"Intelligent tutoring effects on induced emotions and cognitive load of learning-disabled learners".

PURPOSE: This study addresses the learning requirements of learners with learning difficulties by monitoring their learning experience in an Intelligent Tutoring System. Intelligent Tutoring Systems were developed to enrich the teaching-learning process. MATERIALS AND METHODS: In the present work, the interface is designed and developed utilizing the potential of Artificial Intelligence to meet their individual needs. Designing an online learning platform for a learners with learning difficulties requires consideration of their learning problems and preferences. The interface was developed focusing on all the requirements of the LD learners. The objective of the present study is to monitor the learning experience in the form of induced emotions and cognitive load of the learners to determine the impact of learning. RESULTS: 83 learners were observed during various stage of learning. The results obtained through the Support Vector machine (SVM) classification technique showed the positive attitude towards intelligent tutoring. The analysis revealed that a total of 0.23% of learners were positively induced. Their learning experience was positive and effective. The cognition load on learners was minimum with single-mode instruction and least disturbed. CONCLUSIONS: The system was improved based on preference feedback on design features. This helps in improving content design and creating device independent and responsive visual design. The fatigue effect analysis on cognitive load implied that multiple modes of instruction increased drowsiness. Single mode of instruction have a positive impact on the learning process and it reduces the cognitive load of the learners.Implications for RehabilitationThe user interface designed and developed for learners with Dyslexia, Dysgraphia and Dyscalculia has learning disabled-friendly features. These can be used to create a device-independent and responsive design.Learning experience is monitored along with the impact on cognitive load of the learners.The research helps in understanding the stimulation and response of learners with learning disability for different learning conditions.Most existing learning systems are limited to non-learning-disabled learners. The ITS developed during research presents a Universal learning design helpful for all learners with and without learning disability.

Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study.

Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.

Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM).

Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy-based model into the Fuzzy C-Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M-FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management. RESEARCH HIGHLIGHTS: A novel approach is proposed in the article, integrating a neural network energy-based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M-FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.

Underpinning implications of instructional strategies on assistive technology for learning disability: a meta-synthesis review.

PURPOSE: This article reviews the instructional strategies used by assistive technologies and maps their problem manifestation and effectiveness for children with learning disabilities. The objective of this article is to investigate the most common types of assistive tools used in learning to study their attributes, limited to the needs of learners with the condition of dyslexia, dysgraphia and dyscalculia. REVIEW METHODS: It studies currently available low, mid and high-level assistive learning technologies available to deal with problems faced by these learners. Assistive tools studied in this article range from simple hardware tools to multi-sensory software. A simple analytical framework by interlinking, Problem Manifestation, Underpinned Implication, Instructional Strategy and Cognitive Strength Developed (PISC) is formulated to examine the tools. RESULTS: Five assistive tools types (non-electronic products, low-tech products, mid-tech products, high-tech products apps and learning software) for each learning disability are identified, analysed and associated learning implication is studied under PISC framework. This helps to map the problems and learning style of learning disabled and analyses the underpinned implication along with the corresponding development of skills (cognitive, affective or psychomotor) by these assistive technologies. CONCLUSION: Performance of identified assistive tool types using PISC framework is analysed. Findings are reported and discussed. Implications for RehabilitationAvailable assistive tools are not learning disability specific. So, in order to differentiate the learning path of a Learning Disabled learner from that of a Non-Learning Disabled learner, study is conducted under four attributes of PISC framework: Problem Manifestation, Underpinned Implication, Instructional Strategy and Cognitive Strength.Available assistive tools in the field of remedial education are found to be problem centric and only able in dealing with single academic learning need of a learner with specific learning difficulty.The mapping of the available assistive technologies under PISC framework provides a detailed structure for the selection of most suited assistive tool as per learning requirement of a learner with learning disability.This study also conclude the non- availability of the High-tech assistive tools and Educational Software specifically designed for learners with learning disability.

An automated unsupervised deep learning-based approach for diabetic retinopathy detection.

As per the International Diabetes Federation (IDF) report, 35-60% of people suffering from diabetic retinopathy (DR) have a history of diabetes. DR is one of the primary reasons for blindness and visual impairment worldwide among adults aged 24-74 years. Therefore, this research aims to develop an automated technique for the detection of retinal abnormalities associated with DR, such as microaneurysm. Unsupervised learning has a high potential for data classification. The proposed work accomplishes the following objectives. (a) k-means and fuzzy clustering method is discussed, and the objective function is revised to offer the modified version named modified fuzzy clustering method (MdFCM). (b) A modified convolutional neural network is proposed to consolidate the MdFCM and features for better outcomes. (c) The results are compared on three diverse datasets, DIARETDB1, APTOS, and Liverpool, with the fuzzy clustering method, deep embedded clustering, and k-means for generalizability. To the best of our knowledge, the proposed algorithm is the first to detect DR using a hybrid approach of unsupervised and deep learning methodology. The proposed system achieved an improved accuracy rate of 98.6%. The results show that our proposed method outperforms the state-of-the-art algorithm. We intend to design a tool using the proposed system for diabetic retinopathy detection at an early stage. Complete system flow architecture of diabetes retinopathy detection using unsupervised deep learning approach.