Intelligent Computational Tools Research Articles

IACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP. In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively. The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models. The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.

Dataset for estimation of muscle Dysmorphia in individuals from Colombia.

This paper presents data collected through a questionnaire for the estimation of body dysmorphic disorder known as vigorexy, weight level and levels of exercise dependence in individuals from Barranquilla, Colombia who attend local gyms, based on their physical activity, food supplements eating habits, psychological pressure, and risk of muscle dysmorphia. The Data contains 8 tributes and 200 records; labelling obesity range according to WHO into normal, overweight or obesity. All data was collected in person and directly from users. This data can be used to generate scientific research and intelligent computational tools to identify the obesity level and muscle dysmorphia risk of an individual and to build recommender systems that monitor health and mental status.

Representing Operational Modes for Situation Awareness

Operating complex plants is an increasingly demanding task for human operators. Diagnosis of and reaction to on-line events requires the interpretation of real time data. Vast amounts of sensor data as well as operational knowledge about the state and design of the plant are necessary to deduct reasonable reactions to abnormal situations. Intelligent computational support tools can make the operator’s task easier, but they require knowledge about the overall system in form of some model.While tools used for fault-tolerant control design based on physical principles and relations are valuable tools for designing robust systems, the models become too complex when considering the interactions on a plant-wide level. The alarm systems meant to support human operators in the diagnosis of the plant-wide situation on the other hand fail regularly in situations where these interactions of systems lead to many related alarms overloading the operator with alarm floods. Functional modelling can provide a middle way to reduce the complexity of plant-wide models by abstracting from physical details to more general functions and behaviours. Based on functional models the propagation of failures through the interconnected systems can be inferred and alarm floods can potentially be reduced to their root-cause. However, the desired behaviour of a complex system changes due to operating procedures that require more than one physical and functional configuration. In this paper a consistent representation of possible configurations is deduced from the analysis of an exemplary start-up procedure by functional models.The proposed interpretation of the modelling concepts simplifies the functional modelling of distinct modes. The analysis further reveals relevant links between the quantitative sensor data and the qualitative perspective of the diagnostics tool based on functional models. This will form the basis for the ongoing development of a novel real-time diagnostics system based on the on-line adaptation of the underlying MFM model.

Fault Detection and Diagnosis in Deep-trough Hydroponics using Intelligent Computational Tools

The intelligent computational tools of feedforward neural networks and genetic algorithms are used to develop a real-time detection and diagnosis system of specific mechanical, sensor and plant (biological) failures in a deep-trough hydroponic system. The capabilities of the system are explored and validated. In the process of designing the fault detection neural network model, a new technique for neural network designing and training parameterisation is developed, based on the heuristic optimisation method of genetic algorithms. Sensor and actuator faults are detected and diagnosed in sufficient time that the fault detection model can be applied on-line as a reliable supervisor of the operation of an unattended deep-trough hydroponic system. Biological faults were not detected in general. It seems that the interaction between plants and their root-zone microenvironment is not equally balanced, as the condition of the plants is highly influenced by the conditions in their root zone microenvironment, while these microenvironment conditions (as they are represented by the measurable variables) are not influenced in the same degree by the conditions of the plants. Finally, the genetic algorithm system developed here can be successfully applied to a combinatorial problem such as deciding the best neural network architecture, activation functions and training algorithm for a specific model.

Computer vision in architectural design

The visual representations traditionally used by architects form a primary source for the analysis and understanding of architectural design. Computerization and, in particular, computer vision offer a new opportunity for making explicit the general cognitive mechanisms and the domain knowledge involved in these representations, as well as for developing subsequently intelligent computer tools which support and facilitate the representation of design thinking and its products.

AI in Reverse: Computer Tools That Turn Cognitive

This article posits artificial intelligence (AI), as applied in instruction, is a more efficient means to old ends and that newer ends can be thought of. In particular, it is argued on the basis of some past research and on the basis of a Vygotskian view, that intelligent computer tools can not only simulate human cognition but, given specific conditions, humans can simulate computer's intelligence. That is, learners can internalize computers' intelligent tools and use them as cognitive ones. Internalization is discussed in terms of cognitive reconstruction of cultural artifacts of particular characteristics — tool-like nature, relative novelty of function, compatibility with learners' schemata, and most importantly — explicitness of operation. The article also discusses learning conditions that might facilitate the process of tool internalization, particularly “high road” learning whereby learners are intentionally mindful of a tool's mode of operation and logic. A preliminary study is briefly described showing that children are capable of internalizing the metacognitive guidance provided by a semi-intelligent Reading Aid and that they show evidence of internalizing the tool's “intelligence” thereby manifesting improved reading comprehension as well as improved essay writing. Four fundamental questions are raised for future research as well as questions of ethics.