Often, nonlinearity exists in the financial markets while Artificial Neural Network (ANN) could be used to expect equity market returns for the next years. ANN has been improved its ability to forecast the daily stock exchange rate and to investigate several feeds using the back propagation algorithm. The proposed research utilized five neural network models, Elman network, Multilayer Perceptron (MLP) network, Elman network with Self-Optimizing Map (SOM), MLP with SOM filter and simple linear regression, for estimating new values. Results were examined to investigate the predicting ability and to provide an effective feeds for future values. The result of the proposed simulation showed that SOM could greatly improve the convergence of the neuron networks; whereas Elman network did a better performance to capture the temporal pattern of the symbolic streams generated by SOM.A benchmark of linear regression model was also employed to show the ability of neural network models to generate higher accuracy in forecasting financial market index.

The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.

With the technological advancements and its reach Social media has become an essential part of our daily lives. Using social media platforms allows propagandist to spread the propaganda more effortlessly and faster than ever before. Machine learning and Natural language processing applications to solve the problem of propaganda in social media has invited researchers attention in recent years. Several techniques and tools have been proposed to counter propagation of propaganda over social media. This work pursues to analyse the trends in research studies in the recent past which address this issue. Our purpose is to conduct a comprehensive literature review of studies focusing on this area. We perform meta-analysis, categorization, and classification of several existing scholarly articles to increase the understanding of the state-of-the-art in the mentioned field.

Our study aims to analyze the change in coverage of health issues awareness, printed on the front page of Indian E-Papers (The Hindustan Times and The Times of India) for the pre-and- peri coronavirus period. The collected news articles are examined by performing the Latent Dirichlet Allocation algorithm. The sentiment analysis is performed to analyze the change in the emotions aroused from news articles. The outcome regarding the pre-coronavirus period reveals that the focus of the e-papers was mostly on politics, crime, and economy whereas, in the peri-coronavirus period, the e-papers are focusing more (i.e. 40 % topics) on publishing the news related to disseminating the awareness about the Coronavirus disease. The priority of news topics includes the active number of cases, medical facilities, COVID-19 testing. The outcome regarding sentiment analysis reveals that negative sentiments are prominent in the peri-coronavirus period due to fear of the outbreak of the virus.

Training future programming teachers requires an innovative approach. Not only students need to handle the most current trends in technologies and teaching-learning methodologies, but also they must develop the capacity and criteria to search and select the most adequate to their context. This work analyzes the application of a collaborative Research-Based Learning methodology in the Programming subject of a master's degree in teacher training. The objective was to create a digital learning ecosystem and analyze the impact on the development of programming teaching skills. The results show that students perceive positive effects on the development of teaching skills, generating useful resources. However, teamwork has conditioned the quality of such resources. The digital ecosystem has allowed students to share knowledge with their peers and forthcoming students. Students who already had the generated ecosystem available valued it very positively. Future programming teachers require lifelong learning which can be supported by this living ecosystem.

The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of information retrieval to get the most relevant results. In this paper, we propose a new recommendation approach for recommending relevant documents to a search engine’s users. In this work, we proposed a new approach for calculating the similarity between a user query and a list of documents in a search engine. The proposed method uses a new reinforcement learning algorithm based on n-grams model (i.e., a sub-sequence of n constructed elements from a given sequence) and a similarity measure. Results show that our method outperforms some methods from the literature with a high value of accuracy.

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.

User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and Multi-identity cards. In this paper we have tried to provide a secure Multi-identity Multi-application Smart Card Authentication Scheme. Security is provided to user’s data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored which further enhances the security of the underlying system.

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.