Jinat Ara

In recent years, there has been a growing field of research focused on comprehending complexity in relation to web platform accessibility. It has shown that it is quite difficult to accurately assess and identify web accessibility concerns while taking multifaceted factors into account. It is imperative to prioritize multi-dimensional characteristics as they facilitate the integration of many aspects into the assessment process, which is a critical component in enhancing the accessibility evaluation process. Although many existing solutions with varying degrees of computational success have been proposed by scholars, they are confined to (1) following a certain set of rules of a specific guideline; (2) limited evaluation properties; (3) disregard for user criteria; and (4) complex functional properties or architectural design. To address these problems, we present in this work a straightforward yet precise model that assesses webpage accessibility by taking into account common features of the structural and visual elements of webpages that are part of the HTML Document Object Model (DOM) structure. In order to predict a webpage’s accessibility status, we implemented three distinct algorithms to analyze web features/objects considering both semantic and non-semantic aspects. We performed experimental work to validate 20 university webpages in Hungary through our developed tool. The computed result of the developed tool was assessed by comparing the result with a user study where we performed user testing that included 40 users’ 80 reviews on the same 20 university webpages in Hungary. Additionally, we compared our developed tool with other scientific models (that already exist) and existing ten open-source commercial automated testing tools considering several functional characteristics or properties. This two-phase assessment result shows that the developed tool has several advanced properties and the potential to predict the accessibility issues of the tested webpages.

Crop failure is defined as crop production that is significantly lower than anticipated, resulting from plants that are harmed, diseased, destroyed, or influenced by climatic circumstances. With the rise in global food security concern, the earliest detection of crop diseases has proven to be pivotal in agriculture industries to address the needs of the global food crisis and on-farm data protection, which can be met with a privacy-preserving deep learning model. However, deep learning seems to be a largely complex black box to interpret, necessitating a prerequisite for the groundwork of the model’s interpretability. Considering this, the aim of this study was to follow up on the establishment of a robust deep learning custom model named CropsDisNet, evaluated on a large-scale dataset named “New Bangladeshi Crop Disease Dataset (corn, potato and wheat)”, which contains a total of 8946 images. The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. In addition, demonstration of the model’s interpretability gave us insight into our model’s decision making and classification results, which can allow farmers to understand and take appropriate precautions in the event of early widespread harvest failure and food crises.

Since 1996, web accessibility evaluation has been an important aspect of web development to increase social inclusion for people with special needs. Several web accessibility evaluation and testing tools have been developed to automatically evaluate websites in order to identify barriers for people with disabilities. The developed tools are significant since their aim is to effectively represent accessibility issues. However, a few issues with website accessibility cannot be identified through the existing accessibility testing tools due to several limitations, including (i) inappropriate guideline selection, (ii) ambiguities in guideline understanding, (iii) avoiding user and expert suggestions as evaluation criteria, (iv) limited consideration of semantic perspectives, and (iv) unwillingness to incorporate the updated engineering methods. Therefore, reported results may be unclear and inappropriate for some users. Such limitations are critical factors that reduce the effectiveness of the developed tools. These issues cause unwillingness to use a particular tool or possible adoption of other tools. In this situation, we must identify which aspects are important to incorporate in the development in order to make the developed solution more effective as it allows users to make their website accessible to people with disabilities. In this paper, first, we present a literature review of different existing solutions for web accessibility testing to identify their challenges and limitations. Following the reported findings, we propose an automated web accessibility evaluation framework addressing several accessibility aspects to improve the evaluation results by mitigating the limitations of existing solutions. The proposed framework is validated by comparing it with existing automated solutions considering their functional properties. The proposed accessibility framework might be beneficial for web developers, accessibility engineers, and other practitioners to incorporate into their development and research.

Background and Objective: Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement. Methods: In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions. Key Content and Findings: The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn’t consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions. Conclusions: The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

Human perception is complex, and can be influ enced by several factors. The study’s purpose is to understand these factors, thus, an application was developed. This appli cation examines memory colors in the CIELAB color space. Memory colors can be affected by nationality, virtual reality game playtime, and the presence or absence of an image cue. The memory colors of banana and orange had the highest agreement between the students, while the memory colors of river and grass had the largest dispersion. Virtual reality games influenced the memory color of grass the most. It can also be concluded that without an image cue, different colors were se lected in 70.90% of the cases.

Information Technology (IT) plays a vital role in promoting sustainability and enabling independent living. People with cognitive disabilities face numerous challenges in their daily lives, such as social interactions, learning new things, and improving specific abilities. A variety of therapies and treatments have been introduced to help address these challenges. Recently, computer-assisted therapeutic procedures, treatment procedures, and assistive systems have emerged as beneficial tools to improve the lives of people with cognitive disabilities. Advances in technology have made it possible to develop effective applications specifically designed for this group of individuals. The objective of this paper is to identify potential applications of these developed solutions for people with cognitive disabilities, evaluate their effectiveness, strengths, and limitations, and understand their contribution in addressing various difficulties due to cognitive impairments. To achieve this goal, we reviewed 23 studies that demonstrate several applications developed for people with cognitive disabilities to address their unique issues. Our investigation indicates that the developed applications hold promise, although a few issues with cost-effectiveness, device transparency, and specific disability dependency may limit their effectiveness. Hence, this paper aims to shed light on these innovative applications, their implications, and their role in aiding users in tackling their specific challenges.

Several works of literature contributed to the web evaluation process in recent years to promote digital inclusion by addressing several accessibility guidelines, methods, processes, and techniques. Researchers have investigated how the web evaluation process could be facilitated by including accessibility issues to obtain an inclusive and accessible solution to improve the user experience and increase user satisfaction. Three systematic literature reviews (SLRs) have been conducted in the context of past research, considering such research focuses. This paper presents a new SLR approach concerning accessibility in the web evaluation process, considering the period from 2010 to 2021. The review of 92 primary studies showed the contribution of publications on different phases of the web evaluation process mainly by highlighting the significant studies in the framework design and testing process. To the best of our knowledge, this is the first study focused on the web accessibility literature reporting the engineering assets for evaluation of new accessible and inclusive web-based solutions (e.g., websites). Besides, in this study, we aim to provide a new direction to the web designers and developers with an updated view of process, methods, techniques, tools, and other crucial aspects to contribute to the accessible process enrichment, as well as depict the gaps and challenges that may be worthy to be investigated in the future. The findings of this SLR introduce a new dimension in web accessibility research on determining and mitigating the research gap of web accessibility issues for web designers, developers, and other practitioners.

The World Wide Web has become an important platform for sharing a wide array of information within the world community. In the post-COVID-19 scenario, the web become a primary source of information in the context of healthcare information dissemination. Healthcare institutions, such as hospitals and clinics, utilize this platform to provide services to reach their target users. It is essential to evaluate the web pages of healthcare institutions and compute their accessibility score for people with disabilities or special needs. This paper presents a variable-magnitude approach to compute the accessibility score of healthcare web pages, considering several requirements of people with disabilities. To compute the accessibility score through the proposed approach, we considered two different components and integrated them to compute the accessibility score through the proposed algorithm. The proposed approach was experimentally applied to sixteen healthcare institutes’ web pages in Hungary. Based on the experiment’s results and the received feedback from an accessibility specialist, a set of suggestions is provided to minimize the accessibility barrier and improve the accessibility score for people with disabilities to access web resources without difficulty. The main contribution of this work is in enhancing awareness of web platform accessibility for web practitioners to improve accessibility, so that people with disabilities can effectively access web resources.