Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-001087.pdf

Catastrophic cascade of failures in interdependent networks

Picture processing system by computer complex and recognition of human faces

: Education sector has witnessed several changes in the recent past. These changes have forced private universities into fierce competition with each other to get more students enrolled. This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands. To get competitive gain, universities must observe and examine the students’ feedback on their own social media sites along with the social media sites of their competitors. This study presents a novel framework which integrates numerous analytical approaches including statistical analysis, sentiment analysis, and text mining to accomplish a competitive analysis of social media sites of the universities. These techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received, major areas for improvement. A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore Pakistan. Experimental results show that diversity of shared content, frequency of posts, and schedule of updates, are the key areas for improvement for the local universities. Based on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university (RIU) Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

https://www.techscience.com/cmc/v71n3/46524/pdf

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Machine Learning (ML) applications are making a considerable impact on healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the speed and accuracy of physicians' work. Countries are currently dealing with an overburdened healthcare system with a shortage of skilled physicians, where AI provides a big hope. The healthcare data can be used gainfully to identify the optimal trial sample, collect more data points, assess ongoing data from trial participants, and eliminate data-based errors. ML-based techniques assist in detecting early indicators of an epidemic or pandemic. This algorithm examines satellite data, news and social media reports, and even video sources to determine whether the sickness will become out of control. Using ML for healthcare can open up a world of possibilities in this field. It frees up healthcare providers' time to focus on patient care rather than searching or entering information. This paper studies ML and its need in healthcare, and then it discusses the associated features and appropriate pillars of ML for healthcare structure. Finally, it identified and discussed the significant applications of ML for healthcare. The applications of this technology in healthcare operations can be tremendously advantageous to the organisation. ML-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care. Shortly, ML will impact both physicians and hospitals. It will be crucial in developing clinical decision support, illness detection, and personalised treatment approaches to provide the best potential outcomes. 

Significance of machine learning in healthcare: Features, pillars and applications

For the past few years, business intelligence has been a major field that uses data analysis to produce key information as part of business decision-making. Data collected from social media sites and blogs are analyzed to make business decisions, a process called social media analytics (SMA). This method, which goes beyond ordinary monitoring or a basic analysis of retweets, develops an in-depth insight into the social consumer. After reading the whole report, add the pertinent figures to the table. Add pertinent data from the Brand24 report to the table. During a social media audit, any followers, impressions, engagement, copy/traffic, and brand mentions are key parameters to analyze. For companies and research institutions, the great interest is to analyse and gain knowledge from user-produced data. These data contain useful knowledge, including customer perceptions feedback and product/service suggestions. Due to content saturation, social media's true meaning regarding business data is hardly ever found. Therefore, in this paper, the business decision making system (BDMS) has been proposed to develop business using social media data analytics. BDMS provides a clear understanding of the key principles, issues and functionality, and big social data developments. Besides, BDMS concentrates on marketing and describes an operational approach for obtaining valuable information from social data. BDMS performs a short and precise description of current use scenarios from the evidence, as per the help of decisions and investment opportunities companies get when using social data analytics. The experimental result shows that BDMS achieves the highest competitive results. With greater accuracy, system dependability, F-1 measurement, and deviation rate of 85.5%, the BDMS system guarantees 93.7%, 86.8%, and 7.0%.

Social media data analytics for business decision making system to competitive analysis

In the world of digital era, an advance development with internet of things (IoT) were initiated, where devices communicate with each other and the process are automated and controlled with the help of internet. An IoT in an agriculture framework includes various benefits in managing and monitoring the crops. In this paper, an architectural framework is developed which integrates the internet of things (IoT) with the production of crops, different measures and methods are used to monitor crops using cloud computing. The approach provides real-time analysis of data collected from sensors placed in crops and produces result to farmer which is necessary for the monitoring the crop growth which reduces the time, energy of the farmer. The data collected from the fields are stored in the cloud and processed in order to facilitate automation by integrating IoT devices. The concept presented in the paper could increase the productivity of the crops by reducing wastage of resources utilized in the agriculture fields. The results of the experimentation carried out presents the details of temperature, soil moisture, humidity and water usage for the field and performs decision making analysis with the interaction of the farmer.

Smart agriculture management system using internet of things

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.

/pdf/performance-analysis-of-sentiments-in-twitter-dataset-using-1vr3ij41jz.pdf

Performance analysis of sentiments in twitter dataset using SVM models

Autonomous vehicles have been invented to increase the safety of transportation users. These vehicles can sense their environment and make decisions without any external aid to produce an optimal route to reach a destination. Even though the idea sounds futuristic and if implemented successfully, many current issues related to transportation will be solved, care needs to be taken before implementing the solution. This paper will look at the pros and cons of implementation of autonomous vehicles. The vehicles depend highly on the sensors present on the vehicles and any tampering or manipulation of the data generated and transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks against the different type of sensors on-board an autonomous vehicle are covered.

/pdf/autonomous-vehicles-a-study-of-implementation-and-security-8jrfkb147f.pdf

Autonomous vehicles: A study of implementation and security

Internet of Things (IoT) refers to the practice of designing and modeling objects connected to the Internet through computer networks. In the past few years, IoT-based health care programs have provided multidimensional features and services in real time. These programs provide hospitalization for millions of people to receive regular health updates for a healthier life. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. In this paper, a disease diagnosis system is designed based on the Internet of Things. In this system, first, the patient's courtesy signals are recorded by wearable sensors. These signals are then transmitted to a server in the network environment. This article also presents a new hybrid decision making approach for diagnosis. In this method, a feature set of patient signals is initially created. Then these features go unnoticed on the basis of a learning model. A diagnosis is then performed using a neural fuzzy model. In order to evaluate this system, a specific diagnosis of a specific disease, such as a diagnosis of a patient's normal and unnatural pulse, or the diagnosis of diabetic problems, will be simulated.

http://ijai.iaescore.com/index.php/IJAI/article/download/20637/pdf

Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms

Internet of Things (IoT) refers to the practice of designing and modelingobjects connected to the Internet through computer networks. In the past fewyears, IoT-based health care programs have provided multidimensionalfeatures and services in real time. These programs provide hospitalization formillions of people to receive regular health updates for a healthier life.Induction of IoT devices in the healthcare environment have revitalizedmultiple features of these applications. In this paper, a disease diagnosissystem is designed based on the Internet of Things. In this system, first, thepatient's courtesy signals are recorded by wearable sensors. These signals arethen transmitted to a server in the network environment. This article alsopresents a new Hybrid Decision Making approach for diagnosis. In thismethod, a feature set of patient signals is initially created. Then thesefeatures go unnoticed on the basis of a learning model. A diagnosis is thenperformed using a neural fuzzy model. In order to evaluate this system, aspecific diagnosis of a specific disease, such as a diagnosis of a patient'snormal and unnatural pulse, or the diagnosis of diabetic problems, will besimulated.

Maytham N. Meqdad

Papers

Smart agriculture management system using internet of things

Performance analysis of sentiments in twitter dataset using SVM models

Autonomous vehicles: A study of implementation and security

Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms

Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms