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The motive of the article is to investigate the effective teaching standards for MOOC academicians at public universities in Sindh, Pakistan, and the application of the UTAUT model in the new context. This study utilized the UTAUT model to investigate the factors affecting academician intention regarding the acceptance and usage of MOOC in higher institutions. To achieve the research objectives, 497 responses were collected from MOOC academicians through a survey questionnaire to address the suggested hypotheses. Our findings represent the constructs DL, DP, ATM, EE, and FC have a greater impact on BI, which play a significant role in shaping ICT-based teaching and learning outcomes. However, the two constructs PE and SI did not show a significant impact on BI. This research study implication can lead to improved academicians teaching standards that align with the international teaching benchmarks and enhance the technology education landscape at national and international levels. [ABSTRACT FROM AUTHOR]

The innovative ChatGPT AI-based application is considered a supplemental tool for learners to assist and guide broader academic information and research opportunities in various domains. This study uses the modified UTAUT model to investigate the significant predictors influencing the learners' intention to adopt ChatGPT in academia in Sindh, Pakistan. The learners' intention to adopt the ChatGPT tool is assessed based on significant predictors such as DS, HCI, PE, EE, SI, and FC as key indicators influencing the ChatGPT adoption. The research model and suggested hypotheses were addressed using a quantitative technique. Data gathered from 497 respondents as learners from top-ranked public institutions in Sindh, Pakistan, was examined using the PLS-SEM approach. This research found that all significant predictors influencing learners' intention to adopt the ChatGPT tool and all predictors have a more significant impact. This research study can help administration and IT experts incorporate the skills and techniques associated with the ChatGPT tool in education in Sindh, Pakistan. [ABSTRACT FROM AUTHOR]

This study aims to examine factors influencing the learner's perception and expectations regarding the MOOC platform adoption and usage in the education sector in Sindh, Pakistan. Past literature revealed that learning through MOOC is challenging for learners due to the insufficient digital skills, outdated course quality and design, inadequate course evaluation mechanisms, and IT infrastructure compatibilities that meet the traditional classroom to the online classroom setting. For the quantitative approach method, there were sixty-two learners from top institutions were given their initial responses for the reliability analysis. Cronbach's Alpha is declared as reliable since its coefficient Alpha is above or equal to 0.7 using SPSS tool. All the constructs have significance values ranging between 0.792 to 0.924 respectively, which seems to be higher than the Cronbach Alpha coefficient (α ≥ 0.7). In our analysis, the reliability test ensures evidently that the measurement instrument and data quality are compatible and sufficient. This research will contribute to the strong status of the progressive MOOC platform in education and align its teaching and learning standards that are associated with international standard education. [ABSTRACT FROM AUTHOR]

Detection of abnormalities in human eye is one of the well-established research areas of Machine Learning. Deep Learning techniques are widely used for the diagnosis of Retinal Diseases (RD). Fovea is one of the significant parts of retina which would be prevented before the involvement of Perforated Blood Vessels (PBV). Retinopathy Images (RI) contains sufficient information to classify structural changes incurred upon PBV but Macular Features (MF) and Fovea Features (FF) are very difficult to detect because features of MF and FF could be found with Similar Color Movements (SCM) with minor variations. This paper presents novel method for the diagnosis of Irregular Fovea (IF) to assist the doctors in diagnosis of irregular fovea. By considering all above problems this paper proposes a three-layer decision support system to explore the hindsight knowledge of RI and to solve the classification problem of IF. The first layer involves data preparation, the second layer builds the decision model to extract the hidden patterns of fundus images by using Deep Belief Neural Network (DBN) and the third layer visualizes the results by using confusion matrix. This paper contributes a data preparation algorithm for irregular fovea and a highest estimated classification accuracy measured about 96.90%. [ABSTRACT FROM AUTHOR]

Flash memory is popular because of its well-known properties like low power consumption, shock resistance, cost and small size. Beside its properties itthere aresome challenges in hiding erase operation of a flash desk when we come to its practical implementation. Superblock Flash Transition Layer (FTL) Scheme hasalready been proposed by researchersas a very good way to hide erase operation in flash memory. This also has certain limitations. In this paper we present a new scheme named Extended Superblock (eSuperblock) scheme, which uses the structure of Superblock Scheme butwith varying working principles. In this scheme, wehave suggested away to share the U-blocks of adjacent Superblocks. Using eSuperblock scheme, Update Blocks (U-blocks) can be efficiently managed delay in erase operation also be increased. [ABSTRACT FROM AUTHOR]