Reviewerly/exHarmony huggingface dataset:
Enhancement of motor speed identification using artificial neural networks

In this study have been utilized a modified version of ant colony optimization to improve the thresholds of neural networks and weights by including therank-weight approach. Furthermore, this technique easily overcome the drawbacks speed up convergence into the minimum while training the backpropagation neural network. The improved ant colony optimization-backpropagation neural.not only has the capacity to map extensively, but it also enhances operating efficiency noticeably, according to the simulation findings. The simulation results revealed that the speed sensor replaced with the ant colony optimization rw-optimized back propagation neural network-speed identification and motor’s speed determined using this approach the result is satisfactory.
Interference immunity and energy efficiency of digital communications systems in multipath channel with fading

In the study of measures to improve channel efficiency, we developed and presented a performance analysis of a digital communication system for image transmission in a multipath channel with fading, in the MATLAB/Simulink environment, one of the main advan tages being that it provides high interference immunity in the presence of noise and interference and signal delays. The scheme has been investigated for bit error rate (BER) on additive white Gaussian noise (AWGN) channel and multipath Rice and Rayleigh fading channel operating under the influence of inter-symbol interference (hereinafter ISI) and different parameters for the channel, where we considered cases of signal delay similar for EPA (extended pedestrian a model (EPA) standards). In this paper, we estimate the effectiveness of compensation for signal distortion in radio channels with fading on the basis coded orthogonal frequency-division multiplexing (COFDM) technology, an adaptive equalizer, amplifiers on the transmitter side and a normalizer on t he receiver side, which is an acute problem in the field of wireless communication. This system can be applied to transmit better quality high-resolution radiological images over a wireless network inside a building and for a model analogous of pedestrian traffic.
Adaptive weight grey wolf algorithm application on path planning in unknown environments

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A comparison study result between the proposed method and two other algorithms revealed that the proposed algorithm performed better in avoiding obstacles, which include the situation with the local minima. Finally, when put to comparison with Hybrid Fuzzy-Wind Driven Optimization and Adaptive Particle Swarm Optimization the average improvement rates in route length are 2.86% and 4.70391%, respectively.
Puzzle optimization algorithm with capsule networks based Telugu character recognition model

Recently, &lt;span&gt;Telugu character recognition (TCR) becomes a hot research topic because of drastic increase in technological advancements such as multimedia, and smartphones. Though numerous works have been concentrated on offline TCR models, it is still needed to develop automated and intelligent online TCR models. This paper presents a novel puzzle optimization algorithm with capsule networks-based Telugu character recognition (POACN-TCR) model. The presented POACN-TCR model intends to effectively identify and recognize distinct Telugu characters online. To accomplish this, the POACN-TCR model primarily undergoes pre-processing in different ways such as normalization, smoothing, and interpolation. In addition, the POACN-TCR model designs an effective capsule network (CapsNet) model to generate feature vectors and hyperparameter optimization takes place using puzzle optimization algorithm (POA). Finally, the C4.5 decision tree classifier is utilized for the effectual recognition of Telugu characters. The utilization of POA for hyperparameter optimization of the CapsNet model helps in achieving improved recognition performance. For ensuring the enhanced outcomes of the POACN-TCR model, a wide-ranging experimental analysis is performed and the outcomes pointed out the betterment of the POACN-TCR model on the recent approaches.&lt;/span&gt;
Chaotic map technique for enhancement security for android mobile system based on image encryption

Network &lt;span&gt;security continues to be the priority of many organizations. To ensure the protection of their data, they pay great attention to Encryption systems. Moreover, because of the enormous developments in networks of connection particularly the internet which has used by several people to share a variety of data kinds. The security of data has been a significant issue. As a result, there is a significant focus in using methods of decryption and encryption. Numerous encryption techniques have become advanced to preserve data protection, chaotic encryption systems are one of these methods widely used in recent years, where several techniques were proposed to use a chaotic map for encrypt images for the reason that of their characteristics e.g., random action unpredictability and initial conditions sensitivity. In this paper, proof authentication of sent information is used chaotic encryption algorithm to provide cipher text and hidden in image then send to another user. This approach is applied in cellular operating system environment (android). Thus, the sending and receiving of text will be safe and secure. The proposed approach is tested on different types of mobile. The proposed system gives good results.&lt;/span&gt;
Autoregressive prediction analysis using machine deep learning

Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model.
Deep Learning Based Recognition of Turkish Sign Language Letters with Unique Data Set

With its development, artificial intelligence has formed the basis for many studies aimed at facilitating people's lives. More successful results have been tried to be obtained with the increasing data and developing equipment in these studies. It is seen that these developments in artificial intelligence are reflected in the studies related to sign language conversion. In this study, a data set belonging to the letters in the Turkish Sign Language Alphabet was created, and the classification process was carried out with both the deep learning model we created and VGG16, Inceptionv3, Resnet, and Mobilnet models, which are frequently used in image classification. In addition, an open-source data set containing the letters in the American Sign Language Alphabet was organized similar to the data set containing the letters in the Turkish Sign Language Alphabet we created, and Deep Learning models were used to classify the letters in the American Sign Language Alphabet by using this data set. Performance evaluations of the classifications made by Deep Learning Models using both data sets were made. With this study, the results obtained from training Deep Learning methods with different data sets were compared. In addition, it is thought that the study will be useful in determining both the data set and the deep learning method to be used for the studies on the recognition of Sign Language Letters.
PELATIHAN APLIKASI SOFTWARE GEOGEBRA DI SMP DAN SMK YAMAS DI JAKARTA

The use of information technology in the learning process can help visualize abstract concepts and be able to involve students' active roles in the learning process. The use of instructional media in the classroom is also an important component of the learning process in constructing basic mathematical concepts. The importance of using information technology in the learning process also has a positive impact on student motivation and learning outcomes. One form of application is the use of learning media using GeoGebra software. Through this Community Service (PKM) activity, the PKM team helps partners by providing training on the use of GeoGebra software.&#x0D; This training aims to make mathematics teachers more skilled in utilizing media in learning. This PKM activity was carried out in a demonstration, namely the PKM team first gave a seminar on GeoGebra software, then continued by providing training using GeoGebra software. With this, it is hoped that teachers can use GeoGebra software in the learning process.
Security System of Logistics Service Transaction Record Based on Wireless Network

Wireless Networks (WNs) and their associated technology paradigms are employed for smart and secure logistics services. The wireless logistics transactions are secured through end-to-end authentication, verification, and third-party watchdog systems. This manuscript introduces a Preemptive Security Scheme for Transaction Verification (PSS-TV) in wireless network-aided logistics services. Different logistics services are secured based on the sender and receiver’s signature in mutual consent. Signature generation and implications are varied using the key size and validity based on the previous transaction recommendation. The conventional random forest classifier learning is used for detecting transaction breaches and validity requirements. This is feasible based on the transaction interruptions and failed mutual verifications. These classifications are performed using the learning paradigm for improving the key size in generating stealthy signatures. In the signature generation, the conventional elliptic curve cryptography is relied upon. The proposed scheme’s performance is analyzed using success ratio, failure rate, verification and authentication time, and complexity.
A New Paradigm of Literacy

The industrial revolution is a term to label industrial development from era to era. The first phase started in the early 18th century and has now entered the fourth phase, or 4.0. Davies (2015) stated that the 1.0 industrial revolution began with the use of steam engines and machines to replace human force. In the 2.0 industrial revolution, steam engines were replaced by electrical production machines as mass production engines. The 3.0 industrial phase was characterized by the emergence of computers in the 1970s. Finally, the rapid development of interconnected technology, sensor, and data analysis became the signs of the 4.0 industrial revolution emergence. The development has impacted conventional industries in the form of digitalization. Online transportation mode is one of the most prominent impacts of the fourth industrial revolution. This phase also creates massive shifts in many sectors, such as the use of video calls, fingerprints, and face scanners as security locks, social media to connect people from all around the world, and the digitalization of books, magazines, and journals
Possibilities of Self-Control When Drawing and Copying in Older Preschool Children With Mental Retardation (Message 1)

The article is devoted to the study of the possibilities of correcting mistakes made when drawing and copying by children of older preschool age with mental retardation. The article presents options for correcting children of older preschool age with a mental retardation of their drawings in the course of organized self-control.
The Level of Digital Skills for Amman Arab University students in Light of Some Variables

The current study aimed to investigate the level of digital skills among students of Amman Arab University in light of some variables. The study sample consisted of (308) students from various faculties and academic programs, to achieve the goal of the study, a scale for digital skills was applied to the study sample after assuring validity and reliability of it. The study results showed the existence of moderate level of digital skills among the students of Amman Arab University; moreover, the results showed no statistically significant differences between the means of digital skills according to gender, faculty, GPA variables. The results displayed statistically significant differences for the interaction between the faculty and the students’ PGA in favor of students with a "very good" average in scientific faculties. The study recommended developing students ‘digital skills through holding training workshops, scientific lectures and focusing on courses that create and establish appropriate digital skills.
The conversion of the higher education and literature ontology into RelOntoUML

In this article, I will transform an ontology representing higher education and an ontology representing a library into the RelOntoUML model. RelOntoUML modeling is a modeling created from a combination of relational model, ontology, and UML modeling. The relational model is used for database storage. The data is stored in relations. Ontology is a tool for knowledge representation. The ontology describes the elements of a topic. The UML model visually represents the elements of a topic. One of the disadvantages of the ontology model is that software developers are not familiar with it, but UML diagrams and the relational model are well known. The two models (CollageMngtSys, Literature) that will be presented and converted can be downloaded from the gihub in OWL format, and are open source. In this article, after introducing the ontology, relational model, and UML, these two systems are introduced and converted.
Clinical characteristics of lurasidone-treated patients in Spain using Natural Language Processing – A real-world data study with Electronic Health Records

Introduction Schizophrenia is a chronic neuropsychiatric disorder which affects over 20 million people worldwide. Atypical antipsychotics are the first-line choice for the treatment of schizophrenia due to improved tolerability and diminished risk of extrapyramidal symptoms. Lurasidone is an atypical antipsychotic approved in Spain for the treatment of schizophrenia in September 2019. An RWD-based picture of lurasidone use is necessary to better understand its impact in routine clinical practice. Objectives To set up a methodology based on Natural Language Processing (NLP) and machine learning for the analysis of the free-text information contained in the EHRs of patients treated with lurasidone in Spain. Methods A multicenter, retrospective study based on RWD collected in EHRs of lurasidone users will be conducted in hospitals from the Spanish National Healthcare System. Information extracted from the free text in EHRs using NLP will be treated and analyzed as big data. Results A study database for lurasidone-treated patients in Spain has been instituted using the EHRead® technology ( Figure 1 ), which applies machine learning and deep learning to extract, analyze, and interpret the free-text information written in their de-identified EHRs. Sociodemographic and clinical variables in EHRs from September 2019 until the most recent data available are being collected to describe the target patient population and address treatment-related outcomes. Conclusions NLP of free text in EHRs of lurasidone-treated patients renders a real-world picture of lurasidone usage in Spain. Studies using artificial intelligence techniques represent a novel source of information regarding psychiatric disorders and their clinical management. Disclosure I. Gabarda is employee at Angelini Pharma España, S.L.U. and C. de la Pinta is employee at Medsavana.
Bitcoin and Digital Currency: Difficulties, Open Doors and Future Works

Bitcoin and other conspicuous digital forms of money definitely stand out enough to be noticed since the most recent quite a while. All around the world known as advanced coin and virtual cash, this cryptographic money is acquired and exchanged inside the blockchain framework. The blockchain innovation taken on in utilizing the digital currency has caused a stir inside the financial area, government, partners and individual financial backers. The ascent of the cryptographic money inside this long time starting from the beginning of Bitcoin in 2009 has surprised the market. Cryptographic money is expected as the future cash that would supplant the ongoing paper money around the world. Despite the fact that the interest has grabbed the eye of clients, many don't know about its chances, disadvantages and difficulties for what's to come. Explores on cryptographic forms of money are as yet missing nevertheless at its outset stage. In giving significant aid and view to the scholarly field and clients, this paper will examine the valuable open doors in digital money, for example, the security of its innovation, low exchange cost and high venture return. The inventiveness of this paper is on the conversation inside regulation and guideline, high energy utilization, probability of crash and air pocket, and goes after on network. The future endeavors of cryptographic money and its application will be methodically evaluated in this paper.
An improved normalized gain-based score normalization technique for spoof detection algorithm

A spoof detection algorithm supports the speaker verification system to examine the false claims by an imposter through careful analysis of input test speech. The scores are employed to categorize the genuine and spoofed samples effectively. Under the mismatch conditions, the false acceptance ratio increases and can be reduced by appropriate score normalization techniques. In this article, we are using the normalized Discounted Cumulative Gain (nDCG) norm derived from ranking the speaker’s log-likelihood scores. The proposed scoring technique smoothens the decaying process due to logarithm with an added advantage from the ranking. The baseline spoof detection system employs Constant Q-Cepstral Co-efficient (CQCC) as the base features with a Gaussian Mixture Model (GMM) based classifier. The scores are computed using the ASVspoof 2019 dataset for normalized and without normalization conditions. The baseline techniques including the Zero normalization (Z-norm) and Test normalization (T-norm) are also considered. The proposed technique is found to perform better in terms of improved Equal Error Rate (EER) of 0.35 as against 0.43 for baseline system (no normalization) wrt to synthetic attacks using development data. Similarly, improvements are seen in the case of replay attack with EER of 7.83 for nDCG-norm and 9.87 with no normalization (no-norm). Furthermore, the tandem-Detection Cost Function (t-DCF) scores for synthetic attack are 0.015 for no-norm and 0.010 for proposed normalization. Additionally, for the replay attack the t-DCF scores are 0.195 for no-norm and 0.17 proposed normalization. The system performance is satisfactory when evaluated using evaluation data with EER of 8.96 for nDCG-norm as against 9.57 with no-norm for synthetic attacks while the EER of 9.79 for nDCG-norm as against 11.04 with no-norm for replay attacks. Supporting the EER, the t-DCF for nDCG-norm is 0.1989 and for no-norm is 0.2636 for synthetic attacks; while in case of replay attacks, the t-DCF is 0.2284 for the nDCG-norm and 0.2454 for no-norm. The proposed scoring technique is found to increase spoof detection accuracy and overall accuracy of speaker verification system.
Considerations of accounting standards that can be used to disclose Cryptographic assets in financial reports

Cryptographic assets are a digital form of currency that uses mathematical equations and block chain technology to encrypt data. The popularity of this type of currency has grown exponentially over the past few years due to more people and companies using it despite the refusal of central banks and country authorities to recognize cryptocurrencies as money. However, some companies have begun to accept it as payment for goods and services. It is expected that more people will turn to cryptocurrencies in the future. With this, companies will have to find ways to account for and report them in the financial statements, however, the Financial Accounting Standards Board (FASB) has not yet determined how to account for cryptocurrency, leaving companies without formal guidance on the subject. The purpose of this paper is to present potential accounting policies to display how companies will report cryptocurrencies in the future based on evidence derived from a review of the accounting literature. The need to address digital currency problems was recommended by the International Accounting Standards Board.
A Legal Analysis on Resolving Recently Growing Online Business Frauds in Bangladesh

In the recent time, the E-commerce sector has been facing serious challenges due to the fraudulent behaviors of a number of E-commerce businesses. Thousands of consumers have lost their money due to these fake transactions. The main purpose of this article is to help to mitigate online business frauds by reviewing the current legislation and regulations in Bangladesh. This research is a descriptive type of research and secondary data and information have been used for study purpose. The study has analyzed existing business laws and provisions, their applicability and deficiencies in reducing online business frauds and dangers. This study finds that there is no committed code or law establishing consumer rights in online businesses. The current laws have not been changed in such a way that online business matters can be handled properly. The study concludes that the legislature may amend existing laws and provisions and also they may consider some foreign countries provisions for taking actions against the fraudulent e-commerce as well as online business organizations. The outcomes of the present study obviously indicate that the risks of online business frauds directly affect consumer behavior when shopping online. Therefore, reducing these risks through undertaking just and proper laws and provisions can increase the trust of the online shopping consumers.