http://kth.diva-portal.org/smash/get/diva2:1539693/FULLTEXT01

Blockchain-based Framework for Supply Chain Traceability : A Case Example of Textile and Clothing Industry

The attendance system found in an institution or university uses an id card, finger print or using the manual method. This method still has many shortcomings such as signature forgery, loss of ID cards, queue up which can waste a lot of time. In this study, a QR CODE-based Online Attendance System with REST architecture was developed which resulted inweb and mobile-based applications, so that students can make attendance more practical and efficient with their gadgets, besides that lecturers can monitor data and student attendance graph on a web-based application. The attendance System was developed using the REST architecture because the architecture is language and platformagnostic, so it can be used by many programming languages and many platforms, and the REST architecture has a design and philosophy closer to the web, using the HTTP protocol.

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Analisis perspektif pada penerapan e-money menggunakan delone and mclean is success model di bandara sultan syarif kasim ii pekanbaru

Effective Pattern Discovery for Text Mining

The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character The opinions expressed by the public can take form support, criticism and input Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media Data used in this study was obtained by crawling on social media Twitter This study uses a classification algorithm, Naive Bayes and Support Vector Machine Comparison two algorithms produces predictions obtained that the Naive Bayes algorithm with SMOTE gets accuracy value 7032%, and AUC value 0954 While Support Vector Machine with SMOTE gets accuracy value 6692% and AUC value 0832 From these results can be concluded that Naive Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naive Bayes and the vector machine support is 34% Naive Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum

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Sentiment analysis on e-sports for education curriculum using naive bayes and support vector machine

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

/pdf/improved-machine-learning-based-predictive-models-for-breast-2dmeo7vq.pdf

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Twitter has become a popular social media network where people express their opinions and views on political and other topics. Social media analysis of Twitter can be used to understand which sentiment and opinions are implicit in these social media data. The purpose of this paper is to present an approach of natural language pre-processing, text mining, and sentiment analysis techniques to analyze Twitter data related to Afghanistan through a case study. Our article analyzes the Twitter English data about Afghanistan. The value of the proposed approach was to understand the most discomforts and happiness of people, their opinions, and the country situation in the different time through a case study. We found that from 29 March 2018 to 12 Jun 2018 almost always negative comments are higher than positives while from 13 Jun 2018 to 21 Jun 2018 it is just opposite, the positive comments are higher than negative comments on Twitter. The reason for this was the interim peace for a few days that had taken place between Afghan government and the Taliban terrorist group. The outcomes of this research can help the palpitations, companies, and stockholders to use social media network as a great information source for their better political strategies and better business decision-making for their current and future intentions. It provides a feasible approach and a case study as an example to assist the researchers to apply the sentiment techniques more effectively.

Sentiment Analysis on Twitter: A text Mining Approach to the Afghanistan Status Reviews

Through summarizing expert experience and knowledge of clothing, the clothing recommendation system is developed based on a kind of clothing recommendation method. According to color matching rules, this method has refined the six factors that affect the customer’s choice of clothing, establish the clothing knowledge base and clarify the recommendation rules. Considering the characteristics of the customers and the selection criteria, this system can make personalized clothing recommendation scheme for customers and ensure the rationality of the recommendation results.

A Clothing Recommendation System Based on Expert Knowledge

Online simulation systems are mostly model-driven without considering about real-time interactive controlling with physical systems. In order to enhance the two-way interaction control capability of digital twins and physical systems, this paper proposes a digital twin-driven model based on Petri Net. It defines a state incremental update place for enhancing the ability of real-time simulation, and defines a feedback control structure to control the function of opening and closing the real industrial pipelines. Taking the industrial parameters of the suit processing line as experimental data, the feasibility of online synchronization communication and feedback control of the digital twin driven model and the actual production line was verified.

Digital Twins Driving Model Based on Petri Net in Industrial Pipeline

Smart contracts are often used to automate the execution of transactions so that the fairness, credibility and traceability can be ensured. Generally, for different trading events, different smart contracts are needed to be developed so as to meet the needs. This may lead to a reduction in efficiency, especially when large-scale transactions occur. In addition, trading contracts have complex life cycle and terms. Developing a smart contract based on the context of a contract is extremely difficult. These characteristics may do harm to the application of smart contracts in e-commerce. To this end, this paper proposes a trading model based on legal contracts using smart contract templates. The model can dynamically construct, store, and invoke smart contracts based on a smart contract template and the context of a trading contract, thereby improving the reusability of smart contracts and reducing the difficulty for use. Third-party information, like logistics information, is used as the triggering condition of smart contracts to enhance the reasonableness of execution. In this paper, the garment acquisition contract is token as an example to describe the construction method of a smart contract template. Finally, combined with the experiment and the life cycle of a legal contract, the validity and rationality of the model is discussed. The comparison with legal trading contracts in performance is also conducted.

A Trading Model Based on Legal Contracts Using Smart Contract Templates

The health of older people is always a matter of concern, and falls can cause injuries and even death in severe cases. At present, the fall detection algorithm based on computer vision has a large amount of computation and is easy to be interfered by shielding objects. In addition, there are also problems such as a high rate of missed detection and poor real-time performance. This paper proposes a fusion hybrid attention mechanism of human body fall detection algorithm, a YOLOv3-tiny as a benchmark algorithm, add channel attention in the process of feature extraction and spatial attention mechanism, using the channel the different characteristics of attention to change the network the attention of the weight, the spatial attention change characteristics of pixels of attention weights in the figure, by increasing attention in the process of the fall detection selectivity of focusing not obscured human body parts, filter other essential features. Experimental results show that the optimized human fall detection algorithm improves the mAP of the test set by 8.93% compared with the benchmark YOLOv3-tiny algorithm and can also solve the problem of obstructing the human body with obstacles. In addition, this algorithm can detect falling and fall to the ground in real-time. On average, the falling action is detected 625.58ms in advance, and the FPS is 45.35. Therefore, it has practical feasibility and effectiveness.

Ran Tao

Papers

Sentiment Analysis on Twitter: A text Mining Approach to the Afghanistan Status Reviews

A Clothing Recommendation System Based on Expert Knowledge

Digital Twins Driving Model Based on Petri Net in Industrial Pipeline

A Trading Model Based on Legal Contracts Using Smart Contract Templates

Human Fall Detection Algorithm Based on Mixed Attention Mechanism