Iraqi Journal for Computer Science and Mathematics

In this paper, we presented a special type of submodule named W-visible submodule, which is weaker than the visible submodule, where a proper submodule W of a T-module X is said to be W-visible if W = IW for some non-zero proper ideal I of T. Also, we presented the concept of W-fully visible module, where a module X over a ring T is said to be W-fully visible if any proper submodule of X is W-visible submodule. This concept is considered weaker than the concept of fully visible module. In this study, we achieved numerous results and characteristics that belong to this concept.

The proliferation of online platforms recently has led to unprecedented increase in data generation;this has given rise to the concept of big data which characterizes data in terms of volume, velocity, variety, andveracity. One of the common multivariate statistical data analysis tools is linear discriminant analysis (LDA) whichrelies on the concept of obtaining the separation among groups through LDA. The prediction of the class of a givenclass of data points can be achieved through classification, a supervised learning technique but prior to a classificationprocess, a classification model must first be built using classification algorithms. Several classification algorithms areavailable for prediction tasks. LDA is commonly used for the reduction of the dimensionality of datasets. In thisarticle, the use of LDA to improve the classification performance of different classification model was presented.

This paper introduced a new class of operators called (A-n)-potent operator on a complex HilbertspaceH. An operatorT∈B(H)is called (A-n)-potent operator ifATA=Tn,wherenis a positive integer greaterthan or equal to 2. Some basic properties of such operators were also investigated; the relationship between (A-n)-potent operators and some kinds of operators was also established.

There are a few houses that indicate an application for various implementations, they can indicate all types of systems that direct any door lock as well as many other apparatuses. Facial identification that can be considered as a significant section for safety and monitoring, specifically for the handicapped, has the ability to be a way which treats with biometrics and employed for identifying any facial image by employing basic face characteristics. A system of face recognition based on Raspberry Pi utilizing recognition mechanisms as well as conventional face detection will be provided. Thus, the approach that image-built biometrics utilizes a Raspberry Pi has been depicted. The goal here could be to transfer face recognition towards a level where the system has the ability to change RF I-Cards’ use and a password for getting access into all security systems, and reviving the system as well as keeping the door safe hacking people, specifically via utilizing an authorized person’s photo, we turn raspberry pi off and do not turn it on just by an order from cell phone of the authorized person. The presented proposal’s outcome can be a system which utilizes face recognition via employing Raspberry Pi and OpenCV, and it works on Android, and its percentage can be 99.63%. It ought to be secured cost-effective, easy to use and of high performance, that is employed in all applications of smart homes.

Most of the intrusion detection systems are developed based on optimization algorithms as a resultof the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in theperformance of the human-based methods in terms of their training time and classification accuracy. This articlepresents the development of an improved intrusion detection method for binary classification. In the proposed IDS,Rao Optimization Algorithm, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and LogisticRegression (LR) (feature selection and weighting) were combined with NTLBO algorithm with supervised MLtechniques (for feature subset selection (FSS). Being that feature subset selection is considered a multi-objectiveoptimization problem, this study proposed the Rao-SVM as an FSS mechanism; its algorithm-specific and parameter-less concept was also explored. The prominent intrusion machine-learning dataset, UNSW-NB15, was used for theexperiments and the results showed that Rao-SVM reached 92.5% accuracy on the UNSW-NB15 dataset.