Data mining (Data Analysis)

In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented ...

This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period

International Conference on Data Science and Applications (DSA 2020) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Science and Applications. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest
issues and advancement in the area of Data Science & Applications

Social media data possess the characteristics of Big Data such as volume, veracity, velocity, variability and value. These characteristics make its analysis a bit more challenging than conventional data. Manual analysis approaches are unable to cope with the fast pace at which data is being generated. Processing data manually is also time consuming and requires a lot of effort as compared to using computational methods. However, computational analysis methods usually cannot capture in-depth meanings (semantics) within data. On their individual capacity, each approach is insufficient. As a solution, we propose a Conceptual Framework, which integrates both the traditional approaches and computational approaches to the mining and analysis of social media data. This allows us to leverage the strengths of traditional content analysis, with its regular meticulousness and relative understanding, whilst exploiting the extensive capacity of Big Data analytics and accuracy of computational methods. The proposed Conceptual Framework was evaluated in two stages using an example case of the political landscape of Botswana data collected from Facebook and Twitter platforms. Firstly, a user study was carried through the Inductive Content Analysis (ICA) process using the collected data. Additionally, a questionnaire was conducted to evaluate the usability of ICA as perceived by the participants. Secondly, an experimental study was conducted to evaluate the performance of data mining algorithms on the data from the ICA process. The results, from the user study, showed that the ICA process is flexible and systematic in terms of allowing the users to analyse social media data, hence reducing the time and effort required to manually analyse data. The users’ perception in terms of ease of use and usefulness of the ICA on analysing social media data is positive. The results from the experimental study show that data mining algorithms produced higher accurate results in classifying data when supplied with data from the ICA process. That is, when data mining algorithms are integrated with the ICA process, they are able to overcome the difficulty they face to capture semantics within data. Overall, the results of this study, including the Proposed Conceptual Framework are useful to scholars and practitioners who wish to do some researches on social media data mining and analysis. The Framework serves as a guide to the mining and analysis of the social media data in a systematic manner.

Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for avoiding the risk factors. Then the earlier detection system has been created by utilizing optimized and hybridized techniques to recognize the heart disease in earlier stage. So, artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system. Initially , heart disease related information is collected from Heart Disease Data Set-UCI repository. The collected information is huge in dimension which is difficult to process, that reduces the efficiency of heart disease identification system. So, the dimensionality of the features are reduces according to the behavior of gravitational cuckoo search algorithm. The selected features are processed by above defined associative memory classifier. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results. Keywords Heart disease · Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network · Heart disease data set-UCI repository

Intelligence Collection in the Era of "Persistent Surveillance" . H Παγκόσμια Στρατηγική Ασφάλειας της Ε.Ε. (EUGS , 2016), που υπογράφεται από την Ύπατη Εκπρόσωπο της Ένωσης για Θέματα Εξωτερικής Πολιτικής και Πολιτικής Ασφαλείας και Αντιπρόεδρος της Ευρωπαϊκής Επιτροπής, F. Mogherini,  περιλαμβάνει ανάμεσα σε άλλες, και την επιχειρησιακή απαίτηση, της «μόνιμης παρατήρησης Γης», η οποία χρειάζεται για να παρέχει τις απαραίτητες πληροφορίες για την υποστήριξη της εξωτερικής της πολιτικής.
Η απαίτηση αυτή, αν και έχει καταγραφεί στην Ευρώπη από τα μέσα της δεκαετίας του 2000, είναι η πρώτη φορά που αναδεικνύεται έντονα σε ένα τέτοιου υψηλού επιπέδου κείμενο. Ο όρος έχει πρωτοπαρουσιαστεί στην άλλη πλευρά του Ατλαντικού στις αρχές της δεκαετίας του 2000, ως η εποχή της «επίμονης επιτήρησης» και σχετιστεί με την επιχειρησιακή λειτουργία των μη επανδρωμένων συστημάτων.
Όμως και τα συστήματα αυτά δεν μπορούν να ξεπεράσουν αυτό που η Κοινότητα Πληροφοριών εξακολουθεί να αναφέρει ως «άρνηση της επικράτειας»,  που παραπέμπει στην «πολιτική ευαισθησία» χρήσης εναέριων μέσων που κάνουν προβληματική μια υπέρπτηση πάνω από μια εχθρική χώρα, η οποία παραβιάζει το διεθνές δίκαιο. Όπως έδειξαν οι περιπτώσεις στη Σοβιετική Ένωση με το αμερικανικό φωτοαναγνωριστικό U-2 το 1960 και στη Βόρεια Κορέα με το αεροσκάφος ηλεκτρονικής συλλογής EC-121 το 1969, τα εναέρια μέσα μπορεί να καταρριφθούν.
Η χρήση του Εξωατμοσφαιρικού Διαστήματος φαντάζει ως η κύρια εναλλακτική λύση, αφού οι δορυφόροι, σε γενικές γραμμές, είναι, ακόμα, άτρωτοι.. Αν και οι δορυφόροι δεν μπορούν να αιωρούνται πάνω από μία θέση για μεγάλο χρονικό διάστημα, εν τούτοις παρουσιάζονται λύσεις που τείνουν να υποστηρίξουν σε μεγάλο βαθμό την ως άνω επιχειρησιακή απαίτηση. Η παρουσίαση αυτή θα αναφερθεί σε συστήματα που αναφέρονται στην ως άνω προσέγγιση, έχοντας πάντοτε υπόψιν ότι για την υλοποίηση της συλλογής πληροφοριών, χρειάζονται όλα τα συστήματα. Αλλά ακόμα και τότε θα υπάρχουν κάποια άγνωστα στοιχεία.

The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social lifestyle while those who previously struggled due to family distractions may be able to focus away from home, and as a result excel at the university. University admission in Nigeria is typically based on cognitive entry characteristics of a student which is mostly academic , and may not necessarily translate to excellence once in the university. In this study, the relationship between the cognitive admission entry requirements and the academic performance of students in their first year, using their CGPA and class of degree was examined using six data mining algorithms in KNIME and Orange platforms. Maximum accuracies of 50.23% and 51.9% respectively were observed, and the results were verified using regression models, with R2 values of 0.207 and 0.232 recorded which indicate that students' performance in their first year is not fully explained by cognitive entry requirements.

The process required to gain admission into tertiary institutions is challenging for Nigerian students. This is due to the various examinations and requirements that must be met to be qualified for admission among innumerable applicants. It is therefore imperative that after admission, students must pursue academic excellence to justify the opportunity given to them, and this will also improve their chances of success after graduation. In a university, some first-year students struggle because of cultural disadvantages, as well as their economic and social backgrounds. This has resulted in poor performance by some students and inevitably led to bad grades at graduation and some drop out of the university without graduating. Female students are often said to perform a bit poorer in terms of academic performance. This study is a comparative performance analysis of male and female students in Science, Technology, Engineering and Mathematics (STEM), conducted using regression models. Trend analysis shows that in this case study, female students have a tendency to improve on their academic performance from their first to their final year. The highest R-squared value of 0.7069 was achieved based on a regression analysis of the performance of 1,093 female students.

Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our kn...

Bisnis TV berlangganan merupakan salah satu bisnis masa depan bagi sebuah perusahaan yang memiliki bisnis inti telekomunikasi seperti Telkomvision. Pada akhir tahun 2011 jumlah pelanggan Telkomvision mencapai lebih dari satu juta pelanggan, namun demikian pada tahun 2012 menurunnya pendapatan per pelanggan TV berlangganan atau average revenue per user (ARPU) menjadi penyebab perusahaan ini mengalami kerugian.
Tingkat churn pelanggan yang tinggi harus diprediksi secara akurat, karena hasil prediksi yang akurat dapat menentukan strategi apa dan promosi bagaimana yang tepat untuk retensi pelanggan. Kemudian dilakukan segmentasi pelanggan untuk merumuskan program retensi yang tepat sesuai dengan kebutuhan pelanggan yang berpotensi churn tinggi.
Metode data mining digunakan untuk memprediksi potensi churn dan segementasinya pada pelanggan PT Indonusa Telemedia, dan dapat hal ini dapat mendukung proses monitoring, pengendalian, serta penyusunan strategi pada management. Metode Decision Tree dan Clustering merupakan metode data mining yang populer karena sangat mudah dipahami dan diinterpretasikan, sehingga dapat dengan mudah untuk digunakan sebagai teknik untuk melakukan prediksi churn dan segmentasinya.
Tujuan utama dari prediksi pelanggan churn adalah untuk memperoleh informasi dari pelanggan yang mempunyai potensi churn tinggi sehingga dapat disusun rencana strategis dan promo perusahaan yang tepat sesuai dengan segmentasinya.
Metode Decision Tree dengan pemodelan menggunakan algoritma C4.5 menghasilkan tingkat akurasi 90,89%. Kemudian pelanggan yang mempunyai potensi churn tinggi dilakukan clustering menggunakan algoritma K-Means dan dari 5956 total pelanggan dapat merekomendasikan 5792 pelanggan yang mendapatkan penawaran program retensi.

Kata kunci : churn, segmentasi , data mining, decision tree, clustering, k-means

— The Data-leak cases, human mistakes are one of the causes of data loss. Deliberately planned attacks, inadvertent and human mistakes lead to most of the data-leak incidents. The detecting solutions of inadvertent sensitive data leaks caused by human mistakes and provide alerts for organizations. A common approach is to screen content in the storage and transmission for exposed sensitive information. Such an approach requires the detection operation to be conducted in secrecy. The data-leak detection (DLD) privacy-preserving solution to solve the special set of sensitive data digests is used in detection. The advantage of data owner is safely delegate the detection operation to a semihonest provider without revealing sensitive data to the provider. Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. Evaluation results support accurate detection with very small number of false alarms under various data-leak scenarios. Host-assisted mechanism for the complete data-leak detection for large-scale organizations. To design the Host-assisted mechanism for DLD, using data signature and fuzzy fingerprint.

Customer churn is a significant issue that is regularly related with the existence cycle of the business. At the point when the business is in a development period of its life cycle, deals are expanding exponentially and the quantity of new clients to a great extent dwarfs the quantity of churners. On the other side, organizations in a develop period of in their life cycle, set their attention on lessening the rate of customer churn. This research work proposes an efficient computational intelligence model comprising of clustering achieved through improvised K-Means algorithm and classification achieved through Non Linear Support Vector Machine.

Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.

This dissertation aims at conducting a quantitative research to significantly reduce food wastage at retails stores using business intelligence techniques, thus supporting demand forecasting decisions for precised inventory management and increasing overall business profitability. Perishable and deli food items have short freshness-life and this is why the volume of food wastage is higher in this particular segment of supply chain system. Most of the retail store managers are struggling to predict a relatively precised demand and supply of such products to control food wastage at the minimum levels. The forecasting and decision-making is mostly done using sales reports which is not a completely reliable data-source for decision-ing. It is also observed that an educated guessing by store managers is another common practice for inaccurate demand forecasting that causes stock in-availability, over-storage and sizable wastage of food items among fruits, vegetables, cereals, bakery, dairy and meat products etc. These issues are empirically analysed in this research dissertation using BI concepts and methods.

Computer-aided technologies (CAx) mean the use of computer technology to aid in the design, analysis and manufacture various products. International Journal of Computer-Aided technologies (IJCAx)  is an open access, peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Aided technologies such as CAD, CAM, CAIM,CAR, CARD, CASE etc. The journal focuses on all technical and practical aspects of Computer Aided technologies. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced CAx tools and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in all Computer Aided technologies

The Internet may be free, but service provider’s indispensable to access services are not, to the extent that while the complexity and burden of the sites increases, it is becoming more and more expensive to surf the net. Blocking access under the guise of protecting us from offensive or sexually explicit content, to pages, chat rooms, newsgroups and other Web options is not anymore an excuse, but a lie. Government surveillance in the Internet, uncontrolled practices of data harvesting and restriction of free speech, open discussion of issues and even political activism has spread in the last 20 years to include countries that are considered democracies such as the U.S. Internet has become a battleground of power and therefore become increasingly militarized.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data

7th International Conference on Data Mining & Knowledge Management (DaKM 2022) provides
a forum for researchers who address this issue and to present their work in a peer-reviewed forum.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in the
following areas, but are not limited to these topics only.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Every second plethora of reviews on various product lines are being posted in a trending e-commerce website. The objective of a review section in such websites is to analyze customer satisfaction for sales growth and to aid buyers make right purchase decisions. This becomes a herculean task for the business analyst when the search space for interesting patterns is vast. Buyers could benefit from a trusted recommendation label for the product and an overall product rating purely based on data mining of the customer generated live review dataset. The proposed sentiment analysis system makes use of data mining and natural language processing algorithms. The words in the corpus selected for the sentiment classification is associated with fuzzy scores to indicate degree of polarity strength. Naïve bayes algorithm forms the basis for the project. It is simple yet yields effective results. Sentiment scoring of the review is used for automated five star rating that replaces the conventional manual rating. Context based anomaly detection is done to remove irrelevant portions of each review. Selective-feature analysis is performed to assist vendors as well as buyers. A threshold value is compared against the overall rating for the recommendation label. The polarity detection gave fair results. It was observed that the richness of corpus and grammar rules contributed to the accuracy of the model. Threshold chosen for product recommendation gave mediocre credence when it was solely based on reviews. Therefore Threshold was set using more complex criteria that involves purchase patterns. Results obtained from the proposed model is ideal for e-commerce websites. Model can be improved by addition of sarcasm detection algorithm which is quite challenging.

Data mining is one of the best ways to identify patterns and problems in large amount of data to support problem solving process. In this paper the causes business problems under a company’s will be identified from the data collected from the day to day activities of the company. iTelecom, the provider of telephone and broadband services, is facing customer management problems. There were high customer churning in company but the company does not know what is exactly causing it.  The company collected vast amount of data related to its customer, the plan customers
signed on and the services it provides. iTelecom has placed a high emphasis on churning problem and it is trying to find new ways of reducing it. The directors of iTelecom would like to answer the following questions.
-    What is it that makes a customer churn?

-    Are some customers more likely to churn than others?

-    How can we identify these customers before they churn?

In an attempt to answer these questions the company data was organised and analysed using Weka software. The data is composed of 21 attributes with nominal and numeric data types.  There were
3333 instances used to analyse the data. The data is pre-processed tested with at different levels for missing data, for inconsistencies. Then, appropriate attributes were selected. The appropriateness
an attribute was measure with its ability in predicting churning. Then, the data mining performed using different classifiers and tests. Then the data is divided into sub datasets to find out which attribute or group of attribute can better predict churning. In conclusion the best group was selected and used to spot why these attributes are linked to the risk of churning.

کاهش هزینه و زمان پروژه توأماً، در شرایط رقابتی حاکم بین شرکتهای پیمانکاری، امروزه به امری حیاتی تبدیل شده است. لازمه این امرمصالحه بین زمان و هزینه است. از اینرو سازمان های پیمانکاری باید به دقت رویکردهای مختلف را جهت رسیدن به یک موازنه بهینه زمان-هزینه بررسی کنند. اگر چه تا کنون مدلهای مختلفی برای بهینهسازی زمان-هزینهTCO گسترش یافتهاند، ولی اکثراً حالتی را در نظر میگیرند که زمان پروژه بر طبق قرارداد، مشخص و ثابت است. بنابراین هدف از بهینه سازی در این موارد، به یافتن راه حلی برای مینیمم کردن هزینه پروژه محدود میشود. با افزایش رواج سیستم پیشنهاد زمان تحویل پروژه، کارفرما و پیمانکار هر دو در پی افزایش سود و جذب موقعیتهای حاصل از اتمام هر چه سریعتر پروژه هستند. مدل چند هدفه پیشنهاد شده در این مقاله، بر مبنای جستجوی فراکاوشی توسط الگوریتم ژنتیک GAs) و بر پایه تکنیک های NSGA II جهت بهبود جبهه پارتوی دو بعدی زمان- هزینه گسترش داده شده است. مفهوم بهینه سازی چند هدفه مدل TCOبا یک مثال دستی ساده نمایش داده شده و نتایج بیانگر این موضوع هستند که مدل میتواند به تصمیم گیران پروژه جهت رسیدن به زمان و هزینهی بهینه به طور همزمان، کمک کند

Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month the rainfall occurs in India which also cause damage to the farming industry as the crop seeds gets damaged. In the proposed study the future rainfall is predicted using Linear Regression Technique as we also compared various model such as SVM, Random Forest Regression, Neural Network model so we obtained better results in Linear Regression so we further consider this model for future prediction of rainfall.

The breast cancer is a very rigorous disease mainly found among females throughout the world. This type of cancer emerges from human breast tissue cells, normally from the lobules or the inward covering of the milk pipes that give the conduits milk. A latest medicinal overview uncovers that throughout the world. Of all the diseases, breast cancer happens in 22.9% females and it finally causes 13.7% of cancer related deaths. Breast cancer, being very dangerous to all women, might be the reason of breast loss or might even cost their life. Detection of breast cancer disease is an essential area of data mining research. In this paper, we have included a detailed study about the cause and symptoms of breast cancer. We have also conducted a review of data mining based approaches which are used to detect the risk of presence of breast cancer using various methods

The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning
techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database.
The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the
experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.

This report provides insights into the global sportswear market as well as a comprehensive analysis of Adidas to develop an understanding about the company’s internal situation in line with the external environment concerning surrounding competitors, customers, and suppliers…etc. Accordingly, opportunities and threats can be identified, which helps realize the reason why Adidas implements proactive changes to not only grasp these opportunities but also address those threats.  At the same time, this finding serves as a reference for anyone to evaluate their profession possibility in this field. At the end of the paper, it is concluded weather the sportswear is an attractive industry and Adidas is an attractive company for employees.

This project was centered on online news editing system. The current process of news editing is being operated manually and due to this procedure numerous problem are been encountered. A design was taken to computerized the manual process in order to check this problem. The problems were identified after series of interviews and examination of documents after which analysis was made and a computerized procedure recommended. This project will also suggest how to successfully implement the computerized procedure and to overcome the obstacle that would hinder the successful implementation of the system. The new system will be designed using top-down design methodology and implemented using XAMPP IDE on windows 7,8,8.1 system using PHP, HTML, CSS and MySQL technologies.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data

Monitoring, Evaluation, Accountability, and Learning (MEAL) are part of everyday programme management and are critical to the success of all programmes. MEAL technical quality is very important across a portfolio of projects. Without an effective MEAL system we would be unable to track progress, make adjustments, discover unplanned effects of programmes, or judge the impact that we have made on the lives of those with whom we are working. A MEAL system also helps individuals and teams to be accountable to stakeholders through information sharing and developing complaints or feedback mechanism which can help to guide programme implementation.

The course provides participants with a strong foundation in monitoring and evaluation, programme design and implementation. Participants will explore the variety of tools and techniques used to measure project progress and to report outcomes to the appropriate internal and external stakeholders-including donors, funders, supervisors, or the population being served. Participants will design relevant and effective frameworks, and learn the required methods to conduct effective data collection, statistical analysis, and reporting. Through the interactive sessions and through exposure to real case studies drawn from relevant organizations, participants will gain the skills needed to conduct a monitoring and evaluation programme for development organizations, NGOs, or private organizations in the development, peacebuilding, energy, gender studies, transnational security sectors among other sectors. The course  introduces participants to MEAL concepts and practices. It will stimulate ideas on how to design and implement monitoring and evaluation processes that strengthen accountability and learning, and so promote project, programme and strategy effectiveness.

Mash Industries ltd is a Kenyan manufacturing company specializing in the production of hygiene products. The company was established in 1962 in Nairobi, Kenya the company is committed to developing new products for the east African market, as well as innovating products to satisfy the ever changing needs & preferences of the fast growing middle-class consumers. The company also manufactures branded products such as soft tissues and sanitary towels for various other supermarket chains, that sell them as own brands. Mash Industries ltd has branches across east Africa, including in Uganda, Tanzania, and Rwanda. They have a catalogue of over 10 brand products. Mash Industries ltd is involved in wholesale of its products through its warehouse outlets, as well as retail sales by way of its chain of five retail shops across the city of Nairobi.

Estatística Descritiva e Análise Exploratória de Dados

Ciclo PPDAC - Problema, Plano, Dados, Análise e Conclusão (Spiegelhater, 2019)

SPIEGELHALTER, D The Art of Statistics : How to learn from data , New York: Basic Books,

Problema: Traçar o perfil dos funcionários da Secretaria de Turisimo.

Plano: Obter dados de remuneração dos servidores da Secretaria de

Turismo (Fonte: Portal da Transparência).

Dados: 40 servidores ativos, variávaies: Sexo, Carga Horária, Tempo no Cargo, Remuneração.

Análise: Cálculo das distribuições de frequencias e estatísticas descritivas.

Conclusão: Perfil dos funcionários da Secretaria de Turismo.

https://rpubs.com/PESSANHA/642558

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Stress will exist once operating or paying attention to somebody shouts at you. It is caused through conflicts, creating selections, or otherwise straining skills, or it will exist owing to various social demands on time. it's noted that emotional states tends to not be long lasting, whether or not they are unpleasant emotions like worry, or pleasant ones, like joy. Nonetheless infrequently such states might persist for long periods of your time, or they will reach too high levels. Once this happens, the result typically is labeled "stress", and it's psychological as well as physiological terms. Stress and its manifestations, such as anxiety, depression, and burnout, have always have been a common problem among people in different professions and occupations. In the last few decades, alarm has already been provoked by the proliferation of books, research reports, popular articles and the growing number of organized workshops, aiming to teach people how to cope up with it. The purpose of the study is to find out the level of academic stress among higher secondary students. The present study consists of students studying in higher secondary schools situated in Tamil Nadu, India. The sample was selected by using simple random sampling technique. The present study reveals that the higher secondary students are having moderate level of academic stress and irrespective of sub samples of the higher secondary students are having moderate level of academic stress. The male student's academic stress is higher than female students. The urban student's tutorial stress is higher than rural students. The government college student's tutorial stress is a smaller amount than school students. The science subject student's tutorial stress is over arts student. The scholars whose parents are literates, the educational stress is over their counterparts.

El comportamiento desplegado de cada equipo, es importante. Indicar una razonable aproximación a la necesidad de determinar esta información mediante los gráficos que ayudarán a las interpretaciones es probablemente la mecánica a adoptar en el presente trabajo. La finalidad es proporcionar al ingeniero de minas herramientas que no solo permitan conocer e interpretar condiciones / índices de  operación; sino también facilitar en la toma de decisiones sobre reorientaciones del curso en las operaciones tan dinámicas que son una permanente característica en mina. Por ello se trabaja en el rendimiento de equipos y proyecciones para el aumento de la productividad. La toma de datos fue realizada en operaciones principales de producción tales como perforación,  voladura, movimiento de tierras y extracción. La obtención de índices de productividad óptimos, derivará en mejores condiciones y por consiguiente el nivel de competitividad irá en aumento, un objetivo tan común; pero a la vez tan primordial.

There is growing effort to evaluate the usability of web-based systems and mobile-based systems. With the increase in the development of web-based, applications coupled with the limitations and associated challenges, it becomes imperative to evaluate the web-based systems in the context of usability. Furthermore, a number of international standards/models on usability are available, but rarely use for practical usability evaluation. In this study, a survey was conducted on an online web-based system of Malaysian Road Transport Department MySIKAP. It is a new system for accomplishing road transport related transaction via online. The popular ISO 9241-11 standard was used to evaluate MySIKAP portal, a survey of five hundred (500) participants was conducted to measure users' perspective. The results revealed some pointers to usability issues as well as confirmation that the web-based transaction portal is relatively usable within the components defined in the models. Findings show that, contents, organization and readability contribute more to the usability of MySIKAP, while the user design interface of the portal is not. Although the navigation and links; effectiveness and efficiency are usable to the users, however the level of usability calls for greater improvement to provide users with better experience for an improved usability. Usability Evaluation of a Web-Based Portal (Mysikap) Using ISO 9241-11 Model