Faculty of Computing

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Subject: search.filters.subject.Neural Network

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Now showing 1 - 7 of 7
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    CricSquad: A System to Recommend Ideal Players to a Particular Match and Predict the Outcome of the Match
    (IEEE, 2023-06-12) Lekamge, E. L.; Wickramasinghe, K. R.; Gamage, S. E.; Thennakoon, T. M. K. L.; Haddela, P.S; Senaratne, S
    Selection of the cricket squad plays a very important role in the outcome of the match. This work is about selecting ideal players for a cricket match and predicting the outcome of the match according to the selected cricket team. A cricket squad consist of around 15 to 16 players, with different expertise in batting, bowling, fielding. To select players for the squad, points were calculated using a statistical approach considering player’s overall career data. And then for the further use of selecting players for the squad next match performance of each and every player were predicted using Machine Learning techniques. Association rule mining was used to find frequent winning player combinations with day/night, home/away, batting first/second, against different opponent combinations. Finally calculate points for each player in both teams, then predict the outcome of the match with classification algorithms by considering the calculated total points of each team and other factors such as toss outcome, batting inning, day night conditions and venue. As for the results, XG boost regressor has produced the highest R2 score of 0.92 for batsman runs prediction model while random forest regressor has produced the highest R2 score of 0.66 for bowler wickets prediction model. The Gradient Boost Classifier predicted the Outcome of a match with the highest accuracy of 0.92 while the K Nearest Neighbor achieved the lowest accuracy of 0.82 score.
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    Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network
    (IEEE, 2021-12-02) Thudawehewa, H; Rathnayake, P.; Thudawehewa, T; Silva, C
    Breast cancer is now a common health problem among most women. Breast cancer is the world’s second-largest cause of mortality for women, and it affects mostly women over the age of 50. The major reasons are that most women do not have proper knowledge about breast diseases/conditions, and the inability to detect abnormalities in the initial stages. A mammogram is one of the best imaging modalities recommended by doctors to diagnose breast cancers. Consultant radiologists are necessary for the identification of those breast pathologies by mammogram images. For a human, it takes some time to read and have an opinion about the condition. Also, the pandemic situation makes the diagnosis processes even more difficult due to the unavailability of doctors and other medical staff. Deep learning approaches are applied for breast cancer detection, and it helps radiologists to identify breast pathologies quickly and accurately. In this work, the mammogram images are collected using MIAS, DDSM, and INbreast databases. The proposed system identifies the location of the lump within the breast, if the lump is malignant or benign, the size of the lump, and the state of the nipple (It is abnormal or not). Convolutional Neural Network (CNN) method for classifying screening mammograms obtained outstanding performance compared to the previous methods. This CNN method produces 96.5% accuracy for breast tumor classification and produces the 80% accuracy for nipple classification.
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    Traffic Density Estimation and Traffic Control using Convolutional Neural Network
    (IEEE, 2019-12-05) Ikiriwatte, A. K; Perera, D. D. R; Samarakoon, S. M. M. C; Dissanayake, D. M. W. C. B; Rupasignhe, P. L
    The existing traffic light control systems are inefficient due to the usage of predefined algorithms on offline data. This causes in numerous problems such as long delays and a wastage of energy. Estimation of traffic density indirectly affects in decreasing the high traffic congestion which will occur due to the less planning of transportation infrastructure and the policies. The goal of this research is to introduce an applicable method to improve the existing static traffic signal system into a dynamic system. As an approach we analyze the use of machine learning algorithms to measure the traffic density to tackle this research problem of high traffic congestion. The main target is to implement this system for the four-way junctions since it is a place where the possibility of having a traffic congestion seems to be high. With use of these traffic density estimation algorithm, crowd density estimation and signal handling we conduct experiments on minimizing the congestion at four-way junctions. We decided on using convolutional neural networks as an advanced machine learning method to increase the accuracy of the learning algorithm.
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    Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network
    (IEEE, 2021-12-02) Thudawehewa, H; Silva, C; Rathnayake, p; Thudawehewa, T
    Breast cancer is now a common health problem among most women. Breast cancer is the world’s second-largest cause of mortality for women, and it affects mostly women over the age of 50. The major reasons are that most women do not have proper knowledge about breast diseases/conditions, and the inability to detect abnormalities in the initial stages. A mammogram is one of the best imaging modalities recommended by doctors to diagnose breast cancers. Consultant radiologists are necessary for the identification of those breast pathologies by mammogram images. For a human, it takes some time to read and have an opinion about the condition. Also, the pandemic situation makes the diagnosis processes even more difficult due to the unavailability of doctors and other medical staff. Deep learning approaches are applied for breast cancer detection, and it helps radiologists to identify breast pathologies quickly and accurately. In this work, the mammogram images are collected using MIAS, DDSM, and INbreast databases. The proposed system identifies the location of the lump within the breast, if the lump is malignant or benign, the size of the lump, and the state of the nipple (It is abnormal or not). Convolutional Neural Network (CNN) method for classifying screening mammograms obtained outstanding performance compared to the previous methods. This CNN method produces 96.5% accuracy for breast tumor classification and produces the 80% accuracy for nipple classification.
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    Real-Time Greenhouse Environmental Conditions Optimization Using Neural Network and Image Processing
    (IEEE, 2020-11-04) Wickramaarachchi, P; Balasooriya, N; Welipenne, L; Gunasekara, S; Jayakody, A
    Agricultural business is one of the biggest areas in world economy. With the growth of population losing agricultural lands is the major issue in world food production. Therefore, controlled environment agricultural systems under vertical farming have been introduced with greenhouses. Within greenhouses there is not a mechanism to continuously monitor the growing community and change the climate conditions. Existing systems only predict the required conditions for the plant and once predicted that value is provided to the plants continuously or change the values from season to season. To address these issues, a working prototype of an IoT based smart hydroponic system is introduced, which uses computer vision to gain maximum profits by growing a specific cultivation by providing endemic environmental conditions and addressing the problems over its growing process. There, this research presents a way of external environmental condition optimization. Regression type Feed Forward Neural Network is considered for this research to optimize the required conditions for tomato plants. Based on the current height of the plant, expected height for next 24 hours, and growth date of the plants neural networks predict the CO2, temperature and humidity level for next 24 hours with the accuracy of 88.33%, 89.21% and 92.65% respectively. The objectives of the research can be achieved by this retrieved results. The successful implementation of neural networks results a cost-effective modern farming solution for growers. This research will be supportive to attain a fundamental comprehension on the concept of the research area.
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    A Geophone Based Surveillance System Using Neural Networks and IoT
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
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    A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems
    (IEEE, 2005-12-05) Chatterjee, A; Pulasinghe, K; Watanabe, K; Izumi, K
    This paper shows the possible development of particle swarm optimization (PSO)-based fuzzy-neural networks (FNNs) that can be employed as an important building block in real robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs that can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by a user. The FNN is also trained to capture the user-spoken directive in the context of the present performance of the robot system. Hidden Markov model (HMM)-based automatic speech recognizers (ASRs) are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system has been successfully employed in two real-life situations, namely: 1) for navigation of a mobile robot; and 2) for motion control of a redundant manipulator.