Contribution of cloud computing in the reduction of carbon dioxide emission

The Information Technology industry is rapidly expanding and as a result its contribution to carbon dioxide emission is also rapidly increasing. Fortunately, the cloud computing industry is perceived by many to be a viable solution for reducing carbon dioxide emissions. Accordingly, there are numerous studies which try to prove that cloud computing can reduce carbon dioxide emissions up to more than half of the current carbon dioxide emissions. In this paper, two of such studies where reviewed to assess whether cloud computing is indeed a viable candidate for limiting and reducing the amount of carbon dioxide emitted by the IT industry. All the information gathered in this paper prove that; cloud computing is a promising technology which could reduce carbon dioxide emissions. The percentage of decrease can range from 10% to 90%. The effectiveness of the carbon dioxide emission reduction process is highly dependent on the size of the business organization. Accordingly the size of the organization is negatively correlated to the efficiency of carbon dioxide reduction. This means that as the size of the organization increase, carbon dioxide emission reduction decrease. This paper also presented the four reasons why cloud computing can reduce carbon dioxide emissions, which are: dynamic provisioning, multi-tenancy, server utilization, and data center efficiency

Detecting change from social networks using temporal analysis of email data

Social network analysis is one of the most recent areas of research which is being used to analyze behavior of a society, person and even to detect malicious activities. The information of time is very important while evaluating a social network and temporal information based analysis is being used in research to have better insight. Theories like similarity proximity, transitive closure and reciprocity are some well-known studies in this regard. Social networks are the representation of social relationships. It is quite natural to have a change in these relations with the passage of time. A longitudinal method is required to observe such changes. This research contributes to explore suitable parameters or features that can reflect the relationships between individual in network. Any foremost change in the values of these parameters can capture the change in network. In this paper we present a framework for extraction of parameters which can be used for temporal analysis of social networks. The proposed feature vector is based on the changes which are highlighted in a network on two consecutive time stamps using the differences in betweenness centrality, clustering coefficient and valued edges. This idea can further be used for detection of any specific change happening in a network. © Springer International Publishing AG 2018

Experimental Results of the Tribology of Aluminum in the Presence of Polytron Additive

Friction is an ever-present obstacle that causes energy loss in mechanical parts. To alleviate this nuisance, we carried out experimental studies on a brand new additive called Polytron to assess its role in the minimization of friction and wear. The wear, the volume wear rate, the wear coefficient, and the coefficient of friction of the aluminum surface were measured at room temperature with pin-on-disk tribometer without and with 10% Polytron in Helix oil. In the base oil Helix, their values were found to be 70 μm, 1.28×10−3mm3/min, 1.27×10−10m2/N, and 0.012, respectively, which with the incorporation of Polytron additive in the Helix oil correspondingly reduced to 20μm, 6.08×10−5mm3/min, 4.22×10−11m2N, and 0.004. The experimental verdict points to an ionic character of the additive in that it impregnates the crystal structure of the metal, thereby prompting a hard surface layer which subsequently curtails wear and friction

A Review of Deep Learning Techniques for Crowd Management

Crowd Management is extremely important for maintaining safety and order in areas, such as events, transportation hubs, and urban centers. In the years deep learning methods have become tools for dealing with the complexities associated with crowd management. The adoption of this technology represents a change in how we analyze, predict, and respond to crowd dynamics. Deep learning algorithms can process amounts of data using CNNs. Learn intricate patterns enabling the creation of advanced models that can anticipate crowd behavior, identify unusual occurrences, and optimize crowd flow. By utilizing information from surveillance cameras, social media platforms, and other sources these models can provide real-time insights that empower authorities to make decisions and take measures to ensure public safety. To sum up, integrating deep learning techniques into crowd management offers a path toward improving situational awareness and effectively addressing the challenges presented by large gatherings of people. Further research and application of these technologies have the potential for enhancing crowd

Identification of valued users to generate more telecom filigence

Stay connected is a well-known advantage of telecommunications. Telecom diligence have introduced an easy and enormous way to people to keep in touch. From the last decades, a remarkable growth is noted in telecommunication industry. The number of telecom users has enormously increased. With increasing demands, it is becoming challenges for CSPs (Communication Service Providers) to provide best services. In order to provide finest services a very important step is to monitor the activities of subscribers on the network. Call detail records (CDRs) can play a vital role in monitoring process. CDRs provide a diversity of information about the subscribers. Even though Call Detail Records were initially gathered and stored for billing purpose, but the huge amount of digital data is generated by calling, texting and internet usage etc., generates events, sate and errors that provide useful insights. These digital footprints can be analysed to produce worthy information like network optimization, user's patterns and behavior identification, valued users identification, suspects and their connected association and much more. This paper has identified an important use of call detail records to discover and analyze the networks generating more income for offering new services and promotions to those valued users's networks

Calculating customer experience management index for telecommunication service using genetic algorithm based weighted attributes

The Customers are the hearts of any industry. Telecommunication being a service oriented industry always prioritizes to find ways of making customers happy, satisfied and loyal. By recognizing this prominence, this paper presents a survey based analysis. A study is conducted to determine what makes customers of Telecommunication Industry satisfied. This paper presents a genetic algorithm (GA) based technique for assigning weights to different attributes of a service based on survey data to find overall customer experience management index (CEMI). Six attributes of service i.e. network coverage, voice call quality, drop call rate, SMS delivery, internet service and call setup duration have been considered in this research to find overall CEMI. The weights for each attribute are optimized by minimizing the error between weighted attributes based calculated CEMI and actual CEMI provided during survey process. The study has been confined within Islamabad City, the capital of Pakistan. The data is gathered through telephonic survey by calling 200 targeted customers of a mobile service provider network in Pakistan. The results indicate that network coverage, signal strength and voice quality are the major factors that highly effect the customer satisfaction. The result of this research proved that there is positive and significant relationship between dependent variables

Important attributes of customer satisfaction in telecom industry: a survey based study

Customer satisfaction has been acknowledged as critical success factors in any organizations. Recent developments in telecom sector shows that communication services providers (CSPs) are engaged in various marketing and survey activities to discover the satisfaction level of their customers. In general, some subscribers complain about the poor network coverage, voice call quality, internet service etc. while other are satisfied with the quality of services. The aim of this paper is to conduct a servery based study to identify and highlight major attributes that effect customer's satisfaction which will eventually help telecom services providers to improve their customer experience management index. Consequently, this paper examines the factors that have resilient or fragile influence on customer satisfaction. Based on the contemporary research six attributes i.e. network coverage, voice call quality, drop call rate, SMS delivery, internet service and call setup duration have been considered and tested in this research to find the customer satisfaction. For this purpose of study, 200 respondents of a CSP are selected from Islamabad, Pakistan. A telephonic survey is conducted to rate each of the factor and their overall satisfaction. The results reveal that network coverage, voice call quality and internet service have the highest impact on the level of customer satisfaction

Detecting changes in context using time series analysis of social network

Researchers are getting great benefits of big data as information gathered from social media like Facebook, Twitter and being used to perceive the lot from family planning to predicting postpartum depression. Detecting behavioral changes in social networks represents an exciting new area of this progression that is used to counter organizational behavior and terrorism. Such analysis in social networks in categorized as dynamic data analysis. To process data dynamically time series considers as an essential component. This research proposes a novel technique that uses time series analysis in cyber space based social networks to detect variances or changes in human context overtime