Technical University of Malaysia Malacca

gases and many other chemicals and molecules can either donate or accept electrons, resulting in an alteration of the overall conductivity. Such properties make CNTs ideal for nano-scale gas-sensing materials. Conductive-based devices have already been demonstrated as gas sensors. However, CNTs still have certain limitations for gas sensor application, such as a long recovery time, limited gas detection, and weakness to humidity and other gases. Therefore, the nanocomposites of interest consisting of polymer and CNTs have received a great deal of attention for gas-sensing application due to higher sensitivity over a wide range of gas concentrations at room temperature compared to only using CNTs and the polymer of interest separately.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

Dissolved gas analysis (DGA) is used to assess the condition of power transformers. It uses the concentrations of various gases dissolved in the transformer oil due to decomposition of the oil and paper insulation. DGA has gained worldwide acceptance as a method for the detection of incipient faults in transformers.

Polylactic acid (PLA) is a thermoplastic polymer produced from lactic acid that has been chiefly utilized in biodegradable material and as a composite matrix material. PLA is a prominent biomaterial that is widely used to replace traditional petrochemical-based polymers in various applications owing environmental concerns. Green composites have gained greater attention as ecological consciousness has grown since they have the potential to be more appealing than conventional petroleum-based composites, which are toxic and nonbiodegradable. PLA-based composites with natural fiber have been extensively utilized in a variety of applications, from packaging to medicine, due to their biodegradable, recyclable, high mechanical strength, low toxicity, good barrier properties, friendly processing, and excellent characteristics. A summary of natural fibers, green composites, and PLA, along with their respective properties, classification, functionality, and different processing methods, are discussed to discover the natural fiber-reinforced PLA composite material development for a wide range of applications. This work also emphasizes the research and properties of PLA-based green composites, PLA blend composites, and PLA hybrid composites over the past few years. PLA's potential as a strong material in engineering applications areas is addressed. This review also covers issues, challenges, opportunities, and perspectives in developing and characterizing PLA-based green composites.

Wire arc additive manufacturing (WAAM) is steadily increasing with significant research work are underway. The ability of WAAM to manufacture a large-scale product at a lower cost and shorter lead times has increased its development. The efficiency or potential of WAAM to become a new lead in the additive manufacturing industry is believed to be realized by a few factors. High efficiency and performance require a higher metal deposition rate, which implies higher heat input that negatively affects the manufacturing process. This paper review works for factors associated with heat input on the WAAM process. The factor affecting these properties were explained to identify the optimized WAAM process in terms of heat input. The paper focuses on the impact of heat input on the macrostructure, microstructure and mechanical properties of the parts deposited in the WAAM process. This review also discussed the effect of heat input used by eight different types of arc welding technologies, with appropriate use of wire materials. The study also highlights that heat input affected the microstructure of the WAAM, causing significant changes in grain structure, grain size and pore area percentage. Besides, it can be suggested that grain structure has a strong influence on the WAAM materials' mechanical properties.

: Studies of stroke patients undergoing robot-assisted rehabilitation have revealed various kinematic parameters describing movement quality of the upper limb. However, due to the different level of stroke impairment and different assessment criteria and interventions, the evaluation of the effectiveness of rehabilitation program is undermined. This paper presents a systematic review of kinematic assessments of movement quality of the upper limb and identifies the suitable parameters describing impairments in stroke patients. A total of 41 different clinical and pilot studies on different phases of stroke recovery utilizing kinematic parameters are evaluated. Kinematic parameters describing movement accuracy are mostly reported for chronic patients with statistically significant outcomes and correlate strongly with clinical assessments. Meanwhile, parameters describing feed-forward sensorimotor control are the most frequently reported in studies on sub-acute patients with significant outcomes albeit without correlation to any clinical assessments. However, lack of measures in coordinated movement and proximal component of upper limb enunciate the difficulties to distinguish the exploitation of joint redundancies exhibited by stroke patients in completing the movement. A further study on overall measures of coordinated movement is recommended.

This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

The COVID-19 pandemic has forced many organizations around the world to make full use of a variety of emerging online communication platform technologies. Universities are among the organizations that have asked students, tutors, and lecturers to use a number of different online communication platforms to ensure the education process remains uninterrupted. However, the COVID-19 pandemic has generated considerable challenges for the global higher education community while using such emerging technologies. This research has two main goals. First, this paper will begin by investigating whether the online learning platforms used by university students during the COVID-19 period have presented any challenges to their learning. Second, the paper will then go on to address proposed solutions by developing a conceptual model to reduce the impact of such challenges. This research uses an exploratory qualitative research approach, supported by literature content analysis techniques. The data set for this study was collected during the first peak of the pandemic period in Malaysia, between the 16th of May 2020 and the 5th of June 2020. We used SPSS to conduct a descriptive analysis and NVivo12 to analyse data collected from 486 students from different universities in Malaysia. These students disclosed various obstacles they encountered when they used IT platform applications for online learning. These obstacles include (a) work and information overload received from instructors, (b) inadaptability and unfamiliarity of the new online learning environment, and (c) personal health challenges related to stress and anxiety. Based on previous relevant research, this study introduced a set of motivational factors and developed a conceptual motivational model for sustainable and healthy online learning.

An analysis is carried out to study the steady two-dimensional stagnation-point flow of a micropolar fluid over a shrinking sheet in its own plane. The shrinking velocity and the ambient fluid velocity are assumed to vary linearly with the distance from the stagnation point. The features of the flow characteristics are analyzed and discussed. Different from a stretching sheet, it is found that the solutions for a shrinking sheet are nonunique.

Recent developments within the topic of biomaterials has taken hold of researchers due to the mounting concern of current environmental pollution as well as scarcity resources. Amongst all compatible biomaterials, polycaprolactone (PCL) is deemed to be a great potential biomaterial, especially to the tissue engineering sector, due to its advantages, including its biocompatibility and low bioactivity exhibition. The commercialization of PCL is deemed as infant technology despite of all its advantages. This contributed to the disadvantages of PCL, including expensive, toxic, and complex. Therefore, the shift towards the utilization of PCL as an alternative biomaterial in the development of biocomposites has been exponentially increased in recent years. PCL-based biocomposites are unique and versatile technology equipped with several importance features. In addition, the understanding on the properties of PCL and its blend is vital as it is influenced by the application of biocomposites. The superior characteristics of PCL-based green and hybrid biocomposites has expanded their applications, such as in the biomedical field, as well as in tissue engineering and medical implants. Thus, this review is aimed to critically discuss the characteristics of PCL-based biocomposites, which cover each mechanical and thermal properties and their importance towards several applications. The emergence of nanomaterials as reinforcement agent in PCL-based biocomposites was also a tackled issue within this review. On the whole, recent developments of PCL as a potential biomaterial in recent applications is reviewed.

Biochars were prepared by conducting slow pyrolysis of rubber wood sawdust (RWSD) derived from sawn timber. Eventhough researches on preparation of biochar from biomass have been reported by many researchers, limited work has been reported for investigation of biochar RWSD for its surface porosities and functional groups. Surface porosity of biochars provides a suitable dimension for cluster of microorganism to grow and higher porosity for better water holding capacity. Surface functional groups contain oxygen may help to improve the soil fertility by increasing the cation and anion exchange capacities to reduce the nutrient leaching in soil. The pyrolysis process was carried out at temperatures ranging between 300 °C to 700 °C at the heating rate of 5 °C/min for 3 hours with continuous nitrogen purging. The influence of pyrolysis temperatures on the biochars pores were investigated by using X-Ray Diffraction (XRD), Brunauer-Emmett-Teller (BET) surface analysis and Scanning Electron Microscopy (SEM). The surface functional groups were examined by Fourier Transform Infrared (FT-IR). SEM analysis clearly showed the development of well-defined pores distributed on biochars surface. It was found that the maximum BET surface area and total pore volume were 5.493 m2/g and 0.0097 cm3/g respectively for biochar pyrolysis at 700 °C. The FT-IR spectrum analysis showed the functional groups decreased with the increasing of pyrolysis temperature. The results highlighted the effect of pyrolysis temperature on biochar pores accumulative that associated with soil fertility and nutrient retention in soil which could be beneficial to the agricultural industries.

Sugar palm (Arenga pinnata) fibres and starches are considered as agro-industrial residue in the agricultural industry. This paper aims to investigate the effect of different concentrations (0–1.0 wt%) of sugar palm nanofibrillated cellulose (SPNFCs) reinforced sugar palm starch (SPS) on morphological, mechanical and physical properties of the bionanocomposites film. The SPNFCs, having a diameter of 5.5 ± 0.99 nm and length of several micrometres, were prepared from sugar palm fibres via a high-pressure homogenisation process. FESEM investigation of casting solution displayed good miscibility between SPS and SPNFCs. The FTIR analysis revealed good compatibility between the SPS and SPNFCs, and there were existence of intermolecular hydrogen bonds between them. The SPS/sPNFCs with 1.0 wt% had undergone an increment in both the tensile strength and Young’s modulus when compared with the SPS film, from 4.80 MPa to 10.68 MPa and 53.97 MPa to 121.26 MPa, respectively. The enhancement in water barrier resistance was led by reinforcing SPNFCs into the matrix, which resulted in bionanocomposites. The properties of bionanocomposites will be enhanced for short-life applications, such as recyclable container and plastic packaging through the incorporation of SPNFCs within the SPS bionanocomposites.

Vehicular communication networks is a powerful tool that enables numerous vehicular data services and applications. The rapid growth in vehicles has also resulted in the vehicular network becoming heterogeneous, dynamic, and large-scale, making it hard to meet the strict requirements, such as extremely latency, high mobility, top security, and enormous connections of the fifth-generation network. Previous studies have shown that with the increase in the application of Software-Defined Networking (SDN) on Vehicular Ad-hoc Network (VANET) in industries, researchers have exerted considerable efforts to improve vehicular communications. This study presents an exhaustive review of previous works by classifying them based on based on wireless communication, particularly VANET. First, a concise summary of the VANET structure and SDN controller with layers and details of their infrastructure is provided. Second, a description of SDN-VANET applications in different wireless communications, such as the Internet of Things (IoT) and VANET is provided with concentration on the examination and comparison of SDN-VANET works on several parameters. This paper also provides a detailed analysis of the open issues and research directions accomplished while integrating the VANET with SDN. It also highlights the current and emerging technologies with use cases in vehicular networks to address the several challenges in the VANET infrastructure. This survey acts as a catalyst in raising the emergent robustness routing protocol, latency, connectivity and security issues of future SDN-VANET architectures.

Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%.

Sugar palm (Arenga pinnata) fibre is considered as a waste product of the agricultural industry. This paper is investigating the isolation of nanofibrillated cellulose from sugar palm fibres produced by a chemo-mechanical approach, thus opening a new way to utilize waste products more efficiently. Chemical pre-treatments, namely delignification and mercerization processes, were initially involved to extract the sugar palm cellulose. Then, mechanical pre-treatment was performed by passing the sugar palm cellulose through a refiner to avoid clogging in the subsequent process of high pressurized homogenization. Nanofibrillated cellulose was then characterized by its chemical properties (Fourier transform infrared spectroscopy), physical morphological properties (i.e. scanning electron microscopy, transmission electron microscopy, X-ray diffraction analysis), and thermogravimetric analysis. The nanofibres were attained at 500 bar for 15 cycles with 92% yield. The results showed that the average diameter and length of the nanofibrillated cellulose were found to be 5.5 ± 1.0 nm and several micrometres, respectively. They also displayed higher crystallinity (81.2%) and thermal stability compared to raw fibres, which served its purpose as an effective reinforcing material for use as bio-nanocomposites. The nanocellulose developed promises to be a very versatile material by having a huge potential in many applications, encompassing bio-packaging to scaffolds for tissue regeneration. Keywords: Agricultural waste, Sugar palm fibre, Nanocellulose, Sugar palm nanofibrillated cellulose, High pressurized homogenization (HPH)

The aim of this paper is to carry out analysis of Maximum Likelihood (ML) classification on multispectral data by means of qualitative and quantitative approaches. ML is a supervised classification method which is based on the Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and can be estimated from the training pixels of a particular class. In this study, we used ML to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of ML.

Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it’s compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learning-based back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.

<p class="0abstract"><strong>Abstract—</strong> Over the past decade, digitalization shapes the overall educational structure worldwide, with the attention received from practitioners, researchers, and policymakers for educational development. Digital technologies are bringing massive changes across education, skills, and employment. These changes mirror how technology is increasingly central to education 4.0. Digital technologies are expanding beyond innovative and less traditional techniques of teaching and learning via education collaboration. However, the present study will explore the research conducted on digital technologies and education. Data is selected from the Scopus database reputed journals. The final 47 studies are chosen for the review process using PRISMA statement 2015, and bibliometric analysis is done to find the occurrence of keywords. The findings of the study are strengthening the value of educational growth and development of high-tech skills. Education's Future focuses on digital technologies, and the traditional modes of education will be replaced entirely.</p>

Thermoplastic starch composites have attracted significant attention due to the rise of environmental pollutions induced by the use of synthetic petroleum-based polymer materials. The degradation of traditional plastics requires an unusually long time, which may lead to high cost and secondary pollution. To solve these difficulties, more petroleum-based plastics should be substituted with sustainable bio-based plastics. Renewable and natural materials that are abundant in nature are potential candidates for a wide range of polymers, which can be used to replace their synthetic counterparts. This paper focuses on some aspects of biopolymers and their classes, providing a description of starch as a main component of biopolymers, composites, and potential applications of thermoplastics starch-based in packaging application. Currently, biopolymer composites blended with other components have exhibited several enhanced qualities. The same behavior is also observed when natural fibre is incorporated with biopolymers. However, it should be noted that the degree of compatibility between starch and other biopolymers extensively varies depending on the specific biopolymer. Although their efficacy is yet to reach the level of their fossil fuel counterparts, biopolymers have made a distinguishing mark, which will continue to inspire the creation of novel substances for many years to come.

Artificial Intelligence has greatly revolutionized education in many aspects. Today, AI-enabled language models, such as ChatGPT, are gaining popularity due to their characteristics and benefits. However, users also consider them a threat to educational integrity and purposes. This research examined ChatGPT usage among students in the United Arab Emirates (UAE), their views, concerns, and perceived ethics. The data was gathered from 388 students from two universities in Al Ain city using Yamane's formula. Findings showed that students consider ChatGPT a revolutionary technology that helps students in many ways. The gathered data showed that the effect of ChatGPT Usage remained significant on students' views. The path analysis also supported the second hypothesis, proposing the significant effect of ChatGPT on Students' Concerns. Finally, the findings also indicated the validation of the final hypothesis, showing the significant effect of ChatGPT Usage on the Perceived Ethics among the students in the UAE. Therefore, this study concluded that using ChatGPT in education has useful and concerning effects on educational integrity. However, implementing practical guidelines can assist in making informed decisions and shaping policies within educational institutions. Recognizing the complexities and importance of ChatGPT usage, teachers and policymakers can keep a balance by leveraging Artificial Intelligence technology to improve education while upholding ethical practices that promote critical thinking, originality, and integrity among students.