Page 11 - AKSES vol3

AKSES AR TIKEL PEN Y ELIDIK AN ADV ANCING KNOWLEDGE FOR SUC CESS FT SM UKM

SUMBER TENAGA INTERNET BENDA DETECTING DYSLEXIA NEURAL-BIOMARKERS PRIVACY-
PRESERVING METHOD BASED ON ENCRYPTED MRI DATASET

Norazuwana Shaari, Azana Hafizah Mohd Aman, Roszita Ibrahim Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad
azana@uk m.edu .m y
r a vie@uk m.edu .m y

Internet Benda atau lebih dikenali sebagai Internet Due to the privacy sensitivity of the MRI dataset The pre-processed MRI datasets are then encrypted
of Things (IoT) telah mencipta fenomena peranti associated with dyslexia neural-biomarkers, this using a special moduli set of homomorphic residue
bersambung dalam kegunaan pelbagai jenis study present a method for detecting dyslexia neural- number system (HoRNS) encryption scheme, and
perkhidmatan, proses, dan aplikasi. IoT membolehkan biomarkers from the encrypted MRI dataset. The the DL classification experiment repeated. This was
peranti berhubung dan berkomunikasi antara satu proposed modified histogram normalisation (MHN) accomplished by employing HoRNS to design and
sama lain untuk berkongsi pelbagai data. Antara method ensures the biological interpretability of develop pixel-bitstream encoder/decoder circuits
fenomena IoT yang telah berkembang pesat adalah neural-biomarker features in all MRI datasets collected capable of concealing the 7-bit binary value of each
seperti pengangkutan pintar, penjagaan kesihatan from wide-range of publicly available data sources pixel in the training and testing datasets. The proposed
pintar, dan rumah pintar. Bagi membolehkan sesuatu characterized by inconsistent acquisition parameters. pixel-bitstream encoder is a combinational circuit
sistem IoT berjalan lancar, antara perkara yang perlu We were able to map the intensities of pixels in low- that requires fewer fast adders, with area complexity
dititikberatkan adalah sumber tenaga peranti IoT. quality input images to range between the low- of 4nAFA and time delay (latency) of (3n+3)DFA for
Penggunaan tenaga IoT untuk aplikasi pintar seperti intensity region of interest and high-intensity region n3. The proposed encoder’s FPGA implementation
grid pintar, bangunan pintar, dan pengangkutan of interest of the identified high-quality image by also improves critical path delay by 23.5% and saves
pintar bergantung pada seni bina IoT. Seni bina ini R A JAH 1 . implementing the proposed MHN. This pre-processing up to 42.4% power. After encryption, the proposed
menentukan tahap penggunaan sesuatu sumber operation was preceded by the implementation pre-trained DL models performed significantly
tenaga, sama ada tinggi atau rendah penggunaannya. of image smoothing based on the Gaussian filter better at distinguishing dyslexia neural-biomarker
method with an isotropic kernel of size 4mm. Based features from normal (control) features. The results
Peningkatan penggunaan tenaga yang ketara Sistem tenaga IoT umumnya perlu mempertimbangkan on the experiment results, the proposed MHN method of DL models provide efficient, accurate, and scalable
disebabkan oleh pertumbuhan populasi IoT telah penyimpanan tenaga, penggunaan tenaga, dan outperforms the normalization method of the state- privacy-preserving predictions, demonstrating that
membawa cabaran besar kepada penyimpanan pengurusan tenaga. Antara jenis sumber kuasa of-the-art histogram matching. CNN models can learn over encrypted datasets.
tenaga dan pengurusan tenaga sistem IoT. Operasi sistem IoT adalah, (a) penyimpanan tenaga, (b)
rumah pintar dan peralatan grid pintar menunjukkan pengagihan tenaga, dan (c) penuaian tenaga. Sel This study investigates the abilities of CNN to
kepentingan mengambil kira pengurusan kawalan fuel adalah salah satu contoh pembekal tenaga distinguish cases of dyslexia from control subjects
tenaga untuk penjimatan serta mengoptimumkan alternatif bagi menggantikan bateri yang biasa using encrypted neural-biomarker features. Because
penggunaan tenaga. Begitu juga di dalam penjagaan digunakan untuk rangkaian peranti IoT tanpa wayar. of the educational and medical importance of dyslexia,
kesihatan, peranti IoT yang diguna pakai oleh pesakit Sumber tenaga yang dihantar ke rangkaian peranti this type of research becomes necessary. Figure 2,
memerlukan bekalan kuasa yang optimum dan IoT tanpa wayar, perlu memperluas kebolehkerjaan presents the overview of the system architecture.
bersesuaian. Peralihan dari sistem IoT yang terpencil dan menggabungkan antara penyimpanan tenaga,
kepada aplikasi pintar memerlukan reka bentuk dan pengedaran, dan teknologi pemulangan seperti yang
seni bina IoT yang bersesuaian untuk memudahkan ditunjukkan dalam Rajah 1.
pengurusan dan kawalan sumber tenaga.
FIGURE 2 .
System Architecture