Status of Learning Amid Covid -19 For Learners with Disabilities and the Accompanying School Closures: A Case of Selected Counties in Kenya

The Covid-19 pandemic brought changes to the scenario of teaching and learning in institutions of education. The shift from face-to-face traditional mode to online platforms brought many challenges along with it. This ‘new norm’ of pedagogy also posed challenges to learners with disabilities in learning institutions across Kenya especially when using online platforms to study. This study presents preliminary results obtained from the study conducted in selected Counties to map out the learning experiences of learners with disabilities during the learn-from- home time related to the COVID-19 lock down in Kenya. The objective of the study was to gather information about the situation in the community, how children continued with their learning, and what challenges there. The study was conducted in selected Counties to learners, teachers and parents that were purposively selected for this exercise. Qualitative data collection methods were used by the researcher to collect primary data. The researcher’s aim was to use the data collected to examine the consistencies, contradictions, milestones, achievements among other important issues relevant to the effects of COVID- 19 in education. Semi-structured interviews were employed to interview the participants and the collected data was analysed qualitatively using the self-determination theory. The findings showed that most of the learners with disabilities students faced mental stress challenges as they were leveraging in poverty and hunger due to low economies as a result of lack of sells hence affecting learning at home, sexual harassment for learners with disabilities and no learning was going on due to lack of electricity and lack of gadgets to enable e-learning, lack of materials, lacked of parental responsibility, they were idling and roaming around at home, anxiety was affecting especially class eight candidates and that both parents and learners had different attitudes towards learning. The findings further revealed that learners with disabilities were not ignorant in their learning and their disabilities were not obstacles to excel in education

Evaluating Linear and Non-linear Dimensionality Reduction Approaches for Deep Learning-based Network Intrusion Detection Systems

Dimensionality 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

An extended k-means cluster head selection algorithm for efficient energy consumption in wireless sensor networks

Effective 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