The Transformer-based neural machine translation (NMT) model has been very successful in recent years and has become a new mainstream method. However, using them in lowresourced languages requires large amounts of data and efficient model configuration (hyperparameter tuning) mechanisms. The scarcity of parallel texts is a bottleneck for high quality (N) MTs, especially for under resourced languages like Amharic. As a result, this paper presents an attempt to improve English-Amharic MT by introducing three different vanilla Transformer architectures, with different hyper-parameter values. To obtain additional training material, offline token level corpus augmentation was applied to the previously collected English-Amharic parallel corpus. Compared to previous work on Amharic MT, the best of the three Transformer models have achieved state-of-the-art BLEU scores. In fact, we were able to achieve this result by employing corpus augmentation techniques and hyper-parameter tuning.

There is a demand for a periodical re-designing and re-structuring of operational practices and policies in Higher Education Institution (HEI). Hence, it is essential to define the critical factors that governs service operational quality to achieve excellence in Higher education. An extensive literature review on similar studies relevant to the present research has been carried out and it is noted that there is a strong need to assess Service Quality (SQ) of selected Higher Education Institution (HEI). The objective of this present research has binary folds: first, to determine the various factors affecting SQ. Second, to measure Service Quality Score (SQS) of selected HEI. The sample of 316 respondents studying in Engineering/ Technology, Management and PG courses is considered for the present research. The investigation is focused on elicitation of critical factors which influences SQ of selected HEI. The result of the study reveals that key factors that affect the Service Quality of HEI are Institutional assurance, Academic and support services, Empathy of staff, Responsiveness and Add on Provisions. The relevant hypothesis was formulated to know the inter relationship between various factors that influences SQ of the HEI. The outcome of present research helps to understand the administrators of HEI’s to know the factors that are to be nurtured in order to strengthen the service quality which shall help to gain competitive advantage and to achieve operational excellence.

One major work in the analysis of DNA sequence is to find out the densities of particular nucleotides. The approach of graphical representation of DNS sequences give ways of comparability, testing as well as storing of various sequence. In this paper we draw 3 characteristic curves, as per categorization of 4 basis DNA sequence. First off to represent the DNA sequence fluctuation and geometrical centers of 3 curves grouped in 12 component vectors. For 10 particular species the Euclidean distances between calculated vectors are applied to analyze and comparing of coding sequence.

The science of artificial intelligence is suppressed in solving problems more quickly and processing or saving more data that is derived from the human mind in addition to processing data, regardless of its nature or size, in a mechanism or semi-automatic and an appropriate manner and compatible with a specific goal. And thus it leads to innovation, innovation, and change, and in the other, it leads to the creation of machines capable of simulating human intelligence. Artificial intelligence has witnessed remarkable development in the past few years and has become a spearhead in the face of the challenges we face on the planet, the most recent of which is the Coronavirus, which has become the concern of the world. Artificial intelligence can diagnose infection with the Coronavirus to accurately, quickly, and early identify the infected and thus help the competent authorities to take the necessary measures and measures quickly enough to address the disease, such as quarantine measures to curb the further spread of infection and provide the necessary health care to the injured promptly. When health care providers and authorities use AI to identify patterns from these huge amounts of data, it helps them with good decision-making and planning. The use of artificial intelligence leads to tracking is especially valuable in discovering \"specific groups\" of people, because the population is not fully mixed, and it tends to divide into groups that rarely interact with other groups

This study examines the impact of the covid-19 pandemic on small and medium scale enterprise Operation in Calabar, Cross River State, Nigeria. The cross-sectional survey method was used in collecting data from 474 SMEs in Calabar, Cross River State using the stratified and purposive sampling technique. The instrument of data collection was a self-developed semi-structured questionnaire. Data collected from the field was coded, analyzed using descriptive statistics such as tables and graphs and correlation analysis at 0.05 level of significance. Out of the 474-instrument distributed only 422 was returned and used for analysis. From the analysis, the result revealed that 88.6 per cent of the SMEs reported being negatively affected by the pandemic, 71.3 per cent reported laying off employees during this period. Also result from the correlation analysis revealed that there is a significant relationship between covid-19 pandemic and the operations of SMEs in Calabar, Cross River State, Nigeria. Based on these findings, there is a need for policy adjustment toward SMEs operations in Calabar.

Nigeria ranks among the highest consumers of marijuana global and the youths are among the highest demographic of consumers. But there is a dearth of evidence that people know about the dangers inherent in being addicted to marijuana. This study examined public knowledge of marijuana addiction and risk of psychosis mental health outcome among youths in Calabar. The survey method through the distribution of self-administered semi-structured was used to elicit data from a sample of 384, selected from Calabar, Cross River State, Nigeria using the stratified, purposive and random sampling technique. Data collected was analyzed using descriptive statistics using such as frequency distribution, percentages, figures and inferential statistics in the form of independent T-test. Results revealed that there is public knowledge of marijuana addiction and psychosis mental health outcome in Calabar, Cross River State, Nigeria. there is need for the government and its Drug enforcement agency to formulate drastic laws and put in place punitive measures that will curb the distribution, sale and consumption of marijuana.

The era of fast and spontaneous communication do require effective utterance interpretation which in turn requires lexical, syntactic and semantic familiarity acquaintance. This acquaintance is required at acoustic, phonetic, linguistic and application, which are the speech grades. It is proven indeed that vocal mechanism of a particular speaker is unique but, still there is a utterance difference do exists with respect to speakers mood, his/her speaking style, the context of speech and individuals intentional utterance control, perturbations and frame of his/her mind. Acronymic letter prosody uses characteristics such as pattern, accentuation, pitch and tonality to convey information and meaning pertaining to uttered letter or word. Acronymic letter prosody recognition is a challenging task in all languages. A three letter approach, heuristic approach, logistical regression approach and SVM classifier and HMM are some of the previous works carried out on this aspect. In comparison with these developed approaches, this paper brings out an effective algorithm based on MFCC-HMM approach in order to arrest variability such as to get good accuracy. The work is implemented using MATLAB platform with feature extraction using MFCC, dimensional reduction through principle component analysis (PCA), Modelling and classification through HMM.

Cryptocurrencies and Bitcoin in particular are proned to wild swings resulting in frequent\njumps in prices, making them historically popular for traders to speculate on. Understanding\nthese fluctuations can be of great benet to crypto investors by allowing them to make\ninformed decision. Jump-diffusion models appear to be more suitable for the analysis of these\ndigital currencies, as it is able to account for sporadic events and shocks of a larger amplitude\nthan what a continuous diffusion process can explain. Shortly after the development of the\nBlack{Scholes option valuation formula, Merton developed a jump-diffusion model to account\nfor the excess kurtosis and negative skewness observed in log-return data. This study reveals\nthat Merton jump-diffusion model fit perfectly Bitcoin log-return data and analysis its jump\nfeatures. This study focuses on the probability of jump occurrence (PJO) as indicator of\nrisk and its correlation to the known risk measures Value-at-Risk (VaR) and the Expected\nShortfall (ES). This analysis reveals that POJ is positively correlated to VaR and ES, thus it\nis a useful indicator for risk to be considered by practitioners. It is also noticed that, though\nthe Merton Jump diffusion model is a perfect t for Bitcoin, it is only suitable for short-term\nforecast (less than six months).