Unsupervised Text Extraction from G-Maps

This paper represents an text extraction method from Google maps, GIS maps/images. Due to an unsupervised approach there is no requirement of any prior knowledge or training set about the textual and non-textual parts. Fuzzy CMeans clustering technique is used for image segmentation and Prewitt method is used to detect the edges. Connected component analysis and gridding technique enhance the correctness of the results. The proposed method reaches 98.5% accuracy level on the basis of experimental data sets.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Gabor Filter and Rough Clustering Based Edge Detection

This paper introduces an efficient edge detection method based on Gabor filter and rough clustering. The input image is smoothed by Gabor function, and the concept of rough clustering is used to focus on edge detection with soft computational approach. Hysteresis thresholding is used to get the actual output, i.e. edges of the input image. To show the effectiveness, the proposed technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often used for recommendation, fluctuate over time. Thus, the QoS prediction is essential to identify a suitable service among functionally equivalent services over time. The contemporary temporal QoS prediction methods hardly achieved the desired accuracy due to various limitations, such as the inability to handle data sparsity and outliers and capture higher-order temporal relationships among user-service interactions. Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e.g., collaborative features) to comprehend the relationship among the user-service interactions. This paper addresses the above challenges and proposes a scalable strategy for Temporal QoS Prediction using Multi-source Collaborative-Features (TPMCF), achieving high prediction accuracy and faster responsiveness. TPMCF combines the collaborative-features of users/services by exploiting user-service relationship with the spatio-temporal auto-extracted features by employing graph convolution and transformer encoder with multi-head self-attention. We validated our proposed method on WS-DREAM-2 datasets. Extensive experiments showed TPMCF outperformed major state-of-the-art approaches regarding prediction accuracy while ensuring high scalability and reasonably faster responsiveness.Comment: 10 Pages, 7 figure

Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring

A study on automated handwriting understanding

University of Technology Sydney. Faculty of Engineering and Information Technology.Handwriting is a concatenation of graphical symbols drawn by a pen or other writing instruments, using a hand in order to represent linguistic constructs for communication and knowledge storage. These graphical marks/writing symbols have deep orthographic relation to the phonology of a spoken language. However, to a machine, handwriting is nothing but a pattern. Therefore, recognition of this pattern is performed in order to read a manuscript by a computer. Such a process of automatic character pattern recognition from an optically scanned document image is called OCR (Optical Character Recognition). Nowadays, the computer vision method is not limited to simply recognizing patterns/objects. It tries to endow the machine a human-like intelligent ability. The main goal of this research is using computer vision to bridge the gap between pattern recognition and human perception of handwriting. In this thesis, we focus on understanding the handwriting, which is beyond simply recognizing the characters by OCR. Towards this aim, we peek into the implicit information of handwriting to understand some inherent characteristics. In this thesis, we concentrate on three aspects. First, understanding the generation of handwritten information by the writing body; second, understanding the writing strokes in regards to the quality of handwriting; third, understanding the content revealing handwritten word entities. Thus, the thesis contains three parts. Regardless of past researches on writer inspection, it is hard to find an empirical study performed on intra-variable handwriting, although such variation should be an important concern. The first part of this thesis addresses this concern. Besides, this part inspects the writer on some unconventional aspects, e.g., writing variability over struck-out texts, multiple scripts, etc. The second part makes a pioneering contribution to understanding writing stroke information in multiple facets, such as legibility, aesthetics, difficulty, and idiosyncrasy of strokes. The third part of the thesis approaches to comprehend the content of the handwritten document using computer vision, without the aid of a transcription engine or the natural language processing which, according to our knowledge, is the earliest attempt of its kind. This research has adapted the traditional machine learning approaches as well as state-of-the-art deep learning approaches and has proposed new techniques to automate the process of handwriting understanding. The performed experiments have produced encouraging results, which ensure the applicability of the proposed research. This study has an impact on general image processing, pattern recognition, machine learning, and deep learning domains, especially on document image processing and handwriting processing. Moreover, this research contributes to forensics for questioned document examination, biometrics for behavioral analysis through handwriting, library science/archival science for e-archiving of the manuscript, and data science. According to us, this study has pushed the frontiers of handwriting-related research