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Special Issue on Computational Intelligence Software
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 2) |
| Halaman | : | 13-14 |
| Abstrak | : | - |
CIS Publication Spotlight
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 2) |
| Halaman | : | 10-12 |
| Abstrak | : | - |
The Quantum Tortoise and the Classical Hare: When Will Quantum Computers Outpace Classical Ones and When Will They Be Left Behind
| Pengarang | : | Sukwoong Choi; William S. Moses; Neil Thompson |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 2) |
| Halaman | : | 113-124 |
| Abstrak | : | In the children’s story of the Tortoise and the Hare, the speedier Hare is outpaced by a Tortoise with other advantages (diligence). An analogous contest is happening in computing, between a Quantum Tortoise and a Classical Hare. Here, the Classical Hare’s speed advantage is literal—classical computers run faster than quantum ones. Like his namesake, the Quantum Tortoise is slower, but also has an advantage—in this case, the ability to run algorithms that are unavailable to classical computers. When this algorithmic advantage is substantial enough, the Quantum Tortoise can beat the Classical Hare and solve a problem faster. This article analyzes when the Quantum Tortoise will beat the Classical Hare—and when it will not. |
An Ambient Intelligent and Energy Efficient Food Preparation System Using Linear General Type-2 Fuzzy Logic Based Computing with Words Framework
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 66-78 |
| Abstrak | : | Ambient Intelligence (AmI) is a multidisciplinary paradigm, which positively alters the relationship between humans and technology. Concerning home environments, the functions of AmI vision include home automation, communication, entertainment, working and learning. In the area of communication, AmI still needs better mechanisms for humancomputer communication. A natural human-computer interaction necessitates having systems capable of modelling words and computing with them. For this purpose, the paradigm of Computing With Words (CWWs) can be employed to mimic human-like communication in Ambient Intelligent Environments (AIEs). This paper demonstrates the extendibility of Linear General Type-2 (LGT2) Fuzzy Logic based CWWs Framework to create an advanced real-world application, which integrates a semi-autonomous, safe and energy efficient electric hob. The motivation of this work is twofold: 1) there is a need to develop transparent human-computer communication rather than embedding obtrusive tablets and computing equipment throughout our surroundings, and 2) one of the most hazardous and energy consuming household devices, the electric hob, does not have competent levels of intelligence and energy efficiency. The proposed Ambient Intelligent Food Preparation System (AIFPS) can increase user comfort, facilitate food preparation, minimize energy consumption and be a useful tool for the elderly and people with major disabilities including vision impairment. The results of real-world experiments with various lay users in the intelligent flat (iSpace) show the success of AIFPS in providing up to 55.43% improved natural interaction (compared to Interval Type-2 based CWWs Framework) while achieving semi-autonomous, safe and energy efficient cooking that can save energy between 11.5% and 35.2%. |
An Ant Colony Optimization and Tabu List Approach to the Detection of Gene Gene Interactions in Genome-Wide Association Studies
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 54-65 |
| Abstrak | : | In this paper, a novel ant colony optimization and tabu list approach for the discovery of gene-gene interactions in genome-wide association study data is proposed. The method is tested on a number of diseases drawn from the large established database, the Wellcome Trust Case Control Consortium which contains hundreds of thousands of small DNA changes known as single nucleotide polymorphisms. To analyze full scale genome-wide association study data, the standard ant colony optimization algorithm has been adapted, with tournament path selection, a subset based approach, and tabu list included in the algorithm. These modifications, in addition to the use of a statistical test of significance of single nucleotide polymorphism interactions as a fitness function, greatly increase execution speeds and permit the discovery of combinations of single nucleotide polymorphisms that can discriminate cases and controls. The methodology is applied to several large-scale genomewide association study disease datasets namely, inflammatory bowel disease, rheumatoid arthritis, type I diabetes and type II diabetes patients to discover putative gene-gene interactions in reasonable time on modest hardware. |
BEBERAPA MASALAH DARI SISTEM KOMUNIKASI MASSA DI INDONESIA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-1, JANUARI (No. 1) |
| Halaman | : | 65-78 |
| Abstrak | : | - |
WAWASAN BURUH DI INDONESIA
| Pengarang | : | Murwatie B. Rahardjo |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-1, JANUARI (No. 1) |
| Halaman | : | 51-64 |
| Abstrak | : | - |
The Hebbian-LMS Learning Algorithm
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 37-53 |
| Abstrak | : | Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, pattern recognition, and artificial neural networks. These are very different learning paradigms. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm that has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? "Nature's little secret," the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm. |
KEADAAN EKONOMI INDONESIA BERDASARKAN TABEL INPUT-UTPUT 1975
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-1, JANUARI (No. 1) |
| Halaman | : | 32-50 |
| Abstrak | : | - |
Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 26-36 |
| Abstrak | : | Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense com- puting in order to improve the accuracy of computa- tionally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that out- performs state-of-the-art statistical methods in terms of both accuracy and training time. |