MSc in Enterprise Application Development

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2480

Students in the MSc in Enterprise Application Development programme are required to submit a thesis as a compulsory component of their degree requirements. This collection features merit-based theses submitted by postgraduate students specialising in Enterprise Application Development. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.

Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Novel Predictive Text Entry Method for Sinhalese Mobile Users
(2021) Ishara, H. A. N.
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Virtual Musical Performance In Pandemic Situation
(2021) Prasadinie, H. A. M.
The 2020 Covid – 19 global epidemics had a significant impact to the entertainment industry. That’s greatly affected societies around the world. And also due to government policies citizens are unable to participate for public gatherings, public businesses and public areas. In music industry, major part of income of musicians depends on tours, musical show. But in this pandemic situation, had a huge negative effect to the musicians’ income because they cannot perform face-to-face and cannot arrange huge gatherings. However, nowadays musicians have to turn to digital media and pushed music industry to the new reality of online concerts-virtual concerts. So, musicians are taking the show/concert online. Musicians should send links for the virtual concerts individually. Apart from that, the research proposes to implement an application to put all the details of the concerts. And through the application people can pay and get the link easily. If needed people can participate to the concert through the application and requesting songs from the band by commenting and can see the views for the performance. Advertisers can show their advertisements while performance going on.
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Time Series Prediction of Medical Records Incorporating Stationary Personal Details
(2021) Rajasinghe, R.L. Navod
Improved blood glucose monitoring techniques have emerged over the last century. Adequate glycemic control and minimal glycemic variability necessitate a perfect, accurate, and dependable glucose monitoring system. [2]. There is still research being done on blood glucose monitoring systems in order to find the best one. According to the research proposal, the goal is to model and predict multiple blood glucose time-series from different users efficiently from limited training data in order to control and model their blood glucose levels. Individuals must anticipate blood glucose levels in order to take preventive measures against health risks in good time. There are high quality highlights and plan expectation models for the past endeavors, which lead to low exactness because of incapable component portrayal and limited preparing data for each individual. According to the findings of this study, the best way to predict blood glucose levels is to use a multi-time-arrangement profound LSTM model (MT-LSTM). It uses an individual learning layer for customized forecast and naturally learns highlight portrayals and transient conditions of blood glucose elements by sharing information among different clients. MT-LSTM outperformed traditional predictive relapse models in assessments of 100 clients.
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Indoor Crowd Interaction Surveillance Using Image Processing in Post-COVID-19 Situation
(2021) Piumal, M. K. I.
Working title: Indoor crowd interaction surveillance using image processing in post-COVID19 situation Human interaction is limited in today’s society because of Covid 19 health restrictions, which are in place to prevent the virus from spreading. According to the rules, individuals must be at least one meter apart, and the number of individuals in an indoor environment is limited to a certain number. However, most people do not follow the instructions, putting the disease’s spread at risk. The severity is substantially higher if the environment is indoor. If a single infected person is detected in the area, health officials should trace the close contacts of the person. To answer this problem, the research project was conducted by providing a solution for contact trace. The research is conducted by implementing a convolutional neural network to obtain the risk footage from the CCTV footage and determine the health guideline violations. With the violated information digital contact tracing was done through the face search framework.
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Machine Learning Assisted Risk Management And Responsible Conduct Of Gambling
(2021) Sankalpa, S P Sachintha
Managing risk in a proactive manner is the most important factor that contributes to the longterm success of a bookmaker as well as their patrons (punter). Traditionally, risk management is done by skilled traders. But the amount of data and information that traders can access at any given time is limited. Machine learning assisted risk management (MLARM) module that has been developed in this research can classify and identifying gambling patters of different punters with an accuracy over 92%. It makes use of two artificial neural networks that are developed specifically to handle two types of data. One being betting data and the other being notes and comment from traders. MLARM will be a helping hand for traders in risk management while supporting punters combat gambling addiction.
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Peer to Peer social media platform
(2021) Dissanayaka, R.M.C.L.B
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Cloud Oriented Micro Services Resource Optimization by Content Delivery Networks
(2021) Jayasundara, P.P.A.S.
In the field of modern information technology, the most intriguing topic is cloud-based application development. After several decades of rapid development and research on cloud technologies, nowadays almost all cloud service providers are providing a massive range of services with higher reliability but there are a couple of business domains that having additional technical requirements and these are unique to their business domains. Capital Market and Finance is one of such specific business domains which need to address additional technical and compliance requirements. The main technical barrier in this domain is providing business functionalities for all users across the globe with micro-second level latency. Therefore, when developing and maintaining such a system, we are highly concern about system throughput and hardware resource allocation. While on the subject, the cloud-based system architecture is an ideal infrastructure for this kind of application development because we can upgrade hardware resources within a couple of minutes. however, there are significant issues remains as it is.  Message queues are growing unexpectedly until resource upgrade.  Lack of accurate cloud services to identify duplicate API requests.  User connectivity and API access are limited due to service back off  Peek time is limited to sort period and resources are billing on hours.  System recovery in machine terminate is very costly mechanism As a matter of above technical concerns, we are conducting this research to propose a better solution to handle these types of technical barriers in without upgrading hardware resources unnecessarily and the proposed solution will not be limited to Capital market but it can be used for any application service to utilize their hardware resources while high network traffic
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Cognitive Code Analyzer
(2021) Thirunayan, D.J.
Source code is the building block of any form of software and maintaining efficiency and readability of source code is crucial for the long-term maintainability and usability of any software product. And it is the responsibility of software engineering teams to maintain consistent standards for their source code. The most common approach used by software teams to maintain source code readability and identify bugs is through source code review. Source code review is a process in which when an engineer finishes a project component, functionality, or module, before the developed functionality is released the source code changes in the newly developed functionality are reviewed by another software engineer who is typically more experienced. Although code review was proven to be an effective method for maintaining code consistency, one of the biggest problems in source code review is the amount of time spent by engineers to review code. Maintaining consistent efficiency of source code is an even tougher task because there is no single metric to measure the efficiency of source code. And even metrics like time complexity do not have an algorithmically straightforward method of evaluation from source code. In this work we propose a “Hydranet” inspired deep learning based model architecture which can effectively learn the underlying patterns in the structure of source code code through it’s syntactic and semantic representations and use the learned representations to perform two primary downstream tasks : generating source code review and predicting time complexity.
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IoT For Sustainable Farming Without Soil: Reinforcement Learning For Device Interaction
(2021) Liyanage, D. L. K. S.
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Mobile Crime Reporting System with Blockchain Based Data Provenance
(2021) RAJAPAKSHA, H.M.R.P
The security situation in Sri Lanka has deteriorated over time due to the low number of police personnel in the country and the authorities lack concrete to solve crime incidents since there is no proper crime reporting system. Crime incidents happen everywhere but the witness to these crime incidents nonexistence a convenient and efficient method to report them. Security challenges have increased from mere theft to carjacking attacks and to more serious and evolved challenges like murder and terrorism. With increase of smartphones in Sri Lanka, an opportunity exists because of the untapped gap of incidents reporting. The proposed solution was to develop a mobile application that can be used to report any crime incidents. The mobile application was developed on the Android platform and will integrate the use of GPS location services. It was developed concurrently with a web application developed in ReactJS language to supplements its functionality and MYSQL used as the database server. The solution has an administrative webbased backend that will be accessed by the police force to ensure they get detailed information of criminal activities. The web application was adapted the MVC architecture with object-oriented environment. In addition to that online UML tool is being used to draw UML diagrams. Thus, the mobile application comes in trend to provide a solution to the way users report crime incidents. The suggestions made by users were used to enhance the application functionality and performance. The application will allow users to report crime incidents that happen in anywhere anytime. Based on the overall statistics of user testing and evaluation, can say that the application fulfills its simplicity and usability requirement and based on the questionnaire responses, the application is generally considered easy to understand and use.
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Smart Traffic Lights System using Google API
(2021-05) Perera, P. V. C. N.
There is a lot of traffic in urban zones, which can lead to long traffic jams and large traffic delays. Hence overcrowding in traffic is a demanding problem at present. Due to traffic lights, a vehicle that has stopped at an intersection happens to stop at the next intersection very often. And its ever-growing environment makes it essential to distinguish the time taken to travel among two intersections in real-time for improved signal control and effective traffic management. There can be different causes of congestion in traffic like insufficient capacity, unrestrained demand, large Red-Light delays, etc. While insufficient capacity and unrestrained demand are somewhere interrelated, the delay of respective light is hardcoded and not dependent on traffic. Therefore, the need for simulating and optimizing traffic control to better accommodate this increasing demand arises. Traffic light control can be done by isolated or coordinated control. Isolated control is used for the control of a single intersection and coordinated control is used for the control of a network of intersections. This paper it is attempted to implement an improved coordinated traffic light control system with the help of Google map APIs.
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Extended User Experience For Data Entry Process In The ERP Systems
(2021-05) Perera, G.D.M.
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Detecting rosacea skin disease severity level from selfie images with help of Transferee Learning Regression
(2021-05) Wickramarathna, R M Dilan
The aspect of detecting and preventing or avoiding a disease is a significant aspect of the health care industry when taking into consideration the behavioral protection that is needed against diseases and pandemics. Presently, due to the prevailing pandemic situation, the healthcare industry is being over-whelmed and facing a large and unmanageable workload when considering the anomaly detection pertaining to the patients. When healthcare workers, researchers and advocates do not possess the in-depth knowledge needed pertaining to a disease as well as its anomaly symptoms, it is a challenging task to identify reluctant individuals. In most of the situations, an average individual will not be able to determine the symptoms of the disease by simply glancing or looking at the facial features. Furthermore, it is difficult to identify dermatological changes that cannot be recognized from a general clinical observation. This influences the need for an accurate, effective and efficient automation pertaining to detection of anomaly and symptoms by simply observing the surface of the face to evaluate diseases such as rosacea, acne, shingles, Covid-19 rashes etc. that portray similar face diseases. Rosacea is identified as a skin disease that is able to affect an individual in the long-term pertaining to the skin surface of the face. Its symptoms are pimples on the skin, redness, swelling as well as superficial dilated blood vessels that is found around the face, nose and neck. Rosacea is identified to be one of the most severe yet common skin conditions or disorders across the globe. Due to its severity, most of the time the tests as well as assessments are conducted by trained and specialized dermatologists in a special and controlled environment. The disease is seen to be mostly spread around the European region since the skin of European citizens are quite sensitive. The real reason behind the disease is unknown. The symptoms can be shown at unexpected instances as well. The need to detect the symptoms of rosacea can be frequent. Therefore, there is a dire need of a certain media to detect the symptoms of the condition with ease and accuracy. Considering the proposed topic, the ultimate goal of the thesis is to develop a mobile application that is able to observe a selfie image at any given time and receive the feedback from the app with regard to the skin condition, the severity as well as preventative measures or remedies pertaining to the skin condition, similar to how a trained and professional dermatologist would.
Open Access
A Sinhala Based Programming Assistance Tool For Java Programmers
(SLIIT, 2024-12) Athukorala, K. S. N.
This research presents an extensive programming assistance tool, which is implemented especially for native Sinhala-speaking Java programmers. The tool provides several features to overcome challenges faced by Java programmers, including real-time code generation, diagram creation based on user queries and repository assistance for Java-related projects. It utilizes advanced NLP techniques and large language models (LLMs) to convert Sinhala queries into Java code, visualize code flow through diagrams, generate detailed reports from repository data and provide answers according to user given queries. Additionally, the tool incorporates a transformer-based translation model that converts Sinhala queries into English for code and diagram generation. The tool demonstrates high reliability and technical accuracy, with an overall translation accuracy of 95.79%, indicating strong alignment with reference translations. These capabilities significantly enhance the accessibility and productivity of Sinhala speaking developers. The system provides precise and contextually relevant responses to programming queries related to specific repositories by integrating large language models (LLMs) with a Retrieval-Augmented Generation (RAG) architecture. Core functionalities include repository assistance, enabling users to clone, query, and obtain targeted insights into codebases. Response generation is powered by LLMs, including GPT-3.5-turbo, GPT-4-turbo, GPT-4o-mini, GPT-4o, and ChatGPT-4o-latest. To evaluate the system’s effectiveness, the tool was tested using the RAGAS (Retrieval-Augmented Generation Answer Scoring) framework, assessing performance across three key metrics: answer relevance, answer similarity, and answer correctness. This study demonstrates how such tools can democratize iii programming education, overcome language barriers, and increase inclusivity in technical learning, ultimately contributing to the growth of Sri Lanka’s technology sector.
Open Access
Complexity Analysis and Visualization Tool
(SLIIT, 2024-12) Sampath, B. M. W. G. K. R.
The paper presents an original software metrics tool for measuring complexity which goes beyond the limitations of current tools and measurements. However, standard metrics such as those developed by Chidamber and Kemerer, they focus only on the technical aspects of software development ignoring cognitive perspectives of complexity. This research introduces advanced metrics like including Cyclomatic Complexity measure, Cognitive Functional Size and Improved CB that incorporate cognitive complexity into the evaluation of software quality. Moreover, the research describes a new tool incorporating traditional, object-oriented and these advanced measures to offer a thorough evaluation methodology. The tool has user friendly interfaces with visualizations and lack of standardization of current practices. In developing this tool and gathered observations from industry experts, including project managers and architects, to understand their needs and expectations when visualizing calculated metrics. This technique is geared towards improving software quality measurement through a more holistic appraisal system for its complexity to help get better decision in maintenance or creation processes. This advanced tool aims to explain the existing metrics and their limitations in relation to software complexity from the perspective of cognitive inclusion. In addition, this paper outlines the iterative strategy used in the design and construction of the tool, highlighting the use of the end user’s feedback to refine the operations of software developers, project managers. This combination of inputs assists in ensuring that the tool is not just better at estimating complexity but is also better suited to address practical problems associated with the needs of the software development industry.
Open Access
Enhancing Sinhala Hate Speech Detection in Online Platforms
(SLIIT, 2024-12) Silva, W. M. R. D.
The rise of deep learning methodologies has indeed revolutionized text analysis, enabling more sophisticated and nuanced understanding of language dynamics. With the proliferation of social media platforms, these advancements have been particularly crucial in navigating the vast amounts of data generated by online interactions. However, amidst the benefits of this digital age, the prevalence of hate speech has emerged as a pressing concern, transcending linguistic and cultural boundaries. In the context of Sinhala, a language rich in nuances and deeply intertwined with cultural complexities, the challenges in detecting and mitigating hate speech are further compounded. Language is not merely a tool for communication but also a reflection of societal norms, values, and power structures. In the Sinhala-speaking context, historical legacies, religious beliefs, and political tensions intertwine to shape discourse in multifaceted ways. Consequently, any hate speech detection mechanism must navigate these intricate layers of meaning, accounting for cultural sensitivities and contextual nuances to ensure accurate identification of harmful content. The integration of deep learning techniques and advanced semantic analysis holds promise in enhancing hate speech detection in Sinhala. By leveraging the power of neural networks to discern patterns and contexts within textual data, such mechanisms can offer a more nuanced understanding of language dynamics. Moreover, the evaluation of these tools on real-world social media data not only validates their effectiveness but also provides insights into the evolving nature of online discourse. Ultimately, addressing hate speech in Sinhala and similar low-resource languages requires a multifaceted approach that combines technological innovation with cultural sensitivity and community engagement to foster safer and more inclusive online spaces.
Open Access
Development of an Integrated IoT System for Remote Monitoring and Enhanced Safety Assurance in Outdoor Environments
(SLIIT, 2024-12) Dawlagala, D. S. D. M. D.
Making places safer outside for freely operating has also risen in priority especially in places with little or no communication facilities and people can go out for camping and hiking. In this study, a distributed Internet of Things (IoT) system incorporating geofencing, environmental monitoring and real-time positioning system to improve outdoor navigation and safety has been developed and tested. The system includes one main hub and a number of sub devices connected to ESP32 microcontroller and LoRa 433 MHz for communication in addition to GPS (Neo-7M) for position location purposes. Linear and angular displacement measurements and their data processing and recording were performed using sub-devices that incorporated GPS and other modules used for receiving and transmitting data over a LoRa network. Sub and the main devices demonstrated data on 0.96 inches OLED both devices which enable users to receive up to date responses. There is also a type of geofencing action in the system, where an alert is sent when a sub-device departs an area that has been defined. The geofencing alerts can be managed though a web dashboard that utilizes Node.js, Next.js, and Web-Sockets for dashboard and main hub interaction. The main hub was also able to send and receive updates from the internet and then transmit this information to the sub-devices over the LoRa network thus bridging the gap that existed between local and remote operation. From the initial results, the newly proposed system is able to provide secure communication, precise location coordinates and prompt geofencing alerts even in outdoor environments with poor network support. The subsequent steps will seek to enhance the energy consumption of the system, improve communication coverage, and make more tests in real-life scenarios to assess the system as a whole.
Open Access
Exploring how Natural Language Processing Techniques can be used for Personalized Learning
(SLIIT, 2024-12) Samarasinghe, K
Artificial intelligence (AI) has had a profound impact on many industries, and education is no exception. Large Language Models (LLMs), including GPT-3 and GPT-4, stand out among AI-driven technologies as ground-breaking instruments that have the power to revolutionize conventional learning settings. These models allow for a realistic, conversational interface between students and instructional information because they were trained on massive amounts of text data. They produce logical, contextually appropriate answers to a range of queries by examining linguistic patterns, giving students the opportunity to participate in individualized learning experiences. Though LLMs offer chances for dynamic learning, their effectiveness is constrained by the static nature of their knowledge, which is dependent on the training data. Tasks requiring current or specialized knowledge are made more difficult by this constraint. To close this disparity, In particular, this thesis investigates the use of RAG and LLM models for personalized learning in educational environments that prioritize tailored instruction and flexible learning pathways. The limitations of conventional educational systems can be overcome by incorporating these AI-driven technologies, giving students access to a more flexible, personalized, and interactive learning environment. Essentially, personalized learning is adjusting the pace, approach, and substance of training to meet the individual requirements and preferences of each student. When LLMs and RAG are used together, the system can comprehend the demands of the learner and adjust in real-time to provide feedback, fresh knowledge, and questions for critical thought. This makes for an interactive learning process. Personalized learning is based on the necessity of flexibility. Because different learners have different comprehension levels, learning styles, and rates of advancement, it is critical to have a flexible system that can change in real time to meet the demands of each unique user. Because of their extensive language comprehension skills, LLMs are excellent at offering this flexibility. LLMs can help learners learn by having adaptive discussions in which they provide factual answers to basic queries, in-depth explanations of difficult subjects, or even the introduction of new ideas. When studying Sri Lanka's history in the 1980s, for example, a student may begin by asking general questions about significant occurrences and then go further into topics like the country's political climate or economic shifts at that time. A very participatory and interesting learning environment is made possible by the LLM's capacity to understand and answer such questions in a conversational manner. But personalized learning uses LLMs for more than just conversation. The capacity of LLMs to give learners rapid feedback is one of its main advantages. This is particularly crucial in educational environments as pupils frequently need their misconceptions cleared up or corrected. In order to make sure the subject is understood, LLMs can spot areas where a learner might be having difficulty and provide thorough explanations or alternative explanations. Additionally, by emulating the Socratic technique of guided questioning, LLMs can motivate students to consider other viewpoints and reflect critically on their own responses. To encourage a deeper cognitive engagement with the material, the LLM could, for instance, ask the student to consider the reasons and effects of particular events rather than just providing a historical answer. Because LLMs are trained on static data, they are intrinsically constrained even if they offer a strong framework for individualized learning. As a result, when it comes to current events or specialized issues, their expertise is limited to what they learned during their training, which may result in inaccurate or obsolete information. This restriction may make it impossible for the LLM to give precise information about certain historical events, people, or policies. Retrieval-Augmented Generation (RAG) models are useful in this situation. RAG models provide a link between real-time information retrieval and LLMs. Through the integration of a retrieval mechanism that combs through databases or outside sources, RAG models enhance the generative process by adding highly relevant and current data. Because of this dual structure, the learning platform can deliver timely, factually accurate knowledge in addition to responses that are coherent and appropriate for the given situation. For instance, the LLM may provide a generic response based on its prior knowledge if a student asking about a particular political incident in Sri Lanka's history during the 80s asks about that event. In contrast, the RAG component enriches the learning process by retrieving recent documents, articles, or academic papers to offer a more accurate and fact-based response. Enhancing the range and depth of information that an educational chatbot or system may offer requires the integration of RAG models. RAG models make sure the system can serve both broad learners and individuals looking for more specialized or up-to-date information by drawing from a dynamic knowledge base. This is especially helpful in subjects like history, where having access to original sources, research papers, and other academic materials may greatly improve the caliber of instruction. Furthermore, by directing students toward more resources, the retrieval element of RAG models can support reinforcement of learning by allowing them to delve further into a subject and carry on learning after their first engagement with the system. The Socratic method is one of the most effective ways to apply LLMs and RAG models in individualized learning. A well-known instructional approach is the Socratic method of inquiry, which entails posing open-ended questions that promote introspection and more in-depth thought. The approach can support active learning, where students are more involved and take charge of their education, by encouraging them to consider their responses carefully. For example, the LLM may ask follow-up questions to encourage the student to delve deeper into the subject matter, instead than giving a direct response to a query from the student. This method promotes the growth of critical thinking abilities in addition to reiterating the students' comprehension of the material. Beyond technological considerations, LLMs and RAG models are being implemented for individualized learning. The user experience must be prioritized in order to guarantee the efficacy of these solutions. With the use of these technologies, an interactive chatbot or virtual tutor may be created that allows students to easily ask questions, get helpful answers, and participate in insightful conversations. Furthermore, learning routes should be dynamically adjusted by the system based on the student's performance and development, which will allow it to modify the level of difficulty, recommend new subjects, or provide remediation as needed. The provision of an effective learning experience that is customized for each individual depends on this adaptability. Ultimately, even if the application of RAG models and LLMs has great potential for individualized learning, there are certain issues that need to be resolved. For example, it's crucial to make sure the data retrieved is precise, dependable, and suitable for the student's needs. This necessitates the meticulous selection of outside information sources and the creation of algorithms capable of determining the reliability of content retrieval. Furthermore, both RAG and LLM models have large computational requirements, thus preserving high-quality outputs while maximizing efficiency is essential. In conclusion, the integration of LLMs and RAG models into personalized learning platforms represents a significant step forward in the evolution of education. By combining the language understanding and generation capabilities of LLMs with the real-time retrieval capabilities of RAG models, educators can create adaptive, engaging, and highly effective learning environments. The addition of the Socratic method further enhances these platforms by encouraging critical thinking and active engagement. As AI continues to evolve, the potential for creating even more personalized, responsive, and impactful learning experiences will grow, ultimately transforming how we learn and interact with educational content.
Open Access
Optimizing Edge Computing and IoT for Affordable and Portable Vibration-Based Machinery Condition Monitoring Solutions in Sri Lankan SMEs
(SLIIT, 2024-12) Samaraweera, R. P.
This research focuses on developing an affordable and portable system for monitoring the condition of machinery in Sri Lankan SMEs, utilizing Edge Computing and IoT technologies. The study is conducted in three stages. First, vibration data is collected from sensors attached to gearboxes to monitor for anomalies. In the second stage, the collected signals are processed using wavelet transform to extract relevant features from the data. Finally, machine learning classifiers are employed to identify anomalies, with a comparison of models including Convolutional Neural Networks (CNN), Random Forest (RF), and Autoencoders (AE). The goal was to create an effective solution for early detection of machinery issues, reducing unexpected maintenance costs, and improving operational efficiency in SMEs. This research aims to support SMEs in Sri Lanka by offering a costeffective method to prevent machinery failures and enhance business modernization. Using IoT, signal processing, and machine learning models combination for gearbox fault detection along with the Python GUI interface called Gearbox Monitoring System (GMS) significantly improves predictive maintenance in the industrial sector. Provide reliable anomaly detection by implementing the RF model in the application, which can help prevent costly downtimes and improve the longevity of machinery.
Open Access
Developing an Enhanced Soft Sensor for Wastewater Treatment Plants: A Comparative Study of Multiple Machine Learning Approaches
(Sri Lanka Institute of Information Technology, 2025) Kaluarachchi, C.D
Wastewater treatment plants (WWTPs) require continuous monitoring of critical water quality parameters to ensure operational efficiency and regulatory compliance. Traditional physical sensors are accurate but expensive and maintenance-intensive, creating a need for cost-effective alternatives. This research investigates the development of enhanced soft sensors using advanced machine learning techniques to estimate key wastewater parameters including Chemical Oxygen Demand (COD) and Total Phosphorus (TP) concentrations at both influent and effluent points. The study addresses fundamental limitations of existing soft sensor implementations particularly their inability to capture complex non-linear relationships which is suspected to have sensor drift and degradation due to seasonal variations and equipment aging. Through comprehensive evaluation of multiple machine learning approaches including Neural Networks and Decision Tree-based methods with the aim to develop robust, adaptable soft sensor models that maintain accuracy over extended periods with reduced recalibration requirements. The methodology involves systematic data collection from a Norwegian WWTP, comprehensive preprocessing to handle data quality issues, feature engineering and rigorous comparative evaluation based on prediction accuracy, computational efficiency and adaptability. Expected outcomes include deployable soft sensor models offering reliable real-time monitoring capabilities, significant cost savings, and improved operational efficiency for WWTPs. The research contributes both theoretical insights into soft sensor design and practical solutions for the wastewater treatment industry.