Social Network sites are one of the most prominent websites used in almost every facet of life. A social network reflects relationships among social entities, including acquaintances, co-workers, or co-authors. With the extreme success of Social networks, the misuse has also been escalated and unlocked the way for various illegal behavior and security threats. Social Network anomaly detection has become a critical topic to be explored by researchers. The nature of the input data is a significant aspect of an anomaly detection technique. In the field of anomaly detection in the social network, input networks can be classified as static or dynamic, Attributed or unattributed. The number of nodes and the connections between the nodes in static networks would not change with time. Attribute networks are pervasive in various domains and constitute a vital element of modern technological architecture, where node attributes support the topological structure in data exploration. While social networks build up over time, evaluating them as if they had been static is very beneficial. Numerous studies have been done for Anomaly detection, but to the best of our knowledge, research in static attributed anomaly detection has been very limited. This review tries to portray earlier research on detecting anomalies for social static attributed networks and thoroughly discusses state-of-the-art embedding approaches
WSN is a low-power system and are often used in numerous monitoring uses, such as healthcare, environmental, and systemic health surveillance, in addition to military surveillance. It is important to reduce network resource usage since many of these applications need to be installed in locations that are virtually inaccessible to humans. Many protocols for WSN to extend the presence of the network have been established to solve this problem. In the energy efficiency of WSN networks, routing protocols play an important role since they help minimize power usage and response time and provide sensor networks with high data density and service quality. This study also employed a Hopfield neural network and the findings from this study are presented next to each other to enable comparison. This paper also discusses how to easily and accurately capture and handle WSN collisions. Future experiments that require the usage of neural networks and so many fuzzy structures will be able to prevent a crash in these respects.
Cloud computing is used to achieve sustainability in terms of computing. It reduces energy and resource consumption. Most of the companies have been moving their applications to the cloud to reduce power, energy re-source, and carbon emission. Today's computing landscape is rapidly shifting toward creating applications to leverage Cloud platforms to have necessary features such as elasticity, virtualization, low cost, and pay-per-use. Cloud computing's rising demand and versatility are achieving acceptance in the research community as a means of implementing large-scale electronic systems in the format of workflows (set of tasks). One of the most important objectives of this effort is to trim down makespan which is the total period taken by the resources to complete all workflow activities. Another foremost objective of this work is to satiate all the user-delineated time constraints while scheduling workflow activities.
The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use a machine learning ensemble approach for the automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners. The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in human history before. Besides other use cases, news outlets benefitted from the widespread use of social media platforms by providing updated news in near real-time to its subscribers. The news media evolved from newspapers, tabloids, and magazines to a digital form such as online news platforms, blogs, social media feeds, and other digital media formats. It became easier for consumers to acquire the latest news at their fingertips. Facebook referrals account for 70% of traffic to news websites. These social media platforms in their current state are extremely powerful and useful for their ability to allow users to discuss and share ideas and debate over issues such as democracy, education, and health. However, such platforms are also used with a negative perspective by certain entities commonly for monetary gain and in other cases for creating biased opinions, manipulating mindsets, and spreading satire or absurdity. The phenomenon is commonly known as fake news.
The main theme of this paper is to implement the mobility model in the Cooja simulator and to investigate the impact of mobility on the performance of Routing Protocol over Low power Lossy networks (RPL) in the IoT environment. In the real world, mobility occurs frequently. Therefore in this paper, a frequently used mobility model - Random Way Point (RWP) is used for analysis. RWP can be readily applied to many existing applications. By default, the Cooja simulator does not support mobility models. For this, the Bonn Motion is introduced into Cooja as a plugin. As IoT deals with the resource-constrained environment, a comparison is done between the static environment and the mobile environment in terms of power consumption. As expected, the results indicate that mobility affects the RPL in terms of Power Consumption