Reviewerly/exHarmony huggingface dataset:
Optimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application

There were 50 data sample obtained from industries in Malaysia that practice sustainable manufacturing. Input file is presented in matrix 4x50 and 1x50 matrix as target file. However, there is no suitable number of hidden neuron that can be applied for the neural network model with 4 inputs and 1 output. An experiment has been done to identify the suitable hidden neuron through the observation of values from MSE and Regression. The hidden neuron must be no overfitting. The same goes for output and targets value must have close or linear relationship. The sample of tested hidden neuron is from 5 to 40 hidden neurons. The final answer obtained after look into Mean Square Error (MSE) values, Regression values and plots is hidden neuron 29. Hidden neuron 29 shows positive result in all criteria and should be implemented for this type of neural network model.
Fraud Detection: A Study of AdaBoost Classifier and K-Means Clustering

Fraud is a problem which can affect the economy greatly. Billions ofdollars are lost because of fraud cases. These problems can occur throughcredit cards, insurance and bank accounts. Currently there have beenmany studies for preventing fraud. Machine learning techniques havehelped in analysing fraud detection. These include many supervised andunsupervised models. Neural networks can be used for fraud detection.The dataset for the present work was collected from a research collaborationbetween Worldline and the Machine Learning Group of Université Libre deBruxelles on the topic of big data mining and fraud detection. It consistsof the time and amount of various transactions of European card holdersduring the month of September in 2013. This paper gives an analysis ofthe past and the present models used for fraud detection and presents astudy of using K-Means Clustering and AdaBoost Classifier by comparingtheir accuracies.
Empirical validation of object-oriented metrics on cross-projects with different severity levels

An object-oriented (OO) metrics has become crucial desideratum for software effort and fault predictions. To strengthen the adequacy of object-oriented metrics, it becomes important to know relationship between OO metrics and fault proneness at different levels of severity. It is inconceivable to build model of accurate estimate due to the inherent uncertainty in development projects. Empirical validation of software metrics is essential issue to determine applicability of prediction model. In this study, empirical validation is done on OO metrics given by Chidamber and Kemerer (CK suite) for predicting faults at different severity levels. This paper also instanced on defect prediction using cross-projects (CP) because of the unpredictability in selection of software attributes by analogy-based approach that deliver imprecise and ambiguous solution. This paper depicts detection of fault-proneness by extracting the relevant OO metrics and such models helps to focus on fault-prone modules of new projects by allocating more resources to them with use of regression and other machine learning methods. Combination of CP data with regression techniques improves effectiveness of prediction by extracting similar features impacted by all datasets.
emoji summaries

The White Pube present the emoji summaries that accompany their exhibition reviews on thewhitepube.com. The emoji summaries are there to replace the quantitative 1–5 star rating that normally heads typical art and film reviews. Rather than claim the same authoritative objectivity, The White Pube writers will respond to the exhibition with three selective emojis to offer a description of their personal experience.
A personalised ontology ranking model based on analytic hierarchy process

Ontology ranking is one of the important functions of ontology search engines, which ranks searched ontologies based on the ranking model applied. A good ranking method can help users acquire the exactly required ontology from a considerable amount of search results, efficiently. Existing approaches to rank ontologies take only a single aspect into consideration and ignore users' personalised demands, hence produce unsatisfactory result. It is believed that, the factors that influence ontology importance and the users' demands both need to be considered comprehensively in ontology ranking. A personalised ontology ranking model based on the hierarchical analysis approach is proposed in this paper. We build a hierarchically analytical model and apply analytic hierarchy process to quantify ranking indexes and assign weights to them. The experimental results show that the proposed method can rank ontologies effectively and meet users' personalised demands.
R Markdown

You use R Markdown to create reports that incorporate the features discussed in the previous chapters. R Markdown leverages freely available open source technology like HTML, CSS, and Markdown to create rich reports in a variety of formats.
Poet Attribution for Urdu: Finding Optimal Configuration for Short Text

&#x0D; &#x0D; &#x0D; &#x0D; This study presents a machine learning system to identify the poet of a given poetic piece consisting of 2 lines (i.e. a couplet) or more. The task is more difficult than the general task of author attribution, as the number of words in verses and poems are usually less than the number of articles present in author attribution datasets. We applied classification algorithms with different sets of feature configurations to run several experiments and found that the system performs best when support vector machine using a combination of unigram and bigram are used . The best system (for 5 Urdu poets) has the accuracy of 88.7%.&#x0D; &#x0D; &#x0D; &#x0D;
Do Quality of Governance, Remittance and Financial Development affect Growth?

The newly elected government of the Pakistan considered corruption as a main hurdle in long-term development of the country on one hand. On the other hand, it is put emphasis on enhancing remittances to maintain Marco-economic stability in Pakistan. Therefore, the aim of this study is to highlight the effects of remittance, quality of governance and financial development in Pakistan for the time 2000 to 2016 using Ordinary Least Square Method (OLS). The aftermaths of the study has shown the positive influence of remittance, quality of governance and financial development on economic development in Pakistan. Conversely, as expected the sign of inflation is negative with in-significant effect. Furthermore, the present study has categorized financial development into banking sector and stock market development to explore their effects separately. For this purpose, two indicators have chosen for banking sector and two for stock market development. Lastly, this paper uses various diagnostic tests to check the various econometric issues in the given models. The results of the diagnostic tests have showed that residual is normally distributed, homoscedastic and absence of serial correlation in all the given models. Moreover, descriptive statistics also shows that variables of present study are normally distributed. In sum, based on the findings this research recommends that by improving the quality of the governance and by increasing the remittances long-term economic growth is possible in Pakistan. In addition, financial development is also an important determinant of growth in Pakistan. Therefore, government focus on eradicating corruption and increasing remittances are the right policies for economy of Pakistan in the long run.
Minerals Yearbook, volume III, Area Reports — International — Africa and the Middle East

For additional information, contact: Director, National Minerals Information Center U.S. Geological Survey 12201 Sunrise Valley Drive 988 National Center Reston, VA 20192 (703) 648-4961 Email: nmicrecordsmgt@usgs.gov The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:Volume I, Metals and Minerals, contains chapters about virtually all metallic and industrial mineral commodities important to the U.S. economy. Chapters on survey methods, summary statistics for domestic nonfuel minerals, and trends in mining and quarrying in the metals and industrial mineral industries in the United States are also included.Volume II, Area Reports: Domestic, contains a chapter on the mineral industry of each of the 50 States and Puerto Rico and the Administered Islands. This volume also has chapters on survey methods and summary statistics of domestic nonfuel minerals.Volume III, Area Reports: International, is published as four separate reports. These regional reports contain the latest available minerals data on more than 180 foreign countries and discuss the importance of minerals to the economies of these nations and the United States. Each report begins with an overview of the region’s mineral industries during the year. It continues with individual country chapters that examine the mining, refining, processing, and use of minerals in each country of the region and how each country’s mineral industry relates to U.S. industry. Most chapters include production tables and industry structure tables, information about Government policies and programs that affect the country’s mineral industry, and an outlook section.The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcome.
Reckoning Examination for Precip Soothsaying

Rainfall is important for food production plan, water resource management. India is an agricultural country and its economy [1],[ 3],[5]is largely based upon productivity. Thus rainfall prediction becomes a significant factor in agricultural countries like India. On the growing importance of Rainfall studies in the climate change scenario and High Performance Computing, different Users starting from a farmer to a scientist to a policy maker needs the rainfall prediction well in advance for their application like crop planning, water storage etc. Data discovery from temporal, spatial and spatio- temporal data is critical for rainfall analysis. However, recent growth in observations and model outputs, combined with the increased availability of geographical data, presents new opportunities for the users to implement new techniques such as predictive analytics for developing a predictor which can be used for multi-scale forecasting of rainfall that is from 24 hour forecast to long-range forecast say 2-3 month in advance forecast. Hence we developed predictive analytics system for the efficient and real time prediction of rainfall over India. [2 ],[ 4],[6]
Ontology-based data integration for the Internet of Things in a scientific software ecosystem

The Internet of Things (IoT) enables a smart observation of the environment, producing a large amount of heterogeneous data. On the one hand, it allows the remote collection of data, either providing a ready field data set compilation or serving as a secondary source of information to better analyse the research context. On the other hand, all the raw data generated by disparate sensors need to be integrated to leverage the power of IoT in scientific experiments. This paper proposes an ontology-based data integration architecture that allows data from different sources, formats, and semantics to be integrated and organised by a mediated ontology that provides knowledge inference. The architecture is thus evaluated as a use case testing in a scientific software ecosystem that supports all stages of the experiment life cycle.
Kernel-based tensor discriminant analysis with fuzzy fusion for face recognition

This paper proposes a novel kernel-based image subspace learning method for face recognition, by encoding a face image as a tensor of second order (matrix). First, we propose a kernel-based discriminant tensor criterion, called kernel bilinear fisher criterion (KBFC), which is designed to simultaneously pursue two projection vectors to maximise the interclass scatter and at the same time minimise the intraclass scatter in its corresponding subspace. Then, a score level fusion method is presented to combine two separate projection results to achieve classification tasks. Experimental results on the ORL and UMIST face databases show the effectiveness of the proposed approach.
Use of Charades in Enhancing Higher Institute Students’ Speaking Skills

English speaking skill is one of the most challenging skills that has to be taught by teachers, and there are many factors affecting students' low speaking proficiency. Hence, charades are implemented in this study. The purpose of this research is to study the improvement of English-speaking skills among college students through implementing Charades in the classroom. This study consists of two objectives. The first objective is to determine the effectiveness of charades in improving learners’ speaking skills, and the second objective is to investigate learners’ perception of charades. This study involves 18 students from Chenderoh Community College. Charades were implemented in their speaking lessons. This is single-group quasi-experimental research. Data was collected through pre and post-test and questionnaire. Speaking scores were analyzed through paired T-test. After the data analysis, there is a significant difference between pre and post-test. Students showed a positive perception of charades.
Sarawak Energy, Shell study future hydrogen economy in Malaysia

Sarawak Energy and Shell MDS (Malaysia) Sdn Bhd (SMDS) have signed a Memorandum of Understanding to work together in assessing potential opportunities in lower-cost hydrogen production technology via electrolysis, in a major boost to including hydrogen in the exploration of a ‘green’ energy agenda in Malaysia's largest state.
Phishing Diagnosis: A Multi-Feature Decision Tree-based Method

Phishing is an electronically connected criminal activity in which the attacker steals the user’s personal information like username, countersign, internet banking account, credit/debit card number with the expiration date, password, pin, legitimacy, confidential patient record, CVV number, etc. to boon financially. Email-based phishing is the most common and traditional way of phishing scams, in which the phisher will send a suspicious email with an embedded URL and ask the user to click the URL. When the user clicks on the link, the link will be redirected to a spoofed site that looks the same to the original site to steal their credentials and displays some error message. Later the phishing uses those credentials for malicious purposes. To overcome these scams, many anti-phishing tools have developed. Among that the machine learning-based approaches can give a better result. This paper is an extensive study of the various machine learning-based anti-phishing approaches and their results that detect the phishing URL's from the URLs with URLs features. Six most important models of machine learning have been examined for the phishing detection problem. The Decision Tree-based method outperforms other methods.
Research of Clustering Algorithms using Enhanced Feature Selection

In Present situation, a huge quantity of data is recorded in variety of forms like text, image, video, and audio and is estimated to enhance in future. The major tasks related to text are entity extraction, information extraction, entity relation modeling, document summarization are performed by using text mining. This paper main focus is on document clustering, a sub task of text mining and to measure the performance of different clustering techniques. In this paper we are using an enhanced features selection for clustering of text documents to prove that it produces better results compared to traditional feature selection.
A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Retrieval: Motivation, Applications and Future Trends

Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.
Prediction of Personality Traits from Text using Time Efficient Preprocessing and Deep Convolution Neural Network

Persons express their sentiments as part of day-by-day communiqué. Personality traits reveals persons thoughts, about their feelings and persons behavior. Hence personality traits sets psychology how one person different from one another. Person thoughts expressed by what he/she write about any situation. Henceforth personality traits prediction is the vital research area. This research area belongs to NLP (Natural Language Processing), the most widely accepted of these traits are as: Openness (OPE), Conscientiousness (CON), Extraversion (EXT), Agreeableness (AGR), and Neuroticism (NEU). In this paper we have proposed to use time efficient sentence tokenization algorithm, efficient text preprocessing prominence on emoji’s followed by CNN deep learning classifier , proposed prediction model uses convolution filter for feature selection, further we have compared prediction model with machine learning based prediction model. We have also compared brute force tokenization method with proposed tokenization algorithm over dataset different in size.
A new bio-inspired algorithm: lizard optimisation

A new bio-inspired, lizard algorithm (LA) is proposed for optimisation of soft computing used in data mining. Here, an effort has been made to mimic the anole lizard behaviour to optimise problems of the dataset. Furthermore, the experiments have been carried out on five benchmark problems with ten benchmark algorithms like statistical, fuzzy, neural network, tabu search, simulated annealing, HS, DEA, PCO, ABC and ACO on dataset. The concept of average error and residual error were conducted to compare the performance of LA with that of other used algorithms. The concepts of residual analysis and chi test (χ2) have also been performed on the proposed algorithm to check the righteous among the algorithms. And the result has shown that LA has achieved good optimisation results in terms of both optimisation accuracy and robustness.
HHDSSC: harnessing healthcare data security in cloud using ciphertext policy attribute-based encryption

The advancement of cloud computing has great impact on the medical sector. Due to its storage facility, e-healthcare has emerged as a promising healthcare solution for providing fast and immediate treatment to patients. The PHRs collected and outsourced in the cloud leads to security concern. The data outsourced in the cloud is no more under the direct control of the patient, hence data should be encrypted prior its storage. Existing works based on group signature require high amount of computation. Other issues like confidentiality of private data, efficient key distribution, scalable and flexible fine-grained data access, revocation and tracing the malicious user is yet to be addressed to maintain the integrity of the patients. In this manuscript, we propose EPOC-1-based multi authority CP-ABE which can trace and revoke the malicious user who leaks the real identity and confidential data of the patient without any storage overhead. This methodology of white-box traceability presented in this manuscript, traces the malicious user efficiently. The proposed scheme is validated with some existing policies and makes the healthcare domain more securable under the cloud setup.
Pearl Pixel Steganographic Method for Grayscale Images using Location-Array Method

Nowadays, the user of the internet is growing very fast, in which sends and receiving a messages become very easy using social media applications, meanwhile using these applications, the security is a very big issue. Today providing security for the essential data becomes too hard, intruders become smarter. They are using advanced techniques and models to access our data. For a long period cryptography algorithms were operated to protect the important data, but nowadays these algorithms are easily broken by the intruders. The steganography algorithms are considered as the next generation of cryptography; every user is able to create own algorithms to send and receive the important data. In this method, the secret data will embed into image pixel; many more algorithms are designed by the researcher using this idea. All of these algorithms embed the data into an image and transfer the stego image from one end to another end with stego-key at the receiver end with the help of a stego-key, they do the reverse engineering process in the stego image to get the original data which embedded by the sender side. In most of the algorithms, transfer the stego image and key is a very big concern. Since during the transmission time of stego image and key, anyone can make changes into that; like resize the image or cropping the same. If the receiver gets the damaged version of stego image, they can’t get the original message back. In that circumstances transmission of stego-image with stego-key, it needs more space and time to reach the destination as well as need to pay attention to the security. To overcome these problems, the proposed method does not transfer the stego image, due to which it is not required to compare the image before and after data insertion and no need to calculate the peak signal-noise ratio (PSNR). It shares stego key with the proper security key to recognize if any intruder made an attack on that. This method provides good security for the data.
Gesture Recognition using CNN and RNN

Gesture Recognition is a major area in Human-Computer Interaction (HCI). HCI allows computers to capture and interpret human gestures as commands. A real-time Hand Gesture Recognition System is implemented and is used for operating electronic appliances. This system is implemented using the deep learning models such as the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN). The combined model will effectively recognize both static and dynamic hand gestures. Also the model accuracy while using VGG16 pre-trained CNN model is investigated.