The intrusion detection systems (IDSs) have attracted more researchers from last two decades. The much more work has been done in IDS. But still, there are some problems remain unsolved like false positive rate and detection accuracy. The various approaches are used in developing IDS; some of these are data mining, machine learning, statistic-based, and rule-based approaches. In this paper, we ...

Reinforcement learning models can explain various aspects of two-way avoidance learning but do not provide a rationale for the relationship found between the dynamics of initial learning and those of reversal learning. Artificial Intelligence planning offers a novel way to conceptualize the learners’ cognitive processes by providing an explicit representation of and reasoning about internal pro...

Non-Player-Characters (NPCs), as found in computer games, can be modelled as intelligent systems, which serve to improve the interactivity and playability of the games. Although reinforcement learning (RL) has been a promising approach to creating the behaviour models of non-player characters (NPC), an initial stage of exploration and low performance is typically required. On the other hand, im...

While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of legal and argumentative reasoning [6]. In this paper, we present a new hybrid model of computation that allows for the deduction and learning of argumentative reasoning. We do so by using Neural-Symbolic Learning Systems to translate ...

This paper proposes the Named Entity Recognition (NER) system for Punjabi language using a hybrid approach in which rule based approach and machine learning approach i.e. Hidden Markov Model (HMM) is combined. With no Dataset available, the Named Entities (NEs) were manually tagged which led us to the creation of training and testing dataset, under the linguistic supervision. Using hybrid appro...

Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in o...

In this paper we present the INSS system, a new hybrid approach based upon the principles of KBANN networks. It represents an important improvement in comparison with its predecessor because the learning and the knowledge extraction process are faster and are accomplished in an incremental way . INSS offers a new approach applicable to constructive machine learning with high-performance tools, ...

Critical role of the internet things (IoT) in various domains like smart city, healthcare, supply chain, and transportation has made them target malicious attacks. Past works this area focused on centralised intrusion detection system (IDS), assuming a central entity to perform data analysis identify threats. However, such IDS may not always be feasible, mainly due spread across multiple source...

Abstract Precise prediction on vacant parking space (VPS) information plays a vital role in intelligent transportation systems for it helps drivers to find the quickly reduce unnecessary waste of time and excessive environmental pollution. By analyzing historical zone-wise VPS data, we that number VPSs, there is not only solid temporal correlation within each lot, but also an obvious spatial am...