3D Face Analysis using Tensor Approach

The advancement of multimodal technology has enabled the creation of large face datasets. Multidimensional characteristics such as covariates and multimodal aspects in 2D, 2.5D, and 3D data are included in these datasets. Early studies in face research used matrix-based and vector-based algorithms to represent faces. According to studies, these methods have the potential to prevent the loss of critical and significant data, which could lead to lower recognition performance. The goal of this research is to develop and validate a tensor-based face recognition method that can overcome the drawbacks of matrix-based Principal Component Analysis (PCA). A face dataset consists of faces with a combination of multiple underlying causal factors such as facial expression, expression intensity, angle of view, gender and race, and the bilinear technique alone is incapable of accurately representing the dataset’s multidimensionality. PCA’s shortcomings can be overcome by using the tensor decomposition approach to separate these distinct variations. Experimental results have shown that the multilinear tensor approach could statistically outperform the bilinear PCA approach in face recognition applications. This study has added to the understanding of the centring strategy used in tensor models. The median projection operator could maximise the variation for each principal axis in the tensor space and the results have shown that in the median-centred strategy, recognition rates for emotional expressions increased from 0.4 percent to 1.4 percent. Only fear expressions have demonstrated similar recognition performance in mean-centred and median-centred experiments. Current face recognition using PCA is unable to distinguish between different types of centring approaches and cluster them. As a result, this research adopted a hybrid combined framework of a tensor model with an ANOVA (Analysis of Variance) model to uncover the substantial effects of within-subject differences (expression types and expression strengths) on recognition performance. Apart from that, experimental results revealed an interaction effect between those two covariates, showing that the effect of expression types on recognition performance is not continuous and is influenced by intensity levels. This new evidence may well be useful in a variety of recognition processes

Predictors of important CT findings and neurosurgical intervention in minor head injury

Di antara semua kecederaan kepala, kecederaan kepala nngan merupakan suatu kumpulan kecederaan kepala yang paling kerap berlaku dimana ia menyumbang sebanyak 70 - 80 % kes yang ditemui di Jabatan Kecemasan. Kecederaan kepala ringan didefinasikan sebagai seseorang yang mengalami hentakan di kepala yang mengakibatkan pengsan dan I atau hilang ingatan. Sementara itu, skala koma Glasgow pula adalah di antara 13-15. Penggunaan imbasan tomografi berkomputer ke atas semua pesakit yang mengalami kecederaan kepala ringan adalah suatu perkara yang kontroversi. Ini disebabkan peratusan keputusan penting yang di perolehi dari imbasan berkenaan adalah kecil berbanding dengan kos tinggi yang membabitkan penggunaan imbasan tomografi berkomputer. Of all patients with head injury, minor head injury represents the most common visitors of the emergency department. It contributed to about 70 - 80 o/o of all head injury cases that are seen in the Accident and Emergency department. Minor head injury is generally defined as those with history of blow to the head in which has resulted in loss of consciousness and I or amnesia with Glasgow Coma Score of 13 to 15. It is controversial whether cranial CT scan should be performed on all patients, as the yield of positive CT scan is low as opposed to its high cost. The rate of neurosurgical intervention is even lower to justify the routine use of CT scan on this patient. The use of clinical variables as a screening tool before deciding to embark on CT scan is appropriate in order to reduce the number of CT scan performed on all patients with minor head injury

The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing

In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing

A review on region of interest-based hybrid medical image compression algorithms

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms

Systematic review of using machine learning in imputing missing values

Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values

Hydrogen and Carbon Nanotubes from Pyrolysis-Catalysis of Waste Plastics: A Review

More than 27 million tonnes of waste plastics are generated in Europe each year representing a considerable potential resource. There has been extensive research into the production of liquid fuels and aromatic chemicals from pyrolysis-catalysis of waste plastics. However, there is less work on the production of hydrogen from waste plastics via pyrolysis coupled with catalytic steam reforming. In this paper, the different reactor designs used for hydrogen production from waste plastics are considered and the influence of different catalysts and process parameters on the yield of hydrogen from different types of waste plastics are reviewed. Waste plastics have also been investigated as a source of hydrocarbons for the generation of carbon nanotubes via the chemical vapour deposition route. The influences on the yield and quality of carbon nanotubes derived from waste plastics are reviewed in relation to the reactor designs used for production, catalyst type used for carbon nanotube growth and the influence of operational parameters

Peningkatan prestasi proses melalui peralatan kualiti berstatistik (improving process performance through statistical quality tools

Persekitaran pasaran yang kompetitif pada hari ini memerlukan syarikat perusahaan kecil dan sederhana (PKS) menekankan kepada kualiti bagi meningkatkan proses dan prestasi pengeluaran. Matlamat ini boleh dicapai dengan mempraktiskan peralatan kualiti berstatistik melalui program-program proses kawalan statistik (SPC). Penggunaan peralatan kualiti seperti carta kawalan membenarkan pihak pengeluar membuat keputusan proses dan memenuhi kualiti produk yang tinggi. Bagaimanapun, terdapat sesetengah pihak PKS gemar menggunakan kaedah SPC secara manual (helaian kertas graf) yang lebih cenderung kepada beberapa kelemahan, seperti kesilapan manusia dan penggunaan masa untuk mengenal pasti masalah. Oleh itu, dalam kajian ini dibincangkan mengenai pembangunan terbaru kaedah SPC berasaskan komputer yang bertujuan untuk melakukan analisis statistik, dan menguruskan data kualiti. Konsep dan maklumat pembangunan sistem ini secara asasnya diperoleh daripada kaedah kajian kes yang dikendalikan ke atas syarikat pembuatan PKS, termasuklah temu ramah, soal selidik serta pemerhatian. Tambahan lagi, pembangunan sistem juga difokuskan kepada set data, operasi statistik yang mudah dan kumpulan pengguna tertentu. Hasil kajian menunjukkan bahawa sistem yang dinamakan Small and Medium Enterprises - Statistical Process Control (SMEs-SPC) adalah amat praktikal sebagai alat menganalisis data berbanding dengan penggunaan SPC secara manual. Di samping itu, sistem ini berpotensi tinggi untuk memberi galakan kepada operator atau pekerja pengeluaran dan jurutera perindustrian dalam memahami kepentingan pengumpulan data kualiti demi meningkatkan prestasi pengeluara

Homestays - community programme or alternative accommodation? a re-evaluation of concept and execution

Homestay programmes - which form a part of Community-based tourism (CBT) vital in the development agenda of Third World Countries- provide tourists with a unique opportunity to experience the atmosphere, lifestyle practices and activities of rural communities in the countryside. The Ministry of Tourism and Culture, Malaysia has drawn up a set of guidelines and requirements for operating a homestay program that must be adhered to before approval is granted. The question that has arisen is whether homestay programs as practised in Malaysia truly present visitors with the opportunity to experience the host community’s lifestyle, or merely serve as an alternative form of accommodation. This article overviews the various scenarios and dilemmas faced in implementing the homestay programmes in the Malaysian context, the causes that lead to its ‘abuse’, and some practical solutions that may be proposed to address the arising issues and challenges in an integrated manner

Halangan pelaksanaan SPC: satu pandangan praktikal daripada syarikat pengusaha kecil dan sederhana

This study presents the outcome of case studies conducted in ten Malaysian Small and Medium Enterprises (SMEs) manufacturing companies (named as company A to J) by focusing on the obstacles or problems faced in the context of statistical process control (SPC) software or system application, particularly among production workers (operators). Furthermore, the data collection is carried out in stages through interviews, questionnaires, and observations with company representatives. The results show that there are several related barriers faced by the SMEs companies; these issues include top management support, commitment, costs to develop the system, as well as education and training of SPC. In addition, the study also revealed that manual SPC method is subjected to several limitations such as human errors and inefficient data management. In conclusion, several issues and practical reflection provided by SMEs would be used as a medium to further improve the SPC application in the futur