Background: Headache disorders are the most common complaints worldwide. Migraine, tension type and cluster headaches account for majority of primary headaches and improvise a substantial burden on the individual, family or society at large. There is a scanty data on the prevalence of primary headaches in sub-Saharan Africa in general and Ethiopia in particular. Moreover there is no population based urban study in Ethiopia. The purpose of this study is to determine the prevalence and burden of primary headaches in local community in Addis Ababa, Ethiopia. Methods: Cross-sectional sample survey was carried out in Addis Ketema sub city, Kebele 16/17/18 (local smallest administrative unit). Using systematic random sampling, data were collected by previously used headache questionnaire, over a period of 20 days. Results: The study subjects were 231 of which 51.5% were males and 48.5% were females. The overall one year prevalence of primary headache disorders was 21.6% and that for migraine was 10%, migraine without aura 6.5% migraine with aura was 2.6% and probable migraine was 0.9%. The prevalence of tension type of headache was found to be 10.4%, frequent episodic tension type headache was 8.2% followed by infrequent tension type headache of 2.2%. The prevalence of cluster headache was 1.3%. The burden of primary headache disorders in terms of missing working, school or social activities was 68.0%. This was 78.3% for migraineurs and 66.7% for tension type headache. Majority 92.0% of primary headache cases were not using health services and 66.0% did not use any drug or medications during the acute attacks and none were using preventive therapy.Conclusion: Prevalence and burden of primary headache disorders was substantial in this community. Health service utilization of the community for headache treatment was poor.

Over the past few decades, the rate of diagnosing depression and mental illness among youths in both genders has been emerging as a challenging issue in the present society. Adequate numbers of cases that have been prevailing had unheard of symptoms linked to mental depression that are able to be detected using their voice recordings and their messages in social media websites. Due to the wide spread usage of mobile phones, services and social sites emotion prediction and analyzing have been an indispensable part of providing vital care for the eminence of youth’s life. In addition to dynamicity and popularity of mobile applications and services, it is really a challenge to provide an emotion prediction system that can collect, analyze, and process emotional communications in real time and as well as in a highly accurate manner with minimal computation time. Few depression prediction researchers have analyzed and examined that various social networking sites and its activities may be merged to low self-confidence, particularly in young people and adolescents. Moreover, the researchers suggest that several objective voice acoustic measures affected by depression can be detected reliably over the smart phones. And also in some observational study, it is stated that speech samples of patients from the telephone were obtained each week using an IVR system, and voice recording files from smart phones have been under process for predicting the depression. Such that several telephonic standards for obtaining voice data were identified as a crucial factor influencing the reliability and eminence of speech data. Hence, this article investigates on different process applied in different machine learning algorithms in recognizing voice signals which in turn will be used for scrutinizing the techniques for detecting depression levels in future. This will make a blooming change in the youth’s life and solve the social unethical issues in hand.

A pentaband antenna is presented based on the conducting copper material printed on an FR4 substrate for the applications operating in the Gigahertz frequencies. The antenna has a substrate material with a dielectric constant of 4.4. The conducting copper is printed on the FR4 substrate acting as the radiating element and ground. The antenna radiating element has a defected circular structure with a cross stub. The proposed structure is operating at 2.64 GHz, 4.87 GHz, 7.86 GHz, 10.74 GHz, and 13.67 GHz. The antenna is simulated using CST software. The antenna is fabricated and validated with the measurement of return loss. The antenna simulated results like surface current distribution, gain, directivity, and radiation pattern prove that the proposed structure with its compact size is the right candidate for the GHz application.

In this paper, a novel 2 × 2 multiple-input multiple-output (MIMO) antenna array with four patch elements is designed. The proposed antenna is the first dual band, operating at two prominent working frequencies: 24 (24.286-25.111) GHz and 77 GHz (75.348–79.688), of automotive radars. This structure is composed of two antenna modules colocated on a single substrate, whereas each module is made up of a corporate fed planar array of two elements. This attractive feature enables us to utilize the antenna in two different ways; either both modules serve as the transmitting/receiving antenna of a monostatic radar or one module serves as a transmitter and the other one as the receiver of a bistatic radar. Most of the existing autonomous radar applications operating at 24 GHz are going to become obsolete, and all countries have plans of shifting towards the 77 GHz band. Hence, our design is very attractive as it operates with the required performance in both the bands with another added feature of the MIMO structure. The placement of antenna elements is also optimized in terms of inter- and intraelement separation of greater than λ/2 so as to ensure high diversity gain of 9.6 dBi. Moreover, the proposed antenna structure with only two antenna elements is able to achieve a high gain of around 11.8 dBi and 11.3 dBi at the dual operating modes of 24 GHz and 77 GHz, respectively. In addition to the above-mentioned benefits, this design also addresses mutual coupling reduction that is a common problem in MIMO structures by using complementary split ring resonator (CSRR) structures. State-of-the-art comparison with the recent literature shows that the proposed antenna has less number of antenna elements, an adequate gain, an excellent VSWR value, and high isolation.

Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing to its security and the private issue, analyzing the computed tomography dataset has become a tedious process. This study puts forward the convolutional autoencrypted deep learning neural network to assist unsupervised learning technique. By carrying out various experiments, our proposed method produces better results comparative to other traditional methods, which efficaciously solves the issues related to the artificial image description. Hence, the convolutional autoencoder is widely used in measuring the lumps in the bronchi. With the unsupervised machine learning, the extracted features are used for various applications.