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Diabetes is a life-threatening syndrome occurring around the world; it can have huge complications and is documented by large amounts of medical data. Therefore, attempts at early detection of this disease took a large area of research and many methods were used to deal with diabetes. In this paper, different types of KNN algorithm have been used to classify diabetes disease using Matlab. The dataset was generated by the criteria of the American diabetes association. For the training stage, 4900 samples have been used by the classifier learner tool to observe the results. Then, 100 of the data samples were used for the test. The results show that the KNN types (Fine, Weighted, Medium and Cubic) give high accuracy over the Coarse and the Cosine methods. Fine KNN is considered the most suitable according to its accuracy of classified samples. ABSTRAK: Penyakit kencing manis adalah sindrom penyakit ancaman nyawa yang berlaku di seluruh dunia dan ia mempunyai data perubatan yang besar serta komplikasi tinggi. Oleh itu, cubaan dalam mengesan awal penyakit ini mempunyai potensi luas dalam kajian dan banyak kaedah telah digunakan bagi mengkaji penyakit kencing manis. Dalam kajian ini, pelbagai jenis algoritma KNN telah digunakan bagi mengelas penyakit kencing manis menggunakan Matlab. Setdata dihasilkan berdasarkan kriteria Kesatuan Kencing Manis Amerika. Pada peringkat latihan, sebanyak 4900 sampel telah digunakan oleh pelatih alat pengelasan bagi memantau dapatan kajian. Kemudian, 100 daripada sampel data telah digunakan bagi ujian. Keputusan menunjukkan jenis KNN (Halus, Berat, Sederhana dan Kubik) lebih tepat berbanding kaedah Kasar dan Kosinus. KNN Halus di dapati lebih sesuai berdasarkan ketepatan sampel pengelasan.

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In this paper, the performance of Linear Antenna Array Element (LAAE) has been evaluated at the Base Station (BS) with a different number of elements for Unmanned Air Vehicle UAV application. The Switched Beam (SB) and Phase Array (PA) have been used as a steering beam mechanism. The beam steering tracker is based on the GPS points of the UAV and the BS. In addition, the Misalignment angle has been analyzed for SB and PA corresponding to the maximum speed of the UAV. The compression between SB and PA in term of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) and BER vs. Misalignment angle have been examined by using Matlab. The results show that the PA has better performance than SB in both terms under Additive White Gaussian Noise (AWGN) channel with an interference signal. When the number of the elements is eight provides longer distance than four by the factor (1.5 in SB case and 2 in PA case) and wider Misalignment angle range than twelve by factor (2 in SW case and 3 in PA case). Therefore, it is becoming a useful option for many applications.

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