Towards a comprehensive methodology for KBS development

Abstract: The vitality of effective methods and processes for developing knowledge-based systems (KBS) is examined. The paper discusses what should be expected from a methodology. Have their advocates been getting it all wrong all the way? That is certainly a suggested view put forward. The criticism includes an articulation of the major schools of thought in developing KBS; their implicit assumptions and philosophies, especially the prototyping options, are presented in the light of this paper. After the criticism, the philosophical basis of a new comprehensive KBS development methodology is presented. This breeds the line model of development, which in turn creates what is suspected to be an essential concept of thought in the developer's mind.     

Cloning SIM Cards Usability Reduction in Mobile Networks

This paper studies the probability of using cloning subscriber identity module (SIM) cards in Universal Mobile Telecommunications Systems. It also explores how the mobile system can discover the cloning SIM cards as soon as possible and how to reduce the probability of using cloning SIM cards in mobile networks. The illegal mobile station attached to mobile networks can be detected by a Normal Location Area Update, a periodic area location update, and by outgoing calls that are issued from genuine mobiles. An analytical model is developed to investigate the effects of a location area update and outgoing calls issued by genuine mobiles on the use of illegal mobiles. Mobility management, such as registration, cancellation, and outgoing and incoming procedures for legal and illegal users are investigated and analysed. An analytical model to investigate the effect of outgoing call arrival rates, and location area residence time on the detected illegal user has been presented. This paper seeks to increase the security of communication by avoiding fraud of cloned mobile phones by proposing a solution to speed up the detection of cloning SIM cards.     

Heat transfer and fluid flow characteristics in flash desalination units

The present work investigates the effects of different operating and geometrical parameters on the flashing efficiency of water in a single stage flashing chamber. This chamber uses a submerged rectangular orifice as flow control area. Experiments were performed to investigate the effects of flow rate, liquid level in the flashing chamber, liquid inlet temperature and flashing range, on the flashing efficiency under various design conditions. These comprise flow with baffle placed in the flashing chamber, and wire meshes placed at the orifice to increase the turbulence level.It was found that the efficiency increased with the increase of the turbulence levels, flashing range and liquid inlet temperature; the efficiency deceased with the increase of the flow rate and liquid level in the flashing chamber. The application of numerical procedure to predict flow pattern and heat transfer in the flashing chamber is introduced as a powerful design tool.     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

Non-Newtonian Fluids in Spout-Fluid Beds: Mass Transfer Coefficient at Low Reynolds Numbers

Following on from the work of Anabtawi et al. (2003), this study examined how the volumetric liquid-phase mass transfer coefficient, k_La, of oxygen in air in three-phase spout-fluid beds was affected by varying the system parameters of bed height, bed diameter, gas velocity, and liquid velocity. The liquid used was 0.1% CMC solution, displaying a pseudo-plastic rheology, with 1.75 mm glass spheres as packing. The values of the Sherwood number were lower than in previous studies (Anabtawi et al., 2003), in the range 9,000-186,000. Gas velocity had a similar effect on k_La as in a bubble column, with results also giving good agreement with previous work on two-phase and three-phase spouted bed systems. The correlation obtained for the effect of liquid velocity on k_La compared well with that of Schumpe et al. (1989). An increase in the height of packing increased k_La to the power of 0.319, with an increase in column diameter also causing an increase in k_La, which is in agreement with the results of Akita and Yoshida (1973).