Quantum Image Processing
INTRODUCTION
Image processing has become a popular and critical technology and field of study for our everyday lives. The need to extract important data from visual information arose in many fields like biomedicine, military, economics, industry, and entertainment [1]. Analysis and processing of images requires representing our 3D world in 2D spaces, using different complex algorithms to highlight and examine essential features [2]. With the rapid growth of the volume of visual information, these operations are requiring more computing power. According to Moore’s law, computing performance of classical computers doubles every 18 months. However, experts claim that this law will not hold true for very long [1]. Thus, classical computers will not be able to solve image processing problems with big sets of data within reasonable time limits.
Failure of the Moore’s law can be solved with quantum computation. Existence of more efficient quantum algorithms and their ability to perform calculations faster than classical computers was shown by researchers [1]. Quantum computing also can dramatically improve areas of image processing [2]. Applying quantum computation to image processing tasks is rereferred as Quantum Image Processing or QIMP. This paper will review the basics of quantum image processing and computation, go into its use, with focus on security technologies, and discuss the challenges and future of QIMP.
QUANTUM COMPUTATION
A new method of computation known as quantum computing could completely change the field of computer science. In 1982, the late Nobel Prize-winning physicist Richard Feynman began exploring the possibilities of using quantum systems for computing [1]. He was interested in modeling quantum systems on computers. He realized that the number of particles has an exponential effect on the amount of classical memory needed for a quantum system. Thus, when simulating 20 quantum particles, only 1 million values need to be stored, while when simulating 40 quantum particles, 1 trillion values need to be stored. It's impossible to do interesting simulations with 100 or 1000 particles, even with all the computers on Earth [2]. Thus, the concept of using quantum mechanical effects to perform calculations was developed when he proposed the creation of computers that used quantum particles as a computational resource that could model general quantum systems for mass simulation. Researchers have taken a closer look at the processing capability of quantum systems as a result of exponential storage capacity and some disturbing phenomena such as quantum entanglement [4]. Over the past 20 years, quantum computing has exploded, proving that it can solve some problems exponentially faster than any computer [3]. If quantum computers can be built massive enough, the best-known algorithm, Peter Shor's integer decomposition algorithm, will make it easier to break the most common encryption methods currently in use [1].
All modern mainstream computers fall under the category of classical computers, which operate on a "Von Neumann architecture," which is based on an abstraction of discrete chunks of information [1]. Since a computer must eventually be a physical device, scientists recently have moved away from this abstraction of computation and realized that the laws regulating computation should be derived from physical law. One of the most fundamental physical theories, quantum mechanics was a good candidate to investigate the physical feasibility of computational operations [5]. The important finding of this study is that quantum mechanics permits machines that are substantially more powerful than the Von Neumann abstraction.
Along with Shor's factoring algorithm, Lov Grover's search algorithm is a fantastic quantum technique that significantly lessens the amount of work required to look for a certain item. For instance, it takes an average of 500,000 operations on a classical computer to search through a million unsorted names for a given name, and the Von Neumann model of computing offers no faster method [1]. However, using Grover's approach, which takes use of quantum mechanics' parallelism, the name may be obtained with just 1,000 comparisons under the quantum model. Grover's approach outperforms the conventional one considerably more for longer lists.
The subject of quantum computing is huge and diverse today. There are researchers working on a variety of topics, from the creation of physical devices employing various technologies like trapped ions and quantum dots to those tackling challenging algorithmic problems and attempting to pinpoint the precise limits of quantum processing [5]. It has been established that quantum computers are inherently more powerful than classical ones, although it is still unclear how much more powerful they are. And a technological challenge is how to construct a large quantum computer [3].
So, quantum computation is still in its infancy. If the technical challenges are overcome, perhaps quantum computation will one day supersede all current computation techniques with a superior form of computation, just as decades of work have refined the classical computer from the bulky, slow vacuum-tube dinosaurs of the 1940s to the sleek, minimalist, fast transistorized computers that are now widely used. All of this is based on the peculiar laws and procedures of quantum physics, which are themselves anchored in the peculiarities of Nature. What computers will be derived from more complex physical theories like quantum field theory or superstring theory remains to be seen.
BACKGROUND
The field of quantum image processing aims to adapt traditional image processing techniques to the quantum computing environment. Its main focus is on using quantum computing technologies to record, modify, and recover quantum pictures in various formats and for various goals. It is believed that QIMP technologies would offer capabilities and performances that are yet unmatched by their traditional equivalents because of some of the astonishing aspects of quantum processing, including entanglement and parallelism. These enhancements could be in the form of increased computer speed, ensured security, reduced storage needs, etc [3].
The first published work connecting quantum mechanics to image processing was Vlasov's work from 1997. It concentrated on using a quantum system to distinguish orthogonal images. Then, efforts were made to look for certain patterns in binary images and identify the target's posture using quantum algorithms. In 2003 publication of Venegas-Andraca and Bose's Qubit Lattice description for quantum pictures greatly contributed to the research that gave rise to what is now known as QIMP. The Real Ket, which Lattorre developed as a follow-up representation, was designed to encode quantum pictures as a foundation for more QIMP applications [1][3].
The proposal of Flexible representation for quantum images by Le et al. genuinely sparked the research in the context of current descriptions of QIMP. This might be explained by the adaptable way in which it enables the integration of the quantum picture into a normalized state, which makes it easier for auxiliary transformations on the image's contents. Since the FRQI, a wide range of computational frameworks that focus on the spatial or chromatic content of the picture have also been presented, along with numerous alternative quantum image representations (QIRs).
The representative QIRs that can be linked back to the FRQI representation include the multi-channel representation for quantum images (MCQI) and novel enhanced quantum image representation (NEQR). The development of algorithms to alter the location and color information encoded using the FRQI and its several variations has also received a lot of attention in QIMP [5]. For instance, it was initially suggested to use FRQI-based fast geometric transformations, which include swapping, flipping, rotations, and restricted geometric transformations to limit these operations to a specific region of an image [3]. Recent discussions have focused on quantum image scaling and NEQR based quantum image translation, which transfer each picture element's position in an input image to a new position in an output image. While single qubit gates like the X, Z, and H gates were initially used to propose FRQI-based broad forms of color transformations. Later, MCQI-based channel of interest operator, which involves moving the preselected color channel's grayscale value, and channel swapping operator, which involves switching the grayscale values of two channels, were further studied [3].
Researchers always prefer to mimic the digital image processing jobs based on the QIRs that we already have in order to demonstrate the viability and competence of QIP methods and applications. Researchers have so far made contributions to quantum image feature extraction, quantum image segmentation, quantum image morphology, and quantum image comparison using the fundamental quantum gates and mentioned operations [5]. QIMP based security technologies in particular have drawn a lot of interest from researchers.
IV. SECURITY TECHNOLOGIES
The necessity for secure communication has developed along with mankind's need to transfer information. With the development of digital technology, the demand for secure communication has increased. In order to realize secure, effective, and cutting-edge technologies for cryptography and information concealment, QIMP is totally based on the extension of digital image processing to the quantum computing domain [3]. Indeed, quantum computation and QIMP offer the potential for secure communication in fields like encryption, steganography, and watermarking.
Encryption is the practice of hiding information to render it unintelligible to those lacking specialized knowledges as a direct application of the science of cryptography. This is frequently done for confidential communications in order to maintain confidentiality. Information hiding focuses on hiding the existence of messages, whereas cryptography is concerned with safeguarding the content of messages. Since attackers cannot easily detect information hidden using techniques like steganography and watermarking, it appears to be safer [3]. The high requirements for the quantity of information that can be concealed under the cover image without changes to its perceived imperceptibility are one of its key limitations, though. Even though steganography and watermarking are similar, they have different goals and/or applications as well as different needs for those goals [3]:
In watermarking, the carrier image is the obvious content, but the copyright or ownership is concealed and subject to authentication. In the instance of steganography, it aims to safely transmit the secret message by disguising it as an insignificant component of the carrier image without raising any red flags with outside opponents.
Information is concealed through watermarking in the form of a stochastic serial number or an image, such a logo. As a result, watermarked photos typically contain some little copyright ownership information. Steganography frequently needs a huge carrying capacity in terms of the carrier picture because its goal is to conceal the presence of the concealed message.
When watermarking, the content can be subject to many sorts of infringements, such as cropping, filtering, channel noise, etc., whereas steganography pictures don’t face such issues.
FUTURE DIRECTIONS AND CONCLUSIONS
Research is concentrated on what can be accomplished with quantum technologies once increased realization has been achieved, beyond the continuing work toward the physical implementation of quantum computer hardware [3]. One of these is the nexus of quantum computation with image processing, which is known as quantum image processing. Researchers are confronting both enormous potential and problems to create more effective and usable services because it is a relatively new phenomenon.
All the experimental QIP protocol implementations that have taken place so far have been limited to using traditional PCs and MATLAB simulations built on linear algebra using complex vectors as quantum states and unitary matrices as unitary transforms [5]. These provide a fairly constrained implementation of the potential of quantum computation. Therefore, it is crucial to understand the function of quantum computing software needed to implement the various algorithms that we have in order for them to complement the hardware as researchers intensify their efforts to advance and expand QIP technology [3].
REFERENCES
Beach, G., Lomont, C., & Cohen, C. (2003, October). Quantum image processing (quip). In 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings. (pp. 39-44). IEEE.
Anand, A., Lyu, M., Baweja, P. S., & Patil, V. (2022). Quantum Image Processing. arXiv preprint arXiv:2203.01831.
Yan, F., Iliyasu, A. M., & Le, P. Q. (2017). Quantum image processing: a review of advances in its security technologies. International Journal of Quantum Information, 15(03), 1730001.
Cai, Y., Lu, X., & Jiang, N. (2018). A survey on quantum image processing. Chinese Journal of Electronics, 27(4), 718-727.
Ruan, Y., Xue, X., & Shen, Y. (2021). Quantum image processing: opportunities and challenges. Mathematical Problems in Engineering, 2021.
Peli, T., & Malah, D. (1982). A study of edge detection algorithms. Computer graphics and image processing, 20(1), 1-21
Information Science and Technology
↳ Modern Technology
Roles of Core Technologies in an Effective IT System
Abstract
This paper explores the roles of core technologies in an effective IT system and the responsibility of those who manage it. The articles, define the roles of technology by explaining its involvement in IT system. The introduction, research and results are intended to educate the writer on the core of IT Technologies.
There was a time when in order to make a phone call we had to go to a near payphone and insert a couple of coins. Another time, we had to go to the library and wait for a computer to be available in order to do assignments or work. Now, it all has changed, technology has advanced and evolved through the years. With the Wi-Fi revolution and how a simple visit to the local coffee shop can give you access to free Wi-Fi, it’s a great excuse to take technology everywhere you go. In order to understand technology in the IT system we have to examine the core foundations; Computer Programming, Computer Systems Administration, Information Security Analytics and Web Developing.
Computer Programming. For a computer to run a program effectively a series of commands or codes have to be executed. According to Encyclopedia N. (2017), when computer programming started, the use of a IBM punch cards was used to run codes. Then years later, programming languages where implemented such as C ++ and Java. Programming assists developers in building applications and programs that can help prevent computer breaches and detect and combat malware. For example, a fraud analyst has received raw data from a bank with a list of names, addresses, time and date and card numbers of debit cards. In order to organize it and find an easier method to compare it and match it to another list retrieved from a dump of stolen cards the analyst needs an efficient software tool. This is where programming comes in handy and a developer makes a programming language like Python, Ruby or Tableau that can easily help organize and match data.
Computer Systems Administration. A system administrator is responsible for the maintenance and operation of the computer systems in the work place (Sokanu). Any technical problem that occurs in a system the computer administrator will use their operations knowledge to fix it. For example, if an employee sends an email, but is not encrypted, the system administrator will notice that issue and place an encryption on that specific email. Another way the system administrator maintains the system is by updating and upgrading software.
Furthermore, system administrators deal with networks such as Wi-Fi and internet. The Wi-Fi connects to local area networks and the internet connects many local networks together. If either or is down, the system administrator must perform maintenance to support network availability (ONet O. 2018). These maintenances are very important due to network protocol requirement and constraints of accomplishing communication between computer servers, routers and other network devises (Techopedia).
Database Administrator. When an organization is dependent on one or more databases it’s important to have a database administrator (Techopedia). For organizations a database administrator is crucial because access to data has to be given to authorize personnel. As data consumption and usage rises the database administrator must be able to implement necessary changes to accommodate growth (Smullins, 2017). For example, a business analyst needs access to the bank data that holds information of all the customers that have applied to credit card for the past five years. This information will serve him as a lead list for prospect clients that may also want to apply for home mortgage loans. The analyst can’t just enter the database and get the information. The analyst must ask permission and once it’s granted the administrator will be able to transfer the authorize data to the analyst for use.
Information Security Analyst. Integrity, availability, authentication, confidentiality and nonrepudiation are the five terms associated with information assurance (Techopedia). An Information Security Analyst is in charge of protecting a company’s network systems. For example, a hacker wants to gain access to a company’s data and steal confidential customer information. In order to have access, that hacker needs to bypass firewalls and decode encrypted data. But because the Information Security Analyst has implemented a great firewall protection and a strong defense plan the hacker had no chance (Sokanu). Imagine if the company didn’t have a Security Analyst, how much data could’ve been stolen from the company? Even worst, how much money the company would’ve lost due to legal fees and investigations costs. Another important aspect of the Security Analyst is the fight against malware and viruses that penetrate the system. Security data breaches are increasing and hackers are using it to hold data ransom and demanding large sums of money. Security analyst work close with data base administrators and system administrator to protect the data and prevent hackers from stealing information.
Web Developer. Social media is not only to communicate with an old high school classmate or to find love. It’s also a way to promote a business, a personal skill and exposure. A Web Developer will program a code that tells a website how to function, (Sokanu). The developer makes it easy for the user to navigate the page and use various options like a shopping cart, contact information and blogs. Developing a website is not as simple as it sounds. The developer must lay out the vision of the customer, execute the mechanics of the website and run the site efficiently (Sokanu). For example, a customer may have a design in mind for the web page and gives specific instructions on the lay out. The customer wants a blog, a video tab where it shows the last three recent videos from his YouTube channel and forum to discuss relevant topics. The developer has to have all this details in mind in order to make the most efficient and reliable site possible. Most of this developer use programming languages such as HTML, JavaScript, SQL and C++ (Computer Science). Developers can work in different computing platforms such as PC, Mac, mobile, and SQL databases. There are different types of web developers; Front-End, Back-End, Full Stack and JavaScript developers (Sokanu). Each performing different tasks that will make a web development project complete.
Conclusion
In conclusion, Computer Programming, Computer Systems Administration, Information Security Analytics and Web Developing all come hand in hand. All require efficient methods of security, programming and development. In order to understand how the IT system works the core foundations must be understood in its entirety. The present and future is filled with technology advancement and it’s the role of the Cybersecurity expert to understand it and figure out ways to detect and prevent fraud and breaches.
References
Encyclopedia, N. (2017, March 17). Computer programming. Retrieved from http://www.newworldencyclopedia.org/entry/Computer_programming
Sokanu (n.d.). Computer Systems Administrator. Retrieved from https://www.sokanu.com/careers/computer-systems-administrator/
ONet. O. (2018). 15-1142.00 - Network and Computer Systems Administrators. Retrieved from https://www.onetonline.org/link/summary/15-1142.00
Techopedia (n.d.). What are Network Protocols? - Definition from Techopedia. Retrieved from https://www.techopedia.com/definition/12938/network-protocols
Techopedia (n.d.). What is Database Administration? - Definition from Techopedia. Retrieved from https://www.techopedia.com/definition/24080/database-administration
Smullins, C. (2017, August 30). What Does a DBA Do? Retrieved from https://datatechnologytoday.wordpress.com/2010/07/28/what-does-a-dba-do/
Techopedia (n.d.). What is Information Assurance (IA)? - Definition from Techopedia. Retrieved from https://www.techopedia.com/definition/5/information-assurance-ia
Sokanu (n.d.). Information Security Analyst. Retrieved from https://www.sokanu.com/careers/information-security-analyst/
Sokanu (n.d.). Web Developer. Retrieved from https://www.sokanu.com/careers/web-developer/
Computer Science. (n.d.). Web Developer Careers. Retrieved from https://www.computerscience.org/careers/web-developer/
Information Science and Technology
↳ Modern Technology
Numerical Analysis
Computer Software and Modern Applications.
Introduction
In this article, we are going to discuss how computer software has helped students and users in numerical data analysis with practical issues like languages used in programming. In addition, the interaction between numerical computation and symbolic computation will also be reviewed.
Models used in numerical analysis
Mathematical modeling and numerical analysis have been very important in current life affairs. Pragmatic numerical analysis software has been integrated into most software packages, such as programs in the spreadsheet, enabling people to perform mathematical modelling without prior knowledge of the processes involved. This, therefore, demands the installation of numerical analysis software which can be relied on by the analysts. Designing of Problem solving environments (PSE) enables us to solve and model many situations. Interface for graphical users has made PSE for modeling a given situation easy with good models for mathematical theories. There have been modern applications of numerical analysis in computer software of late in many fields. For example, computer-aided manufacturing and computer-aided design in the engineering sector have led to improved PSEs to be developed for both CAD and CAM. Mathematical models in this field are based on the basic laws of newton on mechanics. Algebraic expressions and ordinary differential equations are mostly involved in mathematical models. Manipulation of the mixed systems of these models is very difficult but very important in the modeling of mechanical systems such as car simulators, plane simulators and other engine moving mechanicals needs real-time solving of differential-algebraic systems.
Atmospheric modeling is important in understanding the effects of human activities on the atmosphere. A great number of variables such as the velocity of the atmosphere in a given point at a given time, temperature, and pressure need to be calculated. In addition, chemicals in the atmosphere such as carbon dioxide, which is a pollutant, and their reactions need to be studied—studying velocity, pressure, and time which are defined with partial differential equations and the kinetic, chemical reactions which are defined using ordinary differential equations which very complex needs sophisticated software to handle. Businesses have incorporated the use of optimization methods in decision-making on efficient resource allocation. Locating manufacturing and storage facilities, inventory control problems and proper scheduling are some of the problems which require numerical analysis of optimization. (Brinkgreve, R. B. J. (1996).)
Numerical software sources
Fortran has remained the widely used programming language, and it keeps on being updated to meet the required standards, with Fortran 95 as the latest version. Other useful languages include C++, C, and java. In numerical data analysis, there are several numerical analysis software packages used. The following is a list of the packages used in data analysis with computer software. 1. Analytica software is a wide range of tools used in analyzing and generating numerical models. This is a language programmed visually and linked with influence diagrams. 2. FlexPro program is used in data analyzing and presenting measurement data. It has an excellent interface similar to Excel program with a vector programming language built in it. 3. GNU Octave-this is a high-end language used in the computation of numbers. It has a command- line interface that is used in numerically solving nonlinear and linear problems. Numerical experiments are solved with a language that is mostly suited with MATLAB. There are several newly developed programs of Linux such as cantor and KAlgebra, which offers Octave a GUI front ends. 4. Jacket is a MATLAB GPU tool that enables offloading of computations for MATLAB to the GPU to visualize data and acceleration purposes. 5. Pandas. It is a BSD- licensed python programming language tool used for the provision of data structures and data analysis. 6. Torch provides support for manipulation, analysis of statistics, and tensor presentation. 7. TK Solver is a commercialized tool by the universal technical system used in problem-solving and mathematical modeling software basically on rule-based and declarative language. 8. fit is a statistical analysis and curve-fitting plugin to excel. 9. GNU MCSim is a package for numerical integration and simulation with fast Markov chain Monte Carlo and Monte Carlo capabilities. 10. Sysquake is an application based on MATLAB-compatible language for computing environment with interactive graphics engineering, mathematics, and physics.( Conte, S. D., & De Boor, C. (2017).)
Software development tools
There have been efficient tools in the types of programming languages that creates computer solutions. The following are some of the basic qualities that a mathematical programming language should possess. First, a syntax that enables accurate and fast transformation from mathematical formulae into program statements should be possessed by the language. Also, the language should be rooted on primitives next similar to the basic concepts of mathematics. Lastly, tools for efficient and fast execution should be included in the language. The programming languages have been categorized into different generations: First Generation languages 1954-1958 (Fortran I, ALGOL 58, Flowmatic, and IPL V). Second-generation languages 1959-1961 (Fortran II, ALGOL 60, COBOL, and LISP). Third Generation Languages 1962-1970 (PL/1 (Fortran +COBOL+ALGOL), ALGOL 68, PASCAL, SIMULA, and APL. The Generation Gap 1970-1980. This had many languages which were different. (Bartholomew- Biggs, M. C. (2000).)
Software options for solving problems
There are three classes for software. (1) Compilers for language and graphic packages, which are basic tools. (2) Tools which can solve the users’ problems, such as systems in structuring engineering. (3) Widely applicable generic tools such as mathematical systems and compiler generators.
A course of several actions has to be undertaken for a numerical solution to be attained, which include: (1) using the existing black-box package. Those packages include PAFEC for elementary work and GENSTAT used in the analysis of statistics. (2) Application of library routines like IMSL, NETLIB, and NAG after splitting the problem in hand to components well defined. (3) Writing a whole purpose-built program sometimes which needs deep computing and analytical knowledge.
Numerical libraries
Design issues
Numerical libraries mainly perform normal numerical linear algebra operations, disintegration of singular value, and transformation of Fast Fourier, optimization of nonlinear problems, linear programming, curve fitting, quadrature, and performing special functions.
NAG
In May 1970, a group of six UK universities centers for computing decided to create a numerical routine library. A year later, they released Mark 1 of the NAG library, which contains 98 routines documented in it. In 1989 mark 12 had 688 routines and many other library versions created in Algol 60, Pascal, and Algol 68. In addition, there are specific library versions used by for computers from cray, CDC, Data General, Harris, Telefunken, Xerox, and Philips. The Philosophy of NAG of giving maximum efficiency indicates that the software is trying to excellently calculate mathematical problems within the algorithm domain solution. It also strives to signal and reject a condition erred and returning a best upper bound of the erred condition where possible to the user-supplied tolerance.
International mathematics and statistical libraries (IMSL)
This contains a big mathematical software library. Its main aim is achieving success commercially with the lowest cost and a resulting high volume. Over 350 subroutine-specific software types are readily compatible for computers from Data General, Xerox, DEC, Hewlett- Packard, and Burroughs. (Wang, J. Y., & Garbow, B. S. (1981).)
Tea pack
This is a Fortran-based subroutine library that is assumed to be easy to use than IMSL and NAG which are commercial libraries. Peapack was designed in the 1980s with a documents for teaching numerical analysis introduction. This package contains routines for most basics, polynomial roots, interpolation, and ordinary differential equation calculations.
Machine-Dependent Libraries
Supercomputers contain software libraries like floating point systems kind of machines. In 1989 July, Brad Carlile gave a report to the NA Digest, which informed that computing for FPS had announced "at-cost" FPSmath availability which was a standard library de facto software for scientific algorithms and engineering systems. This speeds the application research and development by allowing institutions to have the same mathematical tools across all their environment for computing a nominal cost hence guaranteeing portability and taking merits of supercomputer and features of acceleration.
Sources of documentation
Users are provided with various documentation categories, including condensed information, encyclopedic, detective, and specialized information.
Comparison and testing algorithm
The following factors affect the choice of which algorithm to choose: efficiency, storage costs, generality, and reliability of solving all problems.
General-purpose computer algebra systems.
A computer algebra system is a package for software used in mathematical formulae manipulation. The computer algebra system's main purpose is to automate tedious calculations and solve hard algebraic expressions. Computer algebraic systems' ability to solve equations symbolically makes the main difference between it and the traditional calculator. Computer Algebra systems provide the user with a programming language to define her procedures and graphing equations facilities. Some of the widely used systems include Mathematica, MathCAD, and maple. They are used in rational functions simplifications, factor polynomials, solving equations, and many other calculations. While Newton and Leibniz's algorithmic processes of solving calculus are very hard and tedious to solve, computer Algebra systems perform these tasks and outdo the man from the processes. The following is a summary of how these computer algebra systems work.
Speakeasy-this was developed in the 1960s with its main being manipulation of matrix, and within the process of evolution, it geared the most common paradigms tools with typing dynamically the structured data objects, collection of garbage and dynamic allocation, overloading operators, and connecting added modules by the groups of users.
Sampath-this is software that is an open-source containing a unified python interface. Examples are proprietary general and open source purposes CAS and other programs such as GP, Magma, GAP, and Maple.
PARI-matrices, algebraic numbers, and polynomials are computed in this computer algebra system.
Mathematica-this has computer algebra capabilities and programming languages that are designed for number theory computations.
Mathcad which has WYSIWYG interface which aid in mathematical equations publication quality.
Trivino - It is an open-source, object-oriented collection of libraries applied in engineering and science, and it solves linear and parallel algebra algorithms. (Wester, M. J. (1999).)
Computer-assisted data analysis
Computer-assisted software for qualitative data analysis like MAXQDA gives the solutions to given data problems without directly giving interpretations to the user. Qualitative data software tools give way to structuring, sorting, and analyzing big data, which facilitates evaluation and interpretation management. Quality data analysis depends on methods of organizing, systemizing, and analyzing non-numeric like those used in the analysis of qualitative content, analysis of mixed methods, group discussions, case and field studies, and Grounded theory. Computer-assisted data analysis packages should facilitate and support methods of sorting, analyzing, and structuring data content despite which approach the researcher chose. Data in the form of image files, video, audio material, and data from social media can also be included in these packages with sophisticated computer-assisted data analysis software authorizes for transcribing and importing the content to the program direct. Software for QDA, such as MAXQDA, provides support to the whole process of analyzing by providing overviews and relationships visualization. It also provides space for the addition of memos to the various analytical processes which aid the user in understanding them better. The first version of MAXQDA computer-assisted data analysis software was created in 1989, and this makes it a pioneer software program in the area. From collecting data to publishing the final report regardless of the approach used, the program provides support to the user. Coding or systematic assignment of portions of texts to themes and probability of making notes and associations are the central elements of MAXQDA. In MAXQDA, evaluation and interpretation of data are performed by sorting materials into portions using a system of hierarchical coding through variables defining, tabular overviews provision, and colors assigning to segments of text. The procedures can be easily tracked, and within a few steps, the results are easily accessed. Creating stunning visualizations helps the user to view the data from a completely different perspective and be able to test theories. The results can be projected and exported to many programs so as to be included in the final publication through these visualizations. (Chitu, C., & Song, H. (2019).)
Data analytics and processing platforms in Cyber-Physical Systems.
The speed of current developments in cyber physical systems and IoT leads to new challenges for business owners and data analysts to come up with new techniques for analyzing big data. Cyber-physical systems is the integration of systems of computer connected to the physical world. The systems process and display signals for the problem in the hand of the user.
Data types
The most important aspect to consider when in the journey of understanding data is to be able to distinguish the different types of data. Therefore, the implementation of a machine learning algorithm in order is very important. Variables are either numerical or categorical types. The categorical data is subdivided into two subcategories: nominal, which means there is no meaningful order, and ordinal type, which shows there is an obvious order. Numerical data is counts or measurements and is grouped into two other types: discrete or integers and continuous data. There are several types of data analysis using computer software as listed here: qualitative analysis, hierarchical analysis, graph analysis, spatial analysis, and textual data analysis.
Hybrid systems
This is a system where the interested area behavior is determined by coupling processes of distinct characteristics in specific discrete dynamics and coupling them continuously. They generate signals consisting of discrete-valued signals and continuous signals. The signals depend on variables that are independent such as time. Hybrid models are used in the control of automotive engines by solving control algorithms that are implemented through embedded controllers, which reduce gas consumption and pollutant emissions with neutral car performance.
B soft computing
This is an approach used in the separation of soft computing knowledge grounded on intelligence on computation from hard computing skills grounded on artificial intelligence computation. Hard computing has characteristics of formality and precision, and it is channeled toward analyzing and designing systems and physical processes. It handles crisp systems, probability theories, mathematical programming, binary logic, approximation theory, and differential equations. Soft computing is used in analyzing and designing intelligent systems, and it handles problems related to fuzzy logic, probabilistic reasoning, and neural networks.
Data structures.
For a program in a computer to manipulate an equation symbolically, the equation has first to be stored in certain computer memory. At the center of any computer algebra system, there is a single data structure or a combination of many data structures responsible for mathematical equation describing. Equations might have other functions references, or be rational functions, and sometimes exist in several variables. Hence, there is no certain specific solution of an equation to a structure of data presentation. A presentation can be Complex in space and time; hence it becomes inefficient, but it may be easy to program. An efficient presentation to a certain mathematical problem does not mean it is efficient to others; hence there is no specific answer to a given problem.
Interface-oriented software
COMSOL- This is a simulation and solver software for doing engineering and physics applications, specifically coupled phenomena.
Baudline- this is used for scientific visualization and numerical signal analysis, and it is a time-frequency browser.
Dataplot, which is provided by the NIST.
Euler Mathematical toolbox. It is a laboratory-powerful numerical programming language which can solve complex, interval, and real numbers, matrices, and vectors.
Hermes which is a library tool for C++ of improved finite algorithms for solving partial differential equations and problems coupled with multiphysics.
DADiSP. It is a DSP-focused program for combining MATLAB numerical capability with a spreadsheet interface.
Flexpro. It is a program used commercially for an automated and interactive presentation and analysis of measurement data. Other programs include IGOR Pro, FEniCS project, Fityk, and Labplot.
Language oriented software
ADMB. It is a C++ software that uses automatic differentiation to model nonlinear statistics.
AcsIX. It is an application software for evaluating and modeling the continuous system performance described by nonlinear differential equations and time-dependent.
AMPL is a language for mathematical modeling for solving and describing problems of high complexity for optimization on a large scale.
Armadillo which is a C++ program for linear algebra which has factorizations, decompositions, and functions of statistics.
APMonitor. It is a language for mathematical modeling used for solving and describing physical system representations inform of algebraic and differential equations.
Clojure. It contains numeric Neanderthal libraries, ClojureCL, and ClojureCUDA to handle linear algebraic functions and optimized matrix functions on GPU and CPU.
R is a system for data manipulation and statistical analysis in which the SAS language is implemented.
SAS is statistics software that includes a matrix programming language.
VisSim. It is a nonlinear dynamic simulation and a visual block-diagram program which supports fast ordinary differential equations with the simulation of real-time complex large scale models.
World Programming Systems (WPS), which supports python mixing SAS and R languages using a single-user program for analysis of statistics and evaluation of data. Other language-oriented software includes Julia, Madagascar, O-Matrix, Optim, GAUSS, pearl data language, and many others.
Conclusion
Due to the current trend in transforming the world in many aspects of life, computers have to be included in the numerical data analysis systems to ease faster and accurate data simulations. Industrialization increased business capital, future reverences demands, increasing populations, and other life affairs have led to the demand for computer-aided data analysis systems in the numerical analysis field, as discussed in this article.
References
Bartholomew-Biggs, M. C. (2000). Software to support numerical analysis teaching. International Journal of Mathematical Education in Science and Technology, 31(6), 857-867.
Brinkgreve, R. B. J. (1996). Geomaterial models and numerical analysis of softening. Chitu, C., & Song, H. (2019). Data analytics and processing platforms in CPS. In Big
Data Analytics for Cyber-Physical Systems (pp. 1-24). Elsevier. Conte, S. D., & De Boor, C. (2017). Elementary numerical analysis: an algorithmic approach. Society for Industrial and Applied Mathematics.
G Golub and C Van Loan. Matrix Computations, 3rd ed., John Hopkins University Press, 1996.
Wang, J. Y., & Garbow, B. S. (1981). Guidelines for using the AMDLIB, IMSL, and NAG mathematical software libraries at ANL (No. ANL-81-73). Argonne National Lab., IL (USA).
Wester, M. J. (1999). Computer algebra systems: a practical guide. John Wiley & Sons, Inc..
Information Science and Technology
↳ Cyber Security
Cyber Security in Healthcare and Machine Learning
Abstract
The main reason for the study of the concept is to ensure that the assessment of machine learning and cyber security is facilitated. Technological advances have also resulted in the need for data collection and analysis. The collection of information from multiple sources has been enabled in businesses based on cyber security and machine learning ventures (Manasrah et al., 2021). Security of data is the other critical venture of analyzing the research topic in healthcare. It ensures that the desired measures of managing data and gaining quality service promotion are reached. Health systems have been advanced to ensure that electronic data management is guaranteed.
The concept being assessed also focuses on the programming languages and the importance of the facilitation of the security of data in organizations. Python is one of the programming languages which has developed the decision trees to engage any weaknesses and threats in companies. It has also evaluated random structures for the machine-learning algorithms in place (Manasrah et al., 2021). Production of quality goods and services in healthcare has been reached with the assessment of cyber security and machine learning. Third-party ventures such as those of the attackers have also been limited in healthcare with the assessment of machine learning. Health rights and information protection for patients have also been improved with technological advances.
Bibliography citations
Manasrah, A., Alkayem, A., Qasaimeh, M., & Nofal, S. (2021). Assessment of Machine Learning Security: The Case of Healthcare Data. In International Conference on Data Science, E-learning and Information Systems 2021 (pp. 91-98).
Authors
Anood Manasrah, Princess Sumaya for the University of Technology in Jordan.
Aisha Alkayem, Princess Sumaya University for Technology.
Malik Qasaimeh, Jordan University of Science and technology.
Samer Nofai, GUJ, Jordan.
Research concern
With technological advances and the use of social media platforms across the globe, the use of machine learning in the healthcare sector has increased. The collection and analysis of enormous amounts of patient data have been reached in healthcare with the evaluation of machine and deep learning prospects. Over the past years, the study has integrated 769 records of pregnant diabetics to ensure that reviews of their health were engaged (Manasrah et al., 2021). Many concerns have also been noted with the assessment of the security of patient data in healthcare. Most of the hackers are using the technologies in place to gain access to the databases in the health centers without authorization or verification. Change in data accuracy is another crucial venture that has been noted with the analysis of cybersecurity and machine learning in healthcare.
The purpose statement of the research
Assessment of data and promotion of security in healthcare is one of the ventures that have been noted in the companies with analysis of machine learning and cyber security. Cyber security is a technological advancement that integrates data security through the machine and deep learning ventures. Growth and development are crucial aspects that can only be analyzed in businesses through healthy structural technology. The study is also crucial in enhancing the security of the confidential data of patients (Manasrah et al., 2021). Since most of the attackers engage in changing patients’ data in health centers, assessment of cyber security with machine learning is a crucial approach. Deep and machine learning has incorporated human data in ensuring that evaluation of efficiency and effectiveness in the storage of information is enabled.
Record keeping is another venture that has been significantly engaged by the company. It has also allowed the promotion of safety systems to healthcare in enhancing security against any unauthorized personnel. Improving the performance of a model and technology requires the evaluation of machines through the deep learning aspect (Manasrah et al., 2021). Decision-making and communication ventures are some of the critical aspects that cyber security and machine learning enhance in the health sector. In this case, it allows the use of decision trees and random forests in creating accurate learning algorithms.
How can machine learning improve cyber security?
How does the healthcare sector facilitate the assessment of cyber security with machine learning?
What are some of the cyber security demerits and merits in healthcare?
Does the evaluation of technology promote communication and decision-making?
Precedent literature
Prior to the research on cyber security with machine learning in healthcare, different angles have been structured to ensure that a descriptive research venture is evaluated in the companies. Engagement of personnel training in the healthcare sector is one of the crucial aspects which can promote technological advancements and limit third-party evaluation. Natural language processing venture has also been created by machine learning to ensure that descriptive cyber security is generated. It has also allowed proper programming ventures which can ensure excellence in quality of service provision in the health sector (Manasrah et al., 2021). The accuracy of the stored patients’ data has been attained with the analysis of python. It has also structured desired decision trees to enable proper opinion-making strategies in the companies. Production of quality goods and services can be analyzed in the companies when the assessment of decision-making is promoted.
Research Methodology
Quantitative and qualitative methodologies were utilized in the research to ensure that accurate results were enabled. In this case, a health system was studied in healthcare in Jordan, and it collected 769 records of pregnant diabetics. In the case of the qualitative aspect, interviews and the use of questionnaires were structured to ensure that the patients were conversant with the research being held (Manasrah et al., 2021). The 769 records collected enhance quantitative methodology, which evaluates figures in promoting research assessments. The methodologies engaged also enhanced the use of programming languages such as that python to ensure that accurate data was collected.
Instrumentation
The research was divided into various categories for the collection of accurate results. In this case, the inclusion of the health rights and privacy concerns of the patients, interviews, and assessment of data had to be critical and was managed through cybersecurity with machine learning (Manasrah et al., 2021). Pages (91-98) were utilized to ensure that the quality of the research had been maintained. With the evaluation of the different sections of the research paper, the studies made were accurate and valuable in the companies. Machine learning algorithms also facilitated the achievement of quality data in the health sector.
Findings
The research showcased the importance of machine learning in cybersecurity based on data collection. Machine learning engages efficiency and effectiveness in assessing data collection to be utilized in cybersecurity (Manasrah et al., 2021). Machine learning has also been evaluated in healthcare through cybersecurity since the assessment of algorithms and security ventures are desired. One of the critical demerits that can be noted with cybersecurity is that firewalls challenge arrangements and assessments.
Conclusion
The use of social media platforms and internet prospects have raised the collection of data in companies. For the health sector, the evaluation of cyber security with machine learning is one of the major ventures which has evaluated growth and development (Manasrah et al., 2021). Python is one of the machine learning attributes that have been used in structuring the decision trees for validation of security measures in healthcare. The main goal of the study was to reduce any weaknesses and threats that were faced by the patients’ data on servers
