Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력

The quality of coffee beans differs from each other based on the geographic locations of its sources. The coffee bean quality is conventionally determined by visual inspection, which is subjective, requiring considerable effort and time and prone to error. This calls for the development of an alternative method that is precise, non-destructive and objective. This paper was conducted with the objective of developing an appropriate computer routine that can characterize coffee beans from the different towns of Cavite, Philippines. Imaging techniques were employed to automatically classify the coffee bean samples according to their specie. Important coffee bean features based in morphology such as area of the bean, perimeter, equivalent diameter, and percentage of roundness were extracted from 195 training images and 60 testing images. Artificial neural network (ANN) and K nearest neighbor (KNN) were employed to automatically categorize the coffee beans. Using ANN, classification scores of 96.66% were achieved while using KNN the following classification scores were achieved 84.12%(k=1), 84.10%(k=2), 81.53%(k=3), 82.56%(k=4), 75.38%(k=5),80.35% (k=6), 38.79%(k=7), 77.44%(k=8), 72.82%(k=9) and 78.45% (k=10). In conclusion, the results of this study have revealed that imaging technique could be used as an effective method to classify coffee bean species. ANN is the more preferred method over KNN in classifying coffee beans.

Classification of coffee bean species using image processing, artificial neural network and K nearest neighbors

The quality of a coffee bean is determined by several factors including color, texture, and size. High quality beans are carefully refined where defects, such as black beans, are removed. The assessment through visual inspection may be subjected to external factors such as light and the amount of beans to be inspected. This study presents a method of controlling the coffee bean quality using Image Processing techniques. Normal beans are identified through the extraction of RGB color components of training image. The RGB values were integrated in an image processing technique that eliminated the black beans in the image. Using the technique in this study, a classification of 100% were achieved for eliminating the black beans in the testing images.

An image processing technique for coffee black beans identification

Teacher evaluation is the systematic procedure done in educational institutions to review the performance of the teachers in a classroom. It aims to provide constructive feedback for teacher's professional growth which benefits students in their education. Students' feedback in the evaluation typically include textual comments which are unstructured but are rich with adequate information and insight about teacher's mastery of the course, teaching style, course content and learning experiences of the students. In this study, sentiment analysis or opinion mining was used to analyze the students' comments. An ensemble approach integrating five individual machine algorithms namely Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree and Random Forest algorithms were applied to classify the comments based on Majority Voting Principle. The experimental result shows that the ensemble classification system outperforms these individual classifiers with 90.32% accuracy. It helps to improve machine learning results producing better predictions compared to a single model.

A Sentiment Analysis Model for Faculty Comment Evaluation Using Ensemble Machine Learning Algorithms

Advanced Encryption Standard (AES) is one of the most frequently used encryption algorithms. In the study, the Advanced Encryption Standard is modified to address its high computational requirement due to the complex mathematical operations in MixColumns Transformation making the encryption process slow. The modified AES used Bit Permutation to replace the MixColumns Transformation in AES since bit permutation is easy to implement and it does not have any complex mathematical computation. Results of the study show that the modified AES algorithm exhibited increased efficiency due to the faster encryption time and reduced CPU usage. The modified AES algorithm also yielded higher avalanche effect which improved the performance of the algorithm.

http://ijeecs.iaescore.com/index.php/IJEECS/article/download/11773/9201

Modified AES for Text and Image Encryption

The motivation of the study is to address the weakness of Term Frequency – Inverse Document Frequency (TF-IDF) in dealing with single terms because single terms can sometimes be vague. That is, a single term when used for indexing, could convey several interpretations. A single term can also be too general, in which, it doesn't have a discriminating power to differentiate terms such as from two individual terms such as "junior" and "college." It is not enough to distinguish "junior college" from "college junior". Thus, this study aims to enhance TF-IDF by integrating collocation as a term feature. The collocated terms are extracted based on the determination of part-of-speech (POS) that forms specific patterns such as adjective + noun, noun + noun, noun + verb, etc. There are three (3) document classifiers which had been considered in this study. These classifiers will be subjected to traditional and modified TF-IDF are RandomForest, MultinomialNB (MultiNB), and SVM. The result of this experiment shows that integrating collocation as part of the enhancement of the TF-IDF process outperforms the traditional TF-IDF by an increase of up to 10 percent.

Ruji P. Medina

Papers

Classification of coffee bean species using image processing, artificial neural network and K nearest neighbors

An image processing technique for coffee black beans identification

A Sentiment Analysis Model for Faculty Comment Evaluation Using Ensemble Machine Learning Algorithms

Modified AES for Text and Image Encryption

Integrating Collocation as TF-IDF Enhancement to Improve Classification Accuracy