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Table 4 Genes’ interaction values for yeast25-alpha. In this section we deliver an example of KSG application to Genetic Regulatory System (GRS) that is a mechanism for determining when and in what form a gene or genes will be expressed [15]. Shortly, KGS is utilized to produce knowledge regarding the genes behavior in GRS for estimating their interaction’ s behavior and what the most expressive gene during a sequence of interaction times. By having much knowledge regarding those phenomena, we can manage the GRS to produce better results. We use the simplest gene database namely yeast25- alpha database which consists of 25 genes with 18t interaction times as presented in Table 4. Figure 6 depicts the knowledge gains by KGS from time-to-time with single gene, 12 genes, and all the 25 genes. The last figure shows the original data.

Table 4 Genes’ interaction values for yeast25-alpha. In this section we deliver an example of KSG application to Genetic Regulatory System (GRS) that is a mechanism for determining when and in what form a gene or genes will be expressed [15]. Shortly, KGS is utilized to produce knowledge regarding the genes behavior in GRS for estimating their interaction’ s behavior and what the most expressive gene during a sequence of interaction times. By having much knowledge regarding those phenomena, we can manage the GRS to produce better results. We use the simplest gene database namely yeast25- alpha database which consists of 25 genes with 18t interaction times as presented in Table 4. Figure 6 depicts the knowledge gains by KGS from time-to-time with single gene, 12 genes, and all the 25 genes. The last figure shows the original data.

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Figure 2. A simplified illustration of HIS where n is the number of sensor [9]. Based on this examination, we find that there are three matters that have to be mathematical-modeled. The first one is the model of the number of fused information, the second one is the model of the information/knowledge fusion, and the last one is the model of the process to obtain the inferencing of the fused information/knowledge. Refer to Figure 2 the number of fused information can be modeled as presented in Equation (1).
Figure 2. A simplified illustration of HIS where n is the number of sensor [9]. Based on this examination, we find that there are three matters that have to be mathematical-modeled. The first one is the model of the number of fused information, the second one is the model of the information/knowledge fusion, and the last one is the model of the process to obtain the inferencing of the fused information/knowledge. Refer to Figure 2 the number of fused information can be modeled as presented in Equation (1).
Table 1 Illustration of BIM+MAP technique. One popular method commonly used for information fusion is Bayes Inference Method (BIM) that is combined with Maximum A Posteriori (MAP) technique for selecting the best estimated inference. In our research, we found that the Bayes+MA P technique lacks of capability when facing multi-hypothesis multi- indication problems that are often faced in real-life. If we have some hypotheses A OA A ann tham nxvicta anmn jindinnhtnnoa D D D Hann thn nan nnctannanm 2.2. Bayes Inference Method Revisited [10]
Table 1 Illustration of BIM+MAP technique. One popular method commonly used for information fusion is Bayes Inference Method (BIM) that is combined with Maximum A Posteriori (MAP) technique for selecting the best estimated inference. In our research, we found that the Bayes+MA P technique lacks of capability when facing multi-hypothesis multi- indication problems that are often faced in real-life. If we have some hypotheses A OA A ann tham nxvicta anmn jindinnhtnnoa D D D Hann thn nan nnctannanm 2.2. Bayes Inference Method Revisited [10]
Figure 3. The Illustration of the MMEO technique [12].
Figure 3. The Illustration of the MMEO technique [12].
Table 2 Illustration of BIM with MMEO technique.
Table 2 Illustration of BIM with MMEO technique.
Figure 4. Topology of AI proposed by Ahmad [1].
Figure 4. Topology of AI proposed by Ahmad [1].
Figure 5. (a) The concept of KGS and (b) the KG-mechanism in KGS. KGS is aimed to emulate HIS in obtaining new knowledge. The main features of KGS are (a) it has ability to fuse information, information-inferencing, and knowledge-inferencing, (b) it has ability to obtain inferencing from the fused information, and fused knowledge, (c) it has a knowledge-base to store the new knowledge. Figure 5 depicts the concept of KGS with A3S as the method for obtaining new knowledge.
Figure 5. (a) The concept of KGS and (b) the KG-mechanism in KGS. KGS is aimed to emulate HIS in obtaining new knowledge. The main features of KGS are (a) it has ability to fuse information, information-inferencing, and knowledge-inferencing, (b) it has ability to obtain inferencing from the fused information, and fused knowledge, (c) it has a knowledge-base to store the new knowledge. Figure 5 depicts the concept of KGS with A3S as the method for obtaining new knowledge.
Table 3 Possible Combinations of Information from Sensors.
Table 3 Possible Combinations of Information from Sensors.
Figure 6. Knowledge of GRS behavior gained by KGS with (a) single gene, (b) 12 genes, and (c) all 25 genes, and (d) the original data as the comparison. A Mathematical Model of Knowledge-Growing System: .
Figure 6. Knowledge of GRS behavior gained by KGS with (a) single gene, (b) 12 genes, and (c) all 25 genes, and (d) the original data as the comparison. A Mathematical Model of Knowledge-Growing System: .
Related topics:
Artificial IntelligenceInformation-Inferencing FusionKnowledge-Growing
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