2nd International Conference on Advancements in Computing [ICAC] 2020
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1317
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Publication Embargo Secure Communication Using Steganography in IoT Environment(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-11-10) Amjath, M.I.M.; Senthooran, V.IoT is an emerging technology in modern world of communication. As the usage of IoT devices is increasing in day to day life, the secure data communication in IoT environment is the major challenge. Especially, small sized Single-Board Computers (SBCs) or Microcontrollers devices are widely used to transfer data with another in IoT. Due to the less processing power and storage capabilities, the data acquired from these devices must be transferred very securely in order to avoid some ethical issues. There are many cryptography approaches are applied to transfer data between IoT devices, but there are obvious chances to suspect encrypted messages by eavesdroppers. To add more secure data transfer, steganography mechanism is used to avoid the chances of suspicion as another layer of security. Based on the capabilities of IoT devices, low complexity images are used to hide the data with different hiding algorithms. In this research study, the secret data is encoded through QR code and embedded in low complexity cover images by applying image to image hiding fashion. The encoded image is sent to the receiving device via the network. The receiving device extracts the QR code from image using secret key then decoded the original data. The performance measure of the system is evaluated by the image quality parameters mainly Peak Signal to Noise Ratio (PSNR), Normalized Coefficient (NC) and Security with maintaining the quality of contemporary IoT system. Thus, the proposed method hides the precious information within an image using the properties of QR code and sending it without any suspicion to attacker and competes with the existing methods in terms of providing more secure communication between Microcontroller devices in IoT environment.Publication Embargo Prediction of CKDu using KDQOL score, Ankle Swelling and Risk Factor Analysis using Neural Networks(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Lokuarachchi, D.N.; Manoj, J.V.T.; Weerasooriya, M.N.H.; Waseem, M.N.M.; Aslam, F.; Kumarasinghe, N.; Kasthurirathne, D.Chronic Kidney disease (Chronic Kidney Disease (CKD)) is a type of kidney disease where gradual loss of kidney function occurs over a period of months to years. But, when CKD cannot identify a manner or causation of the disease or set of causes it is known as Chronic Kidney disease with unknown etiology (CKDu). There are several factors to be considered when analyzing the main causes for CKDu such as socio-economic, environmental, meteorological and health aspects in relation to the CKDu in Sri Lanka. In this research work, identification of CKDu has been done using the relationship of the Kidney Disease Quality of Life (KDQOL) score, ankle swelling with the serum creatinine level of blood and considering risk factors. This research has been done using three major branches of Artificial Intelligence namely neural networks, convolutional neural networks and machine learning. The relationship between the mentioned factors and CKDu has been identified. The sensitivity of 77.27% and a specificity of 89.28% have been marked for the detection of CKDu related to ankle swelling.Publication Embargo Generating 2.5D Motion Graphics from 2D Designs(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ranawake, I.; Guruge, S.; Ahamed, A.; Kasthurirathne, D.As of the year 2020 , the video production industry is worth 31 billion dollars in the United States alone, with more than 6000 businesses and 57000 employees, and keeps growing. The global computer animation market size is anticipated to reach USD 28.30 billion by 2025, according to Grand View Research, Inc. Such significant growth demands the tech industry to introduce better tools for making animations. In this paper, we propose our contribution, specifically for User Interface designers in the field of motion graphics. UI/UX design is found to be one of the top 5 most trending opportunities for motion designers, and our proposed system allows them to generate a 2.5D animation based on a 2D Futuristic User Interface (FUI) design. The ultimate goal is to reduce the production time of FUI animations and minimize the cost of responding to the client’s changes. Obtaining clients’ feedback directly on animations rather than still images would improve the client’s involvement in production, resulting in greater confidence and loyalty. We implement several image processing techniques such as thinning and pixel clustering for pre-processing the 2D designs to segment the given design into an array of atomic shapes. Since the thinned shapes ensure that any pixel in the design does not have adjacent pixels which are also adjacent to each other, it is possible to utilize mathematical means to approximate the shapes. Our system converts a given 2D design to a collection of animated lines and arcs distributed in 3D space that eventually can be exported to the industry-standard tool, Adobe After Effects.Publication Embargo Blockchain based Patients' detail management System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Abeywardena, K.Y.; Attanayaka, B.; Periyasamy, K.; Gunarathna, S.; Prabhathi, U.; Kudagoda, S.In the data technology revolution, electronic medical records are a standard way to store patients' information in hospitals. Although some hospital systems using server-based patient detail management systems, they need a large amount of storage to store all the patients' medical reports, therefore affecting the scalability. At the same time, they are facing several difficulties, such as interoperability concerns, security and privacy issues, cyber-attacks to the centralized storage and maintaining adhering to medical policies. Proposed Flexi Medi is a private blockchain based patient detail management system which is expected to address the above problems. Solution proposes a distributed secure ledger to permits efficient system access and systems retrieval, which is secure and immutable. The improved consensus mechanism achieves the consensus of the data without large energy utilization and network congestion. Moreover, Flexi Medi achieves high data security principles based on a combination of hybrid access control mechanism, public key cryptography, and a secure live health condition monitoring mechanism. The proposed solution results in successfully deployed smart contracts according to the roles of the system, real time patient health monitoring with more scalable and access controlled system. The overall objective of this solution is to bring the entire medical industry into a common platform using a decentralized approach to store, share medical details while eliminating the need to maintain printed medical records.Publication Embargo DenGue CarB: Mosquito Identification and Classification using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mohommed, M.; Rajakaruna, P.; Kehelpannala, N.; Perera, A.; Abeysiri, L.This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.Publication Embargo Influencial Factors of Adopting Digital Banking by Users in Western Province of Sri Lanka(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) RATHNAWEERA, L.; Karunasena, A.Information Technology has transformed entire society while changing our day to day lives immensely in the past few decades. The emergence of digital technologies enables new business models and inline with that establishes much potential in transforming banking industry. In the process of innovation, banks have adopted numerous cutting-edge technologies to develop computer frameworks, computer system networks, and digital communication infrastructures. Distinct to many developed countries, digital banking is a new phenomenon for Sri Lankan context as majority of banking service providers and clients appears to be fairly unacquainted with different aspects of this service. The purpose of this study is to identify influential factors for adopting digital banking of customers residing in Western Province of Sri Lanka. For the above purpose, technology acceptance model (TAM) has been used to define the research framework and the study employs the quantitative research methods for data collection and analysis. The results of the research show perceived usefulness, perceived ease of use, security and trust, attitude towards digital banking and subjective norm has a great influence towards to adopting digital banking.Publication Embargo Stock Market Price Forecasting using ARIMA vs ANN; A Case study from CSE(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) G.W.R.I., Wijesinghe; R.M.K.T., RathnayakaStock market prediction or forecasting is a challenging task to predict the upcoming stock values. Stock prices are nonstationary and highly noisy because stock markets are affected by a variety of factors. Traditionally, the next lag of time series is effectively forecast by a variety of techniques like Simple Exponential Smoothing, ARIMA. In particular, ARIMA has shown its success in accuracy and precision in predicting the next time-series lags. As part of the literature, very few studies have focused on Colombo Stock Exchange (CSE) to find new predictive approaches for the forecasting of high volatility stock price indexes. Different statistical approaches and economic data strategies have been widely applied to define market price movements and trends and the trade volume levels in CSE over the last ten years. This article explores whether and how the newly developed deep learning algorithms for the projection of time series data, such as the Back Propagation Neural Network, are greater than traditional algorithms. The results show that Deep learning algorithms like BPNN outperform traditionally based algorithms like the model ARIMA. The MAE and MSE values relative to ARIMA and BPNN, which suggests BPNN 's superiority to ARIMA.Publication Embargo AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Shanmugarajah, S.; Tharmaseelan, J.; Sivagnanam, L.Road Accidents and casualties incited by drowsiness are an overall important social and monetary issue. The connection between drowsiness and accidents is bolstered by logical confirmations that relate to small-scale sleep. This project has focused on Driver drowsiness detection by using ECG signal extraction. This work expects to extract and arrange the basic four types of sleep through Wavelet Transform and machine learning calculations. The report covers a short theoretical introduction about the medicinal topic, features the extraction, filtering techniques, and afterward trains the extracted information through machine learning software. After that is covered, it demonstrates the results with two types of machine learning algorithms (active or drowsiness status) with WEKA software. The main benefit of this system is it will send a notification to the driver's mobile every second when he goes to sleeping status. Nowadays artificial intelligence cars are available with sleep assistance, however, the devices used on these cars are very expensive. So, our approach is to develop a system to predict the driver's drowsiness to reduce accidents caused by sleepiness at a low cost. The sleep / awake status is determined by both the factors RR peak's distance and R's amplitude.Publication Embargo Vehicle Recommendation System using Hybrid Recommender Algorithm and Natural Language Processing Approach(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Boteju, P.; Munasinghe, L.Owning a vehicle has become a mandatory requirement in the modern world. Automobile industry investing a lot on producing different car models to cater the needs of their customers with different social and economic backgrounds. Thus, Auto makers constantly produce similar car models with different features. In Sri lanka, total number of new vehicles registered at Sri Lanka Registry of Motor Vehicles(RMV) during the period of seven years (from 2008 to 2015) has been increased from 265,199 to 668,907 which is nearly 2.5 times growth. This figure shows the rapid growth of the domestic vehicle market. For a new customer, choosing the most appropriate vehicle requires an extra effort/time and has become a challenging task. For example, matching personal interests and economy with number of available options is a quite complex task. Thus, most of the customers seek support from experts who provide consultancy services. However, customers frequently making complains about the existing services which offers consultancy for new vehicle buyers. The key issues are the people involved in the consultancy are not technically sound and pay minimal attention to customer requirements. Their main focus is to sell the vehicle. Thus, the customers face numerous difficulties before and after buying their vehicle. To address this problem, this research presents a novel vehicle recommender system which guides and gives suggestions to the customers using machine learning technologies. Here, we trained a neural network model using data collected from vehicle users and vehicle sellers. Other than the neural network model, the proposed recommendation system uses natural language processing (NLP) to produce more personalized recommendations. The results shows that the recommendations made by the proposed vehicle recommendation system achieves 96% accuracy in recommending vehicles.Publication Embargo Experimental Determination of CNN Hyper- arameters for Tomato Disease Detection sing Leaf Images(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunarathna, M.M.; Rathnayaka, R.M.K.T.Today, deep learning has become an emerging topic widely used in pattern recognition and classification problems. The design choice of the deep learning models entirely depends on who it's going to create. Still, it requires prior experience because identifying the best combination of parameters is a challenging task. So, the main objective of this study is to develop an accurate model for tomato disease classification while exploring the possible range of parameters that highly affects the performance of the Convolutional Neural Network (CNN). A simple CNN model was first built and train from scratch by using 22930 tomato leaf images collected from the Plant Village dataset in Kaggle. The model was tested for many cases by changing the values of a set of parameters while keeping other parameters constant. Finally, performance metrics were evaluated for every chosen parameter comparing with the base model. The highest prediction accuracy, training accuracy, and validation accuracy achieved from the study are 92%, 94%, and 92%, respectively. Rather than offering a guess, this study can, at most, give a definite answer that will assist new researchers in understanding how the accuracy and loss vary for every parameter within the area of tomato plant disease classification.Publication Embargo An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayaweera, K.N.; Kallora, K.M.C.; Subasinghe, N.A.C.K.; Rupasinghe, L.; Liyanapathirana, C.According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.Publication Embargo Character Modifier Combinations Recognition in Sinhala Handwriting(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Silva, C.M.; Jayasundere, N. D.Sinhala script is categorized as a segmental writing system and therefore consonant–vowel sequences are identified as a unit. Depending on the vowel and the consonant combination, the diacritic can attach above, below, following or preceding the consonant. The identification of characters with the modifier symbols is important in recognizing generally written Sinhala text and has not been addressed sufficiently in the existing research. Sinhala handwriting recognition is much difficult compared to the other popular languages due to the complexity of the shapes of the characters and the presence of the modifier symbols. This paper discusses on a projection profile, distance profile, partial distance profile and contour based approach to identify characters along with character modifiers in Sinhala script. The proposed method has given an average recognition rate of 75% for 283 character modifier combinations. The proposed solution can be used to identify Sinhala handwritten text with a proper segmentation mechanism.Publication Embargo Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gajanayake, R.G.U.S.; Hiras, M.H.M.; Gunathunga, P.I.N.; Supun, E.G.J.; Karunasenna, A.; Bandara, P.Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic perfo rmance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile , CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language Processing to identify the Big Five personality traits, CV and GitHub insights, candidate’s skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented an d shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.Publication Embargo Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gajanayake, R.G.U.S.; Hiras, M.H.M.; Gunathunga, P.I.N.; Supun, E.G.J.; Karunasenna, A.; Bandara, P.Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic perfo rmance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile , CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language Processing to identify the Big Five personality traits, CV and GitHub insights, candidate’s skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented an d shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.Publication Embargo Towards the Development of a User-Centred Health Management Application for Elderly(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Musthafa, F.N.; Mustafa, M.B.; Tajudeen, F.P.; Ramasamy, T.S.; Sinniah, A.As the world is gearing towards an increased older adult population, the need to focus on wellness and healthy lifestyle become essential. Studies reveal evident of delaying the chronic diseases that cause serious health issues among the elderly, which is made possible by educating the elderly on healthy ageing. Mobile applications can be used as a tool to educate the elderly on health management. Hence, the focus on developing health management mobile applications has arisen among the software engineering community as well as the medical experts. Though a considerable amount of health management applications are available online, which some of it are already in use, many still find these application to be less effective and the elderly community is left behind in using them as they don't provide sufficient features preferred by the elderly. Hence, this research focuses on developing a user centered health management application exclusively for the elderly. User requirement related information was collected, and a thorough literature review was conducted with the aim of finding the preferred features needed in the application and the proof of concept prototype was developed. The developed application underwent a functional testing process, where the system performed as expected.Publication Embargo Smart Exam Evaluator for Object-Oriented Programming Modules(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wickramasinghe, M.L.; Wijethunga, H.P.; Yapa, S.R.; Vishwajith, D.M.D.; Samaratunge Arachchillage, U.S.S.; Amarasena, N.Worldwide educators considered that, automate the evaluation of programming language-based exams is a more challenging task due to its complexity and the diversity of solutions implemented by students. This research investigates and provides insight into the applicability and development of a java based online exam evaluator as a solution to traditional onerous manual exam assessment methodology. The proposed system allows students to take online exams in Java for an implemented source code in a practical exam, automatically reporting the results to the administrator simultaneously. Accordingly, this research examines existing methods, identifies their limitations, and explores the significance of introducing a smart object-oriented program-based exam evaluator as a solution. This method minimizes all human errors and makes the system more efficient. An automated answer checker checks and marks are given as human-counterpart and generate a report with possible suggestions for improvement of the answer scripts and generate a classification report to predict the student’s final exam marks. This software application uses a Knowledge base, Abstract Syntax tree (AST), ANTLR, Image processing, and Machine Learning (ML) as key technologies. The proposed system gains a higher accuracy of 95% as performed by a separate human-counterpart. These results show a high level of accuracy and automate marking is the major emphasis to save human evaluation effort and maximize productivity.Publication Embargo Enhanced Symmetric Cryptography for IoT using Novel Random Secret Key Approach(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sittampalam, G.; Ratnarajah, N.The deployment of IoT devices in different domains enables technical innovations and value-added services to users but also creates multiple requirements in terms of effective communication and security. IoT devices are constrained by less computing resources and limited battery power. Generally, the TLS/SSL protocol is used to provide communication security on IoT and the protocol utilizes important encryption algorithms like RSA, Elliptic Curve Cryptography, and AES. However, these conventional encryption algorithms are computationally and economically expensive to implement in IoT devices. Lightweight Cryptography (LWC) algorithms were introduced recently for IoT and the aim of the algorithms is to provide the same level security with a minimal amount of computing resources and power. This paper proposes a novel Random Secret Key (RSK) technique to provide an additional security layer for symmetric LWC algorithms for IoT applications. In RSK, IoT devices do not transmit keys over the network; they share a random matrix, calculate their own RSK, encrypt, and transmit the cipher text. When a random matrix lifetime expires new matrix published and RSK resets. Regular change in the RSK makes the IoT networks resistant to brute-force/dictionary attacks. The RSK added one more simple and effective secure layer to strengthen the security of the original secret key and is successfully implemented in a smart greenhouse environment. The outcomes of the experiments prove that the RSK provides enhanced and efficient protection for symmetric LWC algorithms in any IoT systems, consume a minimum amount of resources and more resistant to key-based attacks.Publication Embargo Intelligent Disease Detection System for Greenhouse with a Robotic Monitoring System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Fernando, S.; Nethmi, R.; Silva, A.; Perera, A.; De Silva, R.; Abeygunawardhana, P.K.W.Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attentiongrabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.Publication Embargo A Geophone Based Surveillance System Using Neural Networks and IoT(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.Publication Embargo A Network Science-Based Approach for an Optimal Microservice Governance(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Siriwardhana, G.S.; De Silva, N.; Jayasinghe, L.S.; Vithanage, L.; Kasthurirathna, D.In today's world of software application development, Kubernetes has emerged as one of the most effective microservice deployment technologies presently available due to its exceptional ability to deploy and orchestrate containerized microservices. However, a common issue faced in such orchestration technologies is the employment of vast arrays of disjoint monitoring solutions that fail to portray a holistic perspective on the state of microservice deployments, which consequently inhibit the creation of more optimized deployment policies. In response to this issue, this publication proposes the use of a network science-based approach to facilitate the creation of a microservice governance model that incorporates the use of dependency analysis, load prediction, centrality analysis, and resilience evaluation to effectively construct a more holistic perspective on a given microservice deployment. Furthermore, through analysis of the factors mentioned above, the research conducted, then proceeds to create an optimized deployment strategy for the deployment with the aid of a developed optimization algorithm. Analysis of results revealed the developed governance model aided through the utilization of the developed optimization algorithm proposed in this publication, proved to be quite effective in the generation of optimized microservice deployment policies.