Potential Approach for Text-Based Emotion Detection Using NLP Coupled With Deep Learning of Sentiment Analysis Gurpreet Singh, Deependra Singh, Ruchi Sharma, Kapil Bhardwaj 2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024 Sentiment analysis is a valuable method for gauging people’s sentiments and emotions towards various subjects. Within this realm, emotion detection stands out by predicting specific emotions rather than categorizing them broadly as positive, negative, or neutral. While previous research has predominantly focused on emotion recognition through speech and facial expressions, text-based emotion detection poses distinct challenges due to the absence of cues like tonal stress and facial expressions. Addressing these challenges, natural language processing (NLP) techniques have been employed in the past, including the keyword approach, lexicon-based approach, and machine learning approach. However, keyword- and lexicon-based strategies have limitations, particularly in handling semantic relations. In this study, a propose novel hybrid model that combines machine learning and deep learning, specifically utilizing a sequential neural network architecture. The model includes embedding layers, flattening, and dense layers. The effectiveness of our approach is demonstrated through training on a diverse dataset encompassing sentences, tweets, and dialogs. Remarkably, our model achieves a high accuracy of the validation data, underscoring its efficacy in text-based emotion detection without relying on existing content.
An Economic Inventory Model Comparing Stock Dependent and Fixed Demand for Random Machine Breakdown, Repair Time and Deterioration Ruchi Sharma, Gurcharan Singh, Ashutosh Pandey, Shiv Kumar Sharma Evergreen, 2023 In this article, effect of random machine breakdown along with random repair time for a manufacture unit exposed to exponentially decreasing rate due to machine breakdown. Production is taken as directly proportional to demand and greater than demand. The model is proposed for demand as stock dependent and also for fixed demand. The unit is also producing deteriorating items. Comparison of expected lost sale cost has been made by considering demand as stock dependent and then as fixed. Using uniform probability density function expected manufacturing time is estimated. The work is done to compare both the demands and then come to a decision for the manufacture system to optimize the expected overall cost with respect to time, subjected to random machine breakdown. To summarize the model a numerical example is also discussed.
Optimizing inventory policy for time-dependent demand with imperfect items R. Sharma, G. Singh Advances in Mathematics Scientific Journal, 2020 Through this paper, an inventory model is proposed for a manufacturing process which produces perfect and after some time imperfect items. It’s been assumed that demand is time-dependent and production is greater than demand. The rate of production of items is directly affected by demand. A further assumption is made that the system starts producing imperfect items after some time of operation due to various factors. For imperfect items, collection and repair work has been considered which optimizes the inventory. Repair of the imperfect items starts when regular production stops. Using the concepts of differential calculus, the optimum inventory is obtained to capitalize on the profit and reduce the cost. An example is discussed to demonstrate the theory.
