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Daron Acemoglu’s article “Root Causes” explores the gaps in living standards and development levels between rich and poor countries. While there are many proximate causes of poverty within countries such as a lack of education or outdated technology, Acemoglu focuses on the fundamental causes. The article provides two fundamental hypotheses to explain the gaps: the geographical hypothesis and the institutional hypothesis. The geographical hypothesis states that the underlying causes for differences in prosperity come from a country’s geography, climate, and ecology. These factors shape a society’s technological advantage and influence the incentives of its inhabitants. For example, a country with fertile soil may have an incentive to excel in agriculture and develop advanced technologies for harvesting. However, while nature does have an impact on economic well-being, it is shown that human influence is stronger. Segwaying into the institutional hypothesis, which focuses on the human factors impacting prosperity. Institutions play a crucial role in determining a country’s economic success. A “good institution” exhibits these key characteristics. Firstly, property rights are enforced which provides individuals with confidence that their assets are protected under the law. This confidence encourages investment which contributes to economic development. Secondly, constraints are placed on the actions of powerful individuals, such as elites and politicians. This ensures a sense of equality throughout the society which encourages broader participation in economic activities. Unfortunately, many countries nowadays lack these good institutions, with individuals lacking confidence to invest due to inadequate protection of property rights and excessive control of the economy by elites. The concept of reversal of fortune challenges the notion that initial advantages within a country always lead to long-term economic success. European colonization is a prime example of this, where initially rich societies such as the Aztecs and the Incas were subjected to a reversal of fortune. The institutions imposed by colonizers show the significance of the reversal of fortune. For example, Belgian colonizers exploited the Congo, a country rich in resources, for their own benefit. Property rights were disregarded as Congolese were forced into slavery and elite power was allowed to go unchecked. Their colonization resulted in a significant divide between the rich and the poor, leading to economic distress. This demonstrates that when the idea of a “good institution” is neglected, the gap between prosperity and distress widens. Institutions must be implemented in a way that meets the needs of all members of society, rather than serving the interests of a select few. The concentration of power among the elite often leads to abuse. For meaningful institutional change, those in power must be willing to act for the benefit of everyone. Failing to do so perpetuates economic inequality and hinders development. Recognizing the importance of institutions in economic development means implementing necessary barriers and reforms to support the disadvantages and educate the elite on responsible use of their power. By addressing these fundamental causes, societies can work towards reducing disparities and fostering sustainable economic growth.  

Introduction The programming languages that are used in Unmanned Aerial Systems play a cardinal role to influence their functionality as well as performance. Operators basically use the languages for various purposes such as data gathering, analysis, mapping, database retrieval, as well as project presentation. According to Ismail et al (2018), UAS systems work by showcasing advanced behaviour as there is a limited level of human intervention. Thus the programming languages that are used act as the main medium to shape how these innovative systems respond and react to diverse situations (Ismail et al., 2018). In the current times there exist a broad range of programming languages such as Matlab, R, Java, Python, etc that can be integrated into drones. But the UAS operators must make sure that a suitable programming language is chosen which simplified how the systems perform. The application of popular languages including MATLAB and R has been critically explored to understand how they contribute to UAS designing. Use of programming language in UAS designing Unmanned Aerial Systems are the autonomous vehicles that can be remotely controlled by the operator. As there is no pilot on board in these vehicles, the operators rely on the communication and engagement that they have with the system which influences the operational and performance aspects. The programming languages that are used act as the medium which connects the operator with a UAS system. According to Mottola, autonomous drones are extremely powerful devices whose capabilities are influenced by the language that is used. The language acts as the chief tool which enables the autonomous system with reason with the external space and proceeded in the intended path in a seamless manner. The use of a suitable programming language plays a key role in drones as it allows the programmers to deal with concurrent issues and problems that could exist between the drone operations in the field and the data processing aspect. The use of a suitable programing language in UAS systems influences the sensing application. It enables the operator to have a better degree of control over how the vehicle functions with limited human intervention (Mottola et al., 2014). The role of programming languages is considered to be indispensable in Unmanned Aerial Systems. This is because the language enables the system to perform a level and stable flight which is the most fundamental and important function of the drone. Use of MATLAB in the designing of Unmanned Aerial Systems MATLAB is a programming language that is commonly known as matrix laboratory. It serves as a multi-paradigm programming language as well as a numeric-based computing environment. It is considered to be very effective as compared to other programming languages. While other languages typically work with numbers in a sequential manner, MATLAB operates on whole matrices as well as arrays (Language Fundamentals. Language Fundamentals - MATLAB & Simulink - MathWorks India, 2020). A number of studies has been carried out in the recent times that shed light on how the programing language influences the functional and technical aspects of an Unmanned Aerial System. According to Alarcon and Santamaria, the model-based design of a UAS system acts as the very backbone of its development phase. The programming language that is implemented in the design has a cardinal impact on the functioning of the core design elements. The authors in their study have made use of MATLAB/Simulink code generation tools. The programming language that is used in drones impacts the degree of control over the robotic components of the system such as the arms (Santamaría et al., 2012). According to the study by Bagheri, MATLAB acts as an extremely powerful tool for engineers working on UAS systems as it enables them to simulate and analyse a diverse range of mathematical models as well as control systems. For instance, the programming language has MATLAB Lab Aerospace Toolbox/ Blockset which helps in the simulate of aircraft control and dynamics. The toolbox in the programming language fundamentally helps to visualize and assess the aerospace vehicle and thus assists in the smooth functioning of UAS systems with limited human interaction (Bagheri, 2014,p . 58). The functional aspects of an unmanned aerial system can be categorized into aircraft or platform, sense, perceive, plan and decide, control and connect or communicate. As humans have restricted involvement when it comes to handling these activities, the responsibility of each of these functions lies on the system and its technical components. An operator merely provides direction to the autonomous machine. In such a scenario, the role of the MATLAB programming language is pivotal as it impacts the management of sensor data. It basically assists in the designing and development of autonomous algorithms and supports interface with sensors and other applications. The properties of the language support persistent memory within the autonomous object which shapes the function for each and every phase of the system including validation, initialization, and reset. Role of Simulink in UAS Simulink is a well-known MATLAB-based graphical programming environment that is used for the purpose of modelling, performing simulation and examining multi-domain dynamical systems. In the Unmanned Aerial System context, the block diagram environment is considered to play an important role as it significantly contributes in the design process. It primarily facilitates system-level design and supports the testing of the device before it is actually modelled as a drone. Simulink is considered to be very useful by operators as well as engineers who work on UAS designing aspects as it assists them while working on automatic code generation, supports continuous testing and facilitates the verification of the embedded systems in the Unmanned Aerial System. According to the study by Casaso, MATLAB-based Simulink offers requisite support in drones by deploying flight control algorithms. It is considered to be very effective in nature as it integrates the ‘model-based design’ method which is considered to be a necessity in the designing of cyber-physical equipment (Casado & Bermúdez, 2021). Simulink also influences the navigation system and the vision subsystem which is responsible to process the images that have been captured by the camera. It also supports the flow of information which impacts the overall operations of the UAS systems (Casado & Bermúdez, 2021, p. 3). Use of R programming language in the designing of Unmanned Aerial Systems R is a common programming language that is used for performing a diverse range of functions such as statistical analysis, reporting as well as graphical representations. This environment is considered to be suitable in different kinds of domains including the field of Unmanned Aerial Systems (What is R, 2020). In the aerospace context, the R language is considered to be one of the best programming languages which influences various elements such as the ability to interface with the hardware and the ease of conducting tests. One of the main attributes of the language that significantly contributes in drone designing is that all the actions of the R language are performed on the objects that are stored in the active memory of the system and thus no temporary files are used. The data that is collected and interpreted by using the language can be easily used from a remote server via the internet by the engineer to work on the technicalities of the UAS system (Paradis, 2005). The high-quality graphical output that is generated by using the programming language simplifies the mapping process. In addition to this, the language enables the operator to carry out a comprehensive spatial analysis by using a broad range of statistical tests. Some of the key functions of the programming language that assist in the designing of Unmanned Aerial Systems include data manipulation, linear and non-linear statistical modelling, etc. The environment allows a UAS operator to create a function and pass on control to it along with suitable arguments. Once the intended actions have been performed, the function gives back control to the operator and thus assists in designing and streamlining the automatic components of a drone. Basic needs of a UAS application An unmanned Aerial System needs a number of elements without which it cannot function. Giordan and Adams have identified several cardinal components of UAS systems. It is necessary to take into account various aspects so that the basic requirements of a UAS application can be taken care. Some of the key considerations relate to the ease of use, cost aspects, transportability, and the ability to function in diverse areas (Giordan et al., 2020). As a UAS system is an aerial system which functions by using technology, it is very important to lay emphasis on the hardware as well as the software requirements. The core technical components that are required for an Unmanned Aerial System to function include the airframe, the navigation system, and the control systems. The programming language that is used in a UAS acts as the binding components that ensure that each of the elements of a drone operates and functions in a streamlined manner (Giordan et al., 2020). It is necessary to take into consideration the security aspects as well while working on a UAS application. According to Siddappaji, a solid cybersecurity framework can safeguard a UAS application and its technologies and processes from unauthorized access by online criminals and attackers (Siddappaji & Akhilesh, 2020). Conclusion Unmanned Aerial Systems rely on various technical elements and components to effectively and successfully function remotely. The programming language that is used by the operators and engineers influences various design aspects of these automated systems. MATLAB and R are two of the most effective programming languages that influence various activities and functions in UAS or drone systems such as data collection and data analysis, mapping, database retrieval, and project presentation. In fact, the language that is selected in a UAS application acts as the ultimate medium between the UAS operator and the autonomous machine. It helps to establish a connection between them so that the autonomous vehicle can be remotely handled. References Bagheri, S. (2014). Modeling, simulation and control system design for civil unmanned aerial vehicle (uav). Casado, R., & Bermúdez, A. (2021). A Simulation Framework for Developing Autonomous Drone Navigation Systems. Electronics, 10(1), 7. Giordan, D., Adams, M. S., Aicardi, I., Alicandro, M., Allasia, P., Baldo, M., ... & Troilo, F. (2020). The use of unmanned aerial vehicles (UAVs) for engineering geology applications. Bulletin of Engineering Geology and the Environment, 79(7), 3437-3481. Language Fundamentals. Language Fundamentals - MATLAB & Simulink - MathWorks India. (2020). https://in.mathworks.com/help/matlab/language-fundamentals.html. Paradis, E. (2005). R for Beginners (pp. 37-71). Institut des Sciences de l'Evolution. Université Montpellier II. Santamaría, D., Alarcón, F., Jiménez, A., Viguria, A., Béjar, M., & Ollero, A. (2012). Model- based design, development and validation for UAS critical software. Journal of Intelligent & Robotic Systems, 65(1), 103-114. Siddappaji, B., & Akhilesh, K. B. (2020). Role of cyber security in drone technology. In Smart Technologies (pp. 169-178). Springer, Singapore. What is R? R. (2020). https://www.r-project.org/about.html.

The quantitative study is the procedure for gathering and processing numerical information.. Quantitative data analysis involves examining number-based and numerical data using various statistical methods. To explain behavior, quantitative analysis entails mathematical and statistical modeling, evaluation, and study (Bloomfield & Fisher, 2019). Quantitative analysts quantify a situation by assigning a numerical value to it. Both inferential and descriptive statistics are used in the data analysis. Quantitative inferential statistics are used to conclude by generalizing the results that are obtained in a random sample. Descriptive data analysis in quantitative research generally describes the construction of tables of means and quantiles and measurements of dispersion like variance that can be used to investigate many different hypotheses. The type of test applied in the quantitative analysis depends on the types of variables used. Variuos quantitative tests are done in quantitative research. Quantitative tests make it possible to observe the behavior (Larson‐Hall & Plonsky,2015). The tests include the testing methods like significance testing, quantitative data tests, and survey. Significance testing in quantitative research consists of applying the statistical tests, mainly unpaired t-tests, and the z- tests. Quantitative data tests are a systematic method for determining whether data sets are significantly different (Gostick et al., 2019. Quantitative user tests can also be used when testing the usability of an application interface. Commonly used data analysis programs include R and Python. R is a statistical programming language that can be used in statistical analysis. R is a powerful program that can also be used in statistical modeling classification clustering and carrying out statistical tests. R can easily manipulate data sets and produce high graphics of the data Python commonly uses indescriptive analysis. Python in quantitative research can be used in quantitative trading, which involves inventing and implementing trading strategies centered on mathematical and statistical studies (Gostick et al., 2019). The various standard fields where Python can be used for data analysis are data mining, data processing, modeling, and data visualization. References Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30. Gostick, J. T., Khan, Z. A., Tranter, T. G., Kok, M. D., Agnaou, M., Sadeghi, M., & Jervis, R. (2019). PoreSpy: A python toolkit for quantitative analysis of porous media images. Journal of Open Source Software, 4(37), 1296. Larson‐Hall, J., & Plonsky, L. (2015). Reporting and interpreting quantitative research findings: What gets reported and recommendations for the field. Language Learning, 65(S1), 127- 159.

While several intriguing smaller cloud service providers are out there, the market is today dominated by a few large manufacturers preparing for public cloud adoption. The three most popular cloud service companies are almost generally acknowledged. Cloud computing services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform have become household names in the fifteen years since its launch in 2006. While Amazon Web Services (AWS) continues to be the industry leader in size, scope, and revenue, Microsoft Azure and Google Cloud are growing at double-digit rates each year. These systems lack the same processing, networking, and storage capabilities despite commercial dominance. Amazon Web Services (AWS) Amazon Web Services (AWS) is better tailored to Amazon Elastic Compute Cloud (EC2) applications (EC2). Specific to its EC2 applications, Amazon Elastic Block Storage (EBS) provides block storage with low latency and high availability. Big data analytics, NoSQL databases, and warehousing database applications are all examples of this type of software. Snapshots, rotating disks, encryption options, and security permissions are some of the other features of AWS products. A further AWS offering is the Elastic File System (EFS). Media processing, analytics, web services, content management, and container storage are some of the use cases for which EFS provides a file system interface. Cloud bursting is possible because EC2 can run on both cloud servers and on-premises hardware to process several simultaneous workloads. A third Amazon Web Services offering is S3 (S3). S3 is meant to store unstructured data in the cloud as objects. This kind of object storing works well for programs that don't require as much processing power. The S3 storage service is exceptional due to its 11-nine durability and scalability, which extends to objects in the trillions. In addition to storing data from IoT devices, websites, and videos, S3 is used for archiving, backing up, analyzing, and warehousing business information. The S3 product also has capabilities that make it possible to encrypt data using various methods that align with the strict privacy standards set by the European Union and the United States. Amazon Glacier is the final product offered by AWS. Glacier on Amazon is used to store data that is rarely accessed and is not expected to be updated (Backes et al., 2018). AWS's services enable it to restore massive amounts of data in less than three hours inside the regions it supports. MICROSOFT AZURE Among its numerous characteristics is blob storage, which is a type of object storage. The term "blob storage container" refers to data blobs created by users within their storage accounts. For cloud or on-premises storage, file serving, backup, streaming video or music, cloud or local analytics, and disaster recovery, use this product (SMB). Volumes and a wide range of other applications are supported by all three major operating systems, Linux, Mac OS X, and Windows. Active Directory, a cloud directory service, and Azure Disk, a cloud file storage service, are both available on Microsoft Azure. Finally, the VM's performance is not limited by Microsoft Azure features, and three tiers of VM performance are available across Azure Disk storage: standard SSD, PREMIUM SSD, and Standard HDD (Antu et al. 2021). Google Cloud Platform Google Cloud's three core services are Persistent Disks for block storage, Filestore for network file storage, and Cloud Storage for object storage. These services are essential to the platform and serve as the foundation for the remainder of the Google Cloud services and, by extension, your systems. NetApp Cloud Volumes ONTAP is now available in the Google Cloud, allowing NetApp users to take advantage of this rapidly developing cloud platform. Let's take a deeper look to see  what these  Google Cloud  storage options are useful for and  how they compare. Google Cloud Storage is the name given to Google Cloud's object storage service. Object versioning and fine-grained permissions (at the object or bucket level) are supplied as standard, simplifying development and lowering running expenses (Hunter & Porter, S. 2018). Google Cloud Storage serves as the backbone for several services.   AWS Azure GCP Features 1. Computing Solutions for storing Connecting Apps to the Cloud Analyses and automated learning Effectiveness Aids Management and development instruments Software architecture in the cloud Techniques based on blockchain Predictive Analytics Integrating all IoT devices Attributes of DevOps 1. Controlling productivity Two: Organizing and Keeping Track of Information CaaS-based app creation Artificial intelligence (AI) and machine learning engines Analytics for business Advantages It is compatible with macOS and other widely used operating systems (unlike other providers) A wide variety of available options The ever- increasing variety of available services Ripeness and accessibility able to withstand a great deal of stress without collapsing under the weight of its users or resources Launching is a breeze, the number Wide availability Intuitive configuration with the Microsoft family of software 3.Consumers of Microsoft's cloud services are eligible for price reductions on service contracts. 4.Applications precompiled for multiple languages (Java, Python,.NET, and PHP) 5.Affordable rates for use "on demand." 6. Reliability through several backups to prevent service interruptions superior scalability Easy installation and configuration The usage of widely-used programming languages, such as Java and Python 4. Generous long-term use discounts 5. Effective data-load balancing and quick reaction times Disadvantages 11. Extremely high price when compared to similar options Inadequate record- keeping Challenges in 1: It doesn't have any fancy extras. 2. There is a     Extra costs for fundamental amenities There are supplementary charges for technical assistance provided to customers. Caps on resources Steep learning curve the following launch Network Management, According to Reports It has been argued that learning is more complicated than comparable systems. The service's design may be less polished than rival offerings. A few users have experienced difficulties when contacting technical support. reduction in the variety of characteristics 3. There are fewer services available. (4) Fewer data centers throughout the world References Hunter, T., & Porter, S. (2018). Google Cloud Platform for developers: build highly scalable cloud solutions with the power of Google Cloud Platform. Packt Publishing Ltd. Backes, J., Bolignano, P., Cook, B., Dodge, C., Gacek, A., Luckow, K., ... & Varming, C. (2018, October). Semantic-based automated reasoning for AWS access policies using SMT. In 2018 Formal Methods in Computer-Aided Design (FMCAD) (pp. 1-9). IEEE. Antu, A. D., Kumar, A., Kelley, R., & Xie, B. (2021, December). Comparative Analysis of Cloud Storage Options for Diverse Application Requirements. In International Conference on Cloud Computing (pp. 75-96). Springer, Cham.

Although there are several security measures in use, professionals in the field of computer security have categorized them into different classes. There are strategies that aid in resisting attacks, spotting attacks, detecting attacks, and detecting attacks as well as strategies that aid in recovering from attacks. Although the focus of this study is anomaly detection, it is important to remember that installing a monitor sensor in one's home is a practical technique to notice an assault. According to Bruce et al., (2020), the practice of comparing definitions of an activity that is thought to be normal versus an observed occurrence in order to discover substantial differences is known as anomaly identification. In many instances, intrusion detection systems are used to identify attacks (Bruce et al., 2020). These systems operate by evaluating the traffic flow toward a computer-based system. However, computer specialists compare the historical baseline when it comes to anomaly detection. Anomaly detection, according to Bruce et al., (2020), is a procedure that is predicated on the idea that intrusive or improper behavior deviates from how a typical system is used. Therefore, the majority of anomaly detection systems are able to identify the activity profile of a typical system and then highlight any system events that statistically differ from the established profile. One advantage of identifying anomalies is that it facilitates the abstraction of data related to a system's typical behavior and aids in the detection of assaults, whether or not the system has previously encountered them. By using metrics generated from system measures including CPU utilization, number and length of logins, memory consumption, and network activity, computer security specialists have created behavior models. The susceptibility of a hacker who manages to enter the system during an anomaly detection learning period, however, is a serious flaw in anomaly detection (Bruce et al., 2020). A cunning hacker might be able to teach the anomaly detection how to interpret invasive events so they appear to be typical system behavior. A number of approaches have been devised in anomaly detection. However, experts have concerns about these approaches regarding the unauthorized access to source computer devices. For instance, the statistical approach to anomaly detection provides a system that allows the anomaly detector to measure the deviance of the present behavior profile from the original profile (Kibria et al., 2018). In this case, only computer that have been trained normally is learned by the system, which tries to extrapolate anomalous behavior by employing low probabilistic outcomes of the testing computer outcomes. Concerns here, however, lie with the fact that, false negatives and positives may be generated due to the inadequacy of the statistical measures that are employed. There are also concerns of whether enough normal training computers were collected (Rahul et al., 2020). To add on that, source computer devices unauthorized access covers a range of concerns, like hackers trying to use them as a way of launching an attack that may involve employing collected source computer or information stealing ((Kibria et al., 2018). In network security and performance context, the source of system log as well as network computer flow is regarded as the infrastructure itself, which is another concern. Different processes have been implemented to govern access rights network infrastructure devices. One of the processes that have been employed by many organizations is to come up with a policy that helps to classify information spelling out the importance of the information stored in the system (Rettig et al., 2019). Detection of anomalies in a network is one of the major deficiencies computer and network security personnel have. The research provides an insight for computer and network security, and to be more specific in the research thesis, sources that provide ample information on securing wireless sensor network ( Kibria et al., 2018). He research which I have decided to use a comparative approach will combine literature from a number of previous and historical researches, to provide a great resource to those who are willing to have solutions to computer and network security issues. In their article Kibria et al., (2018), discuss DoS assaults against WSN. If deployed in an insecure environment, the wireless network devices and sensors are unable to protect the wireless media from attacks and are susceptible to physical tampering. The use of symmetric cryptography, which uses shorter encryption keys and is arguably superior to public key cryptography when used in sensor networks, is one of the generic security mechanisms suggested by the authors. Each protocol layer's weaknesses and suggested protection measures are described in the article. For instance, on a physical protocol layer, jamming and node destruction/tampering are used to assault the network and sensor devices (Munir, 2021). The remaining defenses strategies include detect and sleep, avoid jammed areas by traveling through them, conceal or disguise nodes, tamper-proof packaging, authentication, and interaction protection. The goal is to use prior research to give a more thorough, full list of protection mechanisms and remedies to security concerns. A study on assaults, security measures, and difficulties in wireless sensor networks is another crucial source. According to Xie et al. (2018), there are two primary types of computers in computer science: spatial computers and temporal computers. The first step in developing a spatial computer entails creating an intriguing needed series that is not already recorded in spatial storage. Using a spatial computer, the analyst applies spatial information to generate the necessary data. Identifying series is one of several difficulties in the application of spatial computers, particularly in research analysis. According to Mishra & Jena (2021), Clementine and Enterprise Miner are the two commonly utilized tools for managing spatial computers. These techniques are primarily used for the analysis of various spatial computers, including genomic computers and web computers. Latitude and longitude information are stored in the spatial computer. Additionally, it includes coordinates pointing to a location in space. The spatial computer also has a number of features that help locate various geographic locations and images of those locations. The temporal computer, on the other hand, displays the situation in real time. The temporal computer is seen as transient because it doesn't last for a very long time (Midani et al., 2019). Temporal computers are typically employed for demographic research, traffic management, and weather analysis. The analytics performed during temporal analysis are utilized to pinpoint a problem's root cause, which aids in providing a remedy. According to the pattern of phenomena that has been investigated, the answer is assured. The temporal computer performs a wide range of tasks, such as computer categorization and comparisons, trend analysis, correlation analysis between computers, computer series analysis, and many other things (Tschimben, 2022). The basic goal of a temporal computer is to pinpoint the temporal series, correlation, and sequence that exist within it and to gather the data necessary to display the computer's behavior over a certain time frame. The computation of numerous main key values at various points in a given time is made possible by the temporal computer, which also distributes the computer's time sequence. The two categories are really different even though they seem to be the same. To start, according to its definition, a spatial computer derives data and correlation from local computers contained within a computer base, whereas a temporal computer only extracts trustworthy data from temporal computers, aiding in pattern recognition. In connection, the platform for computer science permits forecasting through the use of codes and powerful computers. The created model makes it easier to develop trustworthy solutions to the problem that needs to be solved. The extent of the inputs and the precision of the varying computer acquired are the main determinants of accuracy during modeling. This system makes use of Hadoop (Bruce et al., 2020). Traditional computer bases and statistical tools are not capable of handling great structured and unstructured computer, but platform tools can computer scientists primarily use the platforms for cleaning, computer visualization using statistical analysis, and modeling code, among other tasks. According to Bruce et al. (2020), business analysts also use the platform to understand their clients' businesses. Replications based on stakeholder information are always supported by the platform. There are related tools from computer science. The computer science tool functions as a platform for computer science to organize, examine, and visualize the computer. The computer science tool can only be used one at a time, whereas the computer science platform can include multiple programming tools. This is the main difference between the two. R is a useful example of a computer science tool. R is open-source and cost-free software that is used for computing and visualizing statistical data (Jha & Sharma, 2021). R has around 9900 packages, including ggpubr, ggplot, tidry, and others that allow computer scientists to conduct analysis, according to Bruce et al. (2020). The integration of R with other languages, including Python, SQL, and others, is quite easy.   Conclusion It has been highlighted that the field of computer science is expanding quickly in the modern technological environment. As a result of this increase, as mentioned, computer scientists and businesses are facing numerous challenges, particularly in managing the computer. Since computers come from diverse sources, management of them is a concern. Additionally, it has been emphasized that the computer analyst must possess the necessary abilities to control and resolve any computer-related issues that may arise. Additionally, it has been highlighted that computers may classify data in two different ways: spatially and temporally. Computer analysis and visualization are required for the desired outcomes in each of these classifications. The life cycle of computer science, which starts with computer collecting and ends with computer visualization, has also been noticed. The computer science platform is referenced in relation to supporting various programming tools for analysis. In conclusion, it has been mentioned that various programming languages are utilized for analysis, including R, which has a number of internal packages that aid in computer visualization for improved decision-making. References Bruce, P., Bruce, A. and Gedeck, P., 2020. Practical statistics for data scientists: 50+ essential concepts using R and Python. O'Reilly Media. Jha, P., & Sharma, A. (2021, January). Framework to analyze malicious behaviour in cloud environment using machine learning techniques. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-12). IEEE. Kibria, M.G., Nguyen, K., Villardi, G.P., Zhao, O., Ishizu, K. and Kojima, F., 2018. Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, pp.32328-32338. Midani, W., Fki, Z., & BenAyed, M. (2019, October). Online anomaly detection in ECG signal using hierarchical temporal memory. In 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME) (pp. 1-4). IEEE. Mishra, B., & Jena, D. (2021). Mitigating cloud computing cybersecurity risks using machine learning techniques. In Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019 (pp. 525-531). Springer Singapore. Munir, M. (2021). Thesis approved by the Department of Computer Science of the TU Kaiserslautern for the award of the Doctoral Degree doctor of engineering (Doctoral dissertation, Kyushu University, Japan). Rahul, K. and Banyal, R.K., 2020. Data life cycle management in big data analytics. Procedia Computer Science, 173, pp.364-371. Rettig, L., Khayati, M., Cudré-Mauroux, P. and Piórkowski, M., 2019. Online anomaly detection over big data streams. In Applied data science (pp. 289-312). Springer, Cham. Tschimben, S. (2022). Anomaly Detection in Shared Spectrum (Doctoral dissertation, University of Colorado at Boulder).