6229 research outputs found
Sort by
Effect of Complaints Handling by Consumer Federation of Kenya on Consumer Protection in Commercial Banks of Kenya
No full textDespite the growth of the financial services industry, the formulation of consumer protection legislation has not been done in many jurisdictions. This calls for the need for basic protections for the clients of financial services to bolster their confidence and encourage the uptake of new products, therefore, this study sought to find out the extent to which the complaints handling by the Consumer Federation of Kenya (COFEK) influences effective consumer protection in commercial banks. The presence of ignorant clients dependent on the trust they place in financial institutions has caused increased abuse of consumer trust. Imposition of excessive charges on customers’ accounts without formal advice or agreement and other insider abuses are becoming common amongst banks and their marketers. The study used the descriptive research design and the population of focus was 87 COFEK employees. The census method was used to reach the COFEK employees owing to their relative manageable numbers. The study used questionnaires as the tools for data collection. Data were analyzed by use of descriptive and inferential statistics. Statistical package for social sciences (SPSS) version 22 was used. Data was presented by the use of frequency tables, percentages, and related statistical abstracts. All the respondents affirmed that COFEK appreciated the responsibility of dispute arbitration and collaboration between COFEK and that commercial banks had assured synchrony in the marketing messages disseminated to the consumers. The analysis of the data showed that complaints handling had a significant coefficient (p-value = 0.001; β=0.408). This means that complaint handling is statistically significant in ensuring consumer protection. The study concluded that the service quality levels had improved occasioned by the capacity of COFEK to monitor consumer complaints
A Review of Chemical Compounds and Bioactivity of Conyza Species
No full textA large proportion of the African population's primary healthcare requirements are still mostly met by traditional medicine. Previous studies have demonstrated the potential of plant extracts disease management. Conyza species are traditionally used for a variety of pharmacological applications including treatment of malaria, smallpox, chickenpox, sore throat, ringworm and other skin related infections, toothache and wounds. The aim of this study was to provide a review of the chemical compounds from Conyza species and their bioactivities. Extracts from Conyza species have a wide range of bioactivities including antioxidant, antiinflammatory, antimicrobial, antitumor, analgesic, antiplasmodial, wound healing, insecticidal, allopathic, antidiabetic, antiviral, anticonvulsant and anti-amnesic effects. These bioactivities are attributed to the bioactive secondary metabolite including terpenoids, phenolic acids, flavonoids and tannins, saponins and steroids which are biosynthesized by the plants. Previous phytochemical test have shown that Conyza species are rich in alkaloids. However, the information about the alkaloids previously isolated from Conyza species is scanty in literature. Further studies should be done isolate and characterize the alkaloids from the plants
Evaluating Linear and Non-linear Dimensionality Reduction Approaches for Deep Learning-based Network Intrusion Detection Systems
No full textDimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ones. The technique aids reduce computational load, avoiding data over-fitting, and increasing model interpretability. Recent studies have revealed that dimensionality reduction can benefit from labeled information, through joint approximation of predictors and target variables from a low-rank representation. A multiplicity of linear and non-linear dimensionality reduction techniques are proposed in the literature contingent on the nature of the domain of interest. This paper presents an evaluation of the performance of a hybrid deep learning model using feature extraction techniques while being applied to a benchmark network intrusion detection dataset. We compare the performance of linear and non-linear feature extraction methods namely, the Principal Component Analysis and Isometric Feature Mapping respectively. The Principal Component Analysis is a non-parametric classical method normally used to extract a smaller representative dataset from high-dimensional data and classifies data that is linear in nature while preserving spatial characteristics. In contrast, Isometric Feature Mapping is a representative method in manifold learning that maps high-dimensional information into a lower feature space while endeavouring to maintain the neighborhood for each data point as well as the geodesic distances present among all pairs of data points. These two approaches were applied to the CICIDS 2017 network intrusion detection benchmark dataset to extract features. The extracted features were then utilized in the training of a hybrid deep learning-based intrusion detection model based on convolutional and a bidirection long short term memory architecture and the model performance results were compared. The empirical results demonstrated the dominance of the Principal Component Analysis as compared to Isometric Feature Mapping in improving the performance of the hybrid deep learning model in classifying network intrusions. The suggested model attained 96.97% and 96.81% in overall accuracy and F1-score, respectively, when the PCA method was used for dimensionality reduction. The hybrid model further achieved a detection rate of 97.91% whereas the false alarm rate was reduced to 0.012 with the discriminative features reduced to 48. Thus the model based on the principal component analysis extracted salient features that improved detection rate and reduced the false alarm rate
Speech by Prof. Dickson Nyariki, Vice Chancellor, Murang'a University of Technology, on the occasion of the 7th graduation ceremony held on Friday, 29th September 2023 at the graduation ground.
No full textSpeech by Prof. Joachim Osur, Acting Chancellor, Murang'a University of Technology, on the occasion of the 7th graduation ceremony held on Friday, 29th September 2023 at the graduation ground.
No full text
An extended k-means cluster head selection algorithm for efficient energy consumption in wireless sensor networks
No full textEffective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches
