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.

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

Deep learning is one of the most unexpected machine learning techniques which is being used in many applications like image classification, image analysis, clinical archives and object recognition.With an extensive utilization of digital images as information in the hospitals, the archives of medical images are growing exponentially. Digital images play a vigorous role in predicting the patient disease intensity and there are vast applications of medical images in diagnosis and investigation. Due to recent developments in imaging technology, classifying medical images in an automatic way is an open research problem for researchers of computer vision. For classifying the medical images according to their relevant classes a most suitable classifier is most important. Image classification is beneficial to predict the appropriate class or category of unknown images. The less discriminating ability and domain-specific categorization are the main drawbacks of low-level features. A semantic gap that exists between features of low-level as machine understanding and features of human understanding as high-level perception. In this research, a novel image representation method is proposed where the algorithm is trained for classifying medical images by deep learning technique. A pre-trained deep convolution neural network method with the fine-tuned approach is applied to the last three layers of deep neural network. The results of the experiment exhibit that our method is best suited to classify various medical images for various body organs. In this manner, data can sum up to other medical classification applications which supports radiologist’s efforts for improving diagnosis.

Internet of things (IoT) is being applied in every aspect of daily living to improve the quality of life. Waste management is a critical issue, especially for developing countries. The purpose of this study is to propose an IoT-driven state-of-the-art system for solid waste management for developing countries and urban areas. The lack of planning and availability of resources in urban areas have made a living in densely populated surroundings even more difficult. This research work provides a framework based on blockchain-enabled vehicular ad-hoc networks (VANETs) in the realm of IoT. The step-by-step methods are proposed for the decentralized solution for solid waste management using blockchain-enabled VANET. Advanced ultra-high frequency (UHF) technology is used along with IoT devices for the real-time location of waste vehicles and the detection of waste bins. Geo-fencing techniques are also applied for the monitoring and timely collection of waste from the dump spots. Lastly, blockchain technology is applied in the proposed solution to make machine-to-machine (M2M) communication more secure, reliable and trustworthy across IoT devices. The experimental results are also obtained by deploying a pilot project in Karachi, Pakistan. The real-time dashboard is also obtained to demonstrate the daily statistics of the waste collection and performance of the waste vehicles. The results indicate the successful implementation of SSWMS to achieve real-time tracking, intelligent identification of waste bins, trash weighing, and keeping tabs on waste collection from dump spots using geofencing. In future, the blockchain-enabled VANETs can be applied for route management, intelligent transportation and fleet management systems (FMS) due to the inherent characteristics of blockchain and IoT.