Reviewerly/exHarmony huggingface dataset:
Mathematical model and dynamical analysis of the human equilibrium seeking training

The purpose of this work is to determine the ability of the partial directed coherence method to identify the directed interaction between nonlinear systems correctly in presence of nonlinear couplings between systems, as well as in the case when the measured signals are generated by objects of high dimension.The another purpose is to determine the dependence of the coupling estimation results on the parameters: series length, sampling rate, model dimension and the coupling architecture.Methods.In this paper, the possibilities and limitations of the frequency-resolved approach (partial directed coherence) to describe the couplings between high-dimensional time series are investigated.Surrogate time series constructed by permutation of realization are used to determine the significance of the results.Results.Coupling architecture in ensembles of small-dimensional oscillators can be correctly identified for linear and nonlinear systems connected in case of both linear and nonlinear coupling.For complex composite signals, when each measured time series is the sum of the signals of many individual oscillators, the technique is not specific enough, revealing non-existent connections, and it is not sensitive enough, missing the existing ones.Conclusion.The criteria for applying the partial directed coherence method to different signals are formulated.The measure does not show indirect couplings at sufficient series length, sampling rate and model dimension in contrast to the pairwise methods of Granger causality and transfer entropy.The measure works well for noisy time series.The method allows to study the connectivity in an ensemble of an arbitrary number of oscillators.The method allows to determine at what frequencies the interaction occurs.The partial directed coherence method gives acceptable results for series of length 80 and more characteristic periods in comparison with the Granger causality method, for which the efficiency is declared already at 4-16 characteristic periods.
Implementation of Text Mining Classification as a Model in the Conclusion of Tafsir Bil Ma'tsur and Bil RA'YI Contents

Studies related to Tafsir Qur'an have only been carried out based on a manual system or on application-based development. The purpose of this study is to build an application which can classify type of interpretation automatically into two classes, tafsir Bil Ma'tsur and tafsir Bil Ra'yi, can provide user convenience in the Al-Qur-an. KNN algorithm is a reliable algorithm in the classification process, also has many parts of the algorithm. This study was done by applying K-Nearest Neighbor (KNN) algorithm with an accuracy of 98.12%. However KNN had been compared firstly with Modified K-Nearest Neighbor (MKNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms, where the two algorithms had 98.01% and 88.3% accuracy respectively. MKNN is the best algorithm with the highest accuracy, but also has a high error value by 4.3% which is higher than KNN, 1.9%. From the research conducted, the more text documents used in KNN modeling, the higher accuracy will be. Therefore, in the implementation of this application KNN is used as modeling in the conclusion of Tafsir Al-Qur'an. Tests performed with BlackBox Testing and User Aceptance Test reached value of 100% and 98.8%.
Email Spam Detection using Machine Learning Techniques

Email spam is one of the biggest threats to today's Internet.To deal with this threat, many anti-spam filters have been developed.One big challenge for these filters is to predict the labels of emails in a personalized mailbox.These spam messages can lead to loss of private data as well.Modern day researchers have used some stylistic features of text messages to classify them to be ham or spam.E-mail spam detection can be greatly influenced by the presence of known words, phrases, abbreviations and idioms.This paper aims to compare different classifying techniques on different datasets collected from previous research works, and evaluate them on the basis of their accuracy, recall, and precision.The comparison has been performed between traditional machine learning techniques.Most of the time such emails are commercial.But many times, such emails may contain some phishing links that have malware.This arises the need for proposing prudent mechanism to detect or identify such spam emails so that time and memory space of the system can be saved up to a great extent.In this paper, we presented the NLP mechanism which can filter spam and non-spam emails and also categorize into different spam mails.Our proposed algorithm generates dictionary and features and trains them through machine learning for effective results.
Performance Examination of Hard Clustering Algorithm with Distance Metrics

Clustering algorithms based on partitions are widely us ed in unsupervised data analysis. K-means algorithm is one the efficient partition based algorithms ascribable to its intelligibility in computational cost. Distance metric has a noteworthy role in the efficiency of any clustering algorithm. In this work, K-means algorithms with three distance metrics, Hausdorff, Chebyshev and cosine distance metrics are implemented from the UC Irvine ml-database on three well-known physical-world data-files, thyroid, wine and liver diagnosis. The classification performance is evaluated and compared based on the clustering output validation and using popular Adjusted Rand and Fowlkes-Mallows indices compared to the repository results. The experimental results reported that the algorithm with Hausdorff distance metric outperforms the algorithm with Chebyshev and cosine distance metrics.
Enhanced Support Vector Machine Based Leukaemia Cancer Classification

Leukaemia is a blood cancer that is characterized by the bone marrow’s unregulated and irregular generation of white blood cells (leukocytes) in the blood. By testing the microscopic blood cell images, diseases can be detected and its early diagnosis can be done. In the current work,at smart decision support systemutilizing microscopic images is proposedto make a diagnosisof acute lymphoblastic leukaemia. Identification through images is a quick and inexpensive technique since there is no equipment is specially needed for lab testing. The improved Bare-Bones Particle Swarm Optimization algorithm is usedto identify the important distinguishing featuresof blast cells and the healthy cells to help in carry out the acute lymphoblastic leukaemia classification with efficiency. To avoid the poor accuracy, the modified median filtering has been introduced in this novel, which helps in removing the noise from the images and maintains the edge which is used to improve the identification of leukocytes and lymphocytes during the segmentation process. Enhanced linear contrast stretching is introduced in image enhancement for enriching the image. In the next step, feature extraction is carried out through the second order statistical features. Bare-bones with Adaptive Bat Optimization (BBABO) is used for feature selection. At last, the classification is carried out by using the combined Fuzzy Neural Network (FNN) and here the Enhanced Support Vector Machine (ESVM) is a classifier called as FNESVM.
Anomaly Detection in Video Events using Deep Learning

The anomaly detection system gives a solution to detect anomaly in crowd event video and sets alarm for public safety in mass gatherings. The deep learning technique CNN(Convolution Neural Network) is used to detect anomaly at the initial stage from
Automatic Online Subjective Text Evaluation using Text Mining

Semantic based text mining is essential in high dimensional data processing in today’s environment. In educational sector Question Answer (QA) evaluation has done using semantic as well as semantic analysis in many approaches. Numerous existing mechanisms have implemented using different machine learning algorithms. Semantic and semantic both works for evaluate the text data, but semantic approach should done same work with low time complexity. In this work system carried out automated text evaluation for online examination system with semi structured dataset. The system has categorized into two phases, NLP and Features base evaluation. Natural Language Processing (NLP) has used for preprocessing of data using tokenization, stop word removal, porter stemmer etc. Similarity technique has used for generate similarity score between test answer and train answer data. Artificial Neural Network (ANN) has used to generate the similarity score between two features vectors. Experimental analysis shows the how proposed system is better than some traditional approaches for semantic text evaluation
Real-Time Realistic Telepresence using a 360 ° Camera and a Virtual Reality Box

This study focuses on the transmission of live 360 video using Ricoh Theta S which is transmitted to Android phone mounted in BoboVR.The proposed solution is to use a 360 camera that captures the scene in all direction and create an application which streams the captured 360 live video from Laptop (Server) into the Android Phone (Client).The Android phone's IMU sensor is responsible for the corresponding viewport selected from the 360 environment.The viewport is subject for Stereoscopic SBS to show the 3D effect in accordance with the user's perception The viewport is subject for Stereoscopic SBS to show the 3D effect in accordance with the user's perception.A 3D video can be produced by applying the Stereogram SBS.Also, depth can be perceived due to the varying distance between the focal baseline and focal length.
Adaptation of cryptocurrencies in listed companies: empirical findings of a CFO survey in the German capital market

This paper examines companies' adaptation of cryptocurrencies and comprises a quantitative empirical study. The emerging potentials of cryptocurrencies but the gap of practical application and respective existing knowledge are addressed in this paper. Technological, economic, social and regulatory aspects are depicted in the literature review. In addition, a comprehensive status quo of on companies' cryptocurrency adaptation research is provided and previous contributions are discussed. This study is based on an online questionnaire that was sent out to CFOs of German Prime Standard listed companies. As suggested in preceding papers the extended technology acceptance model (TAM2) is applied. Results indicate a very low level of adaptation and companies' utilisation of the blockchain technology. Lower potentials are seen in cryptocurrencies than in the underlying blockchain technology. The main obstacles are to overcome regulatory uncertainty and high price volatility. Low transaction costs and the omission of intermediaries are seen as great potential benefits. Suggestions for further research and practical implications are provided.
Application of E-learning System in the World of Education

The purpose of this study is to determine the positive effects of e-learning systems on improving student's understanding of concepts and comparing e-learning with old learning system. Nowadays, the internet is one of the way to find informations easily without reading books and attending classes. The method used in this research is literature study method by obtaining information and data from various sources. The results from this study is that e-learning can improve students' cognitive abilitiesas it is easier to access words, powerpoint, html or PDF in the application. The conclusion is that e-learning can be used as a learning innovation that can help teachers and students using the Software Learning Management System
Multimedia Augmented Reality Technology in Daily Basic Knowledge Learning Media for Early Childhood and Kindergarten

Education for children from an early age is essential because it can affect the growth of children's knowledge at a later age. The introduction of basic daily things can be given to children to make them feel enjoy learning fun objects and make them critical. Educational institutions have widely used the use of information technology from the initial level such as kindergarten. Therefore, the development of learning media is for supporting parents and teachers in providing basic daily knowledge for children. Augmented Reality has features with combining 3D object animation and audio so that the teaching material displayed can be seen in real. This learning media has a very simple and easy to use the display with some menu. When each menu is selected, it will go directly to the camera mode for scanning the teaching marker. This learning media can be used both at school and at home, as well as by teachers or parents and even the child itself.
Sistem Pakar Metode Forward Chaining untuk Mengukur Keparahan Penyakit Gigi dan Mulut

Teeth and mouth are parts of the body that cannot be separated, where the teeth are in the oral cavity so that if there is interference with the teeth it will affect the mouth. Knowledge to recognize dental and oral diseases from an early age is very much needed to be able to maintain healthy teeth and mouth. Lack of knowledge about these oral and dental diseases will result in severe damage to teeth and mouth. This study aims to measure the severity of oral disease and provide a diagnosis of oral and dental disease so that later it can be used as a reference for consultation with a doctor. The method used in this research is Forward Chaining to represent the rules of 27 symptom facts and 8 diseases described by experts. The results of testing for this method are as many as 10 patient data were diagnosed to get the same result as the doctor's analysis so that the accuracy rate is 80%. The expert system designed with the Codeigniter Framework can provide insight by being able to identify the disease suffered by patients and measure the severity of the oral and dental disease suffered.
Techniques for the Discovery of Patterns Hidden in Large Data Sets and Process Flow of Data Mining

Data mining procedures are used to find the concealed or brand new patterns to keep the information. We know that data mining can utilize every sector like service, farming, advertising and marketing etc. There are numerous methods for data mining like clustering, distinction, and so on. There are a variety of techniques and also procedures of data mining which could be used on information to build up a brand new setting to enhance the efficiency of existing data and aid to make the brand-new prophecies on the records. Within this newspaper, our team provide procedures for the discovery of patterns concealed in sizable records sets, paying attention to problems relating to their usefulness, effectiveness, effectiveness, and scalability.
User Interface Design Guidelines for Children Mobile Learning Applications

Mobile learning applications have created new learning opportunities which lead to the transition from traditional learning to modern learning, thus enhancing individuals’ learning experiences. Mobile learning applications that come with good and effective user interface allow users to be aware and mindful about the subject matter. This enables users to fully optimise the applications for learning purposes. In order to develop an effective mobile learning application, the user interface should take into account the requirements of its users for mobile learning. Children in particular are immature learners and mobile users, whose characteristics and insights are not quite the same as the adults. To date, there are design guidelines for mobile learning applications, however they are not specifically intended for children. In fact, the existing design guidelines are mostly targeted for desktop applications which are not suitable for mobile learning applications. Therefore, this study aimed to address this concern by identifying the design elements that are required for creating user interface of mobile learning applications for children. This study adopted the qualitative approach that comprises three main activities; Theoretical Study, Prototype Development and Expert Evaluation. The data were analysed using content analysis method. The guideline consists of eight design elements with their respective essential characteristics. The elements are Navigation, Text, Image and Icon, Audio, Content, Colour, Input/Output Support, and Feedback. The guideline could be used by designers to design an effective user interface for mobile learning applications specifically for children.
Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of 3 Qubits: Two Report to One

The goal of this work is to explore how the relationship between two subordinates reporting to a leader influences the alignment of the latter with the company’s strategic objectives in an Industry 4.0 environment. We do this through the implementation of quantum circuits that represent decision networks. In fact, through the quantum simulation of strategic organizational design configurations (QSOD) through five hundred quantum circuit simulations. We conclude that the alignment probability of the leader is never higher than the average alignment value of his subordinates, i.e., the leader never has a better alignment than his subordinates. In other words, the leader cannot present asymptotic stability better than that of his subordinates. The most relevant conclusion of this work is the clear recommendation to the leaders of Industry 4.0 not to add hierarchical levels to their organization if they have not achieved high levels of stability in the lower levels.
The Hybridization of Neural Network and Particle Swarm Optimization for Natural Terrain Feature Extraction

The optimization of various soft computing and metaheuristic techniques can be ameliorated in a global area network, Swarm intelligence. In this research, a hybrid algorithm of neural network and particle swarm optimization has been presented for remote sensing applications. The terrain features of the land in a remote sensing image have been classified using these algorithms. Remote sensing basically deals with the processing and interpretation of satellite images without any physical contact to that particular region. In addition, the geo-spatial characteristics of the data also recorded during image classification. The hybrid concept used in this research, the implementation of algorithm in this paper based on the neurons network to find the best solution, which is further resolved using the Particle Swarm Optimization approach, an optimization technique. The proposed algorithm easily classifies the terrain features with higher efficiency and kappa coefficient value. The results show that 94.36% accuracy attained from the proposed technique. The overall accuracy improved by 5.24 % and 14.93% and kappa coefficient enhancement of 6.97 % and 18.99 % in comparison to existing studies.
Identification of Bio-Markers for Breast Cancer Detection through Data Mining Methods

Worldwide, breast cancer is the leading type of cancer in women accounting for 25% of all cases. Survival rates in the developed countries are comparatively higher with that of developing countries. This had led to the importance of computer aided diagnostic methods for early detection of breast cancer disease. This eventually reduces the death rate. This paper intents the scope of the biomarker that can be used to predict the breast cancer from the anthropometric data. This experimental study aims at computing and comparing various classification models (Binary Logistic Regression, Ball Vector Machine (BVM), C4.5, Partial Least Square (PLS) for Classification, Classification Tree, Cost sensitive Classification Tree, Cost sensitive Decision Tree, Support Vector Machine for Classification, Core Vector Machine, ID3, K-Nearest Neighbor, Linear Discriminant Analysis (LDA), Log-Reg TRIRLS, Multi Layer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), PLS for Discriminant Analysis, PLS for LDA, Random Tree (RT), Support Vector Machine SVM) for the UCI Coimbra breast cancer dataset. The feature selection algorithms (Backward Logit, Fisher Filtering, Forward Logit, ReleifF, Step disc) are worked out to find out the minimum attributes that can achieve a better accuracy. To ascertain the accuracy results, the Jack-knife cross validation method for the algorithms is conducted and validated. The Core vector machine classification algorithm outperforms the other nineteen algorithms with an accuracy of 82.76%, sensitivity of 76.92% and specificity of 87.50% for the selected three attributes, Age, Glucose and Resistin using ReleifF feature selection algorithm.
Transition from Holistic to Deep learning Face Recognition Methods

Face recognition, the fastest growing biometric technology of computer vision, made a breakthrough in the field of security, healthcare, access control and marketing etc. This technology helps in automatically discern and identify the faces for authentication by comparing available digital image of faces. Various algorithms have been developed for enhancing the performance of face recognition system. The face authentication system entails three major steps, face detection, feature extraction and face recognition. This paper provides some of the major milestones of face representation for recognition like holistic learning approach, feature based approach, hybrid approach and deep learning approach. The various techniques under these categories are reviewed. Finally, implemented face recognition using convolution neural network (CNN). In this method, the image is captured through webcam for the dataset preparation. The detection is carried out by CNN cascade, followed by face landmark and face embedding by FaceNet CNN. Recognition of face is performed after training the network. Implemented faces recognition successfully and accurately for smaller dataset.
Various Classifiers Performance Based Machine Learning Methods

Classification is a form of data mining (regarding machine learning) approach that is helpful in the prediction of group membership for data instances, where the data input is used by the computer program for learning and thereafter this learning is used for classifying the fresh observation made. This data set might just be bi-class or it can be multi-class also. Few instances of the problems in classification include: speech identification, handwriting identification, bio metric detection, document classification etc. Many classification methods exist, which can be utilized for classification. In this research work, the fundamental classification approaches and few important kinds of classification approaches that include decision tree induction, Bayesian networks,k-nearest neighbor classifier and Support Vector Machines (SVM) and fuzzy learning classifiers with their merits, drawbacks, probable applications and challenges faced with the solution available. There are different problems that have an effect on the classification and prediction. The objective of this research work is to render an extensive review of various classification approaches in machine learning. At last, the future work intended on the best classification techniques for the input data are discussed.