Dr Mahadi Hasan Miraz
@uum.edu.my
Universiti Utara Malaysia
RESEARCH INTERESTS
Blockchain, Supply chain, Operation management, Optimization, Information managemnt, Fintech
Scopus Publications
Scopus Publications
Mahadi Hasan Miraz and Tiffany Sing Mei Soo
Emerald
PurposeThe objective of this study is to examine the various factors that exert an influence on the green economy. This study also investigates the impact of foreign direct investment (FDI) on the Malaysian economy, specifically focusing on its position as a mediator. This research also examines the correlation between FDI and its influence on the contemporary green economy.Design/methodology/approachThe authors employed quantitative methodologies and a self-administered survey to evaluate data and derive a definitive conclusion. The result was constructed using SPSS and SEM-PLS as the analytical software.FindingsThe study reveals that technological advancement, investment country and government policy significantly and positively affect the green economy, catalyse SDG goals and restructure the economy in better shape.Originality/valueThe current empirical research bridges the research gap in the context of technology advancement in government policy from emerging economies by exploring important factors, proposing their impact on the performance of the green economy, and empirically testing those hypothesized relationships. This study deciphers that FDI influences the green economy, where the investment country plays a significant role. Also, for a graphical presentation of this abstract, see the online appendix.
Ferdaus Anam Jibon, Alif Tasbir, Mahadi Hasan Miraz, Hwang Ha Jin, Fazlul Hasan Siddiqui, Md. Sakib, Nazibul Hasan Nishar, Himon Thakur, and Mayeen Uddin Khandaker
Frontier Scientific Publishing Pte Ltd
<p>Epileptic seizure is a neurological disorder characterized by recurrent, abrupt behavioral changes attributed to transient shifts in excessive electrical discharges within specific brain cell groups. Electroencephalogram (EEG) signals are the primary modality for capturing seizure activity, offering real-time, computer-assisted detection through long-term monitoring. Over the last decade, extensive experiments through deep learning techniques on EEG signal analysis, and automatic seizure detection. Nevertheless, realizing the full potential of deep neural networks in seizure detection remains a challenge, primarily due to limitations in model architecture design and their capacity to handle time series brain data. The fundamental drawback of current deep learning methods is their struggle to effectively represent physiological EEG recordings; as it is irregular and unstructured in nature, which is difficult to fit into matrix format in traditional methods. Because of this constraint, a significant research gap remains in this research field. In this context, we propose a novel approach to bridge this gap, leveraging the inherent relationships within EEG data. Graph neural networks (GNNs) offer a potential solution, capitalizing on their ability to naturally encapsulate relational data between variables. By representing interacting nodes as entities connected by edges with weights determined by either temporal associations or anatomical connections, GNNs have garnered substantial attention for their potential in configuring brain anatomical systems. In this paper, we introduce a hybrid framework for epileptic seizure detection, combining the Graph Attention Network (GAT) with the Radial Basis Function Neural Network (RBFN) to address the limitations of existing approaches. Unlike traditional graph-based networks, GAT automatically assigns weights to neighbouring nodes, capturing the significance of connections between nodes within the graph. The RBFN supports this by employing linear optimization techniques to provide a globally optimal solution for adjustable weights, optimizing the model in terms of the minimum mean square error (MSE). Power spectral density is used in the proposed method to analyze and extract features from electroencephalogram (EEG) signals because it is naturally simple to analyze, synthesize, and fit into the graph attention network (GAT), which aids in RBFN optimization. The proposed hybrid framework outperforms the state-of-the-art in seizure detection tasks, obtaining an accuracy of 98.74%, F1-score of 96.2%, and Area Under Curve (AUC) of 97.3% in a comprehensive experiment on the publicly available CHB-MIT EEG dataset.</p>
Md Karim Rabiul, Md. Kamrul Hasan, Mahadi Hasan Miraz, and Rashed Al Karim
Emerald
Purpose Drawing on conservation of resources (CoR) and speech act theories, the authors tested the relationship between managers’ motivating language (ML) and employee service quality and psychological relatedness and competence as mediating variables between their associations. Design/methodology/approach Using a convenient sampling technique, the authors collected 366 hotel employees’ opinions in Malaysia and analysed them in partial least squares-structural equation modelling. Findings Three forms of ML, psychological competence and relatedness correlate with employees’ service quality. Although direction-giving language is correlated with competence, empathetic and meaning-making language are not; thus, competence only mediates the relationship between direction-giving language and service quality. Three types (direction-giving, empathetic and meaning-making) of managers’ communication are correlated with relatedness; thus, relatedness mediates the association between the three types of language and service quality. Practical implications Hospitality managers are encouraged to enhance psychological relatedness and competence by practising an appropriate ML. Psychological relatedness and competence are significant mechanisms that enlighten the effects of supervisory communicant on service quality, indicating employees’ need satisfaction should be improved. Originality/value Our study contributes to speech act and CoR theories by explaining the relationship between ML, psychological relatedness, competence and service quality.
Dona Basak, Mahadi Hasan Miraz, Hwang Ha Jin, Ferdaus Anam Jibon, Satyendra Nath Biswas, Saaveethya Sivakumar, and Hasib Mahmud
IEEE
This paper presents a low delay, high speed CMOS comparator in a lowest possible chip area for portable device application. The proposed comparator is designed using 90nm technology with a supply voltage of 0.8V and clock frequency of 6GHz. Delay, power consumption/conversion and offset voltage are analyzed. Transistor count is also reduced compared to several comparators mentioned in literature in order to make the chip area as compact as possible. Simulation is done using Spectre Circuit Simulator tool-Cadence. Some graphical analysis is made to characterize the proposed comparator behavior and also they are compared with other 90nm comparators for performance analysis. Power consumption is found to be 33.5 μw and delay is 54.32ps for 60% duty cycle of clock pulse. Standard deviation of offset voltage is 6.3mv from Monte Carlo method and the chip area is found to be (8.83*8.61) m2. Also it has low parasitic effect and low noise than those conventional approaches.
Mahadi Hasan Miraz, Ferdoush Saleheen, Abba Ya’u, Hasib Mahmud, and Anuwarul Kabir
IEEE
This paper is designed to estimate the role of halal logistics in how people decide to use halal logistics and explore the pathways to achieving satisfaction in a market. Also, this study investigates factors of halal logistics use behaviour in Malaysia and UAE. Finally, this study constructs the inline factor of religious faith, image, trust, facilities, performance, and halal logistics usage behaviour. This study applied quantitative methods to investigate the answers to open-ended questions by three hundred and seventeen respondents from Malaysia and UAE. In order to prove the assumptions, the obtained data is subjected to a structural equation modelling- partial least squares (SEM-PLS) analysis. This study discovered a significant link between religious faith, image, trust, facilities, performance, and halal logistics usage behaviour. However, the relationship between facilities, efficiency, and halal logistics use behaviour became indirectly important through customer satisfaction mediating. This finding confirms that image, confidence, and facility are essential factors in the increased halal logistic activity of the logistics industry. This study will spur new knowledge to companies and industries worldwide to understand how to enhance their business in Muslim (Islamic) countries.
Abba Ya'u, Mahadi Hasan Miraz, Natrah Saad, Hussaini Bala, Dhanuskodi Rangasamy, Oladokun Nafi’u Olaniyi, and Umar Aliyu Mustapha
EconJournals
Environmental regulation is the responsibility of individuals, corporations, and other entities to prevent environmental damage or improve the tarnished environment. The Environmental law of every country works to protect the natural resources of land, water, air, and soil. There are research evidence that environmental regulation influences Corporate taxes. Economic deterrence theory acted as deterrent to threats of punishment for unwanted or illegal behavior. The fundamental concept of the theory is deterring the taxpayers into compliance by the risk of audit, penalty, etc. The objective of the study is to analyze the impact of economic deterrence theory and environmental regulation on corporate tax evasion, particularly petroleum profit taxes in Nigeria. The components of Economic deterrence theory (tax agents, tax complexity, tax knowledge) and environmental regulations are the independent variables and corporate tax evasion particularly PPT is the dependent variable of the study. It is quantitative research based on primary data which was collected from the oil and gas companies’ representatives. Structural Equation Modelling techniques were applied, and the outcome of the research is a positive and significant relationship between tax agents, tax complexity, tax knowledge, and environmental regulations on corporate tax evasion. The result further shows a positive but non-significant relationship between tax audits and perceived petroleum profit tax evasion. The study draws the attention of policymakers to formulate environmental regulations that are more robust, simple, and flexible, to reduce adverse effects of environmental damage on the economic growth and development of oil and gas-producing countries.
Mahadi Hasan Miraz, Ferdoush Saleheen, Abu Sadat Muhammad Ashif, Mohammad Amzad Hossain, Mohammad Tariq Hasan, Ha Jin Hwang, and Anuwarul Kabir
IGI Global
This study investigates the effect on the teacher's performance of collaborative effort and efficiency of inquiry-based learning. It also determines the impact of the mediating role of student performance. The research framework was constructed based on the unified technology acceptance and use of technology theory. A quantitative analysis was done with surveys to collect primary data from the teacher and lecturers of Malaysia. The researcher used a Likert scale of 7 to evaluate elements of the building. This study focuses on the top 10 public university students in Malaysia. The universities are University Malaya (UM), University Kembangan Malaysia, University Putra Malaysia (UPM), University Since Malaysia (USM), University Technology Malaysia (UTM), Universiti Utara Malaysia (UUM), International Islamic University Malaysia (IIUM), University Technology Mara (UiTM), University Malaysia Perlis (UniMap), and University Tun Hossain (UTHM). Finally, researchers selected the total number of students, 368,881, which is the population of this study. Using systematic random sampling with an interval, researchers sent students an electronic link to respond to a Google Doc questionnaire. This is a unique study in the field of teacher performance that used a diverse and necessary variable known as teaching pedagogy. Therefore, it uniquely integrates leading pedagogy variables into teacher performance. The result of this study helps to meet the education qualification requirement (EQR), and the newly acquired knowledge from this study may help spur the development of the education sector. In addition, it may provide an extensive understanding of making government policies for educational institutions.
Ferdaus Anam Jibon, Mayeen Uddin Khandaker, Mahadi Hasan Miraz, Himon Thakur, Fazle Rabby, Nissren Tamam, Abdelmoneim Sulieman, Yahaya Saadu Itas, and Hamid Osman
MDPI AG
Magnetic resonance imaging (MRI) offers visual representations of the interior of a body for clinical analysis and medical intervention. The MRI process is subjected to a variety of image processing and machine learning approaches to identify, diagnose, and classify brain diseases as well as detect abnormalities. In this paper, we propose an improved classification method for distinguishing cancerous and noncancerous tumors from brain MRI images by using Log Polar Transformation (LPT) and convolutional neural networks (CNN). The LPT has been applied for feature extraction of rotation and scaling of distorted images, while the integration of CNN introduces a machine learning approach for the tumor classification of distorted images. The dataset was formed with images of seven different brain diseases, and the training set was formed by applying CNN with the extracted features. The proposed method is then evaluated in comparison to state-of-the-art algorithms, showing a definite improvement of the former. The obtained results show that the machine learning approach offers better classification with a success rate of about 96% in both plain brain MR images and rotation- and scale-invariant brain MR images. This work also successfully classified T-1 and T-2 weighted images of neoplastic and degenerative brain diseases. The obtained accuracy is perfected by several kernel procedures, while the combined performance of the two wavelet transformations and a strong dataset make our method robust and efficient. Since no earlier study on machine learning approaches with rotated and scaled brain MRI has come to our attention, it is expected that our proposed method introduces a new paradigm in this research field.
Xia Chen, Mahadi Hasan Miraz, Md. Abu Issa Gazi, Md. Atikur Rahaman, Md. Mamun Habib, and Abu Ishaque Hossain
Elsevier BV
Nurul Absar, Emon Kumar Das, Shamsun Nahar Shoma, Mayeen Uddin Khandaker, Mahadi Hasan Miraz, M. R. I. Faruque, Nissren Tamam, Abdelmoneim Sulieman, and Refat Khan Pathan
MDPI AG
The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.
Md. Kashedul Wahab Tuhin, Mahadi Hasan Miraz, Md. Mamun Habib, and Md. Mahbub Alam
Emerald
Purpose This study aims to determine direct and indirect ways of strengthening consumer’s halal buying behaviour. For this, the researchers explore the role of religiosity and consumers’ personal norms on consumers’ attitudes and halal buying behaviour. The study also reconnoiters the mediating role of consumer attitudes. Design/methodology/approach With a structured questionnaire, a survey was conducted to collect data on consumer attitudes, personal norms and halal buying behaviour. Finally, 229 valid questioners were retained for data analysis. The structural equation modelling technique was used for data analysis using SmartPLS 3.0 software. Findings The result of this study suggests that consumers’ attitude towards halal purchase depends on consumers’ personal norms and religiosity. Further, the role of consumer attitudes and religiosity on the halal buying behaviour of consumers is significant. However, the personal norm is not a significant predictor of halal buying behaviour. Consumer attitudes mediate the relationships between personal norms and halal buying behaviour, as well as religiosity and halal buying behaviour. Research limitations/implications The findings of the present study indicate that consumers’ personal norms and religiosity are the important determinants of consumer attitude and behaviour towards halal purchase. Marketers of halal products and services should focus on strengthening consumers’ attitudes and religiosity to influence consumer behaviour towards halal purchase. Originality/value In light of recent research studies on the halal purchase, the present research finds the essential predictors of consumers’ halal purchase attitude and behaviour. The study also reveals that consumer attitude is an important role in strengthening halal buying behaviour, as it has both direct and indirect impact halal buying behaviour.
Kamal Imran Mohd Sharif, Mohamad Ghozali Hassan, Mahadi Hasan Miraz, Effendy Zulkifly, Zulkifli Mohamed Udin, and Mazni Omar
Springer Singapore
Mohamad Ghozali Hassan, Kamal Imran Mohd Sharif, Mahadi Hasan Miraz, Effendy Zulkifly, Zulkifli Mohamed Udin, and Mazni Omar
Springer Singapore
Mahadi Hasan Miraz
Institute of Advanced Scientific Research
Mahadi Hasan Miraz
Institute of Advanced Scientific Research
Purpose: The purpose of this study is toanalyses the factors that influence blockchain & bitcoin for peer money transfer. Also, this study identifies the concept of blockchain and cryptocurrency. Furthermore, the conceptual framework of this study developed incorporating trust on software as a mediator for a better understanding of the use of blockchain in a peermoney transaction. Theory: The unified theory of acceptance and use of technology (UTAUT2) by adding trustin software as an independent variable. This research also demonstrates the relationship between the blockchain and bitcoininpeer transaction. The theoretical framework of this study developed to merge trust on software as a synthesis in UTAUT2 for a better understanding of the use of blockchain for peer transaction in Malaysia. Methodology: 142 self-administered questionnaires distributed through simple random sampling technology aimed at blockchain users in Malaysia from 20 to 30 years. Data analysis performed using PLS. This studyalso demonstrate the relationship of peer transaction and trustin software have a positive and significant relationship with using blockchain& bitcoin. Significant: This research will significantly contribute to blockchain diversityin Malaysia. It is also providing them with useful information about the interaction between the critical determinants of the trust and intention of blockchain behaviourin Malaysia. Originality: This is one of the most recent studies that describe the blockchain& bitcoin fundaments and transaction policy in Malaysia.