Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities

Recently, online shopping has turned into a mainstream means for users to purchase as well as consume with the upsurge development of Internet technology. User satisfaction can be improved effectively by doing Sentiment Analysis (SA) of a large quantity of user reviews on e-commerce platforms. It is still challenging to envisage the accurate sentiment polarities of the user reviews because of the changes in sequence length, textual order, along with complicated logic. This paper proposes a new optimized Machine Learning (ML) algorithm called the Local Search Improvised Bat Algorithm based Elman Neural Network (LSIBA-ENN) for the SA of online product reviews. The proposed work of SA encompasses ‘4’ major steps: i) Data Collection (DC), ii) preprocessing, iii) Features Extraction (FE) or Term Weighting (TW), Feature Selection (FS), and polarity or Sentiment Classifications (SC). Initially, the Web Scrapping Tool (WST) is utilized to extract the customer reviews of the products for which the data is gathered as of the E-commerce websites. Next, preprocessing is carried out on the web scrap extracted data. Those preprocessed data go through TW and FS for additional processing by means of Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) and Hybrid Mutation based Earth Warm Algorithm (HMEWA). Lastly, the HM-EWA data is rendered to the LSIBA-ENN, which classifies the customer reviews’ sentiment as positive, negative, and neutral. For the performance analysis of the proposed and prevailing classifiers, ‘2’ yardstick datasets are taken. The outcomes exhibit that the LSIBA-ENN attains the best performance in SC when weighted against the existing top-notch algorithms. The observations of the reviewer are exact. The prevailing ENN proffers recall of 87.79 when utilizing the proposed LTF-MICF scheme, whereas ENN only achieve 83.55, 84.03, 85.48, and 86.04 of recall whilst utilizing W2V, TF, TF-IDF, and TF-DFS schemes respectively.

A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach

Machine learning techniques for hate speech classification of twitter data: State-of-the-art, future challenges and research directions

Sentiment analysis acquired a great area of attention in the microblogging websites and analysis of sentiment is a practice of categorization and identification of opinions that are articulated as speech, text, database sources and tweets to detect if opinion is negative, positive or neutral. The challenge lies in determining sentiment from the tweets due to the unique characteristics of Twitter data. This paper presents an approach for sentiment analysis by adapting a Hadoop framework and deep learning classifier. The Hadoop cluster is used for the distribution of data for extracting the features. Then, the significant features are extracted using the twitter data. The deep learning classifier, namely deep recurrent neural network classifier is used assign a real-valued review to each input twitter data thus, classifying the input data into two classes, such as positive review and negative review. The analysis of the performance is done using metrics like, classification accuracy, sensitivity and specificity. In contrast to classical strategies, the proposed method offered improved classification accuracy of 0.9302, better sensitivity of 0.9404 and high specificity of 0.9157, respectively.

Big data approach for sentiment analysis of twitter data using Hadoop framework and deep learning

Big data and ambient intelligence in IoT-based wireless student health monitoring system

Twitter is a social media that developed rapidly in today’s modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user’s tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as “positive,” “neutral,” and “negative.” Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.

Classifying streaming of Twitter data based on sentiment analysis using hybridization

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density.

Machine Learning Assisted Information Management Scheme in Service Concentrated IoT

https://e-space.mmu.ac.uk/631077/1/IADF.pdf

Senthil Murugan Nagarajan

Papers

Classifying streaming of Twitter data based on sentiment analysis using hybridization

Machine Learning Assisted Information Management Scheme in Service Concentrated IoT

IADF-CPS: Intelligent Anomaly Detection Framework towards Cyber Physical Systems

Ambient intelligence approach: Internet of Things based decision performance analysis for intrusion detection

Security framework for IoT and deep belief network-based healthcare system using blockchain technology