This paper proposes a Machine Learning-based approach for detecting Denial-of-Service (DoS) attacks using different datasets for training and testing. The study evaluates the performance of the proposed approach on a unique dataset; thereafter, compares it to existing approaches in a bid to demonstrate its superiority in terms of accuracy, false-positive rate, and computational efficiency. Furthermore, the impacts of different Machine Learning algorithms and hypertuning configurations on the performance of the proposed approach are investigated. Also, via Feature Importance Analysis, this study examines the influence of each feature (present in the dataset) on our proposed model. The contributions of our work herein demonstrate the potentials of the proposed approach in detecting DoS attacks; and our research highlights the importance of employing Machine Learning in this domain. Future research directions have been suggested based on insights acquired from the investigations of different algorithms and hyperparameters.

The Internet of Things (IoT) is a very crucial aspect of Computing, and it fosters the interconnection of physical nodes on the Internet via enabling them to interact and share data. However, the potentials for security and privacy breaches increase as the number of connected devices/nodes (in the network) rises. Blockchain technology, with its capacity to provide secure and tamper-proof data storage, possess the potentials to mitigate the aforementioned vulnerabilities in the IoT. Hence, this paper gives a detailed review of the present state of blockchain-based applications in the IoT as well as the prospective benefits of employing blockchain technology with the aim/goal of securing IoT devices and its infrastructure. Also mentioned in this paper are the obstacles, research gaps, etc., currently impeding the implementation of some blockchain-based technologies; and the potential solutions toward surmounting these challenges.

Abstract Literature have focused on studying the apparent and latent interactions within social graphs as an n‐ary operation, which yields binary outputs comprising positives (friends, likes, etc.) and negatives (foes, dislikes, etc.). Inasmuch as interactions constitute the bedrock of any given social network (SN) structure; there exist scenarios where an interaction, which was once considered a positive, transmutes into a negative as a result of one or more indicators which have affected the interaction quality. At present, this transmutation has to be manually executed by the affected actors in the SN. These manual transmutations can be quite inefficient, ineffective, and a mishap might have been incurred by the constituent actors and the SN structure prior to a resolution. Our problem statement aims at automatically flagging positive ties that should be considered for breakups or rifts (negative‐tie state), as they tend to pose potential threats to actors and the SN. Therefore, we have proposed ClasReg: a unique framework capable of breakup and link predictions.

Knowledge Graph represents the real world's information in the form of triplets (head, relation, and tail). However, most Knowledge Graphs are highly incomplete. The goal of a Knowledge-Graph Completion task is to predict missing links in a given Knowledge Graph. Various approaches exist to predict a missing link in a Knowledge Graph, but the most prominent approaches are based on tensor factorization and Knowledge-Graph embeddings, such as RotatE and SimplE. The RotatE model depicts each relation as a rotation from the source entity (Head) to the target entity (Tail) via a complex vector space. In RotatE, the head and tail entities are derived from one embedding-generation class, resulting in a relatively low prediction score. SimplE is primarily based on a Canonical Polyadic (CP) decomposition. SimplE enhances the CP approach by adding the inverse relation where head embedding and tail embedding are taken from the different embedding-generation class, but they are still dependent on each other. However, SimplE is not able to predict composition patterns. This paper presents a new, hybridized variant (HRotatE) of the existent RotatE approach. Essentially, HRotatE is hybridized from RotatE and SimplE. We have used the principle of inverse embedding (from the SimplE model) in a bid to improve the prediction scores of HRotatE. Hence, our results have proven to be better than the native RotatE. Also, HRotatE outperforms several state-of-the-art models on different datasets. Conclusively, our proposed approach (HRotatE) is relatively efficient such that it utilizes half the number of training steps required by RotatE, and it generates approximately the same result as RotatE.

Coronavirus disease 2019 (COVID-19) has been an ongoing threat to the world's health system. Millions of people died all over the world because of this deadly virus outbreak. Although health sectors are equipped with modern technologies yet struggling every day to control this outbreak. However, predicting the active COVID-19 cases and hospitalization in advance can be helpful to minimize the catastrophe of this persistent outbreak. This study proposed a novel Agent-based modelling (ABM) framework based on various temporal and non-pharmaceuticals parameters to predict active cases and hospitalization cases. We evaluated the model's performance based on COVID-19 data of Windsor-Essex county region of Ontario, Canada, and eventually achieved satisfactory results in predicting active cases and hospitalization. Experimental results have demonstrated that the simulations provide helpful information that could help take advanced steps to cover up for the shortage in hospital resources and take necessary steps to reduce the number of infections.

Image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. Contextual here means this approach is focusing on the relationship of the nearby pixels also called neighborhood. An open topic of research in computer vision is to devise an effective means of transferring human’s informal knowledge into computers, such that computers can also perceive their environment. However, the occurrence of object with respect to image representation is usually associated with various features of variation causing noise in the image representation. Hence, it tends to be very difficult to actually disentangle these abstract factors of influence from the principal object. In this paper, we have proposed a hybrid model: FUSIONET, which has been modeled for studying and extracting meaning facts from images. Our proposition combines two distinct stack of convolution operation (3 × 3 and 1 × 1, respectively). Successively, these relatively low-feature maps from the above operation are fed as input to a downstream classifier for classification of the image in question.

Link prediction and node classification in social networks remain open research problems with respect to Artificial Intelligence (AI). Innate representations about social network structures can be effectively harnessed for training AI models in a bid to predict ties; and detect clusters via classification of actors with regard to a given social network. In this paper, we have proposed a distinct hybrid model: Representation Learning via Knowledge-Graph Embeddings and Convolution Operations (RLVECO), which hybridizes the strengths of Knowledge-Graph Embeddings (VE) and Convolution Operations (CO) in extracting and learning meaningful features from social graphs via Representation Learning (RL). RLVECO utilizes an edge sampling approach for exploiting features of a social graph via learning the context of each actor with respect to its neighboring actors.

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.

Infectious diseases can spread from an infected person to a susceptible person through direct or indirect physical contact, consequently controlling such types of spread is difficult. However, a proper decision at the initial stage can help control the disease's propagation before it turns into a pandemic. Social distancing and hospital capacity are considered among the most critical parameters to manage these types of conditions. In this paper, we used artificial agent-based simulation modeling to identify the importance of social distancing and hospitals' capacity in terms of the number of beds to shorten the length of an outbreak and reduce the total number of infections and deaths during an epidemic. After simulating the model based on different scenarios in a small artificial society, we learned that shorter social isolation activation delay has a higher impact on reducing the catastrophe. Increasing the hospital's treatment capacity, i.e., the number of isolation beds in the hospitals can become handy when social isolation cannot be activated shortly. The model can be considered a prototype to take proper steps based on the simulations on different parameter settings towards the control of an epidemic.

In recent times, Social Network Analysis (SNA) has become a very important and interesting subject matter with regard to Artificial Intelligence (AI) in that a vast variety of processes, comprising animate and inanimate entities, can be examined by means of SNA. As a result, prediction tasks within social network structures have become significant research problems in SNA. Hidden facts and details about social network structures can be effectively and efficiently harnessed for training AI models with the goal of predicting missing links/ties within a given social network. Thus, important factors such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration because these factors are relevant in understanding the nature and dynamics of a given social network structure. In this paper, we have proposed an interesting hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition herein is designed for examining, extracting, and learning meaningful facts for resolving link prediction problems about social network structures. RLVECN utilizes an edge sampling approach for exploiting the representations of a social graph, via learning the context of each actor with respect to its neighboring actors, with the goal of generating vector-space embeddings per actor which are further harnessed for innate representations via a Convolutional Neural Network (ConvNet) sublayer. Successively, these relatively low-dimensional representations are fed as input features to a downstream classifier for solving link prediction problems in a given social network. Our proposition, RLVECN, has been trained and evaluated on six (6) real-world benchmark social graph datasets.

Node classification and link prediction problems in Social Network Analysis (SNA) remain open research problems with respect to Artificial Intelligence (AI). Inherent representations about social network structures can be effectively harnessed for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. In this paper, we have proposed a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition is designed for analyzing and extracting expressive feature representations from social network structures to aid in link prediction, node classification and community detection tasks. RLVECN utilizes an edge sampling technique for exploiting features of a given social network via learning the context of each actor with respect to its associate actors.

Social network graphs possess apparent and latent knowledge about their respective actors and links which may be exploited, using effective and efficient techniques, for predicting events within the social graphs. Understanding the intrinsic relationship patterns among spatial social actors and their respective properties are crucial factors to be taken into consideration in event prediction within social networks. My research work proposes a unique approach for predicting events in social networks by learning the context of each actor/vertex using neighboring actors in a given social graph with the goal of generating vector-space embeddings for each vertex. Our methodology introduces a pre-convolution layer which is essentially a set of feature-extraction operations aimed at reducing the graph's dimensionality to aid knowledge extraction from its complex structure. Consequently, the low-dimensional node embeddings are introduced as input features to a one-dimensional ConvNet model for event prediction about the given social graph. Training and evaluation of this proposed approach have been done on datasets (compiled: November, 2017) extracted from real world social networks with respect to 3 European countries. Each dataset comprises an average of 280,000 links and 48,000 actors.

Event prediction in social network structures is a crucial research problem in social network analysis. This impels understanding the intrinsic relationship patterns preserving a given social network structure, based on the study of several structural properties computed on the constituent social units, with respect to space and time. In this regard, tackling problems of this nature is considered NP-Complete. Consequently, this paper proposes an original and unique approach which involves making event predictions about a target social unit, y, based on the intrinsic patterns of relationship learnt from one or more neighboring social units. Our methodology is based on Deep Learning (DL) architectures, and is developed using deep-layer stacks of Multilayer Perceptron (MLP) appended with an adjustment-bias (ab) vector at the output layer in a bid to improve the accuracy and precision of predictions made with respect to the target unit (or node). Also, we trained and tested our technique on a real world social clique comprising 5 connected cities; thereafter, we performed a comparative analysis of our approach against 9 other models drawn from the fields of Deep Learning, Machine Learning, and Statistics.