The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efﬁcacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

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Detection of Online Fake News Using Blending Ensemble Learning

https://doi.org/10.1016/j.imu.2022.100937

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia

Competent employees are a rare commodity for great companies. The problem of maintaining good employees with experience threatens the owners of companies. The issue of employee attrition can cost employers a lot as it takes a lot to compensate for their expertise and efficiency. For this reason, in this research, we present an automated model that can predict employee attrition based on different predictive analytical techniques. These techniques have been applied with different pipeline architectures to select the best champion model. Also, an autotuning approach has been implemented to calculate the best combination of hyper parameters to build the champion model. Finally, we propose an ensemble model for selecting the most efficient model subject to different assessments measures. The results of the proposed model show that no model up until now could be considered ideal and perfect for each case of business context. Yet, our chosen model was pretty much optimal as per our requirements and adequately satisfied the intended goal.

/pdf/automated-prediction-of-employee-attrition-using-ensemble-1uq4sbri.pdf

Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms

System which gives recommendations usually filters the given data using various methodology and suggests the relevant one to the customer benefit. In day-to-day life people usually use a powered recommendation system in many areas like movies, books, music, news, items etc. In this paper a wide range of work is reviewed in the field of a recommender system for movies where dataset source, methods used and accuracy are compared to deduce best one and future scope for improvement in this area are analyzed.

A Review on Movie Recommendation System Using Machine Learning

In today’s world, information is of paramount importance in any field and controls our lives. With increasing number of people taking to social media and websites hosted on the internet as their everyday sources of information, the impact of spreading misinformation has high affecting causes rapidly. The proposed system is a Multitask Learning model that can categorize the news articles collected from the web as fake or not. The title and content of the articles are modeled as BiLSTM subtasks and the CapsNet model is the meta classifier. The model predicts with an accuracy of 97.96% which helps to flag the articles posted on the internet so that the readers are well informed. Further, the system is also able to rate the articles on a 5-point scale to determine the degree of misinformation in the article for further analysis.

Fake News Detection and Analysis using Multitask Learning with BiLSTM CapsNet model

Information sharing on the web particularly via web-based networking media is increasing. Ability to identify, evaluate and address such information is significantly important. Fake information deliberately created is purposefully or unintentionally engendered over the internet. This is affecting a larger group of society who are blinded by technology. This paper illustrates model and methodology to detect fake news from news article with the assistance of Machine learning and Natural language processing. In this proposed work different feature engineering methods like count vector, TF-IDF and word embedding are used to generate feature vector. Seven different Machine learning Classification algorithms are trained to classify news as fake or real and are compared considering accuracy, F1 Score, recall, precision and best one is selected to build a model to classify news as fake or real.

Performance Comparison of Machine Learning Classifiers for Fake News Detection

Food, shelter and clothing are essential for human survival. With advancements in science and technology, we’ve seen incredible improvements in the way we design and build modern homes and clothing. From smart homes that use various technologies to make our lives more comfortable and convenient, to high fabrics that provide better insulation and protection, we’re living in the era of incredible innovation and progress. With increased awareness and support, we can create a more inclusive and diverse community in agriculture. However, there are initiatives and programs in place to provide education and training for farmers to improve their knowledge and skills. These programs can cover topics such as crop management, soil health, pest control, and sustainable farming practices, which can help farmers, increase their yields, reduce their environmental impact, and improve their livelihoods. Additionally, there are also scientific advancements in agriculture technology that aim to simplify and automate farming processes, making it easier for farmers to manage their crops and minimize errors. By leveraging technology and data-driven models, we can help farmers make more informed decisions and reduce the need for trial and error. This can save time and resources for farmers, leading to more efficient and sustainable farming practices. By providing farmers with the right tools and information, we can help improve their livelihoods and contribute to the overall growth and prosperity of the agricultural sector. Keywords: Machine Learning, ESP32 microcontroller, DHT 11 sensor, K-Nearest Neighbours (KNN)

/pdf/crop-recommender-system-using-machine-learning-approach-and-2listakc.pdf

Crop Recommender System Using Machine Learning Approach and IoT

This paper focuses on binary and grayscale image transfer by using a graphical programming tool LabVIEW. The LabVIEW platform is used to implement PSK modulation through USRP. AWGN channel was created to test the simulated results. The output image is recorded and calculated PSNR for various Eb/N0 in channel. Eb/No ratio of 50 dB and 15 dB showed better results with PSK as modulation technique. After testing in simulation environment, the results are obtained by transmitting image using B210 USRP hardware. Various test cases are considered to verify hardware implementation of image transmission. The resulted image shows satisfactory reconstruction with respect to transmitted image.

Smitha N

Papers

Performance Comparison of Machine Learning Classifiers for Fake News Detection

Crop Recommender System Using Machine Learning Approach and IoT

Image Transmission using USRP through LabVIEW