Browsing by Author "Jepkoech, Jennifer"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    A Classification Model for Water Quality analysis Using Decision Tree
    (2019-06) Gakii, Consolata; Jepkoech, Jennifer
    A classification algorithm is used to assign predefined classes to test instances for evaluation) or future instances to an application). This study presents a Classification model using decision tree for the purpose of analyzing water quality data from different counties in Kenya. The water quality is very important in ensuring citizens get to drink clean water. Application of decision tree as a data mining method to predict clean water based on the water quality parameters can ease the work of the laboratory technologist by predicting which water samples should proceed to the next step of analysis. The secondary data from Kenya Water institute was used for creation of this model. The data model was implemented in WEKA software. Classification using decision tree was applied to classify /predict the clean and not clean water. The analysis of water Alkalinity,pH level and conductivity can play a major role in assessing water quality. Five decision tree classifiers which are J48, LMT, Random forest, Hoeffding tree and Decision Stump were used to build the model and the accuracy compared. J48 decision tree had the highest accuracy of 94% with Decision Stump having the lowest accuracy of 83%.
  • No Thumbnail Available
    Item
    A Hybrid Of Capsule and Dense Neural Networks with backward Regression model for Coffee leaf disease identification
    (UoEm, 2023-04) Jepkoech, Jennifer
    Diseases that affect coffee have serious repercussions for policymakers, seed industries, farmers, and consumers. Over the years, farmers have had to travel long distances searching for pathologists to check their coffee leaves and inform them which disease affects them. The pathologist approach is time consuming and costly. Automated technology in the agricultural sector has saved farmers’ time. The use of convolutional neural networks (CNN) to classify plant diseases has been implemented and showed reputable results. However, they still experience difficulty due to loss of information through pooling, difficulty in error propagation, computational complexities, and translation invariance, which leads to failure in pose preservation, and loss of shape and texture information. This work developed a hybrid neural network, which adopted three subnets for feature collection. To promote strong gradient flow and signal reception from all preceding layers, hybrid neural network model adopted DensNet loop connectivity where error signals were easily propagated to all the preceding layers in the subnets more directly. The Hybrid neural network model used channel-wise concatenation features from all preceding layers to collect rich feature patterns. This allows the model to combine complex and simple features, hence high generalization. This work used images from Mutira coffee plantation that were taken using a digital camera and with the help of a plant pathologist. The hybrid neural network model displayed a 99.7 % F1 score. In comparison, the conventional CapsNet model displayed 87 % F1 score accuracy on testing our framework based coffee dataset comprising 5 coffee disease categories with 58,000 images. The developed model showed relatively higher and stable accuracy when sensitivity analysis was performed by varying testing and training dataset percentages. Support Vector Machines (SVM), AlexNet, ResNet, VGGNet, Inception V3, Artificial Neural Networks (ANN), and VGG 16 deep learning approaches scored 84.5\%, 88.6\%, 99.3\%, 97.87\%, 99.14\%, and 98.2\%, respectively when the coffee dataset was used. The work used only five features with the target variable instead of the total ten features. This was done through the rigorous backward regression process. Due to the reduced number of features, model computation also reduced. This was also evident from the results of the test error. The overall test error for the developed hybrid neural network model was 0.16 while the test error for the original CapsNet model was 0.26. These findings indicate that hybrid neural network model may be a decent and, in most cases, superior and less expensive alternative for phrase categorization models founded on convolutional neural networks (CNNs). Therefore, several classifiers could be fused to enhance the accuracy of plant diseases classification.
  • Thumbnail Image
    Item
    Implementation of Blockchain Technology in Africa
    (European Centre for Research Training and Development UK, 2019-08) Jepkoech, Jennifer; Anyembe, Shibwabo.C.
    Africa is currently tackling challenges to technologies and innovation in its suitable development agenda.in the journey to embracing the blockchain technology, ICT plays a very vital role in addressing the challenges. The introduction of internet technology and the World Wide Web has brought with it changes in the interactive nature of people. One of the most outspoken changes is "Blockchain", a smart technology that allows people to transact without the need for an intermediary. In Africa, Blockchain is known as "the trusted machine" because one can be sure of doing clean transactions using blockchain. The technology behind "bitcoin" is the decentralized databases and cryptocurrency and its major merits are decentralization, security, and transparency, resistance to outages and efficiency. Almost all the sectors are using this technology some of which are; health, education, governance, financial institutions industries and IT. According to the firm Research and Markets 3 from the United States of America, the worldwide cryptocurrency and blockchain technology market will grow by 35.2 percent during the forecast period 2016–, to touch an aggregate of $42.16 billion by 2022. Most of this growth, however, will take place in the United States, followed by Europe, the Asia-Pacific, and India.
  • Thumbnail Image
    Item
    Staff Profile: Dr. Jepkoech Jennifer
    (University of Embu, 2020-01) Jepkoech, Jennifer
    Jennifer Jepkoech received BSc computer science from Periyar university India and MSc. Computer science from Bharathiar university India. She holds a PhD in Computer science from the University of Embu, Kenya. Research Interests: • To teach Computer Science and to undertake research • To integrate ICT in education, to apply ICTs to solve local social challenges and make learning more interesting and flexible to learners. • To consult in the Computer Science / ICT domain, and develop alternative models and computer systems. (Expertise in Management Systems, programming and Image Processing) • To become a respected researcher and scholar in computing, specializing in Image processing.