Metaheuristic approach on feature extraction and classification algorithm for handwrittten character recognition

Handwritten Character Recognition (HCR) is a process of converting handwritten text into machine readable form and it comprises three stages; preprocessing, feature extraction and classification. This study acknowledged the issues regarding HCR performances particularly at the feature extraction and classification stages. In relation to feature extraction stage, the problem identified is related to continuous and minimum chain code feature extraction at its starting and revisit points due to branches of handwritten character. As for the classification stage, the problems identified are related to the input feature for classification that results in low accuracy of classification and classification model particularly in Artificial Neural Network (ANN) learning problem. Thus, the aim of this study is to extract the continuous chain code feature for handwritten character along with minimising its length and then proceed to develop and enhance the ANN classification model based on the extracted chain code in order to identify the handwritten character better. Four phases were involved in accomplishing the aim of this study. First, thinning algorithm was applied to remove the redundancies of pixel in handwritten character binary image. Second, graph based-metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature of the handwritten character image while minimising the route length of the chain code. Graph theory was then utilised as a solution representation. Hence, two metaheuristic approaches were adopted; Harmony Search Algorithm (HSA) and Flower Pollination Algorithm (FPA). As a result, HSA graphbased metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature for handwritten character. Based on the experiment conducted, it was demonstrated that the HSA graph-based metaheuristic feature extraction algorithm showed better performance in generating the shortest route length of chain code with minimum computational time compared to FPA. Furthermore, based on the evaluation of previous works, the proposed algorithm showed notable performance in terms of shortest route length of chain code for extracting handwritten character. Third, a feature vector was derived to address the input feature issue. The derivation of feature vector based on proposed formation rule namely Local Value Formation Rule (LVFR) and Global Value Formation Rule (GVFR) was adopted to create the image features for classification purpose. ANN was applied to classify the handwritten character based on the derived feature vector. Fourth, a hybrid of Firefly Algorithm (FA) and ANN (FA-ANN) classification model was proposed to solve the ANN network learning issue. Confusion Matrix was generated to evaluate the performance of the model in terms of precision, sensitivity, specificity, F-score, accuracy and error rate. As a result, the proposed hybrid FA-ANN classification model is superior in classifying the handwritten characters compared to the proposed feature vector-based ANN with 1.59 percent incremental in terms of accuracy model. Furthermore, the proposed hybrid FA-ANN also exhibits better performances compared to previous related works on HCR

A legal analysis on marital rape in Malaysia / Muhammad Syafiq Mohd Sahar, Mohamad Arif Aizuddin Masrom and Mohamad Zakwan Mohamad Anuar

The purpose of this research is to analyze the issue of marital rape in Malaysia from the legal perspective. In the first phase of the project, we discuss the current law and situation of marital rape in Malaysia. Then, we compare the law with other countries that have applied it such as Scotland and United States. The second phase of the project, we carried out field work in the context that we handle questionnaires to the respondents that relates to this issue and to the public in order to receive their feedback. The final phase of this project is where we conclude our research and recommend solutions that can be done by the legislature and authorities in Malaysia in order to handle this issue efficiently

Quality control improvement at Jana DCS Sdn. Bhd.

Jana DCS Sdn. Bhd. is one of the companies that run the service of air conditioning system supply in Nusajaya, Johor, Malaysia. Quality improvement is one of the most important part when talking about a company, mostly companies that operate in service industries. Quality control plays the major parts in quality improvement as quality control is an operational technique to ensure efficient and effective operation. Roughly, total net area cooled by Jana DCS Sdn. Bhd. is 590,000 square feet as for Johor State Government Administration Centre. While for Puteri Harbour, the total net area cooled is 614,000 square feet. Jana DCS Sdn. Bhd. operates Iskandar Malaysia’s first district cooling plant, with both thermal energy and chilled water storage capability that produce and supply cooling load for air conditioning to the Johor State Government Complex at Kota Iskandar and to various private sector developments at Puteri Harbour

Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition

This study addresses the concerns regarding the performance of Handwritten Character Recognition (HCR) systems, focusing on the classification stage. It is widely acknowledged that the development of the classification model significantly impacts the overall performance of HCR. The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. The objective of this study is to improve and refine the ANN classification model to achieve better HCR. To achieve this goal, this study proposed a hybrid Flower Pollination Algorithm with Artificial Neural Network (FPA-ANN) classification model for HCR. The FPA is one of the metaheuristic approaches is utilized as an optimization technique to enhance the performance of ANN, particularly by optimizing the network training process of ANN. The experimentation phase involves using the National Institute of Standards and Technology (NIST) handwritten character database. Finally, the proposed FPA-ANN classification model is analysed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy

Smart portable security system

Stealing is a form of criminal which is long-rooted in Malaysia and was recorded increasing in rates as time went by. Based upon those unwanted matters, many researchers have done several studies to minimize the rate of this type of crimes. A research has been done related to a portable security system that is used to monitor and alert user on intruders. It is used to prevent of and protect against assault, damage, fire, fraud, invasion of privacy, theft, unlawful entry, and other such occurrences caused by deliberate action [4]. The project basically focusing on replicating or imitating the security system that are in the market nowadays. The main focus of this project is to construct a reliable and portable security system that are small in size, has a quick and simple setup, can be moved easily and can connect a module to another module wirelessly in a range of distance. The system comprises sensors, an alarm, a keypad, a transmitter, a receiver and a microcontroller

Model updating of crash box structures for crashworthiness study

The crash box structure is an essential structure of the front side members of a car body structure. It absorbs the kinetic energy during the event of a collision by plastically deform to absorb the impact energy efficiently. Various designs are applied towards the structure with different materials, configurations, and imperfections or trigger mechanisms. Crash box with trigger mechanisms is often a subject in crashworthiness studies, however, this research will have an approach to dealing with the structure with modal testing through experimental and computational analysis due to the location of the structure that exposed to vehicle vibration as well. As discrepancies occur, the model updating technique is utilised to identify and update the sensitive parameters that cause the discrepancies. The parameters are then used in the crashworthiness analyses to determine their effect towards the crashworthiness output of the crash box structure. The crash box structures are modelled in finite elements before being analysed with the normal mode analysis in MSC Patran and MSC Nastran and quasi-static analysis in Abaqus. Five different fabricated structures are made up of two parts attached using a spot weld with different designs of trigger mechanisms. Three approaches to joining elements are used for the finite element model: CWELD, CBEAM, and CBAR. The modal behaviour for all modelling is identified by using SOL 103, while the experimental modal analyses are conducted with the use of an impact hammer test with the roving hammer method to obtain the modal responses. The model updating method was conducted to reduce the discrepancies between the experimental and the computational data. Sensitivity analyses are executed to find the most sensitive model updating parameters. The results obtained by this study demonstrate that the use of CBAR joining element is the best to replicate the spot weld joining, where for all five types of crash box structures, the CBAR elements did show a significant percentage of error compared to CWELD and CBEAM for all types of crash box structures, while the most sensitive parameters that affect the modal behaviour of the structures are Young’s modulus of AA-6061, followed by the density of AA-6061 and Young’s modulus of spot welded joint. In terms of crashworthiness analyses, it is identified that the use of updated parameters in crashworthiness analyses compared to the initial results of crashworthiness output did show a small change where the crashworthiness output of the structure is slightly higher for both primary and secondary peaks as well as for the magnitude of the absorbed energy. The outcome of this research will contribute towards the field of mechanical vibration and crashworthiness, especially in the automotive industry, in which this research focuses on the optimization method of the modelling to improve the accuracy and reliability of the computational prediction

The effect of fluidity of palm kernel oil with pour point depressant on coefficient of friction using fourball tribotester

The growing awareness worldwide of the need to promote the use of renewable materials such as vegetable oils is due to increasing concerns about the damage to the environment that is being caused by the use of non-biodegradable mineral oils. Vegetable oils have the potential to replace mineral oils as a lubricant because of their specific properties, namely that they are non-toxic and biodegradable. The main problem with the use of vegetable oils is that they perform poorly at low temperatures. In this research, palm kernel oil (PKO), which behaves as a semi-solid, was used as a bio-lubricant by mixing it with different weight percentages of a pour point depressant (PPD) to investigate the performance of the pour point depressant and also to determine the effect on the lubricity of the bio-lubricant when it is blended with different percentages of PPD (5 wt.%, 10 wt.%, 20 wt.% and 30 wt.%). The experiment was conducted according to ASTM D4172 and ASTM D2783. The results of the experiment showed that at low temperatures the PKO samples with 20 wt.% PPD and 30 wt.% PPD performed well, where they were able to remain in a liquid form at a temperature of 15°C. From all antiwear test result, the coefficient of friction for the PPD sample shows poor tribological performance when adding PPD into the palm kernel oil

APLIKASI METODE OVEN SUHU TINGGI TETAP DAN BENIH UTUH DALAM PENGUJIAN KADAR AIR BENIH KELAPA SAWIT (Elaeis guineensis L. Jacq.)

Seed moisture content is a crucial factor which will determine seed viability during storage and influence successful germination process. By that reason, ability to measure seed moisture content in precise is a basic need for seed producers. For plant species which produces large seeds such as oil palm, International Seed Testing Association (ISTA) demands that seed moisture content can be determined by using high or low constant oven temperature with seeds been sliced or crushed before the oven application. On the other hand, seed producers commonly use intact seeds to determine the parameters. Test results to compare between the two methods showed that low constant oven temperature generated higher seed moisture content than that of the high constant oven temperature method. In addition, moisture contents, which were generated by intact and crushed seeds, were not significantly different. Based on seed component, kernel contained higher moisture than shell, both by utilizing low and high constant oven temperature. Further research is needed due to a large range of shell thickness of oil palm seeds.Kadar air (KA) benih merupakan salah satu faktor penting yang menentukan tingkat viabilitas selama penyimpanan maupun pengecambahan benih. Oleh karena itu, kemampuan untuk menduga KA benih dengan tepat merupakan kebutuhan dasar bagi produsen kecambah. Bagi benih-benih berukuran besar seperti benih kelapa sawit, International Seed Testing Association (ISTA) mensyaratkan penggunaan oven suhu tinggi dan suhu rendah serta penerapan pemecahan benih untuk penentuan KA yang lebih tepat, sedangkan produsen menggunakan benih utuh untuk proses penentuan parameter tersebut. Hasil percobaan memperlihatkan bahwa benih-benih yang diuji dengan oven suhu rendah konstan memberikan nilai KA yang lebih tinggi dibanding KA yang diperlihatkan oleh metode oven suhu tinggi. Selain itu, benih yang dianalisis secara utuh memberikan nilai KA yang tidak berbeda nyata dengan KA benih yang dianalisis dengan metode pemecahan benih. Berdasar komponen penyusun, inti benih memiliki KA yang secara nyata lebih tinggi dibanding KA pada cangkang, baik menggunakan metode oven suhu rendah ataupun suhu tinggi. Percobaan lebih lanjut dibutuhkan mengingat luasnya keragaman ketebalan cangkang benih kelapa sawit

Penerapan model regresi linear untuk estimasi mobil bekas menggunakan bahasa Python

Mobil bekas memiliki nilai transaksi yang signifikan dalam pasar otomotif. Estimasi harga mobil bekas menjadi penting bagi pembeli dan penjual untuk menentukan nilai yang sesuai. Dalam penelitian ini, kami menerapkan model regresi linear menggunakan bahasa pemrograman Python untuk memperkirakan harga mobil bekas berdasarkan atribut-atribut yang relevan seperti tahun produksi, capaian kilometer, pajak mobil, konsumsi bahan bakar, dan juga jumlah mesin. Kami menggunakan dataset mobil bekas yang mengandung informasi penting untuk analisis. Dalam menggunakan model regresi linear pada penelitian ini berhasil mendapatakan akurasi sebesar 0,76% dan untuk hasil estimasi harga mobil yang didapatkan dengan inputan tahun mobil = 2019, KM mobil = 5000, pajak mobil = 145, konsumsi BBM = 30,2, dan ukuran mesin = 2. Maka berhasil mendapatkan nilai estimasi sebesar 21.208,505 dalam satuan Pound dan 393.608,6514549 dalam satuan Rupiah. Sehingga dapat dikatakan model regresi linear terbukti berhasil dalam kategori baik untuk mencari estimasi harga mobil bekas berdasarkan faktor tertentu menggunakan bahasa Python

Whale Optimisation Freeman Chain Code (WO-FCC) extraction algorithm for handwritten character recognition

In order to improve the classification accuracy in the field of handwriting character recognition (HCR), the number of derivative algorithms has improved and the interest in feature extraction has increased. In this paper, we propose a metaheuristic method for feature extraction algorithm with Whale Optimisation Algorithm (WOA) based HCR. WOA is a swarm-based techniques that mimic the social behavior of groups of animals, which mimics the social behavior of humpback whales. Freeman chaincode (FCC) is utilised as a data representations of handwritten text images. Nevertheless, the representations of FCC depends on the length of the path and the branching of the character’s nodes. To solve this problem, we propose a metaheuristic approach through WOA to find the shortest path length and minimum computational time for handwriting recognition. Finally, the results were compared with the existing proposed Flower Pollination Algorithm (FPA) at the time of FCC extraction. The results show that WOA is a bit better at getting shorter path lengths than FPA in terms of path lengths. In terms of calculation time, WOA calculates faster calculation time by feature extraction than FPA