasingly being used in higher education to help students and administrators better understand and resolve educational and administrative difficulties. The majority of educational mining research focuses on replicating students' performance rather than instructors' performance. The course assessment questionnaire is a standard instrument for evaluating teachers' performance based on students' perceptions. There are a number of different ways for creating classification techniques in this system, from decision trees to support vector machines to machinelearning techniques, including differential evolution. Comparing their answers to an actual course assessment question based on its accuracy, retention and sensitivity. Students' online conduct has become more accessible because to the use of learning Management Systems (LMSs) in educational institutions. These statistics have been utilized by several studies to predict student outcomes. A wide range of subjects and predictive characteristics collected from the LMS make it difficult to draw generalized statements about the processes that control student performance. To get started let's take a look at some of the most recent research' theoretical foundations and common predictions in the field of learning analytics. A total of 4,989 students took 17 blended courses with Moodle LMS at the same school, and both multi-level and conventional regressions are utilized to examine the impact of LMS predictor factors and between-course evaluation grades on student performance. Despite the fact that the
Speech impairment is a disability that affects an individual's ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need...
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ISBN:
(纸本)9781665428644
Speech impairment is a disability that affects an individual's ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. My paper is an effort towards filling the gap between differently-abled people like deaf and dumb and the other people. Image processing combined with machinelearning helped in forming a real-time system. Image processing is used for pre-processing the images and extracting different hand from the background. These images obtained after extracting background were used for forming data that contained 24 alphabets of the English language. The Convolutional Neural Network proposed here is tested on both a custom-made dataset and also with real-time hand gestures performed by people of different skin tones. The accuracy obtained by the proposed algorithm is 83%.
Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their ...
Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven machinelearning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2 nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data.
In Indonesia, diarrheal disease is still a global health problem. The degree of morbidity and mortality is quite high, ranking 3rd after TB and pneumonia. In developed countries, even though there have been improvemen...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
In Indonesia, diarrheal disease is still a global health problem. The degree of morbidity and mortality is quite high, ranking 3rd after TB and pneumonia. In developed countries, even though there have been improvements in people’s health and economy, the incidence of infectious diarrhea remains high and is still a health problem. The development of increasingly complex science encourages humans to learn data that can be processed and produce new information and knowledge. In computer science, machinelearning (ML) facilitates computers to read and interpret previously existing data automatically. machinelearning can be used to classify diseases, determine the type of disease, and predict a disease. The machinelearning method used is a decision tree. The step is to collect a data set consisting of input variables in the form of symptoms and output variables in the form of types of diarrheal diseases. Then change the form of the data into a table which becomes a model tree by looking for the entropy and gain of each symptom. After the model is formed, change the tree into a rule and simplify the rule (pruning). The results of identifying diarrheal diseases using the decision tree method were excellent, with an accuracy of 96.4%. Then a simplification of the rules is also obtained from 20 rules from 17 symptoms to 5 rules with a depth of 4 levels. The most influential symptoms in forming rules are G10, G14, and G16.
Oil painting production is a very time-consuming task. This article uses the current generation confrontation network popular in machinelearning to transfer the style of images, and directly convert real-world images...
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