Visually impaired persons (VIPs) comprise a significant portion of the population, and they are present around the globe and in every part of the world. In recent times, technology proved its presence in every domain, and innovative devices assist humans in their daily lives. In this work, a smart and intelligent system is designed for VIPs to assist mobility and ensure their safety. The proposed system provides navigation in real-time using an automated voice. Though VIPs wouldn't be able to see objects in their surroundings, they can sense and visualize the roaming environment. Moreover, a web-based application is developed to ensure their safety. The user of this application can turn the on-demand function for sharing his/her location with the family while compromising privacy. Through this application, the family members of VIPs would be able to track their movement (get location and snapshots) while being at their homes. Hence, the device allows VIPs to visualize the environment and ensure their security. Such a comprehensive device was a missing link in the existing literature. The application uses MobileNet architecture due to its low computational complexity to run on low-power end devices. To assess the efficacy of the proposed system, six pilot studies have been performed that reflected satisfactory results. For object detection and recognition, a deep Convolution Neural Network (CNN) model is employed with an accuracy of 83.3%, whereas the dataset contains more than 1000 categories. Moreover, a score-based quantitative comparative analysis is performed using the supported features of devices. It is found that the proposed system has outperformed the existing devices having a total score of 9.1/10, which is 8% higher than the second-best.

Visually impaired persons (VIPs) comprise a significant portion of the population and they are present in all corners of the world. In recent times, technology proved its presence in every domain of the life and innovative devices are assisting humans in all fields especially, artificial intelligence has dominated and outperformed rest of the trades. VIPs need assistance in performing daily life tasks like object/obstacle detection and recognition, navigation, and mobility, particularly in indoor and outdoor environments. Moreover, the protection and safety of these people are of prime concern. Several devices and applications have been developed for the assistance of VIPs. Firstly, these devices take input from the surrounding environment through different sensors e.g. infrared radiation, ultrasonic, imagery sensor, etc. In the second stage, state of the art machine learning techniques process these signals and extract useful information. Finally, feedback is provided to the user through auditory and/or vibratory means. It is observed that most of the existing devices are constrained in their abilities. The paper presents a comprehensive comparative analysis of the state-of-the-art assistive devices for VIPs. These techniques are categorized based on their functionality and working principles. The main attributes, challenges, and limitations of these techniques have also been highlighted. Moreover, a score based quantitative analysis of these devices is performed to highlight their feature enrichment capability for each category. It may help to select an appropriate device for a particular scenario.

An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in motorbike accidents mainly due to not wearing helmets. Automatic helmet violation detection of motorcyclists from real-time videos is a demanding application in ITS. It enables one to spot and penalize bikers without a helmet. So, there is a need to develop a system that automatically detects and captures motorbikers without a helmet in real time. This work proposes a system to detect helmet violations automatically from surveillance videos captured by roadside-mounted cameras. The proposed technique is based on faster region-based convolutional neural network (R-CNN) deep learning model that takes video as an input and performs helmet violation detection to take necessary actions against traffic rule violators. Experimental analysis shows that the proposed system gives an accuracy of 97.69% and supersedes its competitors.

In Pakistan, existing blood control systems or blood information management systems are limited in terms of efficient data retrieval of donor to consumer. There is no communication network in place for extra blood in one location to be demanded from a region if blood is limited, resulting in blood wastage. Due to a lack of accessibility and sufficient blood quality testing, blood contaminated with illnesses such as HIV has been used for transfusion in some cases. This study proposes a ledger blood management system to address these challenges. The trail has been represented as a supply-chain management problem following the blood. By trailing the blood stream and donation a single platform for transferring blood and the problem results among blood groups, the proposed system, built on the hyperledger fabric model, adds more traceability toward the blood transfusion process. It also helps to reduce unjustified blood wastage by providing an integrated system for transferring lifeblood and the thing extracts among lifeblood banks. A web app is also designed for accessing the network for simplicity of usage and security is enhanced by implementing block chain hyperfebric ledger system through Key Value System (KVS) system.

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This article is a correction on: Zafar, M., Khan, I., Rehman, A. and Zafar, S. 2022. A Novel Blockchain-Based Model for Blood Donation System. EAI Endorsed Transactions on Context-aware Systems and Applications. 8, 1 (Aug. 2022), e8. DOI:https://doi.org/10.4108/eetcasa.v8i1.2546 In the above article, Imran Khan was added as an author of the paper when he had no participation in the research process. Imran Khan requested the removal of his name and all the co-authors agreed with this decision.