539 research outputs found
Water filtration by using apple and banana peels as activated carbon
Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent
Kecerdasan Buatan dalam Teknologi Kedokteran: Survey Paper
Dalam makalah ini, akan diberikan gambaran mengenai penerapan kecerdasan buatan dalam bidang medis, khususnya untuk pembuatan keputusan serta pengklasifikasian dalam ilmu diagnostik berdasarkan gambar biomedis. Beberapa teknologi kecerdasan buatan (AI) terbukti mampu melakukan optimasi klasifikasi gambar biomedis. Studi ini mengumpulkan studi representatif yang menunjukan bagaimana AI digunakan untuk memecahkan masalah pada ilmu diagnostik. Ini juga mengakui metode kecerdasan buatan yang sering digunakan dalam memecahkan masalah pada ilmu diagnostik, seperti metode jaringan syaraf tiruan, support vector machine, pohon keputusan, serta metode particle swarm optimization. Masalah-masalah dalam ilmu diagnostik yang dapat terpecahkan menggunakan metode tersebut diantaranya yaitu analisis tumor otak MRI dan kanker payudara. Berdasarkan hasil survei yang penulis lakukan, untuk metode yang paling efektif dan efisien dalam melakukan diagnosis pada bidang medis adalah metode CNN hanya saja metode CNN membutuhkan data yang cukup besar untuk melakukan klasifikasi
Hybrid ACO and SVM algorithm for pattern classification
Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to
solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the
SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while
the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification
accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
Long-range forces in controlled systems
This thesis investigates new phenomena due to long-range forces and their effects
on different multi-DOFs systems. In particular the systems considered are metamaterials,
i.e. materials with long-range connections. The long-range connections
characterizing metamaterials are part of the more general framework of non-local
elasticity.
In the theory of non-local elasticity, the connections between non-adjacent particles
can assume different configurations, namely one-to-all, all-to-all, all-to-all-limited,
random-sparse and all-to-all-twin. In this study three aspects of the long-range
interactions are investigated, and two models of non-local elasticity are considered:
all-to-all and random-sparse.
The first topic considers an all-to-all connections topology and formalizes the mathematical
models to study wave propagation in long-range 1D metamaterials. Closed
forms of the dispersion equation are disclosed, and a propagation map synthesizes
the properties of these materials which unveil wave-stopping, negative group velocity,
instability and non-local effects. This investigation defines how long-range
interactions in elastic metamaterials can produce a variety of new effects in wave
propagation.
The second one considers an all-to-all connections topology and aims to define an
optimal design of the long-range actions in terms of spatial and intensity distribution
to obtain a passive control of the propagation behavior which may produces
exotic effects. A phenomenon of frequency filtering in a confined region of a 1D
metamaterial is obtained and the optimization process guarantees this is the best
obtainable result for a specific set of control parameters.
The third one considers a random-sparse connections topology and provides a new
definition of long-range force, based on the concept of small-world network. The
small-world model, born in the field of social networks, is suitably applied to a
regular lattice by the introduction of additional, randomly selected, elastic connections
between different points. These connections modify the waves propagation
within the structure and the system exhibits a much higher propagation speed and
synchronization. This result is one of the remarkable characteristics of the defined
long-range connections topology that can be applied to metamaterials as well as
other multi-DOFs systems. Qualitative experimental results are presented, and a
preliminary set-up is illustrated.
To summarize, this thesis highlights non-local elastic structures which display unusual
propagation behaviors; moreover, it proposes a control approach that produces
a frequency filtering material and shows the fast propagation of energy within a
random-sparse connected material
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
A novel approach to data mining using simplified swarm optimization
Data mining has become an increasingly important approach to deal with the rapid
growth of data collected and stored in databases. In data mining, data classification
and feature selection are considered the two main factors that drive people when
making decisions. However, existing traditional data classification and feature
selection techniques used in data management are no longer enough for such massive
data. This deficiency has prompted the need for a new intelligent data mining
technique based on stochastic population-based optimization that could discover
useful information from data.
In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as
a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm
Optimization (PSO) that has a self-organising ability to emerge in highly distributed
control problem space, and is flexible, robust and cost effective to solve complex
computing environments. The proposed SSO classifier has been implemented to
classify audio data. To the author’s knowledge, this is the first time that SSO and PSO
have been applied for audio classification.
Furthermore, two local search strategies, named Exchange Local Search (ELS) and
Weighted Local Search (WLS), have been proposed to improve SSO performance.
SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from
the UCI repository database. Meanwhile, SSO-WLS has been implemented in
Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel
hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed.
The empirical analysis showed promising results with high classification accuracy
rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99
datasets. Therefore, the proposed SSO rule-based classifier with local search
strategies has offered a new paradigm shift in solving complex problems in data
mining which may not be able to be solved by other benchmark classifiers
Digital-Twins towards Cyber-Physical Systems: A Brief Survey
Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS
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