A review of training methods of ANFIS for applications in business and economic

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance

Accelerated mine blast algorithm for ANFIS training for solving classification problems

Mine Blast Algorithm (MBA) is newly developed metaheuristic technique. It has outperformed Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and their variants when solving various engineering optimization problems. MBA has been improved by IMBA, which is modified in this paper to accelerate its convergence speed furthermore. The proposed variant, so called Accelerated MBA (AMBA), replaces the previous best solution with the available candidate solution in IMBA. ANFIS accuracy depends on the parameters it is trained with. Keeping in view the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm, many have trained ANFIS using metaheuristic algorithms. In this paper, for getting high performance, the parameters of ANFIS are trained by the proposed AMBA. The experimental results of real-world benchmark problems reveal that AMBA can be used as an efficient optimization technique. Moreover, the results also indicate that AMBA converges earlier than its other counterparts MBA and IMBA

A single measurement of combined direct and indirect MPPT algorithm for mismatch PV module application / Mohd Najib Mohd Hussain

The use of module integrated converter (MIC) topologies for photovoltaic (PV) system is to harvest the maximum power point of PV module from solar irradiance. MIC is a promising method to keep the electrical power output from PV module at maximum capacity through maximum power point tracking (MPPT) algorithm during full-sun and mismatch module occurrence. This thesis focussed on the development of PV module integrated with positive output (PO) buck-boost converter by intelligent control of MPPT technique. A novel of combination direct and indirect (CoDId) MPPT algorithm is implemented for solving mismatch losses from existing PV system by employing the implementation of system identification approach. System identification is established via utilization of multi-input single-output (MISO) model and incorporation with simple iteration scheme. The main intention of this study is to develop a single sensor measurement of instantaneous output current from PV system in order to track maximum power point of the PV module. The signal from the sensor is then applied for power switching devices of the PO buckboost converter by the attained maximum power point current from CoDID MPPT method

A single measurement of combined Direct and indirect MPPT algorithm for mismatch PV module application / Mohd Najib Mohd Hussain

The use of module integrated converter (MIC) topologies for photovoltaic (PV) system is to harvest the maximum power point of PV module from solar irradiance. MIC is a promising method to keep the electrical power output from PV module at maximum capacity through maximum power point tracking (MPPT) algorithm during full-sun and mismatch module occurrence. This thesis focussed on the development of PV module integrated with positive output (PO) buck-boost converter by intelligent control of MPPT technique. A novel of combination direct and indirect (CoDId) MPPT algorithm is implemented for solving mismatch losses from existing PV system by employing the implementation of system identification approach. System identification is established via utilization of multi-input single-output (MISO) model and incorporation with simple iteration scheme. The main intention of this study is to develop a single sensor measurement of instantaneous output current from PV system in order to track maximum power point of the PV module. The signal from the sensor is then applied for power switching devices of the PO buck-boost converter by the attained maximum power point current from CoDID MPPT method. The system is constructed from model-based design (MBD) of the PV system. The MBD is divided into four parts; executable system properties, design and simulation of the properties, execution and implementation for code generation and verification process. The properties dataset of building integrated PV system located in Malaysian Green Technology Corporation are taken as requirement and specification for the MBD system. The second aim is to provide a constant desired output DC voltage of the system. The criterion of maximum power produce from PV system is to produce the DC voltage from unregulated DC output voltage due to mismatch losses. Two control schemes; logic control and current closed-loop control are developed for retaining the required DC output voltage. The system also offer a flexibility of using different electrical specification of PV module in string connection solving the issue of the selection of PV module should be in similar specification. The development of the system is carried out using Matlab/Simulink incorporated with MPLAB programming for evaluating and validating the system. The ability of the system to track maximum power point regardless mismatching condition are assessed through simulation study and proved in the experimental work for different PV module specification and variable of solar irradiances

A comparative study on the protective effects of ascorbic acid and tualang honey on the skin flap of reverse sural fasciocutaneous flap in a smoking rabbit model

Lower limb traumas impose a great challenge for reconstructive surgeons. Complexity in the tissue constituent of lower limb and the concern regarding vascularity has been the barrier in the process of reconstruction. Introduction of pedicled fasciocutaneous flap based on reverse flow has provided an alternative to free flap reconstruction. Reverse sural fasciocutaneous flap is gaining popularity and widely being used in the reconstruction of distal third of lower limb due to its ease of elevation compared to free flap. Performing flap surgery in smokers has been proven to yield poor outcome. Components in cigarette smoke have an established negative impact on the vascular endothelium of blood vessels. Nicotine and carbon monoxide in cigarette smoke produce oxidative damage and release reactive oxygen species resulting in ischaemic injury. Ascorbic acid and “Tualang honey” are two agents which have been extensively studied for their anti oxidative and wound healing properties. Previous studies were aimed at studying the impact of cigarette smoke and ascorbic acid on the endothelium of large calibre vessel like carotid and aorta. The present study was undertaken to compare the anti oxidative properties of both these agents in improving the outcome in flap survival using a smoking rabbit model. This study is aimed at assessing the beneficial anti oxidative property to improve microvascular circulation in a reverse sural fasciocutaneous flap in a smoking rabbit model. All samples were exposed to cigarette smoke using a smoke chamber for a total of 5 weeks. Study sample was divided into three groups. Control group only received cigarette smoke while the other two groups each received ascorbic acid in the form of intramuscular injection and oral “Tualang honey” on top of exposure to cigarette smoke. Reverse sural fasciocutaneous flap was raised in all the rabbits at day 30 of exposure to cigarette smoke. Samples were evaluated on immediate post operative period, post operative day 1, 3 and 7. Parameters documented throughout the study were temperature, capillary refill time, colour of flap, presence hematoma or infection, and on post operative day 7 the flap survival was calculated. Intervention with smoking, ascorbic acid and “Tualang honey” were continued throughout the period of experiment. We found that, both group intervened with ascorbic acid and “Tualang honey” showed marked improvement in the flap survival. Statistically significant improvement were documented with regard to temperature, improvement in colour of flap and reduction in hematoma. Flap survival improved from 62% in the smoking arm to 88.9% and 81.0% in the ascorbic acid and “Tualang honey” arm. However oneway ANOVA test showed p value of 0.075 which was near to significant value. In conclusion both ascorbic acid and “Tualang honey” improved the survival of reverse sural fasciocutaneous flap in smoking rabbit model. However ascorbic acid is superior compared to “Tualang honey” in improving the oxidative damages produced by cigarette smoke and microvascular circulation of the reverse sural fasciocutaneous flap

Analysis of Deep-Fake Technology Impacting Digital World Credibility: A Comprehensive Literature Review

Deep-Fake Technique is a new scientific method that uses Artificial-Intelligince to make fake videos with an affect of facial expressions and coordinated movement of lips. This technology is frequently employed in a variety of contexts with various goals. Deep-Fake technology is being used to generate an extremely realistic fake video that can be widely distributed to promote false information or fake news about any celebrity or leader that was not created by them. Because of the widespread use of social media, these fraudulent videos can garner billions of views in under an hour and have a significant impact on our culture. Deep-Fakes are a threat to our celebrities, democracy, religious views, and commerce, according to the findings, but they can be managed through rules and regulations, strong company policy, and general internet user awareness and education. We need to devise a process for examining such video and distinguishing between actual and fraudulent footage

The US Subprime Crises and Extreme Market Pressures in Asia

The primary objective of this study is to examine the evidence of occurrences of extreme market pressure of currencies of a number of Asian economies against the US dollar during the period of 2000-2009. In particular, we are interested in investigating the severity of these pressures during the recent US sub-prime crisis of 2007-2009. Were the currencies of these economies subjected to indiscriminate selling pressures during the period of the crisis? Was the heightened severity of the selling pressures associated with a particular event during the subprime crisis, such as the collapse of the Lehman Brothers? Our findings confirm the globally indiscriminate impacts of the sub-prime crisis on the countries examined and the greatest impact was felt and experienced by these economies around the time of the Lehman-Brothers’ collapse during the last quarter of 2008. Our findings offer far-reaching implications in terms of the linkages between macroeconomic and financial stability.Currency Crisis; Exchange Market Pressure; SEACEN; Extreme Value Theory

An Intelligent Approach to Automatic Query Formation from Plain Text using Artificial Intelligence

Man have always been, inherently, curious creatures. They ask questions in order to satiate their insatiable curiosity. For example, kids ask questions to learn more from their teachers, teachers ask questions to assist themselves to evaluate student performance, and we all ask questions in our daily lives. Numerous learning exchanges, ranging from one-on-one tutoring sessions to thorough exams, as well as real-life debates, rely heavily on questions. One notable fact is that, due to their inconsistency in particular contexts, humans are often inept at asking appropriate questions. It has been discovered that most people have difficulty identifying their own knowledge gaps. This becomes our primary motivator for automating question generation in the hopes that the benefits of an automated Question Generation (QG) system will help humans achieve their useful inquiry needs. QG and Information Extraction (IE) have become two major issues for language processing communities, and QG has recently become an important component of learning environments, systems, and information seeking systems, among other applications. The Text-to-Question generation job has piqued the interest of the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System (ITS), and Information Retrieval (IR) groups as a possible option for the shared task. A text is submitted to a QG system in the Text-to-Question generation task. Its purpose would be to create a series of questions for which the text has answers (such as a word, a set of words, a single sentence, a text, a set of texts, a stretch of conversational dialogue, an inadequate query, and so on)

Toxic Effects Of Signal Grass (Brachiaria Decumbens) On Drug- Metabolizing Enzyme Activities In Sheep

Signal grass (Brachiaria decumbens) is widely grown on livestock farms in many countries including Malaysia due to its high productivity and nutritive value. Unfortunately, it is toxic to sheep and goats causing a severe hepatic and renal damage, and death. Two experiments were conducted to determine the effect of the toxicity of signal grass (B. decumbens) on the activity of drug-metabolizing enzymes (DME) in sheep and cattle. It was hypothesized that the activities of selected enzymes would be affected during intoxication and that differences in the activity level of the enzymes in the two species might explain why cattle were safe from the toxic effect of the grass. Twenty-three healthy Wiltshire-Malin crossed rams, aged 1 4- 1 6 months were used, fifteen for the first experiment and eight for the later. In the first experiment, aniline 4-hydroxylase (A4H), aminopyrine N-demethylase (AND), UDP-glucuronyl transferase (UDPGT), glutathione S-transferase (GST) and cytochrome P450 of the liver and kidney of the control and intoxicated sheep wer

Stability model integration for large scale solar photovoltaic system using Western electricity coordinating council model

Due to the increased demand for renewable energy, the interest in the large-scale solar photovoltaic (LSSPV) power plant has recently grown dramatically. However, when a large amount of electricity is produced from the LSSPV power plant to the grid interconnection, the system commonly experiences instability and thus disrupt the grid system in disturbance issues such as bus fault, line-to-line fault, three-phase fault, and tripping. This sudden disturbance occurrence is tended to interrupt the stability of the system from providing balanced electrical production within the electrical grid. A dynamics response from the simulation is used to study the stability and the behavior of the photovoltaic (PV) plant into the grid interconnection by developing 118 bus system. The observation of critical clearing time (CCT) duration shows that the result from the simulation where the duration takes less than t=15 s for the system to get back to its pre-fault condition in three-phase fault and tripping in a dynamic simulation to shows that the system reaches its stability been observed through the simulation result by using from user-specific models to generic models like those advocated by the Western electricity coordinating council (WECC) in power system simulator for engineering (PSSE) software