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ABSTRACT Purpose: Building upon a human-centered approach, knowledge management considers organizations to be complex systems that spring from the unique organizational contexts in which they are developed. The aim of the present study was to empirically investigate how knowledge practices impact innovation by inquiring into the employees’ intention to stay in the organization and the role of employee recognition.

Accurate stock market prediction is highly desirable to corporations and investors. In this study a deep learning model based on LSTM, BiLSTM with attention mechanism used to predict stocks closing price for next 30 days of two banks listed in Pakistan Stock Exchange. For accurate stock price prediction, it is necessary to consider volatile factors such as news sentiments along with historical data. This study covers that aspect by incorporating news sentiments along with historical stock data that is distributed over a span of ten years from Jan 2011 to July 2021. Preprocessing and sentiment analysis of data was performed using python NLTK module. After that we built a univariate deep learning model based on four layers of LSTM and one dense layer to combine all layers and performed a prediction on train and test data followed by a multivariate deep learning model based on BiLSTM with self-attention mechanism and found out that incorporation of news sentiments really improved the prediction accuracy by reducing the values of mean squared error. Finally, we did the prediction for next 30 days of stock closing price of two banks and compared those predicted prices with actual prices and got quite accurate results.

Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research. In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first step, the input image is fed to Resnet-101 for feature extraction. The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign. Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers. The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset. Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials. We aim to release the employed database soon to assist the improvement in the SRC recognition research area. [ABSTRACT FROM AUTHOR]