Research Papers

Paper Title: Custom Deep Neural Network For Message Scoring Authors: Shailendra Singh Kathait, Ashish Kumar and Ikshu Chauhan Summary: This paper offers customized message scoring creation of Technical Literature of Drugs using Text Analytics and Deep Learning. A customized Deep Learning driven model has beendeveloped that feeds scientific data (texts) about medicine(s) (written by medical researchers) & creates multiple sub messages that can be used for marketing research to effectively communicate benefits of drugs. Model helped in reducing manual intervention by automating the complete process and replacing Linguists. Download Research Paper

Paper Title: Application of Image Processing and Convolution Networks in Intelligent Character Recognition for Digitized Forms Processing Authors: Shailendra Singh Kathait, Shubhrita Tiwari Summary: Image processing is a rapidly evolving field with immense significance in science and engineering. One of the latest applications of Image processing is in Intelligent Character Recognition (ICR). Intelligent Character Recognition is the computer translation of handwritten text into machine-readable and machine-editable characters. It is an advanced version of Optical Character Recognition system that allows fonts and different styles of handwriting to be recognized during processing with high accuracy and speed. ICR, in combination with OCR and OMR (Optical Mark Recognition), is used in forms processing. Forms processing is a process by which one can capture information entered into different data fields filled in forms and convert it to an editable text. Forms processing systems can range from the processing of small application forms to large scale survey forms with multiple pages. The Recognition Engine, designed using Image Processing and Convolution Networks helps save time, labor and money in addition to the increase of accuracy. Download Research Paper

Paper Title: Intelligent Recommendation Engine Authors: Shailendra Singh Kathait, Shubhrita Tiwari, Piyush Kumar Singh Summary: Highly engaged visitor is central to the success of any Digital publisher. Having right content and serving it to the right audience at right time is the key to achieving high engagement rates. Publishers have used machine learning based recommendation engines to summarise articles, extract article metadata (keywords, phrases), build user preferences and recommend right content to the visitor in real time on a website, as email digest or app notifications. This has been achieved through collaborative filtering, content-based filtering. Recommendation algorithms based only alone on collaborative filtering or content based filtering overlook important factors that drive suitable recommendations for a user. This paper describes the application of hybrid approach i.e. Collaborative Filtering together with Content-based Filtering for making the most appropriate and relevant recommendations. Download Research Paper

Paper Title: Supplier Evaluation Model on SAP ERP Application using Machine Learning Algorithms Authors: Manu Kohli Summary: For business enterprises, evaluating a supplier is a mission critical process. On ERP (Enterprise Resource Planning) applications such as SAP, the supplier evaluation process is performed by configuring a linear score model, however this approach has a limited success. Therefore, author in this paper has proposed a two-stage supplier evaluation model by integrating data from SAP application and ML algorithms. In the first stage, author has applied data extraction algorithm on SAP application to build a data model comprising of relevant features. In the second stage, each instance in the data model is classified, on a rank of 1 to 6, based on the supplier performance measurements such as on-time, on quality and as promised quantity features. Thereafter, author has applied various machine learning algorithms on training sample with multi-classification objective to allow algorithm to learn supplier ranking classification. The application of supplier evaluation model proposed in the paper can be generalised to any other other information management system, not only limited to SAP, that manages Procure to Pay process. Download Research Paper

Paper Title: Unsupervised Key-phrase Extraction using Noun Phrases Authors: Shailendra Singh Kathait, Shubhrita Tiwari, Anubha Varshney, Ajit Sharma Summary: Increasing volume of digital content in the form of articles, blogs has accelerated the need for an automated process can simplify the extraction of relevant tags and even summarize the content on an article. Traditionally supervised machine learning approaches have been used but their utility needs labeled datasets which become a bottleneck in learning as content grows exponentially. Facing similar scenario we have experimented with an unsupervised approach to key-phrase extraction that doesn’t depend on labeled training datasets. Details are outlined in the research paper. Download Research Paper

Paper Title: Hybrid Intelligent System via Fuzzy Regression Analysis, Bayesian Gaussian Reasoning Model in Healthcare Authors: Shailendra Singh Kathait, Dr Aankita Kaur, Anubha Varshney Summary: In this paper, we propose the architecture of Hybrid Intelligent System with different techniques of pattern recognition and machine learning. Fuzzy Regression and Bayesian Gaussian Neural Network approach are used to build the model. Fuzzy regression deals with the uncertain, vagueness of the system. Naive Bayesian classifier helps in building strong independent relationships whereas Gaussian classifiers correlates high dimensional data with kernel function to yield better performance of the system. A hybridized combined approach of neural network is presented in healthcare. It is due to its flexibility of modeling, and robust nature, learning ability from complex functions and the application of different algorithm for reduction of errors for a better intelligent system. Download Research Paper

Paper Title: Integrating Neural Networks with Time Series Forecasting: Improving Sales Authors: Shailendra Singh Kathait, Dr Aankita Kaur, Shubhrita Tiwari, Anubha Varshney Summary: This paper presents a comparison between different algorithms that are used for time series forecasting on a noisy time series data. The accuracy of forecasting is first evaluated using traditional methods like Moving Average, ARIMA modeling etc. and then Machine Learning based approach with ANN (Artificial Neural Networks) is used for forecasting. ANN algorithms have found to deliver superior & robust predictions. Download Research Paper

Paper Title: Predicting Equipment Failure On SAP ERP Application Using Machine Learning Algorithms Authors: Manu Kohli Summary: A framework model to predict equipment failure has been keenly sought by asset intensive organisations. Timely prediction of equipment failure reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author has proposed an equipment reliability model, for equipment type pumps, designed by applying data extraction algorithm on equipment maintenance records residing in SAP application. Author has initially applied unsupervised learning technique of clustering and performed classes to cluster evaluation to ensure generalisation of the model. Thereafter as part of supervised learning, data from the finalised data model was fed into various Machine Learning (ML) algorithms where the classifier was trained, with an objective to predict equipment breakdown. The classifier was tested on test data sets where it was observed that support vector machine (SVM) and Decision Tree (DT) algorithms were able to classify and predict equipment breakdown with high accuracy and true positive rate (TPR) of more than 95 percent. Download Research Paper

Paper Title: Intelligent System For Customer Sentiment Analysis Authors: Shailendra Singh Kathait, Shubhrita Tiwari, Anvesha Bagaria, Vinod Kumar Singh Summary: Sentiment analysis is widely acknowledged in the web and social media monitoring. It allows businesses to gain a comprehensive public opinion on the organization and its services. The ability to deduce insights from the text and emoticons from social media is a practice that is now widely adopted by the organizations worldwide. Digital media represents an extensive opportunity for businesses of any industry to acquire the needs, opinions and intent that users share on social media and web. Listening to consumer’s voice requires in-depth understanding of what customer’s express in Natural Language. This research paper describes the designing of an Intelligent System to understand the human language and crack the sentiment behind it. Download Research Paper

Paper Title: Unsupervised Automated Clustering of Chinese Articles Authors: Shailendra Singh Kathait, Shubhrita Tiwari Summary: A lot of insights can be drawn from the articles that are published online. Instead of manually reading all the articles and assigning relevant tags to them satisfying the content, it will be highly efficient if there exists an automated process for performing the task. In this paper, an unsupervised approach for the automated tagging of articles in the Chinese language has been implemented. The input is an article and output is the tags to that article. The major challenge is the segmentation of the Chinese characters, which do not make use of separators, unlike the English characters. To overcome this, different approaches are combined together in order to get accurate results. Efficient tagging of articles is required, which can be used for many applications in the analysis, one of which is in Recommendation Engine. The tagging process should consider all the aspects of the article and assign the most relevant tags accordingly. The proposed algorithm was implemented for a Chinese Publication House and relevant tags were assigned to its articles of different categories. At the end of the project, the results were manually checked for, in a corpus of 10000 Chinese articles, which reflected the attainment of the overall accuracy of around 85%, greater than that obtained through different traditional methods. Download Research Paper