Investigating the nonlinear and conditional effects of trust—The new role of institutional contexts in online repurchase

Trust is paramount to developing and maintaining long-term relationships in all stages of the customer lifecycle, including the repurchase stage. This research goes beyond the simple finding documented in the extant trust literature that the effect of trust will diminish. It sheds light on the role of institutional contexts and develops a nuanced understanding of the boundary conditions under which trust operates in the repurchase stage, where knowledge-based trust becomes more predominant. Drawing on a different theoretical tenet, prospect theory, we find that customers exhibit distinctively different transaction intentions in the two perceptual conditions of high and low trust in institutional contexts. Specifically, the nonlinear relationship between trust and repeat online transaction intention is inverted U-shaped curvilinear when trust in institutional contexts is high, but is U-shaped when trust in institutional contexts is low. With data collected from both e-commerce and mobile banking contexts using two different measures of institutional contexts, we employed a new and advanced latent moderated structural (LMS) equations approach for analysis and provided robust results. Our findings largely confirm the hypotheses and offer theoretical, methodological, and practical implications.     

A study of profiled handoff for Diffserv‐based mobile nodes

There is a growing interest in providing Internet services to the mobile nodes. When mobile nodes travel from one router's service area to another, the time‐sensitive applications may see degradation in service. We investigate into the effects of handoff on service quality of mobile nodes. Several experiments are conducted using various packet metering and marking schemes with or without transferring profiles to the new router. Results indicate the relative instability period following handoff, loss of packets and delay encountered by packets in profiled or un‐profiled handoffs, leading to determining suitable mix of metering and marking schemes with or without context transfer. Copyright © 2002 John Wiley & Sons, Ltd.     

IAMonSense: multi-level handwriting classification using spatiotemporal information

International Journal on Document Analysis and Recognition (IJDAR) - Online handwriting classification has become an open research problem as it serves as a preliminary step for handwriting...     

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR de-noising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.     

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms—Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.