Browsing by Author "Ganegoda, GU"

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item: Conference-Abstract
7th International Conference in Information Technology Research 2022 (Pre Text)
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
item: Conference-Full-text
8th International Conference in Information Technology Research 2023 (Per Text)
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
item: Conference-Full-text
Accelerated adversarial attack generation and enhanced decision insight
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Kumarasiri, NKYS; Premaratne, SC; Wijesuriya, WMRM; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Adversarial Attack is a rapidly growing field that studies how intentionally crafted inputs can fool machine learning models. This can have severe implications for the security of machine learning systems, as it can allow attackers to bypass security measures and cause the system to malfunction. Finding solutions for these attacks involves creating specific attack scenarios using a particular dataset and training a model based on that dataset. Adversarial attacks on a trained model can significantly reduce accuracy by manipulating the decision boundary, causing instances initially classified correctly to be misclassified. This alteration results in a notable decline in the model's ability to classify instances after an attack accurately. The above process helps us develop strategies to defend against these attacks. However, a significant challenge arises because generating these attack scenarios for a specific dataset is time-consuming. Moreover, the disparity between the model's prediction outcomes before and after the attack tends to lack clear interpretability. In both above limitations, the common limiting factor is time. The time it takes to devise a solution is crucial because the longer it takes, the more opportunity an attacker has to cause harm in real-world situations. In this paper, we propose two approaches to address the above gaps: minimizing the time required for attack generation using data augmentation and understanding the effects of an attack on the model's decision-making process by generating more interpretable descriptions. We show that description can be used to gain insights into how an attack affects the model's decision-making process by identifying the most critical features for the model's prediction before and after the attack. Our work can potentially improve the security of machine learning systems by making it more difficult for attackers to generate effective attacks.
item: Conference-Full-text
Acoustic signature analysis for distinguishing human vs. synthetic voices in vishing attacks
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Gamage, P; Dissanayake, D; Kumarasinghe, N; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Cybercrimes targeting mobile devices are on the rise, with vishing and smishing attacks being particularly prevalent. These attacks exploit social engineering techniques to manipulate individuals into divulging personal information or engaging in unintended actions. To counter this evolving threat landscape, this research proposes a pioneering methodology rooted in voice feature analysis. By distinguishing between human and robotic voices, this approach aims to discern legitimate calls from potential scams, thereby mitigating the associated financial losses and reputational damage. The research delves into the intricacies of voice feature analysis, leveraging natural language processing (NLP) and machine learning (ML) techniques to extract and analyze audio attributes such as pitch, volume, and temporal patterns. The ultimate objective is to create a binary classification model that accurately differentiates between human voice calls and robocalls, incorporating a comprehensive dataset comprising actual call recordings and synthesized scenarios. This research advances beyond conventional practices by championing a holistic analysis of both human and robocalls, contrary to the prevalent assumption of robocalls exclusively constituting scams. The application of various audio features, coupled with nuanced weightage allocation, enhances the model’s discernment capabilities. The resultant binary classifier is an exemplar of the innovative fusion of technology and human expertise. In conclusion, this research introduces a novel dimension to the combat against vishing and smishing attacks, with a robust voice feature analysis methodology capable of accurately identifying human and robotic voices. By effectively distinguishing legitimate calls from potential threats, this approach presents a promising avenue for safeguarding individuals and organizations against the far-reaching consequences of cybercrimes. The comprehensive analysis, validation, and insights presented in this paper contribute significantly to the field of cybersecurity and voicebased communication analysis.
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Adapting marytts for synthesizing sinhalese speech to communicate with children
(Faculty of Information Technology, University of Moratuwa., 2021-12) Lakmal, MAJA; Methmini, KADG; Rupasinghe, DMHM; Hettiarachchi, DI; Piyawardana, V; Senarathna, M; Reyal, S; Pulasinghe, K; Ganegoda, GU; Mahadewa, KT
The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1–6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM -based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented.
item: Conference-Full-text
An agile project management supporting approach for estimating story points in user stories
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Wanigasooriya Arachchi, KJ; Amalraj, CRJ; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
While significant research has been conducted on software analytics for effort estimation in traditional software projects, limited attention has been given to estimation in agile projects, particularly in estimating the effort required for completing user stories. In our study, we present a novel prediction model for estimating story points, which serves as a common unit of measure for gauging the effort involved in completing a user story or resolving an issue. To achieve this, we propose a unique combination of two powerful deep learning architectures, namely LSTM and RHN. What sets our prediction system apart is its end-to-end training capability, allowing it to learn directly from raw input data without relying on manual feature engineering. To support our research, we have curated a comprehensive dataset specifically tailored for story points-based estimation. This dataset comprises 6801 issues extracted from 6 different open-source projects. Through an empirical evaluation, we demonstrate the superiority of our approach over three common baselines. In summary, our study addresses the gap in research regarding agile project estimation by introducing a prediction model that effectively estimates story points. By leveraging the combined power of LSTM and RHN architectures.
item: Conference-Full-text
Ai-driven user experience design: exploring innovations and challenges in delivering tailored user experiences
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Padmasiri, P; Kalutharage, P; Jayawardhane, N; Wickramarathne, J; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
In today’s digital landscape, providing user experiences is considered paramount in respective of user satisfaction and engagement. Artificial Intelligence (AI) has emerged as a transformative force in the User Experience (UX) design field, offering innovative solutions. Our research delves into key innovations and challenges enabled by AI in UX design particularly guided by Design Thinking (DT) process. The methodology involved administering a questionnaire to UX professionals in Sri Lanka using a snowball sampling method. The questionnaire, distributed through online platforms, explored participants’ familiarity with AI-driven UX design, contributions of AI in the DT process, and challenges faced, and the responses were analyzed using MS Excel and R Studio. The results demonstrate that AI technologies certainly empower UX professionals to design usercentric solutions adhering to DT process. A “Recommendation Guide” is provided, featuring a set of recommended tools for UX professionals to integrate AI technologies into the DT process.
item: Conference-Full-text
Alzheimer’s disease detection using blood gene expression data
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Yasodya, GDS; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Alzheimer's disease is the most prevalent form of dementia with no established cure. Extensive research aims to comprehend its underlying mechanisms. Genetic insights are sought through gene expression data analysis, leveraging computational and statistical techniques to identify risk-associated genes. This study focuses on accurate AD detection using blood gene expression data. Four feature classification methods—TFrelated genes, Hub genes, CFG, and VAE are employed to identify crucial AD-related genes. Five classification approaches—RF, SVM, LR, L1-LR, and DNN—are used, evaluated by AUC. The VAE + LR model yields the highest AUC (0.76). The study identifies 100 influential AD-associated genes where data is sourced from Alzheimer's Disease Neuroimaging Initiative (ADNI). Findings hold promise for advancing early diagnosis and treatment, enhancing AD patients' quality of life.
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Alzheimer’s disease prediction using clinical data approach
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Perera, LRD; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Alzheimer's Disease (AD) is a progressive neurodegenerative condition that profoundly affects cognition and memory. Due to the absence of curative treatments, early detection and prediction are crucial for effective intervention. This study employs machine learning and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI) to predict AD onset. Data preprocessing ensures quality through variable selection and feature extraction. Diverse machine learning algorithms, including Naive Bayes, logistic regression, SVM-Linear, random forest, Gradient Boosting, and Decision Trees, are evaluated for prediction accuracy. The model resulted with random forest classifier together with filter method yields the highest AUC. The study highlights important analysis using Random Forest and Decision Trees, revealing significant variables including cognitive tests, clinical scales, demographics, brain-related metrics, and key biomarkers. By enhancing predictive capabilities, this research contributes to advancing Alzheimer's disease diagnosis and intervention strategies.
item: Conference-Abstract
Analysis and prediction of severity of united states countrywide car accidents based on machine learning techniques
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Boyagoda, LS; Nawarathna, LS; Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
The number of vehicles and road transportation increases rapidly daily. Hence the frequency of road accidents and crashes also gradually increase with it. Analyzing traffic accidents is one of the essential concerns in the world. Due to the considerable number of casualties and fatalities caused by those accidents, taking necessary actions to reduce road accidents is a vital public safety concern and challenge worldwide. Various statistical methods and techniques are used to address this issue. Hence, those statistical implementations are used for multiple applications, such as extracting cause and effect to predict real-time accidents. In this study, a United States (US) Countrywide car accidents data set consisting of about 1.5 million accident records with other relevant 45 measurements related to the US Countrywide Traffic Accidents were used. This work aims to develop classification models that predict the likelihood of an accident is severe. In addition, this study also consists of descriptive analysis to recognize the key features affecting the accident severity. Supervised machine learning methods such as Decision tree, K-nearest neighbour, and Random forest were used to create classification models. The predictive model results show that the Random Forest model performs with an accuracy of 83.95% for the train set and 80.69% for the test set, proving that the Random forest model performs better in accurately detecting the most relevant factors describing a road accident severity.
item: Conference-Full-text
The application of convolutional neural network in the context of Tamil handwritten character recognition
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Thuvarakan, P; Kowreesan, P; Jeyarooban, S; Janotheepan, M; Ekanayake, EMUWJB; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
This research paper presents an in-depth investigation into the application of Convolutional Neural Networks (CNNs) for Tamil handwritten character recognition. We explore existing research, methodologies, and cutting-edge techniques, showcasing CNNs' effectiveness in achieving a remarkable 95% accuracy. Our dataset comprises 247 Tamil characters and 18 North Indian characters, accommodating diverse writing styles. We tailor CNN architectures for Tamil characters, implement advanced preprocessing, data augmentation, and training methods to enhance model performance. Our paper tackles challenges posed by accessible datasets, offering remedies for data scarcity, class imbalance, and writing style variations. Our distinct contribution lies in achieving 95% accuracy across 247 Tamil characters and 18 North Indian characters, demonstrating CNNs' potential for document processing, language preservation, and automation in Tamil-speaking regions. This work advances the field by introducing novel techniques, a comprehensive dataset, and strategic insights, serving as a significant step forward in Tamil character recognition.
item: Conference-Abstract
Automated commentary generation based on fps gameplay analysis
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Mamoru, DLS; Panditha, AD; Perera, ASSJ; Ganegoda, GU; Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
Video games evolved into competitive sports within a short span of time. Commentary plays a key role in any sport. Commentary is useful to understand the game as well as to capture key moments of the game. The balance between play-to-play commentary and color commentary creates the whole value of commentary. Additionally, it has been proven that contribution factors play a major role in the meaning of an automated commentary as well. The proposed commentary generator takes all three factors into account to generate a commentary track for the first-person shooter game Valorant. Human evaluation along with numerical figures are taken to evaluate the quality of the generated commentary. The evaluation results suggest that the proposed model performs on par with the human commentary.
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Automated question and answer generating system for educational platforms
(Faculty of Information Technology, University of Moratuwa., 2021-12) Thiruvanantharajah, M; Hangarangoda, N; Rajapakshe, S; IT; Ganegoda, GU; Mahadewa, KT
Learning through the web gets to be well known which encourages learners to learn any kind of stuff at any time from the internet assets. In exam preparing questions and answering is have moved into the technology world. In Many industries, more activities have begun to shift as a result of the increased changes brought about by the Covid-19 virus to people's usual livelihoods, and one significant component whose technologization has created concerns is education. This paper presents a novel system that has been introduced to improve the standards of instructing via virtual and non-virtual platforms by ensuring that both the educational staff and the students are provided with the same level of understanding of their education. The support system ensures that the students and educational staff are provided with an automatic question and answer generation mechanism which will thereby improve the quality of education by presenting a standardized method of preparing questions to the educational staff, while similarly providing a better opportunity to improve study methods for the students.
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Automatic diagnosis of diabetic retinopathy using machine learning: a review
(Faculty of Information Technology, University of Moratuwa., 2020-12) Gunawardhana, PL; Jayathilake, R; Withanage, Y; Ganegoda, GU; Karunananda, AS; Talagala, PD
Diabetic Retinopathy is a popular cause of diabetes, causing vision-impacting lesions of the retina. Blindness may be avoided by early detection. The ophthalmologist's manual approach of diagnosing diabetic retinopathy is expensive and time consuming. At the same time, unlike computer assisted diagnostic systems, it may cause misdiagnosis. Deep learning has recently become one of the most effective approaches that has obtained better efficiency in the analysis and classification of medical images. In medical image analysis, convolutional neural networks are more commonly used as a deep learning approach and they are extremely effective. This paper assessed and addressed the new state-of-the-art Diabetic Retinopathy color fundus image classification and detection methodologies using deep learning and machine learning techniques. Additionally, various challenging issues that need further study are also discussed.
item: Conference-Abstract
Automating the initial configuration of sdn switches in a hybrid-sdn environment
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Bolonghe, WKN; Rupasinghe, PM; Umayanga, KSB; Weerasiri, KLHI; De Silva, D; Wijesiri, P; Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
At present for SDN environments, there are many proposed mechanisms to improve its reliability, and performance. The challenges faced by SDN networks are mainly related to Scalability, Reliability and Performance. As an example, for scalability related challenges, many SDN networks have problems when replacing or installing new SDN switches to the network. The problems are mainly the cost and errors while the installation process. To overcome this issue, a mechanism is proposed to automate the initial configuration of the newly added SDN switches. When it comes to the performance, there are problems such as load balancing, looping and traffic related issues due to broadcast messages. To minimize these challenges the project also brings solutions by implementing DHCP relays and STP inside the network. Also, with the multi-controller architecture proposed it increase the efficiency and Performance. To automate the initial configuration of the switches, the newly added switch is detected at first and then a relationship is established between the newly added switch and an existing SDN switch. Then the newly added switch establishes a connection with the automation server, which then results the automation process to start. The proposed mechanism is implemented in a testing environment using Mininet which creates a virtual environment, a RYU controller as the SDN controller and the OpenFlow is used as the protocol for communication between interfaces. The proposed mechanism will bring many benefits like minimize the errors and save time due to the automated initial configuration in a hybrid-SDN environment, increase efficiency and Performance comparing to the present hybrid-SDN environments.
item: Conference-Abstract
Automobile product ranking based on the singlish comments in social media platforms
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Warnakulasooriya, A; Sandanayake, TC; Wickramasinghe, GAMPS; Ranasinghe, RADW; Sumathipala, KASN; Sumathippala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
In today's world, many customers buy or choose products based on online reviews. The internet contains a vast collection of natural language. People share their subjective thoughts and experiences with one another in various social media platforms. Product reviews can be analyzed to determine how people feel about a particular product .In Sri Lanka, people widely use Singlish (Sinhala-English) to comment and give reviews on products, rather than a single pure language .Therefore in this research it has extracted data from social media platforms on various brands in the automobile industry and propose a system to rank the automobile brands in Sri Lanka based on the social media comments which are written on Singlish. When ranking products, it is not practical to rank products based only on the frequency of the products. Because a brand having the highest number of comments does not necessarily indicate that it has good market perception compared to other brands. In order to get an accurate overview, the study have considered both the people's sentiment towards the particular brand and the frequency of comments. When ranking the products research has done several rankings based on different aspects namely market value, country of origin and second hand market, vehicle performance, product features which people pay their most attention in the automobile industry and also an overall ranking considering all these aspects together. With that it is possible to identify which vehicle type or brand has the highest and lowest demand in the market, and the automobile manufacturer can get a good understanding where a particular product stands out comparative to other brands and apply their strategies accordingly. When implementing the ranking system 100000 social media comments were extracted and annotated. Convolutionary neural network was used to develop the main model, and out of the different methods tried to predict the sentiment as the part of the main model, random forest method gave a higher accuracy of 96.7 making it a more sophisticated combination.
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Blockchain-based software subscription and licenses management system
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) De Alwis, H; Wijayasiri, A; De Silva, S; De Silva, K; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Current software licensing models exhibit shortcomings in transparency, security, and adaptability. Addressing these challenges, this study presents a novel blockchain-based licensing system using the Ethereum platform. By employing smart contracts and the ERC721 and ERC20 token standards, the system ensures automated, transparent, and secure license agreement enforcement and facilitates license token operations. Influenced by the rise of subscription licenses and the implications of the UsedSoft court decision, the research designs a blockchain-driven subscription license model, analyses the UsedSoft case’s impact on license transfers, and formulates specialized smart contracts for varied licensing models. The approach signifies a marked advancement in contemporary software licensing practices.
item: Conference-Full-text
Cheating detection in browser-based online exams through eye gaze tracking
(Faculty of Information Technology, University of Moratuwa., 2021-12) Dilini, N; Senaratne, A; Yasarathna, T; Warnajith, N; Seneviratne, L; Ganegoda, GU; Mahadewa, KT
Eye-tracking can detect and examine human visual attention, emotional conditions, latent cognitive processes such as efforts to recall a concept or the fear of running out of time, and so on. Hence, we can use eye-tracking to identify deviant behavior patterns in learning and problem-solving. At present, given the existence of a global pandemic, online exams are widely used by educational institutions to evaluate students' performance. However, identifying cheating is challenging due to the absence of a human (invigilator) monitoring students' behavior as done in exams held in a physical location. In an online environment, students' behavior, and attempts to cheat, can only be captured via a computer, thus requiring a mechanism for online proctoring with capabilities for cheating detection. In this research paper, we present a browser based cheating detection approach in online examinations through eye gaze tracking. We developed a browser plugin to track the eye gaze movements through the in-built web camera. Using the plugin, we generate an eye gaze dataset while a student faces an online examination. We then process and analyze this dataset to detect any misbehavior during an online examination. The underlying research work of this paper identifies different eye gaze patterns during online examinations and present a cheating detection mechanism. For anomaly detection in the eye gaze data, we use a One-Class Support Vector Machine (OCSVM). We then use these identified anomalies to predict cheating behaviors of the test takers. The given approach can be used for any web-based quiz examination such as academic institutions' exams, company recruitment exams, and overseas testing exams to detect any anomalous behaviors of the test takers during the examination period. The given eye tracking approach can also be applied to other research domains such as online gaming, and web usability studies to capture information related to user behaviors.
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Classification of fungi images using different convolutional neural networks
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Nawarathne, UMMPK; Kumari, HMNS; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Fungi offer vital solutions to humanity through roles in medicine, agriculture, and ecological balance while presenting potential threats. They have yielded antibiotics, food fermentation, and nutrient recycling however, fungal infections, crop diseases, and spoilage highlight their dark side. Therefore, it is important to identify fungi to harness their potential benefits and mitigate threats. Offering quick and accurate identification through image classification improves the aforementioned features. Therefore, this study classified images of five types of fungi using convolutional neural networks (CNN). Initially, dataset distribution was observed, and it was identified that there was a class imbalance in the dataset. To address this issue, data augmentation technique was used. Several preprocessing techniques were also applied to understand the model training behavior with their application. Then the images were rescaled into six different resolution combinations such as original images, low-resolution images, high-resolution images, a mix of original and low-resolution images, a mix of original and high-resolution images, and a mix of low and high-resolution images. Then these data were trained using 13 pre-trained CNN models such as Xception, VGG16, VGG19, InceptionResNetV2, ResNet152, EfficientNetB6, EfficientNetB7, ConvNeXtTiny, ConvNeXtSmall, ConvNeXtBase, ConvNeXtLarge, ConvNeXtXLarge, BigTransfer (BiT). To evaluate these models, accuracy, macro average precision, macro average recall, macro average f1- score, and loss learning curve assessment were used. According to the results, the BiT model preprocessed with normalization, which used a mix of original and high-resolution images, performed the best, producing a model accuracy of 87.32% with optimal precision, recall, and f1-score. The loss learning curve of the BiT model also depicted a low overfitting aspect proving the model’s optimal behavior. Therefore, it was concluded that the BiT model with the mix of original and high-resolution data can be used to detect fungi efficiently.
item: Conference-Abstract
Closed-loop dc motor embedded control platform based on arm® for distance learning experiments
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Ganganath, R; Ranganath, C; Jayasekara, DC; Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
DC motor controlling is known to be one of the most crucial application in the Engineering world, where precise motion control is commonly found in industrial and commercial applications. Hence, learning the fundamentals of DC closed-loop motor control is beneficial for undergraduate students studying Engineering courses. However Due to the ongoing unprecedented economic crisis in Sri Lanka, the lack of access to electronic components, has become very challenging and in turn, affects implementing and testing of the theories on real prototypes. Hence In this paper, a low-cost ARM® based DC motor embedded control platform is presented so the students who are following control courses can implement their own prototype for an affordable cost using only a few common components. The closed-loop control algorithm is designed by using the PID controller, tuned the system using the classical: trial & error method and tested the system responses in real-time, according to different load conditions. Moreover, ARM® based STM®32F407G micro-controller has been used to program the controller and keil® uVision® software is used as the programming IDE. Using MATLAB® and Simulink® platforms, a comparison of the system responses with no-load, lite load and full-load conditions has been presented.