The special component of malicious software analysis is advanced malicious software analysis which implicates interested the main framework of malicious software that can be executed after executing it and aggressive malicious software investigation depend on inquisitive of the practice of malicious software after running it in a composed habitat. Advanced malicious software analysis is usually performed by contemporary anti-malicious software operating systems using signature-based analysis.
The purpose of this research is to propose also decide a DNN for the progressive identification of portable files to study the features of portable executable malicious software to minimize the occurrence of distorted likeness when aware of advanced malicious software. The model proposed in this study is a NN with a Dropout model contrary to a resolution tree model to examine how well it performs in detecting real malicious PE files. Setup-skeptic methods are used to extract features from files. The dataset is used to train the proposed approach and measure outcomes by alternative common malicious software datasets.
The results from this study illustrate that the use of simple DNNs to study PE vector elements is not only efficient but more fewer system comprehensive than the traditional interested disclosure approach. The model proposed in this study achieves an A-UC of ninety-nine point eight with ninety accurate specifics at one percent inaccurate specific on the R-OC curve. For shows that this model has the potential to complement or replace conventional anti-malicious software operating systems so for future research, it is proposed to implement this model practically.

This research is to clarify how to test the thesis topic similarity, make it easy to check the topic thesis, whether it has been made by a student before. In this regard, an important issue that can be raised is how to make a thesis topic similarity test the manual way to be automated. The purpose of this study is a research method similarity of thesis topics using the TF-IDF method. In this research the system has two stages of process, the first mining the text that is categorizing the thesis that has been categorized using the TF-IDF algorithm, which is to read the appearance of each word in the contents of the document. The second stage results from the TF-IDF algorithm are reprocessed with the VSM algorithm. The end result of this program will get the names of documents that have a degree of similarity with keywords.

Production of freshwater fish farming (ponds) in Kangkung Subdistrict in 2015 reached 4.42 tons or valued at 97.19 million rupiah and the condition is growing more and more. The number of fish seeds is measured conventionally by observing the senses of the eye and causing inaccurate (inaccurate) measurement results. This study offers a solution for counting fish by using a microcontroller based phototransistor technology. Phototransistor has a linear function of the received light intensity that is flowing current (ON) and if there is a condition of light intensity blocked by fish passing through it then the current does not flow (OFF). The trial results of the fish seed count tool have an accuracy of 97% so that it can be used by fish farmers.