Imbalanced classification in faulty turbine data: New proximal policy optimisation

Abstract In industrial and real‐world systems, recognising errors and adopting the best approaches are gaining relevance. The authors’ goal is to identify artificial intelligence apps that provide the most reliable and valuable data‐based fault detection techniques. A system for fault identification is presented based on reinforcement learning and proximal policy optimisation (PPO). Due to the lack of fault data, one of the key issues with the standard policy is its inability to recognise fault classes; this issue was resolved by modifying the cost equation. Using improved PPO, the authors can improve performance, address data imbalances, and forecast possible failures more accurately. The approach utilises policy‐based optimisation, which offers several advantages. Firstly, it directly optimises the advantage quantity, and secondly, it ensures the stability of function approximation. The authors have studied two different turbines in Iran and collected data from them separately when a fault occurred. To demonstrate the efficiency of our algorithm, the authors have included the third and fourth datasets as cyber attack benchmarks. When the authors’ proposed policy is adopted, all evaluation metrics will improve by 3%–4% as compared to the previous policy in the first benchmark, between 20% and 55% in the second benchmark, between 6% and 14% in the third benchmark, and between 4% and 5% in the fourth benchmark, with improved results and prediction times compared to existing studies

Deep Q‐learning recommender algorithm with update policy for a real steam turbine system

Abstract In modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for data‐based fault diagnosis, and the authors are looking for the most reliable and practical ones. A framework based on DL and reinforcement learning (RL) is developed for fault detection. The authors have utilised two algorithms in their work: Q‐Learning and Soft Q‐Learning. Reinforcement learning frameworks frequently include efficient algorithms for policy updates, including Q‐learning. These algorithms optimise the policy based on the predictions and rewards, resulting in more efficient updates and quicker convergence. The authors can increase accuracy, overcome data imbalance, and better predict future defects by updating the RL policy when new data is received. By applying their method, an increase of 3%–4% in all evaluation metrics by updating policy, an improvement in prediction speed, and an increase of 3%–6% in all evaluation metrics compared to a typical backpropagation multi‐layer neural network prediction with comparable parameters is observed. In addition, the Soft Q‐learning algorithm yields better outcomes compared to Q‐learning

Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture

AbstractTo gain access to networks, various intrusion attack types have been developed and enhanced. The increasing importance of computer networks in daily life is a result of our growing dependence on them. Given this, it is glaringly obvious that algorithmic tools with strong detection performance and dependability are required for a variety of attack types. The objective is to develop a system for intrusion detection based on deep reinforcement learning. On the basis of the Markov decision procedure, the developed system can construct patterns appropriate for classification purposes based on extensive amounts of informative records. Deep Q‐Learning (DQL), Soft DQL, Double DQL, and Soft double DQL are examined from two perspectives. An evaluation of the authors’ methods using UNSW‐NB15 data demonstrates their superiority regarding accuracy, precision, recall, and F1 score. The validity of the model trained on the UNSW‐NB15 dataset was also checked using the BoT‐IoT and ToN‐IoT datasets, yielding competitive results

Determination of the Infectivity of Cryopreserved Theileria annu-lata Sporozoites in Tick Derived Stabilates Iran Ak-93 Strain, by In Vivo and In Vitro Methods

Background: The protozoan parasite Theileria annulata is the causative agent of tropical theileriosis in cattle. Vaccination is recommended by administration of attenuated schizont-infected cell lines. The expected protective immunity post-vaccination can be demonstrated by challenge test through inoculation of highly virulent infective sporozoites. The aim of this study was to produce Hyalomma anatolicum anatolicum tick infected with T. annulata (local strain) for preparation of tick-derived sporozoite stabilates for molecular characterization and infectivity test assay.&#x0D; Methods: A local T. annulata strain was used for experimental infection of calves. A field isolate of H. a. anatolicum was isolated, laboratory-reared and infected by blood-feeding on Theileria infected above-mentioned calves. The infectivity of calf, tick and prepared stabilate were confirmed by clinical signs of theileriosis, microscopic inspection, RT-PCR and in vitro cell culture.&#x0D; Results: The tick stabilate was prepared and cryopreserved in liquid nitrogen. The infectivity of the tick stabilate was verified by in vivo bioassay, in vitro cell culture infection, microscopic inspection in salivary glands and RT-PCR assay. The in vitro produced cell line in this study was characterized by T. annulata Cytochrome b gene analyzing.&#x0D; Conclusion: The infectivity of a new prepared tick-derived sporozoite stabilate was confirmed in susceptible calves; by microscopically, post mortem, tick microscopic and molecular assays. Moreover, naïve PBMCs were transformed and proliferated by T. annulata infected tick stabilate to immortal T. annulata schizont infected cell line. The potent infective sporozoite tick derived stabilate could be used for vaccine efficacy and challenge test as well as in vaccine development.</jats:p

Peste des Petits Ruminants Virus in Vulnerable Wild Small Ruminants, Iran, 2014–2016

In 2014–2016, >1,000 wild goats and sheep in 4 northern and central provinces of Iran died from peste des petits ruminants virus (PPRV) infection. Partial nucleoprotein sequencing of PPRV from 3 animals showed a close relationship to lineage 4 strains from China. Control measures are needed to preserve vulnerable ruminant populations

Avian influenza virus monitoring in wintering waterbirds in Iran, 2003-2007

<p>Abstract</p> <p>Background</p> <p>Virological, molecular and serological studies were carried out to determine the status of infections with avian influenza viruses (AIV) in different species of wild waterbirds in Iran during 2003-2007. Samples were collected from 1146 birds representing 45 different species with the majority of samples originating from ducks, coots and shorebirds. Samples originated from 6 different provinces representative for the 15 most important wintering sites of migratory waterbirds in Iran.</p> <p>Results</p> <p>Overall, AIV were detected in approximately 3.4% of the samples. However, prevalence was higher (up to 8.3%) at selected locations and for certain species. No highly pathogenic avian influenza, including H5N1 was detected. A total of 35 AIVs were detected from cloacal or oropharyngeal swab samples. These positive samples originated mainly from Mallards and Common Teals.</p> <p>Of 711 serum samples tested for AIV antibodies, 345 (48.5%) were positive by using a nucleoprotein-specific competitive ELISA (NP-C-ELISA). Ducks including Mallard, Common Teal, Common Pochard, Northern Shoveler and Eurasian Wigeon revealed the highest antibody prevalence ranging from 44 to 75%.</p> <p>Conclusion</p> <p>Results of these investigations provide important information about the prevalence of LPAIV in wild birds in Iran, especially wetlands around the Caspian Sea which represent an important wintering site for migratory water birds. Mallard and Common Teal exhibited the highest number of positives in virological and serological investigations: 43% and 26% virological positive cases and 24% and 46% serological positive reactions, respectively. These two species may play an important role in the ecology and perpetuation of influenza viruses in this region. In addition, it could be shown that both oropharyngeal and cloacal swab samples contribute to the detection of positive birds, and neither should be neglected.</p