Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Publication Open Access 2D Pose Estimation based Child Action Recognition(Institute of Electrical and Electronics Engineers Inc., 2022-11) Mohottala, S; Abeygunawardana, S; Samarasinghe, P; Kasthurirathna, D; Abhayaratne, CWe present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with LRCN on a benchmark dataset containing unconstrained environment based videos.Publication Embargo Accuracy of Diabetes Patient Determination: Prediction Made from Sugar Levels Using Machine Learning(Springer, Cham, 2022) Krishnananthan, S; Puvanendran, S; Puvanendran, RThis study focuses on the prediction of the Diabetic Patients through the sugar levels. The Dataset is analyzed using the data mining techniques such as feature extraction, associate rule mining and classification. The Fast Blood Sugar (FBS) and Post-Prandial Blood Sugar (PPBS) sugar levels are selected as the important features, identification of a rule depending on the selected feature is identified and the performance metric for three classifiers is analyzed based on the selected attributes and choose the classifier with high accuracy. Classification algorithms like random forest, decision tree (J48), and Naïve Bayes were utilized to identify the patients with diabetes disease. The performance of these techniques is considered using the factors relating to the accuracy from the applied techniques. The accuracy is seeming to be higher for Naïve Bayes. The outcomes acquired demonstrated that Naïve Bayes outflanks from different strategies with most noteworthy precision of 74.8%.Publication Open Access Achieving near-zero carbon dioxide emissions from energy use: The case of Sri Lanka(Elsevier, 2023-07-04) Fernando, G.L; Liyanage, M.H; Anandarajah, G; Attalage, R. A; Karunaratne, SSignatories to the Paris Agreement are to achieve net zero Green House Gas (GHG) emissions during the half-century to pursue the efforts limiting global average temperature increase by 2 °C compared to pre-industrial levels. This study models ambitious to challenging scenarios involving energy demand and supply side actions for energy system transition toward net-zero for Sri Lanka. To analyze these scenarios a least cost optimization-based bottom-up type energy system model was developed from 2015 to 2050. A Business-as-usual (BAU) scenario and four countermeasure (CM) scenarios termed Plausible, Ambitious, Challenging, and Stringent were developed. Four different carbon tax rates were used to fathom the level of carbon tax needed to achieve net-zero emissions. The CM scenarios were formulated considering different technology options and policy measures such as the diffusion of efficient technologies, availability of renewable energy sources, use of cleaner fuels, the introduction of nuclear and carbon capture and storage technologies, and green hydrogen for power generation. The result of this study reveals that the stringent scenario which includes aggressive policy measures in both the energy supply and demand sectors, such as nuclear, and renewable energy for power generation, diffusion of efficient Enduse devices, fuel switching, including the introduction of electric cars, and increased share for public transport achieves the near carbon-neutral scenario at a carbon tax trajectory of 32 US$/tCO2 in 2020 and 562US$/tCO2 in 2050. The Net Energy Import Dependency (NEID) of the country decreases to 13 % in 2050 compared to that of the BAU scenario (65 %) under the near carbon neutral scenario, which is a positive sign from the energy security perspective.Publication Embargo Aerodynamic modeling of simplified wind turbine rotors targeting small-scale applications in Sri Lanka(Elsevier, 2020-09-11) Sugathapala, T. M; Boteju, S; Withanage, P. B; Wijewardane, SA design and optimization procedure of simplified wind turbine rotors for small-scale applications is presented. The need for this research has arisen from the recent national initiative of the government of Sri Lanka titled ‘Battle for Wind Energy’ in promoting small scale grid connected wind plants for electricity customers under Net Metering scheme. The main objective of this research is to assist local developers to design optimum rotors for given electrical generators (as determined by customer requirements), suitable for wind characteristics at specific locations. Another objective is to enhance local manufacturing capabilities by providing a design option of a simplified rotor blade geometry. A study on the correlation between population density of electricity customers and wind energy potentials was carried out to categorize the demand centres based on wind energy potentials in proposing series of small-scale wind turbine designs. A unique and improved rotor design procedure is presented which attempts to match the point of maximum performance of a rotor (design tip speed ratio) with the design wind speed of a given location by considering generator performance. The new design procedure showed successful convergence on a unique blade diameter for each rotor configuration that allowed the design tip speed ratio to match the design wind speed. The performance evaluation of rotor designs showed that high solidity rotors work better on the low wind potential region while low solidity rotors dominate medium and high wind potential regions. The performance reductions of simplified rotor designs are not significant and therefore would be an effective way to enhance value addition through local manufacture.Publication Open Access Aggressive strategies of the COVID-19 pandemic on the apparel industry of Sri Lanka using structural equation modeling(PLoS ONE, 2023-06-21) Rajapakshe, W; Karunaratna, D. S. M.; Ariyaratne, W. H. G.; Lakshani Madushika, H. A.; Perera, G. S. K.; Shamila, PDuring the COVID-19 crisis, the apparel industry faced many challenges. Aggressive cost-cutting strategies became a top priority, and in turn, these influenced stressors and adversely affected business sustainability. This study examines the impact of aggressive strategies during the COVID-19 pandemic on business sustainability in the apparel industry of Sri Lanka. Further, it investigates whether the relationship between aggressive cost-cutting strategies and business sustainability was mediated by employee stress, considering aggressive cost reduction strategies and workplace environmental changes. This was a cross-sectional study with data collected from 384 employees in the apparel industry in Sri Lanka. Structural Equation Modelling (SEM) was applied to analyze the direct and indirect effects of aggressive cost reduction strategies and workplace environmental changes on sustainability with mediating effects of stress. Aggressive cost reduction strategies (Beta = 1.317, p = 0.000) and environmental changes (Beta = 0.251, p = 0.000) led to an increase in employee stress but did not affect business sustainability. Thus, employee stress (Beta = -0.028, p = 0.594) was not a mediator in the relationship between aggressive cost-cutting strategies and business sustainability; business sustainability was not a dependent variable. The findings proved that managing workplace stress, particularly improving stressful working environments and aggressive cost reduction strategies, can enhance employee satisfaction. Thus, managing employee stress could be beneficial for policymakers to focus on the area(s) required to retain competent employees. Moreover, aggressive strategies are unsuitable to apply during crisis to enhance business sustainability. The findings provide additional knowledge to the existing literature, enabling employees and employers to predict causes of stress and serve as a significant knowledge base for further studies.Publication Embargo AI - Driven Smart Bin for Waste Management(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Abeygunawardhana, A. G. D. T.; Shalinda, R. M. M. M.; Bandara, W. H. M. D.; W. D. S. Anesta, D.; Kasthurirathna; Abeysiri, L.With increasing urbanization, waste has become a major problem in the present world. Therefore, proper waste management is a must for a healthy and clean environment. Though government authorities in most countries provide various solutions for waste management, solid waste tends to make a significant impact on the environment as they do not decompose easily. This research focuses on AI (Artificial Intelligence)-driven smart waste bin that can classify the most widely available solid waste materials namely Metal, Glass, and Plastic. The smart waste bin performs the separation of waste using image processing and machine learning algorithms. The system also performs the continuous monitoring of the collected waste level by using ultrasonic sensors. A dedicated mobile application will generate the optimal routes for the available waste collectors to collect the filled bins. Moreover, with this smart bin, the challenge of recognizing each waste item is overcome by using visual data as the source. Therefore, the usage of expensive sensor devices and filtration techniques to determine the category is disregarded. The smart bin can recognize the category of solid waste, collect it to the specified container, and notify the garbage level in each container. So, it is a portable waste management system.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 AI Based Depression and Suicide Prevention System(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Kulasinghe, S.A.S.A.; Jayasinghe, A.; Rathnayaka, R.M.A.; Karunarathne, P.B.M.M.D.; Silva, P.D.S.; Anuradha Jayakodi, J.A.D.C.Suicide is a major issue in the world. The number one reason for suicide is untreated depression. That is why it was decided to focus on depression symptoms more and identify them in order to prevent suicidal attempts. To cure depression, the best way is to talk about their feelings with someone they trusted and release their pain inside of them. Because of that this system has a Chat-bot for the user to interact with. Chat-bot will gather information about the users feelings through text and voice analysis. Also by analyzing their Facebook statuses and recent web history, the application gather more information about their mental state so that the system take more accurate conclusions. After analyzing all the information from each component the back brain will decide on how the chat-bot should act on the user. At the end, the product was able to give more than 75% accurate results for each component.Publication Embargo AI Based Monitoring System for Social Engineering(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, K.; Udara, S.W.I.; Wijayawardane, U.P.B.; Kularatne, K.N.P.; Navaratne, N.M.P.P.; Dharmaphriya, W.G.V.USocial media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.Publication Embargo AI-Based Child Care Parental Control System(IEEE, 2022-12-09) Jayasekara, U; Maniyangama, H; Vithana, K; Weerasinghe, T; Wijekoon, J; Panchendrarajan, RDue to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.Publication Open Access Alcohol Consumption and Stroke Mortality: Global Patterns, Risks and Public Health Implications(Springer Nature 2025, 2025-05-07) Kolonne, T; Mudalige, K; Dissanayaka, G; Rathnayake, K; Jayathilaka, R; Rajamanthri, L; Wickramaarachchi, CGlobally, stroke remains a leading cause of mortality and disability, while alcohol consumption continues to vary widely across regions, prompting concern over its health impacts. This study examines the association between different alcoholic beverages and stroke mortality, using secondary data from 1990 to 2020. Alcohol consumption and stroke death rates across 189 countries were categorized into five levels, from very high to very low, and averaged over two periods (1990–1999 and 2011–2020). Multiple Correspondence Analysis (MCA) was applied to assess relationships among four categorical variables. The findings indicate a significant association between very high alcohol consumption and increased stroke mortality, with eight countries showing elevated death rates. Conversely, moderate beer consumption was linked to reduced stroke mortality, suggesting nuanced effects based on beverage type and quantity. These insights offer a foundation for targeted public health policies and emphasize the need for further investigation into the mechanisms driving alcohol-related stroke risks.Publication Embargo Algorithmically Navigating Complex Tabular Structures in Images for Information Extraction(IEEE, 2022-12-26) Nugawela, M; Abeywardena, K. Y; Mahaadikara, HComputer vision has been in the forefront of automating workflows to replace manual repetitive tasks with convenience and accuracy. Recognizing text from images of commercial documents through optical character recognition (OCR) form the initial step of most such workflows where majority of their information are in the form of complex data structures such as tables and nested tables. Although OCR technology has evolved to effectively capture text from images, there is still room for improvement in recognizing complex data structures and extracting tabular data from images. This paper proposes an algorithmic approach based on keyword detection and the position of words relative to each other in order to recognize nested structures and successfully extract tabular data into a program and human readable format, which aims to take a different approach as opposed to using machine learning models or pre-defined templates for layout recognition. Furthermore, this approach is shown to yield successful results in correctly comprehending the layout and data of nested table structures in multiple rows in a table.Publication Embargo Analysis and Quantification of Position Sensor Offset Error in Feedforward Controlled PMSMs(Institute of Electrical and Electronics Engineers, 2022-10-13) Edirisinghe, E. A. Y. G; Rajapaksha, L.T.W; Abeyratne, S. G; Kuruppu, S. SPermanent Magnet Synchronous Machines (PMSM) are popular in transportation electrification applications due to their inherent torque density and ease of control. In torque control applications, field-oriented control (FOC) ensures optimal torque production. However, PMSMs require a rotor position information for FOC, achieved with sensors such as encoder, resolver or linear hall sensor. Feedforward controlled FOC being one key form of PMSM control, the position sensor signal accuracy is paramount. Due to the harsh environments experience by systems in the transportation segment, the position sensor has the potential to misalign from the initially calibrated alignment. This paper focuses on the effects of such position sensor misalignment on feedforward-controlled drives. A novel quantification strategy to detect the failure mode has been presented with analytical details. The methodology is verified through simulation and validated by experimental results. Moreover, a brief analysis-contrasting the effects of position sensor misalignment on feedback and feedforward-controlled drives are presented.Publication Open Access Analysis of Meandering River Morphodynamics Using Satellite Remote Sensing Data—An Application in the Lower Deduru Oya (River), Sri Lanka(MDPI, 2022-07-16) Basnayaka, V; Samarasinghe, J. T; Gunathilake, M. B; Muttil, N; Hettiarachchi, D. C; Abeynayaka, A; Rathnayake, URiver meandering and anabranching have become major problems in many large rivers that carry significant amounts of sediment worldwide. The morphodynamics of these rivers are complex due to the temporal variation of flows. However, the availability of remote sensing data and geographic information systems (GISs) provides the opportunity to analyze the morphological changes in river systems both quantitatively and qualitatively. The present study investigated the temporal changes in the river morphology of the Deduru Oya (river) in Sri Lanka, which is a meandering river. The study covered a period of 32 years (1989 to 2021), using Landsat satellite data and the QGIS platform. Cloud-free Landsat 5 and Landsat 8 satellite images were extracted and processed to extract the river mask. The centerline of the river was generated using the extracted river mask, with the support of semi-automated digitizing software (WebPlotDigitizer). Freely available QGIS was used to investigate the temporal variation of river migration. The results of the study demonstrated that, over the past three decades, both the bend curvatures and the river migration rates of the meandering bends have generally increased with time. In addition, it was found that a higher number of meandering bends could be observed in the lower (most downstream) and the middle parts of the selected river segment. The current analysis indicates that the Deduru Oya has undergone considerable changes in its curvature and migration rates.Publication Open Access Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques(MDPI, 2023-05-06) Zoysa, S; Basnayake, V; Samarasinghe, J. T.; Gunathilake, M.B.; Kantamaneni, K; Muttil, N; Muttil, U; Rathnayake, UCoastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.Publication Embargo Analysis of the ‘Toll Free Agricultural Advisory Service’ Data as Decision Support Tool for the Department of Agriculture(IEEE, 2022-07-18) Dias, N; Rajapaksha, NThe Department of Agriculture’s Toll-Free Agricultural Advisory Service was formed with the 1920 short code and is connected to all land and mobile telephone service providers in Sri Lanka. This short code allowed farmers and other stakeholders to contact technical officers which Agriculture Instructors immediately. All the information was gathered into the 1920 call center database. Farmers all over the island bring their agricultural problems to the 1920 Agricultural Advisory Service. Nevertheless, it can be seen that they do not do any analysis of these problems. This big data if properly examined has the potential to assist the country on a massive scale in the future. This study for carrying out to explore the possibility of introducing decision support for the 1920 reporting system to generate enhanced analytics and to make it easier to make informed decisions by the top management of the Department of Agriculture, more efficiently and effectively than the reporting method previously.Publication Open Access Analysis on Current and Future Training Needs in Health Sector of Sri Lanka(SRAC - Romanian Society for Quality, 2022-08) Rajapakshe, W; Weerarathna, R. S; Pathirana, G.Y; Malage, M. HSri Lanka holds a unique position in South Asia as one of the developing nations to provide universal health. Government spending on welfare mainly involves compromising or constraining a country's development budget. It requires sound management strategies to utilize limited resources, including the human resource of the health sector. Training and development of health professionals can be identified as a critical component in strengthening the quality of health services. Fewer studies exist on Training Needs Analysis (TNA) on health professionals in Sri Lanka, and this study assessed the Training Needs of Sri Lanka's healthcare professionals. This is a cross-sectional study collecting data from 240 health sector professionals in Sri Lanka. The Hennessey Hicks Training Need questionnaire was used to obtain data. Data from the source was analysed through the oneway ANOVA to compare the training needs of various professional groups, and the Word Cloud Online tool was applied to analyse the open-ended questions. The study identifies published research evaluation, communicates with patients, identifies viable research, introduces new ideas, prioritizing works, assessing patients' needs, using technology, undertaking health promotion activities, collecting research information, and undertaking administrative duties as current training needs. While computer literacy training, training on new medical equipment, awareness programs on workplace stress-reducing, training on quality assurance certifications like ISO, programs on effectively communicating with patients, management training, and language training were identified as future training needs of professionals in the health sector. The study concluded that, continuous training programs as the preferred approach to access the performance gap. It also demonstrates that this awareness of information regarding the training needs is beneficial for the health sector policymakers, managers, employees, and the community. Having a better understanding of their job roles, performance needs, and involvement in the decision-making process is likely to motivate healthcare sector employees to improve service quality. These benefits will be reflected in positive outcomes on service quality, improving the overall healthcare system in the country.Publication Open Access Analysis on Future Training Needs in Health Sector of Sri Lanka(SRAC - Romanian Society for Quality, 2022-08) Weerarathna, R; Rajapakshe, W; Pathirana, G. Y; Malage, M. HSri Lanka holds a unique position in South Asia as one of the developing nations to provide universal health. Government spending on welfare mainly involves compromising or constraining a country's development budget. It requires sound management strategies to utilize limited resources, including the human resource of the health sector. Training and development of health professionals can be identified as a critical component in strengthening the quality of health services. Fewer studies exist on Training Needs Analysis (TNA) on health professionals in Sri Lanka, and this study assessed the Training Needs of Sri Lanka's healthcare professionals. This is a cross-sectional study collecting data from 240 health sector professionals in Sri Lanka. The Hennessey Hicks Training Need questionnaire was used to obtain data. Data from the source was analysed through the one-way ANOVA to compare the training needs of various professional groups, and the Word Cloud Online tool was applied to analyse the open-ended questions. The study identifies published research evaluation, communicates with patients, identifies viable research, introduces new ideas, prioritizing works, assessing patients' needs, using technology, undertaking health promotion activities, collecting research information, and undertaking administrative duties as current training needs. While computer literacy training, training on new medical equipment, awareness programs on workplace stress-reducing, training on quality assurance certifications like ISO, programs on effectively communicating with patients, management training, and language training were identified as future training needs of professionals in the health sector. The study concluded that, continuous training programs as the preferred approach to access the performance gap. It also demonstrates that this awareness of information regarding the training needs is beneficial for the health sector policymakers, managers, employees, and the community. Having a better understanding of their job roles, performance needs, and involvement in the decision-making process is likely to motivate healthcare sector employees to improve service quality. These benefits will be reflected in positive outcomes on service quality, improving the overall healthcare system in the country.Publication Embargo Analyzing Payment Behaviors And Introducing An Optimal Credit Limit(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Bandara, H.M.M.T.; Samarasinghe, D.P.; Manchanayake, S.M.A.M.Identifying an optimal credit limit plays a vital role in telecommunication industry as the credit limit given to customers is influence on the market, revenue stabilization and customer retention. Most of the time service providers offer a fixed credit limit for customers which may cause customer dissatisfaction and loss of potential revenue. Therefore, it is essential to determine an optimal credit limit that maintains customer satisfaction while stabilizing the company revenue. Clustering algorithms were used to group customers with similar payment and usage behaviors. Then the optimal credit limit derived for each cluster is applicable to all the customers within the cluster. In order to identify the most suitable clustering algorithm, cluster validation statistics namely, Silhouette and Dunn indexes were used in this research. Based on the scores generated from these statistics KMeans algorithm was chosen. Furthermore, the quality of the KMeans clustering was evaluated using Silhouette score and the Elbow method. The optimal number of clusters are identified by those validation statistics. The significance of this approach is that the optimal credit limits generated by these clustering models suit dynamic behaviors of the customer which in turn increases customer satisfaction while contributing to reducing customer churn and potential loss of revenue.Publication Embargo Anonymo: Automatic Response and Analysis of Anonymous Caller Complaints(IEEE Computer Society, 2022-08-17) Azhar, A; Maweekumbura, S; Gunathilake, R; Maddumaarachchi, T; Karunasena, A; Nadeeshani, MCustomers are considered as the most valued asset in any business organization. Therefore, attending especially to negative feedback provided by customer in form of complaints is important for an organization to identify areas to improve and retain customers. To quickly respond to customer complaints many business organizations have made hotlines available. Such caller hotlines are dedicated for the purpose of receiving complaints or allowing whistleblowers to reveal information. Due to the fear of being identified, there is a hesitancy in the public to use these hotlines. From the perspective of the organizations when a customer complaint is received it is required to evaluate the validity of the call made to hotlines. Furthermore, when complaints are made, it is required to handle them efficiently by transferring them to relevant departments and prioritize complaints This research proposes 'Anonymo', a system to handle customer complaints in a secure and an efficient manner. To do so, the system analyses the complaints obtained by a caller and provides the end users with the appropriate responses and output, that includes the following: i. Conversational AI agent to respond to callers, ii. Wanted and unwanted call classification, iii. Department-based Complaint classification, iv. Caller Emotion detection and caller complaint analysis while establishing the caller's anonymity. An accuracy of 88.26% was obtained for identification of wanted complaints using SVM algorithm, an accuracy of 85% was obtained for department-based classification using SVM algorithm and 67% accuracy was obtained for emotion analysis by LSTM algorithm