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Predictions of the Status of Undernutrition for Children below Five Using Ensemble Metho
(Hawassa University, 2023-08-02) Natnael Abate Choreno
Undernutrition is one of the main causes of morbidity and mortality in children under five in most developing countries, including Ethiopia. It increases the risk of infectious diseases, impairs cognitive and physical development, reduces school performance and productivity, and perpetuates intergenerational cycles of poverty and malnutrition. The primary goal of this thesis is to build an ensemble model that predicts the undernutrition status of children under five using data from the 2019 EMDHS. The experiments covered 15082 instances and 20 attributes. Ensemble methods combine several models to deliver better results. Typically, results from an ensemble approach are more accurate than those from a single model. The selected method consists of preprocessing, feature selection, k-fold cross-validation, model building, an ensemble classifier, and final prediction steps. In this work, different machine learning classification models such as the Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes classifiers have been used as base model algorithms with an accuracy rate of 0.92%, 0.94%, 0.92%, and 0.75% respectively. The final result was combined by the stacking ensemble method with logistic regression. The most accurate predictive model, with a 96 % accuracy rate was created using the stacking ensemble method. HAZ, WAZ, WHZ, age in 5 years groups, region, source of drinking water, education level, type of toilet facility, wealth index, total children born, number of antenatal visits, vaccination, breastfeeding duration, ever had nutritious food and plain water has given are the major features that contribute to undernutrition in children under-five. The findings of this study provided encouraging evidence that using the ensemble method could support the development of a predictive model that predicts the nutritional status of children under five in Ethiopia. Future research could produce better results by combining large datasets from clinical and hospital datasets. Future research may also include children over the age of five and children with obesity as a malnutrition status
Improving delay tolerant network buffer management approach for rural area’s health professionals’ information exchange syste
(Hawassa University, 2022-08-06) Mulusew Abebe
Delay-tolerant networks (DTNs) are mobile networks in the field of wireless network which are emphasized to provide end-to-end connectivity in the areas where the networks are not reliable and often susceptible to interferences. Despite the rapid advancement of communication technology, there are still rural places that are not connected to the Internet. Health information exchange between rural area and the urban areas still hampered by in adequate telecommunication infrastructures coverage, intermittent connectivity and absence of end-to-end connectivity. The term Delay Tolerant Network (DTN) is invented to bridged communication gaps that have not been connected to the Internet. In current TCP/IP technology communication is possible only when end-to-end path is available. As a result, the usual Internet and TCP/IP network cannot be valid for some hard environments which are characterized by lack of direct path between nodes, lot of power outages and intermittent connectivity.
In this work, the researcher investigated the performance of various delay tolerant network routing protocols and selected MaxProp which is convenient for the proposed framework. Most routing algorithm of delay tolerant network assume the nodes buffer space as unlimited but, it is not the case in reality. As flooding-based routing relies on buffer to have a copy of every message at every node, buffer space has substantial impact on delivery probability. The existing buffer management policies compute in biased way, directed by a single parameter in a random manner while other relevant parameters are completely neglected, resulting in an inability to make a reasonable selection. Therefore, the researcher proposed a reasonable buffer management approach on the situations where there is a short contact duration, limited bandwidth and buffer. The proposed buffer management approach improves buffer availability by implementing three buffer management strategies: scheduling, dropping, and clearing buffers entirely for computing purposes, using three parameters: message type, hop count and time to live.
The performance of proposed approach is validated through simulation by using opportunistic Network Environment (ONE) simulator. They were analyzed on three metrics, namely delivery probability, average latency and overhead ratio. The simulation results collected in this thesis shows that when the nodes buffer get constrained the proposed method MaxProp Routing based on Message Type Priority (MPRMTP) perform better than the existing buffer management policy by increasing the message delivery quality and decreasing overhead ratio. However, when there is sufficient buffer space, both MaxProp, and MPRMTP shows comparable performance
CONTEXT-BASED SPELL CHECKER FOR SIDAAMU AFOO
(Hawassa University, 2022-03-04) MEKONEN MOKE WAGARA
A spell checker is one of the applications of natural language processing that is used to detect and
correct spelling errors in written text. Spelling errors that occur in the written text can be non-word
errors or real-word errors. A non-word error is a misspelled word that is not found in the language
and has no meaning whereas a real-word error, that is, the word is a valid word in the language but it
does not fit contextually in the sentence. We designed and implemented a spell checker for Sidaamu
Afoo that can detect and correct both non-word and real-word errors. Sidaamu Afoo is one of the
languages spoken in the Sidaama region in the south-central part of Ethiopia. It is an official working
language and is used as a medium of instruction in primary schools of the Sidaama national regional
state in Ethiopia. To address the issue of spelling errors in the Sidaamu Afoo text, a spell checker is
required.
In this study, the dictionary look-up approach with a hashing algorithm is used to detect non-word
errors, and the character-based encoder-decoder model is used to correct the non-word errors. The
LSTM model with attention mechanism and edit distance is used to detect and correct the context based spelling error. To conduct the experiment, 55440 sentences were used, of which 90% were for
training (i.e., 49,896) and 10% were for testing (i.e., 5544). According to the experimental results, for
an isolated spell checker, dictionary lookup with hashing achieved an accuracy of 93.05%, a recall of
correct words of 91.51%, and a precision of incorrect words of 72.37% for detection. The encoder decoder model achieved a recall of 91.76% for corrections. For a context-sensitive spell checker, the
LSTM model with attention and edit distance achieved an accuracy of 88.8%, recall of the correct
word of 86.18%, and precision of the incorrect word of 62.84% for detection. It achieved a recall
of 74.28% for the correction. The results of the experiment show that the model used to detect and
correct both non-word and real-word spelling errors in Sidaamu Afoo’s written text performed well.
Finally, to improve the performance of the model, we recommend using additional data set and a
state-of-the-art transformer model.
MORPHOLOGICAL ANALYZER AND GENERATOR FOR KAMBAATISSA USING FINITE STATE TRANSDUCER
(Hawassa University, 2023-08-03) LIDIYA TADESSE GETISO
Kambaatissa is a Highland East Cushitic Language spoken in the Kambaata Xambaaro
zone of South Nation Nationality and People Regional State, Ethiopia. It is a strictly
suffixing and morphologically rich language. The language is one of the under-resourced
languages in Ethiopia. For languages with complex morphology, nearly all computational
work depends on the presence of tools for morphological processing. Many researches
have been conducted in morphological analysis extensively for different languages, while
this work is the first work in Kambaatissa natural language processing applications. This
study focused on a morphological analyzer and generator, which is a lower-level natural
language processing application that is used as a base for many higher NLP applications.
A finite state transducer is a framework for modeling morphology. In this study, Foma is
used as an implementation toolkit and lexc formalism for designing the lexicon. The
experiment is done using 860 root verbs. There are seventeen continuous classes and forty
different rules in the lexicon and foma file respectively. Result from the experiment shows
that 92,020 words are generated among them the transducer gives 95.2% correct
Kambaatissa verbs.
Bi-Directional Sidaamu Afoo - Amharic Statistical Machine Translation
(Hawassa University, 2023-04-06) Kebebush Kamiso
Machine translation (MT) is the area of Natural Language Processing (NLP) that focuses on
obtaining a target language text from a source language text using automatic techniques. It is a
multidisciplinary field and the challenge has been approached from various points of view
including linguistics and statistics.
MT usually involves one or more approaches. Our preference for this study is to develop the bi directional Sidaamu Afoo - Amharic machine translation system, make use of a statistical machine
translation (SMT) approach.
To conduct the experiment, a parallel corpus was collected from all possible available sources.
These include mostly the Old and New Testaments of the Holy Bible for both languages. We used
the monolingual Contemporary Amharic Corpus and the Sidama Afoo corpus compiled by a
research team in the Informatics Faculty of Hawassa University. Different preprocessing tasks
such as tokenization, cleaning, and normalization have been done to make the corpus suitable for
the system.
To accomplish the objective of this thesis work, we conducted four experiments using word and
morpheme-based translation units with SMT for Sidaamu Afoo - Amharic language pairs.
The first two experiments focus on word-based SMT and the next two on morpheme-based
translation using unsupervised morphological segmentation tool; Morfessor. For each experiment,
we used 30,100 parallel sentences. Out of the total parallel sentences, we used 80% (24,100) of
randomly selected parallel sentences for training, 10% (3,000) for tuning and another 10% (3,000)
for testing.
The basic tools used for accomplishing the machine translation are Moses for the translation
process which is MGIZA ++ for word and morpheme alignment and KenLM for language
modeling; Morfessor for morphological segmentation. For evaluation SacreBLEU package which
are BLEU, ChrF and TER metrics.
According to the experimental findings, the differences between Amharic to Sidaamu Afoo and
Sidaamu Afoo to Amharic in the Word-based alignment translation were 6.2, 16, and 1.9 for
BLUE, ChrF2, and TER, respectively. In the Morpheme-based alignment, the differences between
Amharic to Sidaamu Afoo and Sidaamu Afoo to Amharic translation were 7.5, 20.4, and 5.1, for
BLUE, ChrF2, and TER respectively.
In conclusion, the results show that morpheme-based alignment performance is better than word based alignment, for Amharic to Sidaamu Afoo than Sidaamu Afoo to Amharic