Deep learning for religious and continent-based toxic content detection and classification

With time, numerous online communication platforms have emerged that allow people to express themselves, increasing the dissemination of toxic languages, such as racism, sexual harassment, and other negative behaviors that are not accepted in polite society. As a result, toxic language identification in online communication has emerged as a critical application of natural language processing. Numerous academic and industrial researchers have recently researched toxic language identification using machine learning algorithms. However, Nontoxic comments, including particular identification descriptors, such as Muslim, Jewish, White, and Black, were assigned unrealistically high toxicity ratings in several machine learning models. This research analyzes and compares modern deep learning algorithms for multilabel toxic comments classification. We explore two scenarios: the first is a multilabel classification of Religious toxic comments, and the second is a multilabel classification of race or toxic ethnicity comments with various word embeddings (GloVe, Word2vec, and FastText) without word embeddings using an ordinary embedding layer. Experiments show that the CNN model produced the best results for classifying multilabel toxic comments in both scenarios. We compared the outcomes of these modern deep learning model performances in terms of multilabel evaluation metrics

Towards building mobile smart-IoT service system

The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system.Peer ReviewedPostprint (author's final draft

Authorship identification using ensemble learning

With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing. Researchers are searching for alternative strategies to identify the author of an unknown text. There is a need to develop a system to identify the actual author of unknown texts based on a given set of writing samples. This study presents a novel approach based on ensemble learning, DistilBERT, and conventional machine learning techniques for authorship identification. The proposed approach extracts the valuable characteristics of the author using a count vectorizer and bi-gram Term frequency-inverse document frequency (TF-IDF). An extensive and detailed dataset, All the news is used in this study for experimentation. The dataset is divided into three subsets (article1, article2, and article3). We limit the scope of the dataset and selected ten authors in the first scope and 20 authors in the second scope for experimentation. The experimental results of proposed ensemble learning and DistilBERT provide better performance for all the three subsets of the All the news dataset. In the first scope, the experimental results prove that the proposed ensemble learning approach from 10 authors provides a better accuracy gain of 3.14% and from DistilBERT 2.44% from the article1 dataset. Similarly, in the second scope from 20 authors, the proposed ensemble learning approach provides a better accuracy gain of 5.25% and from DistilBERT 7.17% from the article1 dataset, which is better than previous state-of-the-art studies

Device for measurement and control of humidity in crude oil and petroleum products

A device with a frequency-modulated output signal has been developed to increase the sensitivity and accuracy of measuring moisture content in crude oil and petroleum products in the range of 0~20%. The main element of the device is a self-oscillator transducer based on a transistor structure with negative differential resistance. A capacitive sensor in the form of a capacitive cylindrical structure with cylindrical electrodes was used to determine moisture content in crude oil and petroleum products. Electric permittivity of a two-component mixture of oil and water was estimated and the capacitance of the humidity-sensitive capacitive cylindrical structure with cylindrical electrodes was calculated. An electrical diagram of the device for measuring and controlling the humidity of crude oil and petroleum products has been developed. The relative error of converting the humidity of oil and petroleum products into capacitance which was caused by the change in oil temperature, was determined to be 0.225%. Values of relative errors of the device for measuring the humidity of oil and petroleum products are as follows: 1.355 · 10 -5% is caused by instability of the oscillator frequency, 0.01% is caused by fluctuations in the supply voltage of the self-oscillator transducer, 0.05% is caused by a change in ambient temperature by 1°C. For the developed device, which used errors of the first and second type, the reliability of humidity control of oil and petroleum products has been determined to be 0.9591

Secure and efficient data storage operations by using intelligent classification technique and RSA algorithm in IoT-based cloud computing

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm

Convergence of intelligent and IP-networks and services

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Industry 4.0 definitely needs Service 4.0! The transformation of services provided by tax firms to create added value for clients.

Tax firms must prepare for the future if they are to survive in the market. The decisive key to this seems to lie in the digitalization of processes in order to be able to automate standard work. With new time and capital resources, law firms can open up new business areas and specialize in certain core competencies. How such a planning structure looks like will be explained here at a glance and made usable as a planning guide