IIUM Engineering Journal
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Editorial
Get PDFCHIEF EDITOR
Ahmad Faris Ismail, IIUM, Malaysia
TECHNICAL EDITOR
Sany Izan Ihsan, IIUM, Malaysia
EXECUTIVE EDITOR
AHM Zahirul Alam, IIUM, Malaysia
ASSOCIATE EDITOR
Nor Farahidah Za’bah, IIUM, Malaysia
LANGUAGE EDITOR
Lynn Mason, Malaysia
COPY EDITOR
Hamzah Mohd. Salleh, IIUM, Malaysia
MALAY TRANSLATOR
Nurul Arfah Che Mustapha, IIUM, Malaysia
EDITORIAL BOARD MEMBERS
Abdullah Al-Mamun, IIUM, Malaysia
Abdumalik Rakhimov, IIUM, Malaysia
Aishah Najiah Bt. Dahnel, IIUM, Malaysia
Alya Naili Binti Rozhan, IIUM, Malaysia
Norsinnira Bt. Zainul Azlan, IIUM, Malaysia
Hanafy Omar, Saudi Arabia
Hazleen Anuar, IIUM, Malaysia
Konstantin Khanin, University of Toronto, Canada
Ma'an Al-Khatib, IIUM, Malaysia
Meftah Hrairi, IIUM, Malaysia
Mohamed B. Trabia, United States
Mohammad S. Alam, Texas A&M University-Kingsville, United States
Mustafizur Rahman, National University Singapore, Singapore
Ossama Abdulkhalik, Michigan Technological University, United States
Mohamed Hadi Habaebi, IIUM, Malaysia
Mohd. Sultan Ibrahim Bin Shaik Dawood, IIUM, Malaysia
Muhammad Ibn Ibrahimy, IIUM, Malaysia
Nor Fadhillah Mohamed Azmin, IIUM, Malaysia
Waqar Asrar, IIUM, Malaysia
INTERNATIONAL ADVISORY COMMITTEE
A. Anwar, United States
Abdul Latif Bin Ahmad, Malaysia
Farzad Ismail, USM, Pulau Pinang, Malaysia
Hanafy Omar, Saudi Arabia
Hany Ammar, United States
Idris Mohammed Bugaje, Nigeria
K.B. Ramachandran, India
Kunzu Abdella, Canada
Luis Le Moyne, ISAT, University of Burgundy, France
M Mujtaba, United Kingdom
Mohamed AI-Rubei, Ireland
Mohamed B Trabia, United States
Syed Kamrul Islam, United States
Tibor Czigany, Budapest University of Technology and Economics, Hungary
Yiu-Wing Mai, The University of Sydney, Australia.
AIMS & SCOPE OF IIUM ENGINEERING JOURNAL
The IIUM Engineering Journal, published biannually (January and July), is a carefully refereed international publication of International Islamic University Malaysia (IIUM). Contributions of high technical merit within the span of engineering disciplines; covering the main areas of engineering: Electrical and Computer Engineering; Mechanical and Manufacturing Engineering; Automation and Mechatronics Engineering; Material and Chemical Engineering; Environmental and Civil Engineering; Biotechnology and Bioengineering; Engineering Mathematics and Physics; and Computer Science and Information Technology are considered for publication in this journal. Contributions from other areas of Engineering and Applied Science are also welcomed. The IIUM Engineering Journal publishes contributions under Regular papers and Invited review papers. It also welcomes contributions that address solutions to the specific challenges of the developing world, and address science and technology issues from an Islamic and multidisciplinary perspective.
REFEREES’ NETWORK
All papers submitted to IIUM Engineering Journal will be subjected to a rigorous reviewing process through a worldwide network of specialized and competent referees. Each accepted paper should have at least two positive referees’ assessments.
SUBMISSION OF A MANUSCRIPT
A manuscript should be submitted online to the IIUM Engineering Journal website at
http://journals.iium.edu.my/ejournal. Further correspondence on the status of the paper could be done through the journal website.
Whilst every effort is made by the publisher and editorial board to see that no inaccurate or misleading data, opinion, or statement appears in this Journal, they wish to make it clear that the data and opinions appearing in the articles and advertisements herein are the responsibility of the contributor or advertiser concerned. Accordingly, the publisher and the editorial committee accept no liability whatsoever for the consequence of any such inaccurate or misleading data, opinion, or statement.
IIUM Engineering Journal
ISSN: 1511-788X E-ISSN: 2289-7860
Published by: IIUM Press, International Islamic University Malaysia Jalan Gombak, 53100 Kuala Lumpur, Malaysia Phone (+603) 6421-5014, Fax: (+603) 6421-629
PERFORMANCE ANALYSIS ON DYNAMIC WIRELESS CHARGING FOR ELECTRIC VEHICLE USING FERRITE CORE
Get PDFThe technology of dynamic Wireless Power Transfer (WPT) has been accepted in the Electric Vehicle (EV) industry. Recently, for a stationary EV charging system, the existence of a ferrite core improves power efficiency. However, for dynamic wireless charging, the output power fluctuates when the EV moves. Two main obstacles that must be dealt with is air-gaps and misalignment between the coils. This paper investigates clear design guidelines for fabrication of an efficient Resonant Inductive Power Transfer (RIPT) system for the EV battery charging application using a ferrite core. Two different geometry shapes of ferrite core, U and I cores, will be investigated and tested using simulation and experimental work. The proposed design was simulated in JMAG 14.0, and the prototype was tested in the laboratory. The expected output analysis from these two techniques was that the power efficiency of the ferrite pair should first be calculated. From the analysis and experimental results, it is seen that the pair of ferrite cores that used a U shape at the primary and secondary side provides the most efficient coupling in larger air-gap RIPT application with 94.69% on simulation JMAG 14.0 and 89.7% from conducting an experiment.
ABSTRAK: Teknologi Alih Kuasa Wayarles (WPT) dinamik telah diterima pakai dalam Kenderaan Elektrik (EV). Baru-baru ini, kewujudan teras ferit dalam sistem pengecasan pegun EV dapat meningkatkan kecekapan kuasa. Namun, kuasa pengecasan ini akan berubah apabila EV bergerak bagi sistem pengecasan wayarles secara dinamik. Dua halangan utama yang harus ditangani adalah ketidakjajaran dan jarak antara dua gegelung. Kajian ini merupakan garis panduan yang jelas mengenai rekaan fabrikasi dan kecekapan sistem Alih Kuasa Induktif Resonan (RIPT) bagi aplikasi pengecasan bateri EV menggunakan teras ferit. Dua bentuk geometri teras ferit, iaitu teras U dan I telah dikaji dan diuji menggunakan simulasi dan eksperimen. Rekaan ini telah disimulasi menggunakan JMAG 14.0 dan prototaip diuji di dalam makmal. Kedua-dua teknik ini diharapkan dapat menghasilkan kecekapan kuasa yang sama. Dapatan kajian menunjukkan kedua-dua teras ferit pada sisi primer dan sekunder berbentuk U mempunyai gandingan paling efisien bagi jarak paling besar antara 2 gegelung menggunakan aplikasi RIPT dengan 94.69% simulasi JMAG 14.0 dan 89.7% secara eksperimen
DEVELOPMENT OF RAINFALL FORECASTING MODEL USING MACHINE LEARNING WITH SINGULAR SPECTRUM ANALYSIS
Get PDFAgriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall.
ABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. Hasil eksperimen menggambarkan bahawa model yang dicadangkan dapat meramalkan hujan secara produktif
CLASSIFICATION MODEL FOR BREAST CANCER MAMMOGRAMS
Get PDFMachine learning has been the topic of interest in research related to early detection of breast cancer based on mammogram images. In this study, we compare the performance results from three (3) types of machine learning techniques: 1) Naïve Bayes (NB), 2) Neural Network (NN) and 3) Support Vector Machine (SVM) with 2000 digital mammogram images to choose the best technique that could model the relationship between the features extracted and the state of the breast (‘Normal’ or ‘Cancer’). Grey Level Co-occurrence Matrix (GLCM) which represents the two dimensions of the level variation gray in the image is used in the feature extraction process. Six (6) attributes consist of contrast, variance, standard deviation, kurtosis, mean and smoothness were computed as feature extracted and used as the inputs for the classification process. The data has been randomized and the experiment has been repeated for ten (10) times to check for the consistencies of the performance of all techniques. 70% of the data were used as the training data and another 30% used as testing data. The result after ten (10) experiments show that, Support Vector Machine (SVM) gives the most consistent results in correctly classifying the state of the breast as ‘Normal’ or ‘Cancer’, with the accuracy of 99.4%, in training and 98.76% in testing. The SVM classification model has outperformed NN and NB model in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage.
ABSTRAK: Pembelajaran mesin telah menjadi topik yang diminati dalam penyelidikan yang berkaitan dengan pengesanan awal kanser payudara berdasarkan imej mamogram. Dalam kajian ini, kami membandingkan hasil prestasi dari tiga (3) jenis teknik pembelajaran mesin: 1) Naïve Bayes (NB), 2) Neural Network (NN) dan 3) Support Vector Machine (SVM) dengan 2000 imej digital mammogram hingga teknik terbaik yang dapat memodelkan hubungan antara ciri yang diekstraksi dan keadaan payudara ('Normal' atau 'Cancer') dapat diperoleh. Grey Level Co-occurrence Matrix (GLCM) yang mewakili dua dimensi variasi tahap kelabu pada gambar digunakan dalam proses pengekstrakan ciri. Enam (6) atribut terdiri dari kontras, varians, sisihan piawai, kurtosis, min dan kehalusan dihitung sebagai fitur yang diekstrak dan digunakan sebagai input untuk proses klasifikasi. Eksperimen telah diulang selama sepuluh (10) kali untuk memeriksa kesesuaian prestasi semua teknik. 70% data digunakan sebagai data latihan dan 30% lagi digunakan sebagai data ujian. Hasil setelah sepuluh (10) eksperimen menunjukkan bahawa, Support Vector Machine (SVM) memberikan hasil yang paling konsisten dalam mengklasifikasikan keadaan payudara dengan betul sebagai 'Normal' atau 'Kanser', dengan akurasi 99.4%, dalam latihan dan 98.76% dalam ujian. Model klasifikasi SVM telah mengungguli model NN dan NB dalam kajian ini, dan ia menunjukkan bahawa SVM adalah pilihan yang baik untuk menentukan keadaan payudara pada peringkat awal
APPLICATION OF HOUSE OF QUALITY IN THE CONCEPTUAL DESIGN OF BATIK WAX EXTRUDER AND PRINTER
Get PDFMalaysian batik production is dominated by two techniques known as hand-drawn batik, or batik tjanting, and stamp batik, or batik block. In comparison to batik block, the more popular batik tjanting takes a longer time to produce. A Standardized Nordic Questionnaire (SNQ) for musculoskeletal symptom examination involving batik artisans in Kelantan and Terengganu identified high rates of musculoskeletal disorders in respondents due to their working posture during the batik tjanting process. It was also observed that the number of workers and artisans willing to participate in the traditional batik industry is on the decline. These problems have led to a systematic Quality Functional Deployment approach to facilitate the decision-making process for the conceptual design of an automatic batik printer. In this study, house of quality (HOQ) was applied to identify the critical features for a batik printer based on the voice of the customer (VOC). A survey done to rate the importance of VOC using an 8-point Likert scale revealed that the batik practitioners topmost priority for the batik printer feature is the 'ability to adjust and maintain the temperature of wax' (17.54%) while the non-batik practitioners chose 'ability to deliver a variety of complex designs' (15.94%). The least required feature for the batik printer was related to the size of the batik printer. The mapping between customer requirements (VOC) and technical requirements identified that the extruder design (21.3%), the heating element (18%), and nozzle diameter (17.8%) were the most critical components for the batik printer. Several conceptual designs of the extrusion unit, cartesian-based batik printer, and 2D image conversion using open-sourced software were proposed at the end of this work.
ABSTRAK: Pengeluaran batik Malaysia telah didominasi oleh dua teknik yang dikenali sebagai batik lukisan-tangan (batik canting) dan batik cap (batik blok). Sebagai perbandingan, batik canting yang popular mengambil masa lebih lama bagi dihasilkan. Soal Selidik Nordic Standad (SNQ) bagi meneliti gejala muskuloskeletal melibatkan tukang batik di Kelantan dan Terengganu telah menunjukkan persamaan kadar muskuloskeletal yang tinggi pada postur badan semasa bekerja canting batik. Bilangan pekerja yang terlibat dalam industri tradisional batik ini turut terjejas. Masalah-masalah ini telah mengarah kepada kaedah Pengerahan Fungsi Kualiti bagi membantu proses membuat keputusan dalam rekaan konsep pencetak batik automatik. Kajian ini telah mengadaptasi Kualiti Rumah (HOQ) bagi mengesan ciri-ciri kritikal pada pencetak batik berdasarkan suara pelanggan (VOC). Kaji selidik telah dilakukan bagi menilai kepentingan VOC menggunakan skala Likert 8-poin. Didapati keutamaan yang diperlukan oleh 17.54% ahli batik adalah; ciri pencetak batik ini perlu mempunyai ‘keupayaan dalam menyelaras dan menetapkan suhu lilin’, manakala sebanyak 15.94% bukan ahli batik memilih ‘keupayaan pencetak ini harus berjaya menghasilkan pelbagai rekaan yang kompleks’. Ciri yang kurang diberi tumpuan adalah berkaitan saiz pencetak batik. Persamaan antara kehendak pelanggan (VOC) dan kehendak teknikal dalam mengenal pasti komponen-komponen penting bagi pencetak batik adalah rekaan penyemperit (21.3%), elemen pemanas (18%), dan diameter nozel (17.8%). Pelbagai rekaan konsep bagi unit penyemperit, pencetak batik canting, dan imej konversi 2D menggunakan perisian sumber terbuka telah dicadangkan di bahagian akhir kajian ini
Potential of Low Carbon Nanotubes Dosage on Chromium Removal from Water
Get PDFThis paper involves a method of eliminating hexavalent chromium (Cr (VI)) from the synthetic water via a low dosage of carbon nanotubes (CNT). The ability of CNT to remove Cr(VI) from synthetic water through the adsorption process was studied in batch experimentation. The findings revealed up to 100% elimination of Cr(VI) in the 0.07 mg/L Cr(VI) concentration. These excessive elimination proficiencies were credited to the powerful adsorption of chromium ions to the physical properties of the CNT. A pattern layout was created in these experimental runs in order to locate the ideal situation of the Cr(VI) deletion from synthetic water. To accomplish the purposes of the experiment, there were 4 independent variables influencing several points, namely the CNT dosage, the pH of the water, the agitation speed, and the contact time. The StatGraphics Centurion XV software has been used to create the adsorption equivalence and to discover the major impacts to the elimination of Cr(VI). The results show that the adsorption capability of the carbon nanotubes was considerably reliant on the pH of the Cr(VI) solution, supported by the CNT dosage, the contact time, and the agitation speed. The expected optimization, using the adsorption equation, shows that a 1 mg CNT dosage with a pH=2, 120 minutes contact time, and moderate agitation rate at 150 rpm is the most optimal.
ABSTRAK: Kajian ini melibatkan kaedah bagi menyingkirkan kromium (VI) dari air sintetik menggunakan karbon tiub nano berdos rendah. Eksperimen kelompok dilakukan bagi menentukan keupayaaan karbon tiub nano menyingkirkan Cr(VI) dari air sintetik melalui proses penjerapan. Dapatan kajian menunjukkan Cr(VI) telah disingkirkan sebanyak 100% dari kepekatan 0.07 mg/L Cr(VI). Kecekapan penyingkiran ini adalah disebabkan penjerapan ion-ion kromium yang kuat terhadap sifat fizikal nano tiub karbon tersebut. Rekabentuk eksperimen telah dibina bagi menentukan peringkat optima penyingkiran Cr(VI) dari air sintetik. Bagi mencapai matlamat kajian, empat faktor yang terdiri daripada dos nano tiub karbon, pH air, kelajuan goncangan dan masa sentuhan diukur. Perisian StatGraphics Centurion XV telah digunakan bagi mendapatkan nilai setara proses penjerapan dan kesan utama yang menyebabkan tersingkirnya Cr(VI). Dapatan kajian menunjukkan keupayaan penjerapan oleh nano tiub karbon sangat bergantung kepada pH larutan Cr(VI), disusuli dengan dos nano tiub karbon masa sentuhan dan kelajuan goncangan. Penjerapan optimum Cr(VI) dapat dicapai pada tahap 1 mg dos nano tiub karbon, larutan pada pH 2, masa sentuhan selama 120 minit dengan kelajuan goncangan sebanyak 150 rpm
WAVELET DETAIL COEFFICIENT AS A NOVEL WAVELET-MFCC FEATURES IN TEXT-DEPENDENT SPEAKER RECOGNITION SYSTEM
Get PDFSpeaker recognition is the process of recognizing a speaker from his speech. This can be used in many aspects of life, such as taking access remotely to a personal device, securing access to voice control, and doing a forensic investigation. In speaker recognition, extracting features from the speech is the most critical process. The features are used to represent the speech as unique features to distinguish speech samples from one another. In this research, we proposed the use of a combination of Wavelet and Mel Frequency Cepstral Coefficient (MFCC), Wavelet-MFCC, as feature extraction methods, and Hidden Markov Model (HMM) as classification. The speech signal is first extracted using Wavelet into one level of decomposition, then only the sub-band detail coefficient is used as the feature for further extraction using MFCC. The modeled system was applied in 300 speech datasets of 30 speakers uttering “HADIR” in the Indonesian language. K-fold cross-validation is implemented with five folds. As much as 80% of the data were trained for each fold, while the rest was used as testing data. Based on the testing, the system's accuracy using the combination of Wavelet-MFCC obtained is 96.67%.
ABSTRAK: Pengecaman penutur adalah proses mengenali penutur dari ucapannya yang dapat digunakan dalam banyak aspek kehidupan, seperti mengambil akses dari jauh ke peranti peribadi, mendapat kawalan ke atas akses suara, dan melakukan penyelidikan forensik. Ciri-ciri khas dari ucapan merupakan proses paling kritikal dalam pengecaman penutur. Ciri-ciri ini digunakan bagi mengenali ciri unik yang terdapat pada sesebuah ucapan dalam membezakan satu sama lain. Penyelidikan ini mencadangkan penggunaan kombinasi Wavelet dan Mel Frekuensi Pekali Cepstral (MFCC), Wavelet-MFCC, sebagai kaedah ekstrak ciri-ciri penutur, dan Model Markov Tersembunyi (HMM) sebagai pengelasan. Isyarat penuturan pada awalnya diekstrak menggunakan Wavelet menjadi satu tahap penguraian, kemudian hanya pekali perincian sub-jalur digunakan bagi pengekstrakan ciri-ciri berikutnya menggunakan MFCC. Model ini diterapkan kepada 300 kumpulan data ucapan daripada 30 penutur yang mengucapkan kata "HADIR" dalam bahasa Indonesia. Pengesahan silang K-lipat dilaksanakan dengan 5 lipatan. Sebanyak 80% data telah dilatih bagi setiap lipatan, sementara selebihnya digunakan sebagai data ujian. Berdasarkan ujian ini, ketepatan sistem yang menggunakan kombinasi Wavelet-MFCC memperolehi 96.67%
MAINTAIN AGENT CONSISTENCY IN SURAKARTA CHESS USING DUELING DEEP NETWORK WITH INCREASING BATCH
Get PDFDeep reinforcement learning usage in creating intelligent agents for various tasks has shown outstanding performance, particularly the Q-Learning algorithm. Deep Q-Network (DQN) is a reinforcement learning algorithm that combines the Q-Learning algorithm and deep neural networks as an approximator function. In the single-agent environment, the DQN model successfully surpasses human ability several times over. Still, when there are other agents in the environment, DQN may experience decreased performance. This research evaluated a DQN agent to play in the two-player traditional board game of Surakarta Chess. One of the drawbacks that we found when using DQN in two-player games is its consistency. The agent will experience performance degradation when facing different opponents. This research shows Dueling Deep Q-Network usage with increasing batch size can improve the agent's performance consistency. Our agent trained against a rule-based agent that acts based on the Surakarta Chess positional properties and was then evaluated using different rule-based agents. The best agent used Dueling DQN architecture with increasing batch size that produced a 57% average win rate against ten different agents after training for a short period.
ABSTRAK: Pembelajaran Peneguhan Mendalam adalah terbaik apabila digunakan bagi mewujudkan ejen pintar dalam menyelesaikan pelbagai tugasan, terutama jika ia melibatkan algoritma Pembelajaran-Q. Algoritma Rangkaian-Q Mendalam (DQN) adalah Pembelajaran Peneguhan berasaskan gabungan algoritma Pembelajaran-Q dan rangkaian neural sebagai fungsi penghampiran. Melalui persekitaran ejen tunggal, model DQN telah beberapa kali berjaya mengatasi kemampuan manusia. Namun, ketika ejen lain berada dalam persekitaran ini, DQN mungkin kurang berjaya. Kajian ini melibatkan ejen DQN bermain papan tradisional iaitu Catur Surakarta dengan dua pemain. Salah satu kekurangan yang dijumpai adalah konsistensi. Ejen ini akan kurang bagus ketika berhadapan lawan berbeza. Kajian menunjukkan dengan penggunaan Rangkaian-Q Dwipertarungan Mendalam bersama peningkatan saiz kumpulan dapat meningkatkan konsistensi prestasi ejen. Ejen ini telah dilatih untuk melawan ejen lain berasaskan peraturan dan sifat kedudukan Catur Surakarta. Kemudian, ejen ini diuji berpandukan peraturan berbeza. Ejen terbaik adalah yang menggunakan rekaan DQN Dwipertarungan bersama peningkatan saiz kumpulan. Ianya berhasil memenangi permainan dengan purata 57% berbanding sepuluh agen lain melalui latihan jangka masa pendek
Forecasting of infection prevalence of Helicobacter pylori (H. pylori) using regression analysis
No full textGlobal warming may have a significant impact on human health because of the growth of the population of harmful bacteria such as Helicobacter pylori infection. It is crucial to predict the prevalence of a pathogen in a society in a faster and more cost-effective way in order to manage caused disease. In this research, we have done predictive analysis of H. pylori infection spread behavior with respect to weather parameters (e.g., humidity, dew point, temperature, pressure, and wind speed) of Istanbul based on a database from Istanbul Samatya Hospital. We developed a forecasting model to predict H. pylori infection prevalence. The goal is to develop a machine learning model to predict H. pylori (Hp) related infection diseases (e.g., gastric ulcer diseases, gastritis) based on climate variables. The dataset for this study covered years from 1999 to 2003 and contained a total of 7014 rows from the Samatya Hospital in Istanbul. The weather information related to those years and location, including humidity (H), dew point (D), temperature (T), pressure (P) and wind speed (W), were collected from the following website: https://www.wunderground.com. In this paper we analyzed the forecasting model, which was used to predict H. pylori infection prevalence, by non-linear multivariate linear regression model (MLRM). We applied the non-linear least square method of minimization for the sum of squares to find optimal parameters of MLRM. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the Hp infection disease is most influenced by humidity. Hp prevalence is modelled using the Multiple Regression Method equation, the average H, D, T, P, and W were the most important parameters to deviation of the datasets (testing dataset was 17% and 18% for training dataset). This showed that the statistical model predicts the Hp prevalence with about 83% accuracy of the testing data set (11 months) and 87% accuracy of the training data set (42 months). Based on the proposed model, monthly infection can be predicted early for medical services to take preventative measures and for government to prepare against the bacteria. In addition, drug producers can adjust their drug production rates based on forecasting results.
ABSTRAK: Pemanasan global mungkin mempunyai kesan langsung terhadap kesihatan manusia kerana pertambahan populasi bakteria merbahaya seperti infeksi H. pylori. Adalah penting bagi mengesan kehadiran patogen dalam masyarakat bagi mengawal penularan penyakit dengan cepat, dan melalui kaedah kurang mahal. Kajian ini berkaitan analisis ramalan penularan infeksi H. pylori secara langsung terhadap parameter cuaca (cth: kelembapan, titik embun, suhu, tekanan, kelajuan angin) di Istanbul berdasarkan data dari Hospital Samatya Istanbul. Kajian ini membentuk model ramalan bagi menjangka penyebaran infeksi H. pylori. Matlamat adalah bagi mencipta model pembelajaran mesin bagi mengjangka penyakit berkaitan infeksi H. pylori (Hp) (cth: penyakit ulser gastrik, gastrik) berdasarkan pembolehubah cuaca. Dari tahun 1999 ke 2003, set data telah digunakan bagi mempelajari di mana sejumlah 7014 baris dari Hospital Samatya di Istanbul. Informasi berkaitan tahun-tahun tersebut dan lokasi mengenai kelembapan (H), titik embun (D), suhu (T), tekanan (P) dan kelajuan angin (W) dikumpul dari laman sesawang https://www.wunderground.com. Kajian ini mengguna pakai model ramalan bagi meramal kelaziman infeksi H. pylori, melalui model regresi berkadaran multivariat tidak-berkadaran (MLRM). Kaedah Kuasa Dua Terkecil tidak linear digunakan bagi pengurangan jumlah ganda dua bagi mencapai parameter optimum MLRM. Kaedah Regresi Gandaan digunakan bagi mencari persamaan antara kriteria pembolehubah dan gabungan pembolehubah ramalan. Dapatan menunjukkan infeksi penyakit Hp adalah disebabkan oleh faktor kelembapan. Penyebaran Hp dimodel menggunakan persamaan Kaedah Regresi Gandaan, purata H, D, T, P dan W adalah parameter terpenting bagi sisihan data latihan iaitu sebanyak 17% dan 18% bagi set data latihan. Ini menunjukkan model statistik menjangkakan penyebaran Hp adalah sebanyak 83% adalah tepat pada set data yang diuji (selama 11 bulan) dan 87% tepat pada set data latihan (selama 42 bulan). Berdasarkan model yang dicadangkan ini, infeksi bulanan dapat di jangka lebih awal bagi membendung servis kepada perubatan dan kerajaan bersiap-sedia memerangi bakteria ini. Tambahan, prosedur jumlah ubatan dapat dihasilkan lebih atau kurang daripada jumlah ubatan berdasarkan dapatan ramalan
Physicochemical and Functional Properties of Composite Flours Based on Mocaf and Tempeh Flour for Making Cookies
Get PDFGluten and casein free cookie products are increasingly in demand by consumers. Gluten and casein free cookies were made using mocaf as a substitute for gluten-free flour and tempeh flour as a casein-free protein source. The characteristics of a good cookie composite flour made from mocaf and tempeh flour need to be known. The purpose of this study was to determine the physicochemical and functional properties of a cookie composite flour based on mocaf and tempeh flour. The composite flour was mixed by dry mixing. The composite flour formulations were: C0 (100% wheat flour); C1 (100% mocaf); C2 (75% mocaf and 25% tempeh flour); C3 (50% mocaf and 50% tempeh flour); C4 (25% mocaf and 75% tempeh flour) and C5 (100% tempeh flour). The results showed that addition of tempeh flour increased the ash, protein, fat, minerals, a* value, b* value, and water absorption capacity. The addition of tempeh flour was proven to reduce moisture content, carbohydrates, lightness, and whiteness index value. The cookie composite flour made from 75% mocaf and 25% tempeh flour had a gelatinization profile similar to 100% wheat flour, so this formula was recommended as a cookie composite flour.
ABSTRAK: Produk kuki bebas gluten dan kasein semakin meningkat dalam permintaan pengguna. Kuki bebas gluten dan kasein dibuat menggunakan mocaf sebagai pengganti tepung bebas gluten dan tepung tempe sebagai sumber protein bebas kasein. Ciri-ciri tepung komposit biskut yang baik diperbuat daripada tepung mocaf dan tempeh perlu diketahui. Tujuan kajian ini adalah untuk menentukan sifat fizikokimia dan fungsian bagi tepung komposit biskut berasaskan tepung mocaf dan tempeh. Tepung komposit telah dicampur dengan adunan kering. Formulasi tepung komposit ialah C0 (100% tepung gandum); C1 (100% mocaf); C2 (75% mocaf dan 25% tepung tempeh); C3 (50% mocaf dan 50% tepung tempeh); C4 (25% mocaf dan 75% tepung tempeh) dan C5 (100% tepung tempeh). Hasil kajian menunjukkan penambahan tepung tempeh meningkatkan kadar abu, protein, lemak, mineral, nilai a*, nilai b*, dan kapasiti penyerapan air. Penambahan tepung tempeh terbukti dapat mengurangkan kandungan lembapan, karbohidrat, ringan, dan nilai indeks keputihan. Tepung komposit biskut yang diperbuat daripada 75% mocaf dan 25% tepung tempe mempunyai profil gelatinisasi yang serupa dengan 100% tepung gandum, jadi formula ini disyorkan sebagai tepung komposit biskut