Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we propose DRLTrack , a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. In DRLTrack , a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks. DRLTrack is said to be trustworthy as it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance of DRLTrack considering the objectives through simulations and it demonstrates superior performance compared with existing work.

Due to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator.

This letter proposes a new approach to making network selection simple yet effective. Under consideration is a heterogeneous environment where a mobile user roams within a two-tier network architecture over different radio access technologies. Allowing for multiple determinants, we design a scheme for a user equipment to deduce whether handoff is imminent by fusing several metrics—received signal strength, data rate, and network latency. Our method harnesses strengths of fuzzy logic while maintaining lower computational complexity by means of combinatorial fusion. Our results demonstrate that the new approach outperforms other previous proposed fuzzy logic-based strategies in terms of time efficiency and operational effectiveness with the same level of vertical handoffs.

Multiple scoring systems (including rank and score functions; MSS) have been widely used in multiple regression, intelligent biometric systems, multiple artificial neural nets, combining pattern classifiers, ensemble methods, machine learning and artificial intelligence (AI), data and information fusion, preference ranking, and deep learning. Combining MSS has achieved numerous successful results in a variety of domain applications. However, the reasons why this happens remains an active area of investigation. Combinatorial fusion analysis (CFA) combines MSS using the rank-score characteristic (RSC) function and cognitive diversity (CD). The RSC function was proposed to characterise the predictive behaviour of a scoring system. It was subsequently used to define the notion of “cognitive diversity”, which measures the dissimilarity in the representation of information between two scoring systems. In this article, we first examine characterizations of and diversity between scoring systems. Then, we review combinatorial fusion analysis with a variety of domain applications, including biometric systems in cognitive neuroscience, and joint decision making with visual cognitive systems. Finally, we demonstrate that multi-layer combinatorial fusion (MCF) on the Kemeny rank space is a viable machine learning and AI framework for preference ranking and reinforcement learning. This work provides a scientific foundation and technological insights for the use of Combinatorial Fusion in ensemble methods, data and information fusion, preference ranking, and deep reinforcement learning with applications to a variety of domains in data science and informatics for secure and sustainable societies.

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks continue to pose significant threats to networked systems, causing disruptions that can lead to substantial financial losses. These attacks exploit vulnerabilities in network architecture to overwhelm systems, rendering them unavailable to legitimate users. The complexity and evolving nature of DoS/DDoS attacks necessitate advanced detection techniques that can operate effectively in real-time environments. This paper comprehensively examines current methodologies for online DoS/DDoS attack detection. We explore integrating sampling techniques and Change Point Detection (CPD) with Machine Learning (ML) approaches to enhance the detection and identification of DoS/DDoS activities in network traffic. We further assess the various sampling methods and their impact on the performance of online detection, considering both the efficiency and accuracy of these techniques in real-world applications. Lastly, we delve into the challenges of deploying these technologies in operational network environments, highlighting practical implications and future research directions. Our review synthesizes findings from recent studies, providing a critical analysis of existing strategies and proposing a unified framework that leverages CPD, ML, and targeted sampling to improve the resilience of networks against these disruptive cyber threats.