Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

https://www.mdpi.com/2078-2489/12/1/38/pdf

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

FAKE news has proliferated to a big crowd than before in this digital era, the main factor derives from the rise of social media and direct messaging platform. Techniques of fake news stories detection ingenious, varied, and exciting. This study aims to apply natural language processing (NLP) techniques for text analytics and train deep learning models for detecting fake news based on news title or news content. Solution proposed in this study aims to be applied in real-world social media and eliminate the bad experience for user to receive misleading stories that come from non-reputable source. For NLP techniques, text preprocessing such as regular expression, tokenization, lemmatization and stop words removal are used before vectorizing them into N-gram vectors or sequence vectors using terms frequency inverse document frequency (TF-IDF) or one-hot encoding respectively. Then, TensorFlow is chosen as the framework to be used with built in Keras deep learning libraries that is having a large community and number of commits on Tensorflow GitHub repository that can be enough to build deep learning neural network models. Results from the models are showing that models trained with news content can achieve better performance with computation time being sacrificed while models trained with news title require less computation time to achieve good performance. Also, overall performance of models fed with N-gram vectors are slightly better than models fed with sequence vectors.

Fake News Detection using Deep Learning

Software defined networks and openflow: a survey

Today, the world is under siege from various kinds of fake news ranging from politics to COVID-19. Thus, many scholars have been researching automatic fake news detection based on artificial intelligence and machine learning (AI/ML) to prevent the spread of fake news. The mainstream research on detecting fake news so far has been text-based detection approaches, but they have inherent limitations such as the difficulty of short text processing and language dependency. Thus, as an alternative to the text-based approach, the context-based approach is emerging. The most common context-based approach the use of distributors’ network information in social media. However, such information is difficult to obtain, and only propagation within a single social media can be traced. Under this background, we propose the use of composition pattern of web links containing news content as a new source of information for fake news detection. To properly vectorize the composition pattern of web links, this study proposes a novel embedding technique, which is called link2vec, an extension of word2vec. To test the effectiveness and language independency of our link2vec-based model, we applied it to two real-world fake news datasets in different languages (English and Korean). As comparison models, we adopted the conventional text-based model and a hybrid model that combined text and whitelist-based link information proposed by a prior study. Results revealed that in the datasets in two languages, the link2vec-based detection models outperformed all the comparison models with statistical significance. Our research is expected to contribute to suggesting a completely new path for effective fake news detection.

A Link2vec-based Fake News Detection Model using Web Search Results

Technology has become one of the greatest needs of humankind and, in every field, has made lives better. In the field of medicine, technology has helped to treat, diagnose, and cure the diseases far better than the traditional method. Atherosclerosis is a condition where the cholesterol in the body gets deposited on the walls of blood vessels, thus narrowing them and reducing the blood flow to the organs. If the rate of atherosclerosis is greater than 50%, the patient stands at a high risk of heart diseases. The healthcare industry collects a large amount of data from the patients, but the potential of these data remains untapped. It retains various relationships and patterns and helps to determine the relationship between clinical parameters like blood pressure, cholesterol, and their association with heart diseases. The objective of the proposed work is to predict the rate of atherosclerosis for a patient based on the clinical parameters like blood pressure and cholesterol and predict the rate using various machine learning classification algorithms. This method will help the medical practitioner to determine the need for an operative procedure based on the outcome. This system predicts the percentage of narrowing down of blood vessels as the output.

Prediction of Heart Disease Using Long Short-Term Memory Based Network

The diagnosing features for Diabetic Retinopathy (DR) comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we presenta probabilistic convolution neural network based algorithms, utilized for the extraction of such features from the retinal images of patient’s eyeballs. The classifications proficiency of various DR systems is tabulated and examined. The majority of the reported systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities.

/pdf/automated-detection-of-microaneurysmsusing-probabilistic-217jam3biv.pdf

Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

Using the channel utility factor, in this work an analytical model for wireless networks IEEE 802.11 is proposed to calculate the delay experienced while serving a request in the scheduler part. The proposed delay sensitive scheduling algorithm considers the nature of the data. The delay incurred due to scheduling overhead is analyzed in order to address unfair treatment of users. The motivation behind this work is to maximize average time channel utilization. The Rated Queue effectively characterizes the unsaturated traffic condition. In addition, different types of services are addressed to quantify Quality of Service to address different delay constraints. This model explores the time and channel utilization to guarantee and enhance the network performance.

A modeling and analysis of delay sensitive scheduling in wireless network

Data visualization is the common term that is used to describe presenting data in a pictorial or graphical format. Data visualization software helps to easily uncover patterns, trends and correlations that might go unnoticed in text-based data. It helps to break down the details of the data and make the data more comprehendible effortlessly. Another emerging field of study is Internet of Things (IoT). IoT deals with the difficulty of diverse device types, various sensors and types of data that gets generated and analysed in real time. An individual managing IoT has neither time nor the patience to decode the information at leisure. Unless the data is comprehendible, taking fast and result-oriented actions is difficult. That is the reason why data visualization becomes fundamental and viable. One of the tasks here is to be able to choose the form of visual depiction that works the best for the information. IBM’s latest contribution to the field of data science brings analytics to your data and not the other way around. They build solutions that accumulate data from any type of source, including web and social. With those generated solutions, one can store, investigate and give an account of information by using analytic engines to drive actionable insights and visualization. In this work, we are making an attempt to communicate, understand and analyse data from societal application as well as enable big data analysis visualization to support real-time data.

Data Visualization and Analysis for Air Quality Monitoring Using IBM Watson IoT Platform

Smart Vehicular Systems prevail at improving the efficiency of vehicular systems; however, the aftereffects of an efficient system are often negated. This chapter focuses on developing a system that identifies a vehicle based on its unique ID using RFID readers. It makes an attempt to integrate the big hub technologies Blockchain and the Internet of Things to develop a viable Smart Vehicular System. The Internet of Things concept is used to maintain a log of the vehicles identified in various parking lots along with the time duration in the corresponding parking space. Blockchain is used to make automatic and secure payments towards parking based on the unique IDs of the vehicle and that of the parking area. The aim of a Smart Contract is to achieve a common parking fee irrespective of places, such as malls, theaters, hospitals, etc., and to reduce waiting time for parking and payment during peak hours. The Smart Vehicular System becomes a part of the Digital India Movement by reducing usage of physical currency notes and promotes the ecosystem by issuing e-receipts. Blockchain and manual transactions based on estimated data provided by the Ministry of Road and Transport were analyzed, and the transaction timing graph was plotted.

K. S. Umadevi

Papers

Prediction of Heart Disease Using Long Short-Term Memory Based Network

Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

A modeling and analysis of delay sensitive scheduling in wireless network

Data Visualization and Analysis for Air Quality Monitoring Using IBM Watson IoT Platform

Smart Vehicular Management System Using Blockchain for Internet of Connected Vehicles