The hierarchical graph neuron (HGN) implements a single cycle memorization and recall operation through a novel algorithmic design. The HGN is an improvement on the already published original graph neuron (GN) algorithm. In this improved approach, it recognizes incomplete/noisy patterns. It also resolves the crosstalk problem, which is identified in the previous publications, within closely matched patterns. To accomplish this, the HGN links multiple GN networks for filtering noise and crosstalk out of pattern data inputs. Intrinsically, the HGN is a lightweight in-network processing algorithm which does not require expensive floating point computations; hence, it is very suitable for real-time applications and tiny devices such as the wireless sensor networks. This paper describes that the HGN's pattern matching capability and the small response time remain insensitive to the increases in the number of stored patterns. Moreover, the HGN does not require definition of rules or setting of thresholds by the operator to achieve the desired results nor does it require heuristics entailing iterative operations for memorization and recall of patterns.

hal ini penulis mengelompokan data siswa baru sekolah menengah kejuruan tahun ajaran 2014/2015. Pengelompokan tersebut berdasarkan kriteria -kriteria data siswa. Pada penelitian ini, penulis menerapkan algoritma K-Means Clustering untuk pengelompokan data siswa baru sekolah menengah kejuruan. Dalam hal ini, pada umumnya untuk memamasuki jurusan hanya disesuaikan dengan nilai siswa saja namun dalam penelitian ini pengelompokan disesuaikan kriteria -kriteria siswa seperti penghasilan orang tua, tanggungan anak orang tua dan nilai tes siswa. Penulis menggunakan beberapa kriteria tersebut agar pengelompokan yang dihasilkan menjadi lebih optimal. Tujuan dari pengelompokan ini adalah terbentuknya kelompok jurusan pada siswa yang menggunakan algoritma K-Means clustering. Hasil dari pengelompokan tersebut diperoleh tiga kelompok yaitu kelompok tidak lulus, kelompok rekayasa perangkat lunak dan kelompok teknik komputer jaringan. Terdapat pusat cluster dengan Cluster-1=1.4;2.2;2.2, Cluster-2= 2.28;1.64;4 dan Cluster-3=5;2;6. Pusat cluster tersebut didapat dari beberapa iterasi sehingga mengahasilakan pusat cluster yang optimal.

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Perform predictive data in the form of time series required a correct method, one method is now often used is the propagation of this method is a method that is able to minimize the error value of the output of the predicted number of people, but still generate quite a lot of iteration that needs to be optimized by minimize iterations and use of time, then the use of conjugate gradient polak Ribiere are expected to minimize the use of time, the number of the epoch of the results of standard backpropagation.

Lots of damages, losses, and costs have been the major concern, why handling natural disasters of tornados is very important. Several attempts using different approaches have been carried out, but up to now the results are not yet satisfactory. More promising approaches through a kind of artificial intelligent forecaster have been started for a while, but the results are still not satisfactory either. The capability of mHGN as a pattern recognizer has opened up a new possibility of recognizing a pattern of tornado many hours earlier. Therefore, it can be used to forecast a tornado more efficiently. The results taken from a simulated circumstances of a multidimensional pattern recognition have shown, that the 91% of accuracy can be regarded as satisfactory. Though, several modifications related to the data representation within the mHGN architecture need to be implemented. The deployment of mHGN in several risky areas of tornados can then be expected as a tool for reducing those damages, losses, and costs.