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Contributed Papers

Contributed Oral Presentations

Title: Classification of Pneumothorax in Chest X-ray images using Deep Neural Network

AuthorsManjeet Kaur, Ankita Pandey, and Arun Kumar from G. B. Pant University of Agriculture and Technology.

Abstract: Currently, lung diseases are extremely common throughout the globe and a few of which include chronic obstructive pulmonary disease, pneumonia, pneumothorax, tuberculosis. Air in the pleural cavity is thought to be “pneumothorax". This is a state when the lung surface or chest wall is breached, allowing air to enter the pleural space and causing the lung to collapse, it is a serious situation and might be life-threatening. In this study, we have taken chest X-ray images and then used the Deep Transfer Learning technique along with Machine Learning to detect the presence or absence of pneumothorax in chest X-ray images. We have aggregated the unique and computational attributes of Deep Learning and Machine Learning. To extract image features, the deep Transfer Learning based pre-trained Residual Network is used. For binary classification of pneumothorax, Support Vector Machine is employed to generate the optimal decision boundary (hyperplane). To achieve effective outcomes, a balancing data technique with augmentation is used to create a balance between the training and validation dataset, as well as an automatic adjusting learning rate technique called "ReduceLROnPlateau" to monitor validation loss and obtain optimal learning rate. This research work has set a new record with a good performance by achieving state-of-the-art results as 0.8831 in terms of AUC and 0.4375 in terms of loss. The Precision, Recall, and the f1 score obtained as 0.9285, 0.8125, and 0.867, respectively.

Title: Opinion Mining of Japanese Product Reviews using Translators, Word Embedding and ML Techniques

AuthorsRamanna T, P Deepa Shenoy, and Venugopal K R, UVCE, Bangalore University.

Abstract: Most individuals rely on translation to communicate and comprehend other languages. The main driving force behind this study is determining which translator, along with the blend of word embedding and Machine Learning(ML)techniques, when working with the Japanese language, has greater accuracy. Business owners struggle to understand how their products perform in Japan and receive feedback to improve it. Japanese is among the most complex languages to read and write. The issue can be resolved by classifying reviews and ratings into separate groups, such as excellent and negative so that business owners can improve their products based on the reviews. This paper analyzes the Amazon Product Review dataset in the Japanese language, which contains reviews and ratings. These reviews are translated into English using three different translators(Google, Microsoft, and Python). Then combination of word embedding(count vector and TF-IDF) and ML techniques(K Nearest Neighbor(KNN), Support Vector Machine(SVM), and Logistic Regression(LR)) is applied to the data for each translator. The outcome of this study is that the Python Translator along with the blend of TF-IDF and SVM outperforms all the translators with an accuracy of 80%.

Title: MDLCSL: A Multilayer Deep Learning Model for Classification of Sweet Lime Leaves

AuthorsDivyansh Thakur ( IIIT Una ), Jaspal Saini ( IIIT Una ), and Srikant Srinivasan (Plaksha University)

Abstract: Artificial Intelligence (AI) has a crucial impact on human life by using automation to ease workload. Many fields show AI's abilities and it's being utilized to accomplish intricate tasks efficiently and effectively. This work explores the use of AI, specifically Deep Learning (DL), in agriculture through the classification of sweet lime leaves. A custom dataset of 4000 images was created and manually classified into four classes (Healthy, Infected, Decayed, Other). A multilayer DL model was developed using Convolutional Neural Networks (CNN) for leaf classification. The model was evaluated using five performance parameters: accuracy, loss, F1-Score, recall, and precision, achieving an accuracy of 99.05% with a low loss of 0.003, F1-Score and recall of 0.96, and precision of 0.98. The proposed model was benchmarked against InceptionV3 and VGG16 and demonstrated its superiority.

Contributed Flash Talks

Title: Prediction of Sugarcane Pol Using Multi-Spectral Time Series Data

AuthorsRajiv Ranjan and Shashank Tamaskar from Plaksha University.

Abstract: This article presents a method for predicting the pol value (Sucrose content) and harvesting date of sugarcane crops using multiple resolution multispectral time series data. Spectral signatures of sugarcane crops enable this system to estimate the above ground biomass (AGB). Normalized difference vegetation index (NDVI) was analyzed for approx. 300 days (about 10 months) after sowing. Sentinel-2 data of 30-meter resolution and PlanetScope data of less than 3-meter resolution used for the NDVI analysis. This analysis was done at Loni-Hardoi, Uttar Pradesh (India) having 305 sugarcane plots of total 419 acre. NDVI maps were obtained at regular intervals from remotely sensed data and corresponding pol values from the lab measurements as a ground truth. By corelating NDVI and pol values it is observed that maximum AGB reached at 250 days (about 6 and a half months) after sowing and maximum pol values obtained at 320 days (about 10 and a half months) after sowing for the sugarcane variety CO-238. We also estimated the pol values through geospatial analysis and obtained correlation coefficient (R2) of 0.8153.

Title: Sequence Classification of Malfunction in CT Examinations with Imbalanced Data

AuthorsHritwika Paul ( Netaji Subhash Engineering College ) and Srijeet Chatterjee ( Friedrich-
Alexander-University Erlangen-Nuremberg ) 

Abstract: Machine learning plays an essential role in industry 4.0 for predictive analytics related problems. The goal of this project is to classify the examinations of the computed tomography(CT) scanners into correct or malfunctioning class. These classifications could enable predictive maintenance of the CT scanners. The proposed models exploit a structured event log of CT-scan examinations, also known as CT-scan workflows. This project attempts to perform the sequence classification technique using deep neural networks. However, the biggest challenge is to overcome the class imbalance, since error sequences are rare events encountered in the log files.

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