SPIRITUAL INTELLIGENCE A SOURCE OF IMPROVED EMPLOYEE PERFORMANCE THROUGH ORGANIZATION COMMITMENT

The purpose of this study is to present an analysis on the impact of the of Spiritual intelligence on the Organization commitment of the employees and also on the employee performance, it also involves a study of the mediating role of the Organization commitment between the Spiritual intelligence and  employee performance in the Telecom sector of Pakistan. Relationship of the seven major themes of the spiritual intelligence also known as dimensions of spiritual intelligence have also been individually tested with organization commitment. Questionnaire distribution method was used to conduct this research. The total numbers of respondents were 258 in which the respondents of belonged to different service providers and mobile phone operators. People surveyed are all employees of the stated companies. The findings of this research paper indicate that all of  the  dimensions spiritual intelligence are positively correlated with the  organization commitment and there is a significant evidence that the organization commitment serves as a mediator between the Spiritual intelligence and Employee performance. This paper encourages the companies that they should on the development and promotion of the spiritual intelligence in the employees in order to increase their level of organization commitment and performance.Keywords –Spiritual intelligence, dimensions of spiritual intelligence , Organization commitment, Employee performance

SPIRITUAL INTELLIGENCE A SOURCE OF IMPROVED EMPLOYEE PERFORMANCE THROUGH ORGANIZATION COMMITMENT

The purpose of this study is to present an analysis on the impact of the of Spiritual intelligence on the Organization commitment of the employees and also on the employee performance, it also involves a study of the mediating role of the Organization commitment between the Spiritual intelligence and  employee performance in the Telecom sector of Pakistan. Relationship of the seven major themes of the spiritual intelligence also known as dimensions of spiritual intelligence have also been individually tested with organization commitment. Questionnaire distribution method was used to conduct this research. The total numbers of respondents were 258 in which the respondents of belonged to different service providers and mobile phone operators. People surveyed are all employees of the stated companies. The findings of this research paper indicate that all of  the  dimensions spiritual intelligence are positively correlated with the  organization commitment and there is a significant evidence that the organization commitment serves as a mediator between the Spiritual intelligence and Employee performance. This paper encourages the companies that they should on the development and promotion of the spiritual intelligence in the employees in order to increase their level of organization commitment and performance.Keywords –Spiritual intelligence, dimensions of spiritual intelligence , Organization commitment, Employee performance

Smart city-ranking of major Australian cities to achieve a smarter future

© 2020 by the authors. A Smart City is a solution to the problems caused by increasing urbanization. Australia has demonstrated a strong determination for the development of Smart Cities. However, the country has experienced uneven growth in its urban development. The purpose of this study is to compare and identify the smartness of major Australian cities to the level of development in multi-dimensions. Eventually, the research introduces the openings to make cities smarter by identifying the focused priority areas. To ensure comprehensive coverage of all aspects of the smart city's performance, 90 indicators were selected to represent 26 factors and six components. The results of the assessment endorse the impacts of recent government actions taken in different urban areas towards building smarter cities. The research has pointed out the areas of deficiencies for underperforming major cities in Australia. Following the results, appropriate recommendations for Australian cities are provided to improve the city's smartness

Identification of Major Inefficient Water Consumption Areas Considering Water Consumption, Efficiencies, and Footprints in Australia

Due to population growth, climatic change, and growing water usage, water scarcity is expected to be a more prevalent issue at the global level. The situation in Australia is even more serious because it is the driest continent and is characterized by larger water footprints in the domestic, agriculture and industrial sectors. Because the largest consumption of freshwater resources is in the agricultural sector (59%), this research undertakes a detailed investigation of the water footprints of agricultural practices in Australia. The analysis of the four highest water footprint crops in Australia revealed that the suitability of various crops is connected to the region and the irrigation efficiencies. A desirable crop in one region may be unsuitable in another. The investigation is further extended to analyze the overall virtual water trade of Australia. Australia’s annual virtual water trade balance is adversely biased towards exporting a substantial quantity of water, amounting to 35 km3, per trade data of 2014. It is evident that there is significant potential to reduce water consumption and footprints, and increase the water usage efficiencies, in all sectors. Based on the investigations conducted, it is recommended that the water footprints at each state level be considered at the strategic level. Further detailed analyses are required to reduce the export of a substantial quantity of virtual water considering local demands, export requirements, and production capabilities of regions

Supervised classification for object identification in urban areas using satellite imagery

This paper presents a useful method to achieve classification in satellite imagery. The approach is based on pixel level study employing various features such as correlation, homogeneity, energy and contrast. In this study gray-scale images are used for training the classification model. For supervised classification, two classification techniques are employed namely the Support Vector Machine (SVM) and the Naive Bayes. With textural features used for gray-scale images, Naive Bayes performs better with an overall accuracy of 76% compared to 68% achieved by SVM. The computational time is evaluated while performing the experiment with two different window sizes i.e., 50x50 and 70x70. The required computational time on a single image is found to be 27 seconds for a window size of 70x70 and 45 seconds for a window size of 50x50.Comment: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET

Routing Hole Mitigation by Edge based Multi-Hop Cluster-based Routing Protocol in Wireless Sensor Network

In Wireless sensor network (WSNs) due to the harsh environments the degradation of energy is major issue. For addressing this issue, clustering techniques equalize energy utilization by distributing the workload among different clusters but energy-unaware path selection in multi-hop clustering technique leads to routing hole problem. To reduce the routing hole problem in WSNs, an energy-efficient least-edge computation (ELEC) cluster-based algorithm is proposed, which consider the value of edge count, link cost and energy level in selecting the next hope neighbor in data transmission. Results of our simulation reveal that ELEC achieves nearly double network lifetime by equal energy consumption in various parts of the network in addition just 5% energy left unused, as compared to existing routing strategies such as LEACH, GRACE, and AODV-EHA. Furthermore the percentage of node failure is half of the other existing routing strategies and 60% of packet drop noticeable decrease is noticed in ELEC as compared to GRACE, LEACH, and AODV-EHA

Design and implementation of Adaptive Neuro-Fuzzy Inference system for the control of an uncertain Ball on Beam Apparatus

Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro-Fuzzy Inference System) using an uncertain Ball and Beam System. The custom-designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle

Movie Tags Prediction and Segmentation Using Deep Learning

The sheer volume of movies generated these days requires an automated analytics for ef cient classi cation, query-based search, and extraction of desired information. These tasks can only be ef ciently performed by a machine learning based algorithm. We address the same issue in this paper by proposing a deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to train a deep learning model. Subsequently, we propose an ef cient shot detection algorithm to nd the key frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top three tags for each frame. The tags are then assigned weighted scores and are ltered to generate a compact set of most relevant tags. This process also generates a corpus which is further used to segment a movie based on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not only ef cacious in predicting the most relevant tags for a movie, but also in segmenting the movie with respect to the selected tags with a high accuracy

An overview of groundwater monitoring through point-to satellite-based techniques

Groundwater supplies approximately half of the total global domestic water demand. It also complements the seasonal and annual variabilities of surface water. Monitoring of groundwater fluctuations is mandatory to envisage the composition of terrestrial water storage. This research provides an overview of traditional techniques and detailed discussion on the modern tools and methods to monitor groundwater fluctuations along with advanced applications. The groundwater monitoring can broadly be classified into three groups. The first one is characterized by the point measurement to measure the groundwater levels using classical instruments and electronic and physical investigation techniques. The second category involves the extensive use of satellite data to ensure robust and cost-effective real-time monitoring to assess the groundwater storage variations. Many satellite data are in use to find groundwater indirectly. However, GRACE satellite data supported with other satellite products, computational tools, GIS techniques, and hydro-climate models have proven the most effective for groundwater resources management. The third category is groundwater numerical modeling, which is a very useful tool to evaluate and project groundwater resources in future. Groundwater numerical modeling also depends upon the point-based groundwater monitoring, so more research to improve point-based detection methods using latest technologies is required, as these still play the baseline role. GRACE and numerical groundwater modeling are suggested to be used conjunctively to assess the groundwater resources more efficiently

Prediction of the amount of sediment Deposition in Tarbela Reservoir using machine learning approaches

Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (Ra), water inflow (Iw), minimum water reservoir level (Lr), and storage capacity of the reservoir (Cr) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of Ra, Iw, Lr, and Cr. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment