Green Energy Sources: Issues and Challenges

Mohammed, Mostafa Abdulghafoor; Mohammed, Inas Adnan; Hasan, Raed Abdulkareem; Țăpuș, Nicolae; Ali, Ahmed Hussein; Hammood, Omar A.

doi:10.1109/roedunet.2019.8909595

Cited by 19 publications

(15 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent metaheuristic, teaching-learning-based optimization (TLBO), has been reported to be an efficient optimization tool that is inspired by the knowledge passing mechanisms of teachers and learners in a classroom [19][20][21][22]. It has been applied to several well-known combinatorial optimization problems, producing good results [23][24][25][26]. The following sections discussed the novel multi-objective TLBO optimization algorithm for attaining better feature selection accuracy.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

et al. 2020

Self Cite

View full text Add to dashboard Cite

One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.

show abstract

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…PBCH is broadcasted every 40 ms and follows the synchronization signals [9,25,26]. PBCH carries system information for all UEs that contains the bandwidth, system frame number (SFN), and hybrid ARQ indication channel (PHICH) length [27,28]. It is QPSK modulated and is transmitted on all antennas [11].…”

Section: Physical Signals and Channelsmentioning

confidence: 99%

Analysis of LTE physical channels overhead

Al-Hilfi¹

2020

TELKOMNIKA

View full text Add to dashboard Cite

LTE Network is the common mobile technology these days around the world and all service providers seek to how improve the network capacity and deliver the best performance in terms of delivered data rates and coverage area. LTE network consists of many protocols that work together to establish network connectivity, these protocols add variable headers that contains many control information that the network needs to operate. At the same time these headers decrease the effective capacity of the network, so there is a need to optimize the overhead size that used in various channels. The study will illustrate the different overheads that effect on the network capacity and investigate the effect of different values on achieving the best network capacity.

show abstract

“…One of the important aspect of haar wavelet transform is to find corners and contours [18][19][20][21]. The reason for choosing the haar wavelet in this work is due to its being required low computational cost, fast and each sub bands (detail) contains a special edges type [22][23][24][25][26].…”

Section: Haar Wavelet Transformmentioning

confidence: 99%

Offline signatures matching using haar wavelet subbands

2020

View full text Add to dashboard Cite

The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems to satisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works tried to develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different sets of features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used as a dataset for testing purpose. The results achieved by this technique indicate a high performance in signature recognition.

show abstract

Green Energy Sources: Issues and Challenges

Cited by 19 publications

References 62 publications

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

Analysis of LTE physical channels overhead

Offline signatures matching using haar wavelet subbands

Contact Info

Product

Resources

About