FAKE NEWS DETECTION ON THE WEB: A DEEP LEARNING BASED APPROACH

The acceptance and popularity of social media platforms for the dispersion and proliferation of news articles have led to the spread of questionable and untrusted information (in part) due to the ease by which misleading content can be created and shared among the communities. While prior research has attempted to automatically classify news articles and tweets as credible and non-credible. This work complements such research by proposing an approach that utilizes the amalgamation of Natural Language Processing (NLP), and Deep Learning techniques such as Long Short-Term Memory (LSTM). Moreover, in Information System’s paradigm, design science research methodology (DSRM) has become the major stream that focuses on building and evaluating an artifact to solve emerging problems. Hence, DSRM can accommodate deep learning-based models with the availability of adequate datasets. Two publicly available datasets that contain labeled news articles and tweets have been used to validate the proposed model’s effectiveness. This work presents two distinct experiments, and the results demonstrate that the proposed model works well for both long sequence news articles and short-sequence texts such as tweets. Finally, the findings suggest that the sentiments, tagging, linguistics, syntactic, and text embeddings are the features that have the potential to foster fake news detection through training the proposed model on various dimensionality to learn the contextual meaning of the news content

Loyalty Programmes: Practices, Avenues and Challenges

<div align=justify>Complexity of modern business requires managers to strive for innovative strategies to acquire and retain customers in any product market field. As acquiring new customers is getting costlier day by day, business organizations have offered continuity/loyalty programmes to retain/reward existing customers and maintain relationships. The premise of CRM is that once a customer is locked in, it will be advantageous to both the organization as well as customer to maintain relationships and would be a win-win situation for both. Consumers find it beneficial to join such programmes to earn rewards for staying loyal. Through loyalty programmes, firms can potentially gain more repeat business, get opportunity to cross-sell and obtain rich customer data for future CRM efforts (Yuping Liu, 2007). This paper, exploratory in nature, attempts to provide a conceptual overview of Loyalty in organized retail sector, outlines practices of grocery retail outlets in Ahmedabad, the largest city in the state of Gujarat and the seventh-largest urban agglomeration in India, with a population of 56 lakhs (5.6 million). It also throws light on consumer expectations, perceptions and problems faced through indepth exploration. Based on literature review and environment in India, an emerging economy, it attempts to predict future of such programmes specifically in Indian organised retail sector and discusses managerial challenges of managing loyalty programmes and provides agenda for future research directions.</div>

Credibility Analysis of Customer Reviews on Amazon: A Design Science Approach

This research examines the problem of identification and elimination of malicious customer reviews on Amazon.com. Online customer reviews are increasingly considered crowd-sourced consumer opinions that significantly influence online purchasing decisions (Hu, 2012). However, most current approaches to detecting fake reviews rely on either manual assessment of the reviews or the use of the mechanical Amazon Turks service (Mukherjee, 2014; Munzel, 2015). Manual assessment of customer reviews is not scalable in practice, leaving the quality of the current approaches to detect fake reviews questionable. The primary goal of our research is to develop a model of credibility analysis that automatically classifies amazon customer reviews as credible or non-credible. This model is developed based on the Design Science Research Methodology (Peffers, 2007) and encompasses a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) as a classification technique. We first identify features of online customer reviews that can be used to effectively separate credible reviews from non-credible ones. Then fed the review dataset based on identified features to our proposed model for the assessment of review’s credibility. The study of existing literature indicates that most of current research on fake review focusses on the content of the reviews (Hu et al., 2012; Munzel, 2015).We, however, believe that content is only part of an effective method for detecting fake reviews. Our proposed model considers not only the textual but also the writing style and user related features of reviews. Further, we will compare our LSTM based model with other algorithms used in detecting misleading information such as Dynamic Series-Time Structure-based Support Vector machine (SVM-DSTS) (Ma, Gao, 2015) and Decision tree ranking (DT-Rank) (Zhao, 2015). Initial Design of proposed model will be presented for this TREO talk and encourage discussion concerning misleading customer reviews, existing fake review elimination initiatives, and Design science as an approach

A Detailed Study of Channel Estimation and BER Optimization in presence of AWGN and Rayleigh Channel of OFDM System

Orthogonal Frequency Division Multiplexing is an important one field communication and that uses parallel information series. Contrast and single carrier adjustment are basic aspects of this technique where OFDM has many favourable circumstances are risky to work on this technique. It is robust, easy to use, and strength to safe the processing channel from distortions. It provides safety from multipath, much lesser computational many-sided characteristic. OFDM has some significant to execute it in commonly using media transmission frameworks. OFDM standard tolerate Packet misfortune, Bit trouble, Bit Error Rate (BER), Signal to Noise Ratio (SNR), Calculation of PAPR, Power Spectrum estimation. This dissertation is targeted to show the comparison of AWGN and Rayleigh channel by using fading process for particularity in superior performance with individual values of spectrums as well as by their scattering plots. In this dissertation each and every signal of these terms are examined and all the four parameters are thought about utilizing AWGN and Rayleigh fading channel by changing the period of a portion of the subcarriers utilizing QPSK in OFDM regulation. The representation of outputs is finished through MATLAB programming

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.Comment: 35 Pages, 6 tables and 11 figures. Consists of Dataset links used for crime prediction. Review Pape

Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using increasingly sophisticated techniques to breach security systems and steal sensitive data. In recent years, machine learning, deep learning, and transfer learning techniques have emerged as promising tools for predicting cybercrime and preventing it before it occurs. This paper aims to provide a comprehensive survey of the latest advancements in cybercrime prediction using above mentioned techniques, highlighting the latest research related to each approach. For this purpose, we reviewed more than 150 research articles and discussed around 50 most recent and relevant research articles. We start the review by discussing some common methods used by cyber criminals and then focus on the latest machine learning techniques and deep learning techniques, such as recurrent and convolutional neural networks, which were effective in detecting anomalous behavior and identifying potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset, and then focus on active and reinforcement Learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. Overall, this paper presents a holistic view of cutting-edge developments in cybercrime prediction, shedding light on the strengths and limitations of each method and equipping researchers and practitioners with essential insights, publicly available datasets, and resources necessary to develop efficient cybercrime prediction systems.Comment: 27 Pages, 6 Figures, 4 Table

DOMESTIC LPG MARKETING IN INDIAN PERSPECTIVE

Liquefied Petroleum Gas (LPG) marketing commenced in India during the year 1955 at (Bombay) Mumbai by then M/s Burma Shell. Since then LPG market in India has evolved over the last five decades or more from a miniscule level to the present position of over 14 crore customers on Industry basis. LPG marketing activities are expected to grow further because of the focus on expansion in rural areas. A survey was carried out for 2000 People in and aroung Bangalore city. This paper focuses on increase in number of LPG users and its marketing strategies adopted

DOMESTIC LPG COMPARATIVE STUDY BETWEEN URBAN AND RURAL MARKETS

In the Present Scenario Fossil fuels, especially petroleum products, occupy a pre-eminent position in all economies of the world. As a key primary source of energy, they necessitate involvement of the Government in pricing, production and distribution. Energy security continues to be of concern to India as the country faces huge challenges in meeting its energy needs. The country depends on imports of crude oil to meet more than 77% of its petroleum products requirement.A survey was carried out for 2000 People in and around Bangalore city. This paper focuses on various aspects of Domestic LPG consumption and comparing different elements of consumers

Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

The recent advancements under the umbrella of artificial intelligence (AI) open opportunities to tackle complex problems related to image analysis. Recently, the proliferation of COVID-19 brought multiple challenges to medical practitioners, such as precise analysis and classification of COVID-19 cases. Deep learning (DL) and transfer learning (TL) techniques appear to be attractive solutions. To provide the precise classification of COVID-19 cases, this article presents a customized Deep Convolutional Neural Network (DCNN) and pre-trained TL model approach. Our pipeline accommodated several popular pre-trained TL models, namely DenseNet121, ResNet50, InceptionV3, EfficientNetB0, and VGG16, to classify COVID-19 positive and negative cases. We evaluated and compared the performance of these models with a wide range of measures, including accuracy, precision, recall, and F1 score for classifying COVID-19 cases based on chest X-rays. The results demonstrate that our customized DCNN model performed well with randomly assigned weights, achieving 98.5% recall and 97.0% accuracy