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Word classification for sign language synthesizer using hidden Markov model

Sign Language Synthesizer is an algorithm developed to provide signing animation from verbal/spoken language. Word classification in Natural Language Processing (NLP) is required to determine grammatically processed sentences for sign language synthesizer. The correct word position of output can provide understanding to users who use sign language synthesizer tools. In this paper, the Hidden Markov Model is proposed and implemented to process the words and locate their corresponding position correctly. The classification was done for Malay language and has resulted in an average accuracy of 74.67 %.

Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding

Over the past two decades, lung cancer has been a dominant malignant form of cancer. Around 80% of major lung cancers are non-small cell lung carcinoma (NSCLC). NSCLC is the major reason for death from a malignant disease worldwide. As a result, there is urgent interest in the improvement of innovative diagnostic noninvasive technologies that may enhance early diagnosis of the disease. One of the most promising techniques for early detection of cancerous cells depends on machine learning based on molecular cancer classification using gene expression profiling data. Current technological breakthroughs in gene expression profiling, specifically with DNA and oligonucleotide microarrays, permit the concomitant analysis of the expression of thousands of genes and also enables surveillance of disease prediction and progression of patient survival at the molecular level. For this reason, we attempted to come up with a machine-learning-based strategy called composite hypercubes on iterated random projections (CHIRP) in order to settle the problem of detection of early NSCLC from DNA biochip gene expression data. Furthermore, we utilized an unsupervised dimensionality reduction approach, named t-distributed stochastic neighbor embedding (T-SNE), to reduce computational complexity and to increase the efficiency of the developed system. The average accuracy obtained by the proposed system in terms of detection and diagnosis of early non-small cell lung cancer was 97.21871%. The empirical results prove that the combination of dimensionality reduction models with machine-learning algorithms can be effectively used for early detection of specific NSCLC tumor type.

Prognosis of early cervical carcinoma using gene expression profiling

Cervical carcinoma remains a prime cause of cancer-related deaths in woman globally. Research into the prediction of the survivability for cervical cancer has been a challenge for researchers. Survival rates increase with earlier detection of cancer of the cervix. Cancer research and associated domains have made significant strides over recent years. For example, cancer prognosis using machine learning techniques is now a promising area of research. Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. Specifically, DNA chip gene expression technology is a promising tool that can identify cancerous cells in early phases of the disease by examining the gene expression of analyzed instances. Furthermore, microarray technology enables researchers to assay the expression of thousands of genes in parallel. In this paper, we present a Gaussian process regression model in order improve the prediction of survivability of patients with early cervical cancer. Additionally, stochastic proximity embedding (SPE) was applied to reducing the number of attributes by selecting the most informative genes of the input dataset. Consequently, the computational complexity was reduced and the performance of the proposed model was increased. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.

Complexity Algorithm Analysis for Edit Distance

Natural Language Processing (NLP) is a method which works on any language processing. Some of the algorithms are based on edit distance analysis. It is a process where the statistical calculations between two words or sentences are analyzed. Some of used edit distances for NLP are Levenshtein, Jaro Wrinkler, Soundex, N-grams, and Mahalanobis. The evaluation of edit distance is aimed to analyze the processing time of each edit distance in calculation of two different words or sentences. The objective of this paper is to evaluate the complexity of each distance, based on the time process.

Computer-Aided Diagnosis of Pulmonary Nodules from Chest X-Rays Using Rotation Forest

A chest X-ray examination is a painless, non-invasive, and cost effective medical examination performed at present day. A pulmonary nodule is a small round lesion or mass in the lungs which can be indicative of an infection or a neoplasm. Chest X-rays can be used to diagnose pulmonary nodules. This paper proposes a three-layered framework to perform automatic diagnosis of pulmonary nodules. The first layer performs pre-processing of X-ray images. The second layer extracts texture features from the gray-level co-occurrence matrix. Finally, the third layer classifies whether the X-ray contains any signs of nodules using an ensemble technique called rotation forest. Experiments have been carried out on a chest X-ray dataset from the Japanese Society of Radiological Technology. The detection accuracy of the proposed system was found to be 75.6%. Satisfactory preliminary experimental results demonstrate the efficacy of our computer aided pulmonary nodule diagnosis system.