Prediction of mung bean production using machine learning algorithms.

Agriculture is the backbone of the Ethiopian economy. It plays a significant role in the growth of the national economy. Among these, mung bean production takes the highest level of income for most smallholder farmers in all regions of Ethiopia who cultivate mung bean crops.

Evaluative assessment of a five-phase and three-phase permanent magnet synchronous machine at varied loads and fault conditions.

Multiphase machines are very essential for industrial applications that require high reliability of operation. In this paper, a comparative study of three-phase and five-phase inverter fed permanent magnet synchronous motors (PMSMs) was evaluated with respect to their performance characteristics under a healthy state and during transient disturbances such as varied load and double line to ground faults. To avert inherent high oscillations in speed and torque ripples during fault condition, the machine load was significantly reduced to 1/4th of the full load and a post fault current limiting series dynamic braking resistance (SDBR) with a bypassed switch was introduced to enhance the machine fault tolerant level and improve its operational performance.

Derived Amharic alphabet sign language recognition using machine learning methods.

Hearing-impaired people use sign language as a means of communication with those with no hearing disability. It is therefore difficult to communicate with hearing impaired people without the expertise of a signer or knowledge of sign language. As a result, technologies that understands sign language are required to bridge the communication gap between those that have hearing impairments and those that dont.

Software defined networking based network traffic classification using machine learning techniques.

The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models.