9 research outputs found
Causative factors of construction and demolition waste generation in Iraq Construction Industry
The construction industry has hurt the environment from the waste generated during
construction activities. Thus, it calls for serious measures to determine the causative
factors of construction waste generated. There are limited studies on factors causing
construction, and demolition (C&D) waste generation, and these limited studies only
focused on the quantification of construction waste. This study took the opportunity to
identify the causative factors for the C&D waste generation and also to determine the
risk level of each causal factor, and the most important minimization methods to
avoiding generating waste. This study was carried out based on the quantitative
approach. A total of 39 factors that causes construction waste generation that has been
identified from the literature review were considered which were then clustered into 4
groups. Improved questionnaire surveys by 38 construction experts (consultants,
contractors and clients) during the pilot study. The actual survey was conducted with
a total of 380 questionnaires, received with a response rate of 83.3%. Data analysis
was performed using SPSS software. Ranking analysis using the mean score approach
found the five most significant causative factors which are poor site management, poor
planning, lack of experience, rework and poor controlling. The result also indicated
that the majority of the identified factors having a high-risk level, in addition, the better
minimization method is environmental awareness. A structural model was developed
based on the 4 groups of causative factors using the Partial Least Squared-Structural
Equation Modelling (PLS-SEM) technique. It was found that the model fits due to the
goodness of fit (GOF ≥ 0.36= 0.658, substantial). Based on the outcome of this study,
39 factors were relevant to the generation of construction and demolition waste in Iraq.
These groups of factors should be avoided during construction works to reduce the
waste generated. The findings of this study are helpful to authorities and stakeholders
in formulating laws and regulations. Furthermore, it provides opportunities for future
researchers to conduct additional research’s on the factors that contribute to
construction waste generation
Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia
The declining of air quality mostly affects the elderly, children, people with asthma,
as well as a restriction on outdoor activities. Therefore, there is an importance to
provide a statistical modelling to forecast the future values of surface layer ozone (O3)
concentration. The objectives of this study are to obtain the best multivariate time
series (MTS) model and develop an online air quality forecasting system for O3
concentration in Malaysia. The implementations of MTS model improve the recent
statistical model on air quality for short-term prediction. Ten air quality monitoring
stations situated at four (4) different types of location were selected in this study. The
first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban
represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban
located in Banting, Kangar, and Tanjung Malim, also the only background station at
Jerantut. The hourly record data from 2010 to 2017 were used to assess the
characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data
of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2),
carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH)
were used to examine the best MTS models. Three methods of MTS namely vector
autoregressive (VAR), vector moving average (VMA), and vector autoregressive
moving average (VARMA), has been applied in this study. Based on the performance
error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and
Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for
VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained
the VMA(2) as the best model. The lowest root mean square error (RMSE) and
normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and
Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the
lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system
for O3 was successfully developed based on the best MTS models to represent each
monitoring station
Pemodelan Deteksi Penyakit Sirosis Hati dengan Menggunakan Jaringan Syaraf Tiruan
Liver (liver) is the largest organ in the human body. In the hearts of many important processes occur in human life. Unfortunately, although the liver is essential for life, the liver is also susceptible to disease. Cirrhosis of the liver is one type of heart disease. Cirrhosis of the liver is a common chronic liver disease, caused by damage to the liver. Our research was made of a disease modeling to simulate using neural networks with backpropagation method to determine the causes and symptoms of liver cirrhosis. Modeling is expected to detect the disease early cirrhosis of the liver so it can be preventative or further action
A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing
Researchers widely have introduced the Artificial Bee Colony (ABC) as an optimization algorithm to deal with classification and prediction problems. ABC has been combined with different Artificial Intelligent (AI) techniques to obtain optimum performance indicators. This work introduces a hybrid of ABC, Genetic Algorithm (GA), and Back Propagation Neural Network (BPNN) in the application of classifying, and diagnosing Diabetic Mellitus (DM). The optimized algorithm is combined with a mutation technique of Genetic Algorithm (GA) to obtain the optimum set of training weights for a BPNN. The idea is to prove that weights’ initial index in their initialized set has an impact on the performance rate. Experiments are conducted in three different cases; standard BPNN alone, BPNN trained with ABC, and BPNN trained with the mutation based ABC. The work tests all three cases of optimization on two different datasets (Primary dataset, and Secondary dataset) of diabetic mellitus (DM). The primary dataset is built by this work through collecting 31 features of 501 DM patients in local hospitals. The secondary dataset is the Pima dataset. Results show that the BPNN trained with the mutation based ABC can produce better local solutions than the standard BPNN and BPNN trained in combination with ABC
The effect of adaptive parameters on the performance of back propagation
The Back Propagation algorithm or its variation on Multilayered Feedforward
Networks is widely used in many applications. However, this algorithm is
well-known to have difficulties with local minima problem particularly caused by
neuron saturation in the hidden layer. Most existing approaches modify the learning
model in order to add a random factor to the model, which overcomes the tendency
to sink into local minima. However, the random perturbations of the search direction
and various kinds of stochastic adjustment to the current set of weights are not
effective in enabling a network to escape from local minima which cause the network
fail to converge to a global minimum within a reasonable number of iterations. Thus,
this research proposed a new method known as Back Propagation Gradient Descent
with Adaptive Gain, Adaptive Momentum and Adaptive Learning Rate
(BPGD-AGAMAL) which modifies the existing Back Propagation Gradient Descent
algorithm by adaptively changing the gain, momentum coefficient and learning rate.
In this method, each training pattern has its own activation functions of neurons in
the hidden layer. The activation functions are adjusted by the adaptation of gain
parameters together with adaptive momentum and learning rate value during the
learning process. The efficiency of the proposed algorithm is compared with
conventional Back Propagation Gradient Descent and Back Propagation Gradient
Descent with Adaptive Gain by means of simulation on six benchmark problems
namely breast cancer, card, glass, iris, soybean, and thyroid. The results show that
the proposed algorithm extensively improves the learning process of conventional
Back Propagation algorithm
Determination of baseflow quantity by using unmanned aerial vehicle (UAV) and Google Earth
Baseflow is most important in low-flow hydrological features [1]. It is a function of a large number of variables that include factors such as topography, geology, soil, vegetation, and climate. In many catchments, base flow is an important component of streamflow and, therefore, base flow separations have been widely studied and have a long history in science. Baseflow separation methods can be divided into two main groups: non-tracer-based and tracer- based separation methods of hydrology. Besides, the base flow is determined by fitting a unit hydrograph model with information from the recession limbs of the hydrograph and extrapolating it backward
Damage of reinforced concrete beams consisting modified artificial polyethylene aggregate (MAPEA) under low impact load
The impact damage of reinforced concrete beams subjected to low velocity impact loading at the ultimate load range are explored. In this study, an impact tests is carried out on reinforced concrete beam consisting Modified Artificial Polyethylene Aggregate (MAPEA), where, an approximately 100 kg of impact weight were dropped three times onto the beam specimens until its fails. The waste plastic bags, that encapsulated by glass powder as known as MAPEA were used as the replacement of coarse aggregate. There are twelve beam specimens of size 120 mm x 150 mm x 800 mm are categorized into three groups, where each group consists of 4 specimens. The three groups denoted as normal reinforced concrete (NRC), reinforced concrete with MAPEA concrete block infill (RCAI) and reinforced concrete with 9% of MAPEA as a coarse aggregate (RC9A). All specimens were tested under low velocity impact loads under 0.32 m and 1.54 m (2.5 m/s & 5.5 m/s velocities) drop height of impact weight. The comparisons were made between the three types of beams under the aspect of failure (shear and flexural) and its final displacement. The result of the laboratory test showed that the RC9A beams produced less crack and low value of residual displacement
Improved cuckoo search based neural network learning algorithms for data classification
Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural
Network (BPNN) algorithm has been used as a tool for recognizing a mapping
function among a known set of input and output examples. These networks can be
trained with gradient descent back propagation. The algorithm is not definite in
finding the global minimum of the error function since gradient descent may get
stuck in local minima, where it may stay indefinitely. Among the conventional
methods, some researchers prefer Levenberg-Marquardt (LM) because of its
convergence speed and performance. On the other hand, LM algorithms which are
derivative based algorithms still face a risk of getting stuck in local minima.
Recently, a novel meta-heuristic search technique called cuckoo search (CS)
has gained a great deal of attention from researchers due to its efficient convergence
towards optimal solution. But Cuckoo search is prone to less optimal solution during
exploration and exploitation process due to large step lengths taken by CS due to
Levy flight. It can also be used to improve the balance between exploration and
exploitation of CS algorithm, and to increase the chances of the egg’s survival.
This research proposed an improved CS called hybrid Accelerated Cuckoo
Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm
Optimization (APSO) algorithm. In the proposed HACPSO algorithm, initially
accelerated particle swarm optimization (APSO) algorithm searches within the
search space and finds the best sub-search space, and then the CS selects the best
nest by traversing the sub-search space. This exploration and exploitation method
followed in the proposed HACPSO algorithm makes it to converge to global optima
with more efficiency than the original Cuckoo Search (CS) algorithm. Finally, the proposed CS hybrid variants such as; HACPSO, HACPSO-BP,
HACPSO-LM, CSBP, CSLM, CSERN, and CSLMERN are evaluated and compared
with conventional Back propagation Neural Network (BPNN), Artificial Bee Colony
Neural Network (ABCNN), Artificial Bee Colony Back propagation algorithm
(ABC-BP), and Artificial Bee Colony Levenberg-Marquardt algorithm (ABC-LM).
Specifically, 6 benchmark classification datasets are used for training the hybrid
Artificial Neural Network algorithms. Overall from the simulation results, it is
realized that the proposed CS based NN algorithms performs better than all other
proposed and conventional models in terms of CPU Time, MSE, SD and accuracy
Heart Diseases Diagnosis Using Artificial Neural Networks
Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions.
Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis.
The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets.
The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases