Youtube Analytics Channel Visualization Results Using Google Data Studio and Klipfolio

Demonstrate the power of Big Data Analytics using Google Analytics as a platform work flow. First open the YouTube channel, then start recording of the channel analytics is done here automatically by Google. This data is exported from YouTube Analytics to Google sheets and then is fed to Google Analytics. After analyzes the data, it is now integrated with Google Data Studio and Klipfolio. Google Data Studio makes use of AI (Artificial Intelligence) insights techniques that can generate artificial intelligence and prediction-based report graphs which can be analyzed by the end user. In the future, not only YouTube, but any Google products or Google service data can be fed to Google Analytics and integrated in Google Data Studio for artificial intelligence based on Big Data Analytics

Advanced Detection System Milkfish Formalin Android-Based Method Based on Image Eye Using Naive Bayes Classifier

In this paper researcher is trying to make an android-based application that can identify fish with formalin with previous version with adding some more data training. The method used in researcher methods naïve Bayes classifier as a detector (detector) with the object input in the form of fish eye image. The steps in the study include the training and testing process. In the training process used to build the model naïve classifier and estimation parameters. While testing process, implement the results of the model and parameter estimation have been built to detect fish formalin or not formalin. The trial results demonstrate the ability-based applications using the naïve Bayes 98.3% for object dimensions 10x10 imag

Advanced Detection System Milkfish Formalin Android-Based Method Based on Image Eye Using Naive Bayes Classifier

In this paper researcher is trying to make an android-based application that can identify fish with formalin with previous version with adding some more data training. The method used in researcher methods naïve Bayes classifier as a detector (detector) with the object input in the form of fish eye image. The steps in the study include the training and testing process. In the training process used to build the model naïve classifier and estimation parameters. While testing process, implement the results of the model and parameter estimation have been built to detect fish formalin or not formalin. The trial results demonstrate the ability-based applications using the naïve Bayes 98.3% for object dimensions 10x10 imag

Modified Of Evaluating Shallow And Deep Neural Networks For Network Intrusion Detection Systems In Cyber Security

Abstract—Intrusion Detection Systems (IDS) have developed into a crucial layer in all contemporary Information and Communication Technology (ICT) systems as a result of a demand for cyber safety in real-world situations. IDS advises integrating Deep Neural Networks (DNN) because, among other things, it might be challenging to identify certain types of assaults and advanced cyberattacks are complex (DNNs). DNNs were employed in this study to anticipate Network Intrusion Detection System attacks (N-IDS). The network has been trained and benchmarked using the KDDCup-'99 dataset, and a DNN with a learning rate of 0.001 is used, running for 10 epochs for using the activation model experiment and 8 epochs for using the TensorFlow experiment. Keywords—Intrusion detection system, deep neural networks, machine learning, deep learnin