Hardeo Kumar Thakur

• Doctor of Philosophy
• Manav Rachna University, Faridabad India

Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce c...

Psychological health problems concern an approximated 92 million people universally. That's essentially 1 in 10 people in general. Therefore, it is advisable to create a chatbot to lessen the stigma as-sociated with mental health, giving people the ability to voice their problems, and filling the gap left by the lack of support systems for those wh...

Recommendation Systems are everywhere, from offline shopping malls to major e-commerce websites, all use recommendation systems to enhance customer experience and grow profit. With a growing customer base, the requirement to store their interest, behavior and respond accordingly requires plenty of scalability. Thus, it is very important for compani...

Interactions are often viewed in the context of social and mental relations, but the reality cannot be captured accurately by measuring the stochastic of its dynamics. This paper demonstrates an operational framework to detect socio-technical attacks through contextual analysis of the social network. It emphasized on a correlation based on the cent...

The rapid and ubiquitous digital revolution has led to acceleration towards a digitally connected world where accepting recommendations digitally has become a part of our e-commerce related lifestyle. Most of these recommendations are based on rating values, wherein some values are fraudulent. This is a vulnerability in the security of the system a...

Crime often disrupts normal life; people can experience lots of complex emotions. Everyone reacts differently in the adverse situations that can impact a person’s mental well-being. The effect of crime last a long time and it doesn’t depend on how ‘serious’ the crime was. Presently, digitization is created new incentives for the violence or new ave...

Trustworthy recommendation of a movie is a highly complex task for the entertainment industry wherein trust is s a crucial metric of recommendation systems. It depends upon various factors, such as preferences, reviews, emotions, promotions and sentiments. However, these factors are specific to individuals and may vary from person to person. Additi...

Distinct non-random quantitative interactions at diverse timestamps formulate real-world dynamic complex networks. The most frequently used class of methods for discovering communities in dynamic networks is modularity optimization that evaluates the quality of the partition of network nodes into distinct communities. The bipartite networks have bi...

Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properti...

Recommender system (RS) has evolved significantly over the last few decades. This revolutionary move in RS is the adoption of machine learning algorithms from the field of artificial intelligence to produce the personalized recommendation of products or services. This literature presents an exhaustive survey on RS to emphasizes its taxonomy pertain...

Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of parti...

Feature ranking can have a severe impact on the feature selection problem. Feature ranking methods refer to the structure of features that can accept the designed data and have a positive effect on the quality of features. Moreover, accessing useful features helps in reducing cost and improving performance of a feature ranking algorithm. There are...

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work,...

Cross-domain recommendation Systems (CDRS) is a significant research area which has been target of many companies these days. From last few years, there is amount of publications in CDRS domain including recommendation systems which have been rising sharply alongside information retrieval and machine learning. Recommender systems help companies to...

The community detection in a given network is the idea to find a cluster in the structure. A community is the most densely populated part of the graph. The observed network is mostly sparse having multiple dense partitions in it, for example, a protein–protein interaction network where different proteins interact with each other. Here, communities...

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work,...

The periodic interactions represented as a dynamic network possess two aspects: structure and weight. This chapter introduces and explores the use of a third aspect that is associated with the periodic interactions, namely the directional aspect. Moreover, the authors have showcased how some applications require mining of patterns on both aspects-1...

Dynamic graphs model time-varying interactions between related entities in a network. Extensive studies have been carried out on the mining of frequent, regular, and periodic behavior of such interactions. Some of the previous research focused on providing users the platform to mine periodic patterns on a single aspect (structure, weight, or direct...

With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data analysis is key to solving arti...

Real world graphs are mostly dynamic in nature, exhibiting time-varying behaviour in structure of the graph, weight on the edges and direction of the edges. Mining regular patterns in the occurrence of edge parameters gives an insight into the consumer trends over time in ecommerce co-purchasing networks. But such patterns need not necessarily be p...

Periodic patterns are mined individually on structural and weight aspects of an interaction in a dynamic network. However, these interactions possess a direction aspect too. Moreover, some applications require patterns on both aspects i) on direction and ii) on weight of directed interactions for a better understanding of their behaviour. To the au...

Static Graphs consist of a fixed sequence of nodes and edges which does not change over time, hence lack in providing the information regarding evolution of the network. In contrast, Dynamic Graphs to a greater extent relate to real-life events and so provide complete information about the network evolution. That is why many researchers [1, 2, 3, 5...

Mining of regular patterns in dynamic networks finds immense application in characterizing the local properties of the networks, like behaviour (friendship relation), event occurrence (football matches). They in then are used to predict their future trends. But if they do not entail weight and direction aspects of the dynamic network, there can be...

Existing Dynamic Graph mining algorithms focus typically on finding patterns in undirected, unweighted and weighted dynamic networks ignoring the fact that some of them could be directed also. In this paper, we focus on finding regular evolution patterns in edges, outdegree and indegree of all the nodes, featuring consecutively at fixed time interv...