The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Malware represents one of the dangerous threats to computer security. Dynamic analysis has difficulties in detecting unknown malware. This paper developed an integrated multi – layer detection approach to provide more accuracy in detecting malware. User interface integrated with Virus Total was designed as a first layer which represented a warning system for malware infection, Malware data base within malware samples as a second layer, Cuckoo as a third layer, Bull guard as a fourth layer and IDA pro as a fifth layer. The results showed that the use of fifth layers was better than the use of a single detector without merging. For example, the efficiency of the proposed approach is 100% compared with 18% and 63% of Virus Total and Bel

Any software application can be divided into four distinct interconnected domains namely, problem domain, usage domain, development domain and system domain. A methodology for assistive technology software development is presented here that seeks to provide a framework for requirements elicitation studies together with their subsequent mapping implementing use-case driven object-oriented analysis for component based software architectures. Early feedback on user interface components effectiveness is adopted through process usability evaluation. A model is suggested that consists of the three environments; problem, conceptual, and representational environments or worlds. This model aims to emphasize on the relationship between the objects

Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating opti

Herein, an efficient inorganic/organic hybrid photocatalyst composed of zeolitic imidazolate framework (ZIF-67) decorated with Cd0.5Zn0.5S solid solution semiconductor was constructed. The properties of prepared ZIF- [email protected] nanocomposite and its components (ZIF-67 and Cd0.5Zn0.5S) were investigated using XRD, FESEM, EDX, TEM, DRS and BET methods. The photocatalytic activity of fabricated [email protected] nanocomposite were measured toward removal of methyl violet (MV) dye as a simulated organic contaminant. Under visible-light and specific conditions (photocatalyst dose 1 g/l, MV dye 10 mg/l, unmodified solution pH 6.7 and reaction time 60 min.), the acquired [email protected] photocatalyst showed advanced photocatalytic activity

In this paper, the concept of fully stable Banach Algebra modules relative to an ideal has been introduced. Let A be an algebra, X is called fully stable Banach A-module relative to ideal K of A, if for every submodule Y of X and for each multiplier ?:Y?X such that ?(Y)?Y+KX. Their properties and other characterizations for this concept have been studied.

Most intrusion detection systems are signature based that work similar to anti-virus but they are unable to detect the zero-day attacks. The importance of the anomaly based IDS has raised because of its ability to deal with the unknown attacks. However smart attacks are appeared to compromise the detection ability of the anomaly based IDS. By considering these weak points the proposed
system is developed to overcome them. The proposed system is a development to the well-known payload anomaly detector (PAYL). By
combining two stages with the PAYL detector, it gives good detection ability and acceptable ratio of false positive. The proposed system improve the models recognition ability in the PAYL detector, for a filtered unencrypt