Publications by students of COPAS http://localhost/xmlui/handle/123456789/5458 2026-05-26T12:20:42Z 2026-05-26T12:20:42Z Murithi, Mary Kanyiri http://localhost/xmlui/handle/123456789/6959 2026-05-12T12:15:03Z 2026-05-12T00:00:00Z

Association of rs4646903 and rs1048943 CYP1A1 estrogen-metabolizing gene polymorphisms with estrogen receptor-positive breast cancer in Kenyan women Murithi, Mary Kanyiri Abstract: Breast cancer is the most prevalent neoplasm and the second leading cause of death among females in Kenya. Estrogen and its metabolites are known risk factors for breast cancer. Polymorphisms in these genes and breast cancer susceptibility are unique among different populations. This study aimed to determine the probable associations between estrogen-metabolizing gene variations and other risk factors for breast cancer risk in Kenyan women. Buffy coat samples were obtained from patients diagnosed with estrogen receptor-positive breast cancer, benign breast disease, and healthy volunteers. Genotyping of target polymorphisms was conducted using polymerase chain reaction (PCR)-restriction frag ment length polymorphism (RFLP) analysis. The rs4646903 variant genotype CC was associated with breast cancer in the case-control model (P=0.001); the heterozygous genotype TC (P=0.01) and the luminal B molecular subtype (P=0.02) showed increased odds of late-stage breast cancer. The rs1048943 variant genotype GG was associated with breast cancer in the case-benign model (P=0.04), whereas CG was associated with breast cancer in the case-control model (P=0.02). These findings imply that the rs4646903 and rs1048943 variant genotypes are involved in breast cancer risk in Kenyan women. Hence, they may be explored further as potential markers for the disease. Keywords: breast cancer; gene polymorphisms; estrogen metabolizing gene; polymerase chain reaction-restriction frag ment length polymorphism; genotype PhD Research Publication

2026-05-12T00:00:00Z Okoth, Jeremiah Otieno, http://localhost/xmlui/handle/123456789/6919 2026-03-19T09:03:11Z 2026-03-18T00:00:00Z

Hidden Markov Model for Cardholder Purchasing Pattern Prediction Okoth, Jeremiah Otieno, Abstract—This study utilizes the Hidden Markov Model to predict cardholder purchasing patterns by monitoring card transaction trends and profiling cardholders based on dominant transactional motivations across four merchant sectors, i.e., service centers, social joints, restaurants, and health facilities. The research addresses shortfalls with existing studies which often disregard credit, prepaid, and debit card transactions outside online transaction channels, primarily focusing only on credit card fraud detection. This research also addresses the challenges of existing prediction algorithms such as support vector machine, decision tree, and naïve Bayes classifiers. The research presents a three-phased Hidden Markov Model implementation starting with initialization, de-coding, and evaluation all executed through a Python script and further validated through a 2-fold cross validation technique. The study uses an experimental design to systematically investigate cardholder transactional patterns, exposing training and validation data to varied initial and transition state probabilities to optimize prediction outcomes. The results are evaluated through three key metrics, i.e., accuracy, precision, and recall measures, achieving optimal performance of 100% for both accuracy and precision rates, with a 99% on recall rate, thereby outperforming existing predictive algorithms like support vector machine, decision tree, and Naïve Bayes classifiers. This study proves the Hidden Markov Model’s effectiveness in dynamically modeling cardholder behaviors within merchant categories, offering a full understanding of the real motivations behind card transactions. The implication of this research encompasses enhancing merchant growth strategies by empowering card acquirers and issuers with a better approach to optimize their operations and marketing synergies based on a clear understanding of cardholder transactional patterns. Further, the research significantly contributes to consumer behavior analysis and predictive modeling within the card payments ecosystem. Keywords—Hidden Markov Model; cardholder transaction patterns; merchant categories; predictive algorithms MSc Research Publication

2026-03-18T00:00:00Z Gebremichael, Sheferaw Kibret http://localhost/xmlui/handle/123456789/6911 2026-03-05T12:04:31Z 2026-03-05T00:00:00Z

Waveform based speech coding using nonlinear predictive techniques: a systematic review Gebremichael, Sheferaw Kibret Speech coding is a technique that compresses speech signals into a smaller digital form, making it easier to transmit or store, while still maintaining the quality and intelligibility of the speech. The review aimed to identify and analyses the most effec tive waveform-based nonlinear speech coding prediction techniques, including the use of neural networks and polynomial f ilters. The study analyzed 29 publications from 2000 to 2023 and found that neural network-based models are widely used for speech compression, with RNN topologies being favored due to their ability to introduce nonlinearity and nonstationary. While nonlinear adaptive speech prediction techniques have been explored for speech coding, further research is needed to optimize the adaptive algorithms used in these models. The review also identified a need for future research to address quality performance and computational cost, and suggested further exploration of RNN predictor models. The methodology used in this study involved a computer science approach that follows three main phases: planning, conducting, and reporting. Six different stages were followed, including determining research questions, defining research approach, study selection criteria, quality measurement criteria, data extraction strategy, and synthesizing extracted data. Overall, this study highlights the need for continued research in the development and improvement of neural network-based speech compression models PhD Research Publication

2026-03-05T00:00:00Z Gebremichael, Sheferaw Kibret http://localhost/xmlui/handle/123456789/6910 2026-03-05T11:59:20Z 2026-03-05T00:00:00Z

Interactive Multimedia Association-Adaptive Differential Pulse Code Modulation Codec With Gated Recurrent Unit Predictor Gebremichael, Sheferaw Kibret Speech coding is important for effective storage and transmission of audio signals. However, current Interactive Multimedia Association Adaptive Differential Pulse Code Modulation (IMA-ADPCM) speech coding techniques that use a fixed predictor have an impact on the encoding of dynamic and non-stationary speech signals. The limitation of the fixed predictor in IMA-ADPCM speech coding is the motivation for this study. Our goal is to improve the fixed predictor by integrating a GRU predictor that can adapt to and make better predictions of dynamic speech signals. We evaluated the performance of the IMA-ADPCMencodingbaselineandtheGRUpredictorembeddedwiththeIMA-ADPCMcodecalgorithm. The proposed pre-trained GRU predictor based encoding system outperformed the maximum Signal-to Noise Ratio (SNR) (43.2 dB and MOS scores 3.8 to 4.3) of 5.0, and our results demonstrated considerable improvements in audio quality. The main contribution of this study is the development of a GRU Predictor that integrates IMA-ADPCM coding algorithms according to the IMA-ADPCM output speech sample and the actual PCM speech sample dataset required. By integrating the GRU predictor model in accordance with these data samples, the newly designed algorithm significantly improved the quality of the IMA-ADPCM speech codec PhD Research Publication

2026-03-05T00:00:00Z