The Agile and DevOps software development methodologies have made revolutionary advancements in software engineering. These methodologies vastly improve software quality and also speed up the process of developing software products. However, several limitations have been discovered in the practical implementation of Agile and DevOps, including the lack of collaboration between the development, testing and delivery sectors of different software projects and high skill requirements. This paper presents a solution to bridge the existing gaps between Agile and DevOps methodologies by integrating DevOps principles into Agile to devise a hybrid DevOps Enabled Agile for software development. This study includes the development of a small-scale, experimental pilot project to demonstrate how software development teams can combine the advantages of Agile and DevOps methodologies to fulfill the gaps and provide further improvements to the speed and quality of software development process while maintaining feasible skill requirements.

Abstract-Introduction: Every year 2-3 million pilgrims with different background and most of them are elderly from 184 countries in the world congregate in the holy place 'Haram' at Makkah in Saudi Arabia to perform Hajj. During the pilgrimage, they come across a great deal of rough and tough environment, physical hassle and mental stress. Due to the hardship of travel, fluctuation of weather, continuous walking during religious rites at specific time and sites, many pilgrims injury, feel tired, sick, and exhausted. These may also create complications and overburden the physiological functions including heart, chest, abdominal, and kidney of those who suffer from chronic diseases. Besides the problem of diseases, crowds could cause some other significant problems including missing and lost pilgrims, injuries, and even death. Objective: To determine the common health problems e.g. diseases and emergency incidents encountered by pilgrims during Hajj was the main objective of this study. Methods: An extensive literature review to determine the common health problems and emergency incidents during Hajj was conducted through a systematic literature review. Numerous scholarly databases were used to search for articles published related to health problems and emergency incidents during Hajj from 2008 to 2016. Eligible articles included case reports, experimental and nonexperimental studies. Only thirty articles out of two hundred and sixty articles had met the specific inclusion criteria. Results: The analysis revealed that respiratory diseases include pneumonia, influenza, and asthma (73.33%) were the main health problems encountered by the pilgrims during Hajj followed by heat stroke or heat attack, sunlight effects (16.67%), cardiovascular disease, heart disease (10%). The analysis also revealed that emergency incidents include traffic accidents, and trauma was 3.33%. Notwithstanding the information given above, according to the analysis, the common health problems during Hajj are mainly divided into two categories: non-communicable diseases (62.5%) and communicable diseases (37.5%). IBM's statistical package for the social sciences (SPSS) version 22 was used to analysis the result. Conclusion: Both communicable and non-communicable health issues are the most common health problems encountered by pilgrims during Hajj. But, due to lack of existing studies associated with this research area, a definite conclusion could not be made. However, our findings demonstrated the necessity of new research to find solutions to pilgrims' health problems during Hajj.

Abstract-Crowded places like Hajj environment in Makkah which host from 2 to 3 million on specific area and time can pose health challenges for pilgrims who need medical care. One of the solutions to overcome such difficulties is to use Wireless Body Area Networks (WBANs). WBAN is one of the new technology using wireless sensor network to gather data about status of patient then to forward collected data to be proceeded. However, various types of challenges in WBAN should be concerned. Power consumption is critical within WBAN system. Furthermore, delay of data transfer may lead to wrong diagnosis or uncorrected report that may lead to death; therefore, the transferred data must be reliable to ensure accuracy in measurement. In this paper, we propose a framework for routing optimization in medical wireless network. The proposed framework optimize shortest path in different stages of collected data to get less energy consumption, and reduce transmission time. The proposed work is based on Bees Algorithm to overcome such challenges and find shortest path for data within shortest time during overcrowded of Hajj environment. Matlab simulation results show good performance of Bees Algorithm in terms of reducing transmission time, energy consumption, delay, and throughput.

<p class="MsoNormal" style="text-align: justify;"><span style="font-size: 12pt; line-height: 115%; font-family: 'Times New Roman', serif;">During Hajj, pilgrims suffer from various emergencies that should be managed in a real-time manner thus require deploying emerging technology. Based on our research it is found that the emergency medical circumstances among the pilgrims are due to the criticality level of certain physiological conditions those are by some means rely on five major types of the physiological data rate. Five major data types include heart rate, respiratory rate, body temperature, high or low blood pressure and blood sugar respectively which are obligatory to be transmitted real-time and ahead of other non-critical traffic as delay in its transmission may jeopardise human life. Hence, by the criticality constraints of pilgrims’ physiological data, we primarily perform a traffic classification through literature review. By using classified critical traffic, we define the different priority levels to be used by WBANs hub or coordinator. Therefore, in this research, we apply an analytical method to develop the priority-criticality index table in such a way that there will be no queuing delay in the system. Since our research mainly focuses on to manage the emergency, therefore, for simplicity of critical data transmission, we modified the existing medium access control (MAC) superframe that obtains only one exclusive access period (EAP) slot. The modified MAC superframe structure is to perform efficiently even when more than one emergency traffic from different sensors aggregate to the WBANs coordinator for further transmission to the healthcare stations.  </span></p><span style="font-size: 9pt; font-family: 'Times New Roman', serif;"><!--[if !supportMisalignedRows]--> </span><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" rowspan="2" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9pt;">During Hajj, pilgrims suffer from various emergencies that should be managed in a real-time manner thus require deploying emerging technology. Based on our research it is found that the emergency medical circumstances among the pilgrims are due to the criticality level of certain physiological conditions those are by some means rely on five major types of the physiological data rate. Five major data types include heart rate, respiratory rate, body temperature, high or low blood pressure and blood sugar respectively which are obligatory to be transmitted real-time and ahead of other non-critical traffic as delay in its transmission may jeopardise human life. Hence, by the criticality constraints of pilgrims’ physiological data, we primarily perform a traffic classification through literature review. By using classified critical traffic, we define the different priority levels to be used by WBANs hub or coordinator. Therefore, in this research, we apply an analytical method to develop the priority-criticality index table in such a way that there will be no queuing delay in the system. Since our research mainly focuses on to manage the emergency, therefore, for simplicity of critical data transmission, we modified the existing medium access control (MAC) superframe that obtains only one exclusive access period (EAP) slot. The modified MAC superframe structure is to perform efficiently even when more than one emergency traffic from different sensors aggregate to the WBANs coordinator for further transmission to the healthcare stations. </span></p><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"> </p></td><!--[if !supportMisalignedRows]--><td style="height: 63.4pt; border: none;" width="0" height="85"> </td><!--[endif]--></tr><tr style="mso-yfti-irow: 1; mso-yfti-lastrow: yes; height: 61.55pt;"><td style="height: 61.55pt; border: none;" width="0" height="82"> </td><!--[endif]--></tr></tbody></table>

Leaf disease in tomatoes is one of the most common and treacherous diseases. It directly affects the production of tomatoes, resulting in enormous economic loss each year. As a result, studying the detection of tomato leaf diseases is essential. To that aim, this work introduces a novel mechanism for selecting the most effective hyperparameters for improving the detection accuracy of deep CNN. Several cutting-edge CNN algorithms were examined in this study to diagnose tomato leaf diseases. The experiment is divided into three stages to find a full proof technique. A few pre-trained deep convolutional neural networks were first employed to diagnose tomato leaf diseases. The superlative combined model has then experimented with changes in the learning rate, optimizer, and classifier to discover the optimal parameters and minimize overfitting in data training. In this case, 99.31% accuracy was reached in DenseNet 121 using AdaBound Optimizer, 0.01 learning rate, and Softmax classifier. The achieved detection accuracy levels (above 99%) using various learning rates, optimizers, and classifiers were eventually tested using K-fold cross-validation to get a better and dependable detection accuracy. The results indicate that the proposed parameters and technique are efficacious in recognizing tomato leaf disease and can be used fruitfully in identifying other leaf diseases.