A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques

E-mail is one of the most secure medium for online communication and transferring data or messages through the web. An overgrowing increase in popularity, the number of unsolicited data has also increased rapidly. To filtering data, different approaches exist which automatically detect and remove these untenable messages. There are several numbers of email spam filtering technique such as Knowledge-based technique, Clustering techniques, Learningbased technique, Heuristic processes and so on. This paper illustrates a survey of different existing email spam filtering system regarding Machine Learning Technique (MLT) such as Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. However, here we present the classification, evaluation and comparison of different email spam filtering system and summarize the overall scenario regarding accuracy rate of different existing approache

Investigation of COVID-19 Vaccine Information Websites across Europe and Asia Using Automated Accessibility Protocols

Websites content accessibility guidelines (WCAG) ensure that websites should be perceivable, understandable, navigable, and interactive. During the SARS-CoV-2 pandemic, the importance of accessible websites and online content grew throughout the world. Therefore, in this study, we examined COVID-19-related official government websites. This research covered 21 government websites, with 13 websites from European countries and 8 websites from Asian countries, to evaluate their accessibility following WCAG 2.0 and WCAG 2.1 guidelines. The overall goal of this study was to identify the frequent accessibility problems that might help the website owners to identify the shortcomings of their websites. The target websites were evaluated in two steps: in step-1, evaluation was performed through four automatic web accessibility testing tools such as Mauve++, Nibbler, WAVE, and WEB accessibility tools; in step-2, evaluation went through human observation, such as system usability testing and expert testing. The automatic evaluation results showed that few of the websites were accessible; a significant number of websites were not accessible for people with disabilities. In addition, system usability testing found some complexity in website organization, short explanations, and outdated information. The expert testing suggested improving the color of the websites, organization of links, buttons, and font size. This study might be helpful for associated authorities to improve the quality of the websites in the future

An Integrated Variable-Magnitude Approach for Accessibility Evaluation of Healthcare Institute Web Pages

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

Technologies Designed to Assist Individuals with Cognitive Impairments

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

Matrib leaf classification using Deep Neural Network : An Integrated Image Processing Technique

Healthy farm plant leaf classification and identification is a critical food security issue. In many places of the world, it remains tough as it needs appropriate infrastructure. Combining the rising worldwide prevalence of the smartphone with current progress in computer vision through deep learning, now it is possible to diagnose inconsistency of various farm plants. In this technology era, automation can help to replace manual prevention efforts in plants by employing image processing methods. This research deployed three pre-trained deep neural models: 3DCNN, ResNet50 and MobileNet, to classify the Matrib leaf into two categories: Good Matrib leaf and Bad Matrib leaf. We employed our own Matrib leaf customized dataset for this research. Experimental results demonstrate that MobileNet outperformed other models with an accuracy of 99.99% on test data, while ResNet50 and 3DCNN followed with an accuracy of 92.67% and 72.80%.</p

Comprehensive Analysis of Augmented Reality Technology in Modern Healthcare System

The recent advances of Augmented Reality (AR) in healthcare have shown that technology is a significant part of the current healthcare system. In recent days, augmented reality has proposed numerous intelligent applications in the healthcare domain including, wearable access, telemedicine, remote surgery, diagnosis of medical reports, emergency medicine, etc. These developed augmented healthcare applications aim to improve patient care, increase efficiency, and decrease costs. Therefore, to identify the advances of AR-based healthcare applications, this article puts on an effort to perform an analysis of 45 peer-reviewed journal and conference articles from scholarly databases between 2011 and 2020. It also addresses concurrent concerns and their relevant future challenges including, user satisfaction, convenient prototypes, service availability, maintenance cost, etc. Despite the development of several AR healthcare applications, there are some untapped potentials regarding secure data trans-mission, which is an important factor for advancing this cutting-edge technology. Therefore, this paper also analyzes distinct AR security and privacy including, security requirements (i.e., scalability, confidentiality, integrity, resiliency, etc.) and attack terminologies (i.e. sniffing, fabrication, modification, interception, etc.). Based on the security issues, in this paper, we propose an artificial intelligence-based dynamic solution to build an intelligent security model to minimize data security risks. This intelligent model can identify seen and unseen threats in the threat detection layer and thus can protect data during data transmission. In addition, it prevents external attacks in the threat elimination layer using threat reduction mechanisms

Crash severity analysis and risk factors identification based on an alternate data source : a case study of developing country

Road traffic injuries are one of the primary reasons for death, especially in developing countries like Bangladesh. Safety in land transport is one of the major concerns for road safety authorities and other policymakers. For this reason, contributory factors identification associated with crashes is necessary for reducing road crashes and ensuring transportation safety. This paper presents an analytical approach to identifying significant contributing factors of Bangladesh road crashes by evaluating the road crash data, considering three different severity levels (non-fetal, severe, and extremely severe). Generally, official crash databases are compiled from police-reported crash records. Though the official datasets are focusing on compiling a wide array of attributes, an assorted number of unreported issues can be observed that demands an alternative source of crash data. Therefore, this proposed approach considers compiling crash data from newspapers in Bangladesh which could be complimentary to the official crash database. To conduct the analysis, first, we filtered the useful features from compiled crash data using three popular feature selection techniques: chi-square, Two-way ANOVA, and Regression analysis. Then, we employed three machine learning classifiers: Decision Tree, Random Forest, and Naïve Bayes over the extracted features. A confusion matrix was considered to evaluate the proposed model, including classification accuracy, sensitivity, and specificity. The predictive machine learning model, namely, Random Forest using Label Encoder with chi-square and Two-way ANOVA feature selection process, seems the best option for crash severity prediction that provides high prediction accuracy. The resulting model highlights nine out of fourteen independent features as responsible factors. Significant features associated with crash severities include driver characteristics (gender, license type, seat belts), vehicle characteristics (vehicle type), road characteristics (road surface type, road classification), environmental conditions (day of crash occurred, time of crash), and injury localization. This outcome may contribute to improving traffic safety of Bangladesh.</p