Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach

In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With the increasing demand for precision agriculture, models like the proposed one show great potential in accurately detecting and managing diseases, ultimately leading to improved crop yields and ecological sustainability

Evaluation of E-Learning Approaches Using AHPTOPSIS Technique

Strategic preparation of e-learning application includes decision making regarding the most suitable type of elearning on different levels. The survey has been carried out on the sample of 95 respondents consisted of administrative and academic staff, and postgraduate students in Malaysia.They were asked to assess the relative importance of five e-learning evaluation criteria to be analysed by using AHP technique.Furthermore, they also rated the performance of five identified e-learning approaches under each of the requirements.The overall performance of each e-learning approach was computed by using TOPSIS method.The results suggested that Flipped Classroom is the most suitable e-learning approach, while ‘Strategic readiness for e-learning implementation’ found to be the most important criterion.The paper is suggesting a quantitative evaluation method for decision-makers who are strategising modern technologies in higher educational settings

A comparative analysis for adopting an innovative pedagogical approach of flipped teaching for active classroom learning

Active learning is a way of education that imparts the responsibility of learning on learners. Active learning pedagogies ranging from simple lectures to structured pedagogies can be applied to online or face-to-face environments or in a combination of both. Multiple studies have shown that active learning can be done by flipped teaching which improves students understanding and retention of information. The flipped classroom approach, with its prime focus on active learning, attempts to address the concerns of academic staff and helps to meet the expectations of students for practical exposure. On contrary to the traditional pattern of teaching using conventional classrooms and other e-learning methods, the flipped classrooms is a form of blended learning in which students first learn the content online by watching video lectures, usually at home, and do the homework in a class by discussing it with their teachers and colleagues. This approach allows having the most personalized interaction of the teacher with students. Flipped classrooms have started to become common on many university campuses. Despite the growing number of flipped courses, however, quantitative information on their effectiveness remains sparse because of very less number of researchers on it. This paper, therefore, investigates the various major aspects of flipped technology to explore the effectiveness of a flipped classroom model on student’s performance and ease of use. The paper also presents a research of comparing traditional class that engages students in some learning to a flipped classroom that creates more time for active learning using PAPRIKA technique of multi-criteria decision-making (MCDM). A group of students and teachers undergone through the different approaches to teaching have been evaluated for various attributes to determine the overall utility of Flipped teaching

Evaluating project management criteria using fuzzy analytic hierarchy process

Project management is one of the most important issues to fulfil organizational objectives. The project management criteria are playing a vital role for completion of any project. The aim of this paper is to evaluate five main project criteria (time, cost, quality, risk, and safety) mathematically by using a multi-criteria method in order to assist stakeholders or project managers in decision-making. The fuzzy Analytic Hierarchy Process (AHP) is selected with the use of triangular fuzzy numbers for pairwise comparison scales in prioritizing the criteria in managing projects. Utilizing the fuzzy AHP technique can facilitate uncertainty in doing evaluation. In this study, one expert who is a project manager with many years of experience was asked to carry out the evaluation. The results show that the expert’s main concern in managing project is time, and cost is the second important. The study demonstrates how uncertainty in making evaluation of multiple criteria can be solved by using fuzzy method such as fuzzy AHP, in contrary to the crisp or the traditional AHP which is based on specific values, the evaluator(s) are always ambiguous and vague to give exact judgment. Hence, the application of this fuzzy method can make the assessment outcomes more accurate, scientific, and objective. It is anticipated that this work may serve as a support tool for stakeholders in improving the project management quality level

An analytical survey on implementing best practices for introducing e-learning programs to students

Today, a need has risen to reduce paper both to become “green” and to save costs.Shifting to an online course, to expand, the number of students who are participating in online courses and programs with significant global reach continues to increase dramatically.However, many studies showed that the failed retention rates for students of online institutions are bit higher than traditional classroom environments. This calls for analysing the current practices in the use of contents in online courses to improve e-learning student persistence.This is a conceptual paper sharing the specific best practice examples, observations, and outcomes from some leading universities, based on surveys of existing methodologies and practical experience. Special emphasis placed on current and future trends in effective online pedagogy. This paper identifies the best practices for introducing the students to e-learning experience in an analytical manner.The paper also analyses the various attributes of best practices in Elearning using Fuzzy AHP method of a Multi-Criteria Decision-Making (MCDM) method.The Fuzzy AHP involves several steps which include the setting of evaluation criteria and their weights, and evaluation of the E-learning technique as compared to the traditional learning technique for identifying the effect on response rates for different aspects of quality and time in order to explore the real worth in the use of e-learning. This paper shows that all the attributes behave comparable very well in the case of E-learning as compared to the traditional method.The analysis of our criteria which are essential for any effective learning demonstrated that the E-learning methods have good potential to grow

Decision framework on selecting the optimal subjective weighting method for evaluating e-learning approaches

Multi-Criteria Decision Making (MCDM) refers to making decisions in the presence of several criteria or objectives. The criteria weights particularly the subjective weights have great influence on the decisions since different weighting methods may yield different weights and ranking on the same problem. Furthermore, the Pairwise Comparisons (PCs) in Analytic Hierarchy Process (AHP) often encounter inconsistency in judgment which forces the decision maker(s) to revise the judgments. This study aims to develop a decision framework for selecting the optimal subjective weighting (SW) method to be applied in the evaluation of five e-learning approaches. Besides, this study proposes the Tripartite Relations for Overcoming Inconsistency (TROI) to address the inconsistency problem in PCs. The performances of nine SW techniques including PCs method on five identified e-learning criteria were compared. Moreover, this study also demonstrates the application of TROI method for processing inconsistency in AHP. Basically, the TROI method would use the first row of the inconsistent PC matrix to generate elements of the rest of the rows. A total of 95 participants in a selected university evaluated the importance of the criteria and rated the quality of each criterion for each of the five e-learning approaches. The optimal SW method has weights with the least total absolute differences compared to the geometric mean of all nine weights. The optimal weight was then used to select the most suitable e-learning approach by using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results of the study show that Graphical Weights is the optimal SW method, while Flipped Classroom is the most appropriate type of e-learning for implementation in the selected university. The proposed TROI method has helped addressing 12 inconsistent judgments in the PC matrices, while a new PC method, PC-TROI, has been established to achieve a consistent pairwise judgment. This study has successfully developed a decision framework to aid decision maker(s) in choosing the optimal SW method while proposing alternative method to AHP

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

his paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems

Lack of Habituation in Migraine Patients Based on High-Density EEG Analysis Using the Steady State of Visual Evoked Potential

Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine in both two types with aura or without aura and plays an essential role in studying pathophysiological differences between these two groups. Several studies indicated that the reason for migraine aura is cortical spreading depression (CSD) but did not clarify its impact on migraine without aura and lack of habituation. In this study, 22 migraine patients (MWA, N = 13), (MWoA, N = 9), and healthy controls (HC, N = 19) were the participants. Participants were exposed to the steady state of visual evoked potentials also known as (SSVEP), which are the signals for a natural response to the visual motivation at four Hz or six Hz for 2 s followed by the inter-stimulus interval that varies between 1 and 1.5 s. The order of the temporal frequencies was randomized, and each temporal frequency was shown 100 times. We recorded from 128 customized electrode locations using high-density electroencephalography (HD-EEG) and measured amplitude and habituation for the N1–P1 and P1–N2 from the first to the sixth blocks of 100 sweep features in patients and healthy controls. Using the entropy, a decrease in amplitude and SSVEP N1-P1 habituation between the first and the sixth block appeared in both MWA and MWoA (p = 0.0001, Slope = −0.4643), (p = 0.065, Slope = 0.1483), respectively, compared to HC. For SSVEP P1–N2 between the first and sixth block, it is varied in both MWA (p = 0.0029, Slope = −0.3597) and MWoA (p = 0.027, Slope = 0.2010) compared to HC. Therefore, migraine patients appear amplitude decrease and habituation deficit but with different rates between MWA, and MWoA compared to HCs. Our findings suggest this disparity between MWoA and MWA in the lack of habituation and amplitude decrease in the inter-ictal phase has a close relationship with CSD. In light of the fact that CSD manifests during the inter-ictal phase of migraine with aura, which is when migraine seizures are most likely to occur, multiple researchers have lately reached this conclusion. This investigation led us to the conclusion that CSD during the inter-ictal phase and migraine without aura are associated. In other words, even if previous research has not demonstrated it, CSD is the main contributor to both types of migraine (those with and without aura)

Exploring the effects of place attachment and positive emotions on place satisfaction and intentional behaviour in Iranian ski resort: a perspective from S-O-R model

This study developed conceptual framework based on the SOR model consisting place attachment, positive emotion and place satisfaction to investigate the behavioural intentions of visitors in an Iranian ski resort. A total of 290 respondents participated in the study, who were ski resort tourists in Pooladkaf, a relatively new and upcoming ski resort in Iran. The data was analysed via Partial Least Squared structural equation modelling. Findings highlighted the importance of place attachment and positive emotions as predictors of place satisfaction and intention behaviour. Place satisfaction was also found to have a full mediating effect on the relationships between place attachment, positive emotions and behavioural intention. The study contributes to the literature by elucidating the mediating effects of satisfaction within the ski resort context, which has not been examined to a great extent in the current literature. Our study also highlighted the crucial role played by positive emotions in enhancing tourists’ sense of satisfaction which consequently influences future behaviours. Destination marketers and operators of ski resorts ought to focus on improving service quality, infrastructure and marketing communications strategies in order to engender a greater sense of satisfaction, attachment and positive emotions, which have been shown to have a strong bearing on tourist behaviour intentions and thus, lead to revisit behaviours and the spread of positive word-of-mouth