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Fulltext A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping

Kzar AA, Mat Jafri MZ, Mutter KN, Syahreza S

Int J Environ Res Public Health, 2016 Jan;13(1).
PMID: 26729148 DOI: 10.3390/ijerph13010092

Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).
Quantum exhaustive key search with simplified-DES as a case study

Almazrooie M, Samsudin A, Abdullah R, Mutter KN

Springerplus, 2016;5(1):1494.
PMID: 27652067 DOI: 10.1186/s40064-016-3159-4

To evaluate the security of a symmetric cryptosystem against any quantum attack, the symmetric algorithm must be first implemented on a quantum platform. In this study, a quantum implementation of a classical block cipher is presented. A quantum circuit for a classical block cipher of a polynomial size of quantum gates is proposed. The entire work has been tested on a quantum mechanics simulator called libquantum. First, the functionality of the proposed quantum cipher is verified and the experimental results are compared with those of the original classical version. Then, quantum attacks are conducted by using Grover's algorithm to recover the secret key. The proposed quantum cipher is used as a black box for the quantum search. The quantum oracle is then queried over the produced ciphertext to mark the quantum state, which consists of plaintext and key qubits. The experimental results show that for a key of n-bit size and key space of N such that [Formula: see text], the key can be recovered in [Formula: see text] computational steps.
Comparison of Alexandrite and Diode Lasers for Hair Removal in Dark and Medium Skin: Which is Better?

Mustafa FH, Jaafar MS, Ismail AH, Mutter KN

J Lasers Med Sci, 2014;5(4):188-93.
PMID: 25653820

INTRODUCTION: To improve laser hair removal (LHR) for dark skin, the fluence rate reaching the hair follicle in LHR is important. This paper presents the results of a comparative study examining the function of wavelength on dark skin types using 755 nm alexandrite and 810 nm diode lasers.
METHODS: The structure of the skin was created using a realistic skin model by the Advanced Systems Analysis Program.
RESULT: In this study, the alexandrite laser (755 nm) and diode laser (810 nm) beam-skin tissue interactions were simulated. The simulation results for both lasers differed. The transmission ratio of the diode laser to the dark skin dermis was approximately 4% more than that of the alexandrite laser for the same skin type. For the diode laser at skin depth z = 0.67 mm, the average transmission ratios of both samples were 36% and 27.5%, but those for the alexandrite laser at the same skin depth were 32% and 25%.
CONCLUSION: Both lasers were suitable in LHR for dark skin types, but the diode laser was better than the alexandrite laser because the former could penetrate deeper into the dermis layer.

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