Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Application of neural network for the prediction of tensile properties of friction stir welded composites(Trans Tech Publications Ltd ttp@transtec.ch, 2017) Shettigar, A.K.; Prabhu B, S.; Malghan, R.; Rao, S.S.; Herbert, M.In this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error. © 2017 Trans Tech Publications, Switzerland.Item Recommending an alternative path of execution using an online decision support system(Association for Computing Machinery acmhelp@acm.org, 2017) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.Prediction of disease severity is highly essential for understanding the progression of disease and initiating an alternative path of execution, which is priceless in treatment planning. An online decision support system (ODeSS) is proposed here for stratification of the patients who may need Endoscopic Retrograde CholangioPancreatography (ERCP) and recommend an alternate path of execution. By this an immediate intervention can be avoided. In this study gallstone disease (GSD) whose prevalence is increasing in India is considered. ODeSS is a versatile non-linear information model which clustered the traces based on the duration of its completion. This is a Retrospective analyses of 575 traces. ODeSS applied the technique of longest common subsequence for identifying the sequence of an online execution and discovering to which cluster of variants it may belong. This discovery assist in taking appropriate clinical decision by recommending an alternative path of execution for such cases which may need emergency interventions. ODeSS performance was evaluated using area under receiver operating characteristic curve (area under ROC curve). This showed an accuracy of 0.9653 in prediction. The proposed model was validated using ROC curve in k-fold cross validation. Hence the proposed ODeSS can be used to conduct a non-linear statistical analysis since, the relationships between the predictive variables are not linear. It can be used as a clinical practice to recommend the path of execution. This would assist in better treatment planning, avoiding future complications. © 2017 ACM.Item What makes a video memorable?(Institute of Electrical and Electronics Engineers Inc., 2017) Kar, A.; Prashasthi, P.; Ghaturle, Y.; Vani, M.Humans are exposed to many pictures and videos on a daily basis, but they have this exceptional ability to remember the details, even though many of them look very similar. This Video Memorability (VM) is mainly due to distinguishable and a fine representation of the frames in human mind that people tend to remember. Videos have an abundance data contained in the frames which can be used for feature extraction purposes. Each feature from each frame has to be carefully considered to determine the intrinsic property of the video i.e. memorability. Using Convolutional Neural Network (CNN), we propose a solution to the problem of predicting VM, by estimating its memorability. A model has been developed to predict VM using algorithmically extracted features. Two types of features (i) semantic features (ii) visual features have been considered. The effectiveness of the model has been tested using publicly available image and video data. The results confirm that the CNN model can predict memorability with a acceptable performance. © 2017 IEEE.Item Network anomaly detection using artificial neural networks optimised with PSO-DE hybrid(Springer Verlag service@springer.de, 2019) Rithesh, K.; Gautham, A.V.; Chandra Sekaran, K.Anomaly Detection is an important field of research in the present age of ubiquitous computing. Increased importance in Network Monitoring and Security due to the growing Internet is the driving force for coming up with new techniques for detecting anomalies in network behaviour. In this paper, Artificial Neural Network (ANN) model optimised with a hybrid of Particle Swarm Optimiser (PSO) and Differential Evolution (DE) is proposed to monitor the behaviour of the network and detect any anomaly in it. We have considered two subsets of 2000 and 10000 dataset size of the NSL KDD dataset for training and testing our model and the results from this model is compared with the traditional ANN-PSO algorithm, and one of the existing variants of PSO-DE algorithm. The performance measures used for the analysis of results are the training time, precision, recall and f1-score. © Springer Nature Singapore Pte Ltd. 2019.Item Tea leaf disease prediction using texture-based image processing(Springer, 2020) Srivastava, A.R.; Venkatesan, M.Nowadays, Tea is commonly used in India as well as in all over the world. Tea is produced in many states of India, i.e., Assam, West Bengal, Tamil Nadu, Karnataka, and so on. But, production of tea is heavily affected by various diseases and pests. There are various kinds of diseases in tea leaves and various pests that can damage the tea crop and affect the tea production. Tea crop damage is reduced by recognizing the tea leaf diseases in an early stage. After detection of the kind of tea leaf diseases, suitable preventive method can be used to reduce the tea crop damage. For the detection of tea leaves diseases, there are different classification methods. Some classification techniques are random forest classifier, k-nearest neighbor classifier, support vector machine classifier, neural network, etc. After training the dataset with classifier, the image of tea leaf is given as an input, the best possible match is found by the classifier system, and diseases are recognized by the classifier system. This project is going to use various classification techniques to recognize and predict the tea leaves disease which helps us to improve the tea production of India. © Springer Nature Singapore Pte Ltd 2020.Item Diagnostic classification of undifferentiated fevers using artificial neural network(American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).Item TORA: Text Summarization Using Optical Character Recognition and Attention Neural Networks(Springer Science and Business Media Deutschland GmbH, 2022) Sneha, H.R.; Annappa, B.Text Summarization is the process of creating a short and coherent version of a longer document that holds the same meaning as that of the original data. This article illustrates the technique to read the text in a printed document (such as newspaper, brochure, web document, etc.) and generate a summary of text. The method proposed is named Text Summarization using Optical Character Recognition and Attention Neural Networks (TORA). TORA can perform extractive summarization of a news article with the aid of Recurrent Neural Networks, Bidirectional Long Short-Term Memory, and Bahadanu Attention Network. The experimental results of the proposed method are promising. The experimental results have shown 80% accuracy in producing the summary from the large text document. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item An Ultralow-Power CMOS Integrated and Fire Neuron for Neuromorphic Computing(Springer Science and Business Media Deutschland GmbH, 2023) Haque, M.N.; Khan, S.R.; Islam, M.T.; Naik, J.D.; Al-Shidaifat, A.D.; Kumar, S.; Song, H.Very large-scale integration (VLSI) implementations of spiking neurons are vital for a range of applications, from high-speed modeling of large neural systems to real-time behavioral systems and bidirectional brain-machine interfaces. The circuit solution utilized to implement the silicon neuron is determined by the application’s needs. This paper describes an ultralow-power analog circuit for realizing a leaky integrate and fire neuron model. The suggested circuit comprises parts for executing spike-frequency adaptation and modifying the neuron’s threshold voltage, in addition to being designed for low-power consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Neural Network-Based Sensorless Control of Flyback Converter for Cell Balancing(Institute of Electrical and Electronics Engineers Inc., 2025) Premarajan, P.; Raushan, R.; Bhushan, R.A conventional control system with an active clamp flyback converter uses a feedforward method that involves feeding the input to a mathematical model beforehand to adjust the duty cycle accordingly. The feedforward method uses a mathematical model that involves a lot of calculations to obtain an efficiency alike feedback system. Integrating a neural network trained with a feedback system data output can be used as a replacement for the mathematical model to have performance as par with the feedback system. Sensors are used to measure the voltages for the feedback circuit to work. This work investigates the potential of utilizing a neural network to enable sensorless operation within a feedforward control system to regulate the voltage input to the cell balancina system. © 2025 IEEE.