- Volume 3
- Issue 1
- (3) Articles
Rabeb Touati
,
Dr. Imen Ferchichi
,
Dr. Imen Messaoudi
,
Dr.Afef Elloumi Oueslati
,
Dr. Zied Lachiri
The first MicroRNAs was discovered 27 years ago in the nematode C.elegans genomes. MicroRNAs (miRNAs) sequences are small and are expressed in various genomes to affect the translation or the stability of target mRNAs. These short RNA sequences are involved in targeting post-transcriptional gene regulation. The mature miRNAs are derived from longer sequence precursors (pre-miRNAs). Previous works have shown that pre-miRNAs can be classified by their species of origin using bioinformatics techniques combined with machine learning tools. In this study, we focus on the classification of Precursor microRNAs sequences, from 16 different species ranging from animals, plants, and viruses, based on the combination of the features extracted from images corresponding to DNA sequences and machine learning algorithms. As a result, our classification shows that the system based on features correspond to energy images of pre-miRNAs signals using the PNUC coding technique corresponding to the DNA sequence is very efficient in terms of miRNAs inter-genomics recognition
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Doi: https://doi.org/10.54216/JCIM.030101
Vol. 3
Issue. 1
PP. 05-13, (2020)
Dr.Sreejith Vignesh B P
Cyber attacks are prevailing to be a great headache for the technical advancements especially when dealt with mobile usage in an android application environment. For a new user, it is difficult to identify the set of harmful permissions. This could be an advantage for malware intruders to access the data or infect the mobile device by introducing malware applications. Thus the face of Cybersecurity has changed in recent times with the advent of new technologies such as the Cloud, the internet of things, mobile/wireless, and wearable technology. The technological advances in data science which help develop contemporary cybersecurity solutions are storage, computing, and behavior. In this paper, the possible investigations are done on the cyber attacks in android by adopting the various malware classification and detection techniques. Various Classifications and Detections are done on various malware prevailing in the android applications.
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Doi: https://doi.org/10.54216/JCIM.030102
Vol. 3
Issue. 1
PP. 14-20, (2020)
Sayali Karmode
The internet of things has taken the world by storm. According to prediction, there will be around 30 billion connected devices in the year 2020. This means that some or all our home applications might have the capability to be controlled remotely. Recently, the use of IoT devices and sensors has been rapidly increased which also caused data generation (information and logs), bandwidth usage, and related phenomena to be increased. To our best knowledge, a standard definition for the integration of fog computing with IoT is emerging now. This integration will bring many opportunities for the researchers, especially while building cyber-security related solutions. In this study, we surveyed about the integration of fog computing with IoT and its implications. Our goal was to find out and emphasize problems, specifically security-related problems that arise with the employment of fog computing by IoT.
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Doi: https://doi.org/10.54216/JCIM.030103
Vol. 3
Issue. 1
PP. 21-28, (2020)
