Browsing by Author "Gebru, Biruk"

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    Determi Nants of Participation In Non Farm Economic Activities: The Case of Wolmera Woreda West Shewa Zone Oromiya Region
    (Addis Ababauniversity, 2007-06) Gebru, Biruk; Negatu, Workneh (PhD)
    Nowadavs Ihe ruml 11011 jarm economic seclOr in riepeloping cOlliliries is growing SubSlal1liallv and il became an imporlanl source of employmenl and income for rural households. Bul nol all rural households are enjoying Ihe lucmlive bene/ilS of Ihis seClor. This paper investigales inlensity and determinanlS of households' parlicipation in non Fmn income genera ling activities in wolmera woreda,Oromiva region. The dala was collected ji'om random I)} selected sample hOllseholds and Ihe informalion was analvzed and processed using simple descriptive statistical lools and logil regressive economic model. The resull show Ihal majority of rural households in Ihe slud)J area do indeed adopt multiple income sources driven by various demographic alld socio economic .(clclors. The economic model shows thai Jaclors like lileracy, ji:!mil)J size, and land size are happen 10 be Ihe main delerminant jiJctors in households' decision 10 engage in dirferenlnon/Cirm income diversifying aClivilies
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    Mood Based Hybrid Ethiopic Music Recommender
    (Addis Ababa University, 2018-09-01) Gebru, Biruk; Getahun, Fekade (PhD)
    Music is one of the most engaging and enormously spreading content on the Internet that plays an important role in our daily life. This has created new demand for easier services that support music navigation and discovery. Several music recommenders are proposed to contribute for this demand. However, many research questions are still open. Some mood based music recommenders are proposed but there is no any system considering Ethiopic Music. They favor popular songs that lack awareness of user’s contextual situation. They require a lot of user’s effort. Here, we proposed mood based context aware music recommender for smartphone. That has three main tasks, including: 1) Constructing mood based Ethiopic song classifier based on a model trained using linear SVM. 2) User modeling that includes user mood detection module built by combining biometric (heart-rate) and text mood expression modalities using Dempster Shafer theory. 3) Creating an association between user contextual interest and songs to draw list of recommendations. High Positive Affect, Low Positive Affect, Pleasantness, Strong Engagement, and Unpleasantness are the primary moods considered in this study. These has shown accuracy of 65% in song classification, accuracy of 95% in user mood detection and a good feedback gained from subjects that participated in overall evaluation of the recommender. We used 600 Ethiopic Songs and 25,800 mood sentences. Generally the study revealed algorithm and audio features to detect mood of Ethiopic song as well as a new way of user modeling for recommender systems. These can be applied on music information retrieval, music streaming websites, media players and systems that involve user mood detection.
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    Mood Based Hybrid Ethiopic Music Recommender
    (Addis Ababa University, 9/1/2018) Gebru, Biruk; Getahun, Fekade (PhD)
    Music is one of the most engaging and enormously spreading content on the Internet that plays an important role in our daily life. This has created new demand for easier services that support music navigation and discovery. Several music recommenders are proposed to contribute for this demand. However, many research questions are still open. Some mood based music recommenders are proposed but there is no any system considering Ethiopic Music. They favor popular songs that lack awareness of user’s contextual situation. They require a lot of user’s effort. Here, we proposed mood based context aware music recommender for smartphone. That has three main tasks, including: 1) Constructing mood based Ethiopic song classifier based on a model trained using linear SVM. 2) User modeling that includes user mood detection module built by combining biometric (heart-rate) and text mood expression modalities using Dempster Shafer theory. 3) Creating an association between user contextual interest and songs to draw list of recommendations. High Positive Affect, Low Positive Affect, Pleasantness, Strong Engagement, and Unpleasantness are the primary moods considered in this study. These has shown accuracy of 65% in song classification, accuracy of 95% in user mood detection and a good feedback gained from subjects that participated in overall evaluation of the recommender. We used 600 Ethiopic Songs and 25,800 mood sentences. Generally the study revealed algorithm and audio features to detect mood of Ethiopic song as well as a new way of user modeling for recommender systems. These can be applied on music information retrieval, music streaming websites, media players and systems that involve user mood detection.