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 New Digital Watermarking Algorithm Using Combination of Least Significant Bit (LSB) and Inverse Bit

In this paper, we introduce a new digital watermarking algorithm using least significant bit (LSB). LSB is used because of its little effect on the image. This new algorithm is using LSB by inversing the binary values of the watermark text and shifting the watermark according to the odd or even number of pixel coordinates of image before embedding the watermark. The proposed algorithm is flexible depending on the length of the watermark text. If the length of the watermark text is more than ((MxN)/8)-2 the proposed algorithm will also embed the extra of the watermark text in the second LSB. We compare our proposed algorithm with the 1-LSB algorithm and Lee's algorithm using Peak signal-to-noise ratio (PSNR). This new algorithm improved its quality of the watermarked image. We also attack the watermarked image by using cropping and adding noise and we got good results as well.Comment: 8 pages, 6 figures and 4 tables; Journal of Computing, Volume 3, Issue 4, April 2011, ISSN 2151-961

Reka Bentuk Sekolah Kebangsaan Daripada Aspek Kecekapan Tenaga : Kajian Kes Di Daerah Perak Tengah [LB3221. N162 2008 f rb].

Pendidikan merupakan nadi pembangunan dan kemajuan sesebuah negara melalui pertumbuhan ekonomi disebabkan wujudnya tenaga kerja yang terlatih, produktif dan komited yang berasaskan daripada adanya ilmu pengetahuan. It is undeniable that education system is one of the major factors in building a nation. A quality framework and system will ensure continues development of human resources by improving skills and commitment in responding to the labor market

Investigating User Perception of High-Performance Schools about Factors Associated with Building Energy Efficiency

Energy demand in buildings can be reduced by improving energy efficiency. MS1525 has recommended that energy efficiency for Non-Residential Buildings in Malaysia to be not more than 135kWh/m²/year. A school building is a non-residential building and has major social responsibilities. Based on the theory of building energy-efficiency, energy efficiency can be achieved through three main factors: a) design of buildings; b) design of services; and c) user behavior. This study aims to investigate the user perceptions in High-Performance Schools. The questionnaire viewed three main perceptions of users: perception of user behavior, the perception of building design and perception of services design.© 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies, Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.Keywords: User perception; building energy index; building energy efficiency; school buildin

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique for addressing various difficult research problems in business. ANFIS (Adaptive Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for modeling highly non-linear, complex and dynamic systems. It has been proved that, with proper number of rules, an ANFIS system is able to approximate every plant. Even though it has been widely used, ANFIS has a major drawback of computational complexities. The number of rules and its tunable parameters increase exponentially when the numbers of inputs are large. Moreover, the standard learning process of ANFIS involves gradient based learning which has prone to fall in local minima. Many researchers have used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify ANFIS architecture to reduce its complexity and improve the accuracy of classification problems. The experiments are carried out by trying different types and shapes of membership functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS and the training error results are measured for each combination. The results showed that modified ANFIS combined with ABC method provides better training error results than common ANFIS model

The Relationship between Crime Prevention through Environmental Design and Fear of Crime

AbstractThe built environment especially in terms of the residential design is believed to be one of the factors influencing crime and the level of fear of crime (FOC). People's perception of FOC varies considerably depending on their attitude and practices towards environmental conditions. CPTED is one of the most effective mechanisms to reduce FOC. Therefore, this paper investigates the relationship between practices and attitudes of CPTED and FOC in gated and non-gated residential areas. This study found that CPTED perception has a positive relationship with FOC (r=0.36, p<0.01) while CPTED practices has a negative relationship with FOC (r=-0.40, p<0.01)

Sense of community in gated and non-gated residential / Siti Rasidah Md Sakip, Noraini Johari and Mohd Najib Mohd Salleh.

Neighbourhood design is one of the factors contributing towards the establishment and maintenance of local community ties. The differences in environmental size and design of neighbourhoods are perceived to influence sense of community networking functions. A physical element such as gated element is also believed to have an influence on local community relationship networking. Therefore, a study on sense of community was conducted in two neighbourhood areas: Putrajaya (non-gated) and Bandar Baru Bangi (gated) using face to face interview method. This study found that residents of nongated residential areas demonstrated higher sense of community (M=6.47 SP=0.08) than residents of gated residential areas (M=6.39, SP=1.08)

Translating Quran verses result using indexed references

Documents translations are still ambiguous when we translate them using by word, by phrase or by context technically separately and based on different readers’ understandings. Typically, the documents translation in different languages is found not well structured technically that leads to the misunderstanding amongst readers. Hence, there is a necessity for improving the Quran documents translation for providing the right sentences (ayats) in other languages as better as possible technically. The concept of source language and target languages is most importantly in designing the right algorithm as new approach for explaining the source of documents language in the form of the target of documents languages. In designing a new approach based on the said concept, the indexing technique is necessarily for retrieving the target translation from target language as called as multilingual information retrieval (MLIR). Thus, this paper proposes the Indexed References for retrieving the target-translated documents based on the structure index of each document (text file). Thus, Quran documents are translated easier based on unique structure index of each documents either indexing each Surah (Chapters) or indexing each Ayah (Verses) plus Surah as a unique reference. The documents translations retrieval based on Indexed Reference technique is more accurate at 99.99% comparing to the experimental separation of by word, by phrase and by concept translation technique. The proposed technique is useful for documents-to- documents translation retrieval in all languages of world