Indian SMEs Perspective for election of ERP in Cloud

With increasing market competition and globalization, SMEs have shown interest to deploy ERP package to integrate their business processes. High investment in terms of cost, manpower and time are the major hurdles while selecting a traditional ERP package. Hence there is a need to look for a cost effective ERP solution. Deploying ERP on Cloud is one such option due to lower infrastructure cost and flexible payment. A SME evaluates an ERP solution from Economical, Technological and People perspectives with the factors that measure them. . . A factor can either be a benefit or a threat. A benefit facilitates whereas a threat holds back SMEs decision to adopt ERP in cloud. This research provides a benefit- threat framework on Economical, Technological and People perspective that a SME perceives while selecting ERP in cloud environment. The framework is validated and it suggests that Economical and Technological are two important perspectives while selection of ERP on cloud for a SME

A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment

AbstractMemory based algorithms, often referred to as similarity based Collaborative Filtering (CF) is one of the most popular and successful approaches to provide service recommendations. It provides automated and personalized suggestions to consumers to select variety of products. Typically, the core of similarity based CF which greatly affect the performance of recommendation system is to finding similar users to a target user. Conventional similarity measures like Cosine, Pearson correlation coefficient, Jaccard similarity suffer from accuracy problem under sparse environment. Hence in this paper, we propose a new similarity approach based on Mean Measure of Divergence that takes rating habits of a user into account. The quality of recommendation of proposed approach is analyzed on benchmark datasets: ML 100K, ML-1M and Each Movie for various sparsity levels. The results depict that the proposed similarity measure outperforms existing measures in terms of prediction accuracy

An analysis of the ethical challenges of blockchain-enabled E-healthcare applications in 6G networks

Developments in blockchain technology coupled with rapid developments in network technologies have disrupted traditional business and service models. One such application is in the domain of healthcare. However, the domain's sensitive nature and complexity require blockchain-enabled e-healthcare to ensure utilitarianism while suitably addressing the associated ethical challenges. In this milieu, the paper attempts to identify and evaluate the parameters of ethical challenges associated with blockchain adoption in e-healthcare. This paper contributes to the extant body of knowledge by presenting a critical review of the ethical considerations at the meso level of blockchains in e-healthcare. Based on findings from the literature, the study identified nine parameters of blockchain ethics. Of these, Accuracy and Right to be Forgotten were found to be most critical in terms of ethical dilemmas in healthcare applications. No evidence of ethical dilemma could be found with respect to Accountability and Data Ownership. As these services are deployed over networks, all these challenges are further evaluated in the context of 6G network-based models. This will not only provide the stakeholders with a holistic view of the ethical challenges in various blockchain-enabled healthcare applications but also enable a meticulous transition to the 6G network

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Furthermore, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users’ tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD-DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weightage to the recent ratings. This non-noisy weighted matrix is fed into the Deep Learning model, consisting of an input layer, a Multi-Layer Perceptron, and an output layer to generate predicted ratings. The model’s performance is tested on three benchmark datasets, and experimental results confirm that TD-DNN outperforms other existing approaches

Deep vs. Shallow: A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection

Internet explosion and penetration have amplified the fake news problem that existed even before Internet penetration. This becomes more of a concern, if the news is health-related. To address this issue, this research proposes Content Based Models (CBM) and Feature Based Models (FBM). The difference between the two models lies in the input provided. The CBM only takes news content as the input, whereas the FBM along with the content also takes two readability features as the input. Under each category, the performance of five traditional machine learning techniques: - Decision Tree, Random Forest, Support Vector Machine, AdaBoost-Decision Tree and AdaBoost-Random Forest is compared with two hybrid Deep Learning approaches, namely CNN-LSTM and CNN-BiLSTM. The Fake News Healthcare dataset comprising 9581 articles was utilized for the study. Easy Data Augmentation technique is used to balance this highly imbalanced dataset. The experimental results demonstrate that Feature Based Models perform better than Content Based Models. Among the proposed FBM, the Hybrid CNN - LSTM model had a F1 score of 97.09% and AdaBoost-Random Forest had a F1 Score of 98.9%. Thus, Adaboost-Random Forest under FBM is the best-performing model for the classification of fake news