Disasters, whether natural or human-made, leave a lasting impact on human lives and require mitigation measures. In the past, millions of human beings lost their lives and properties in disasters. Information and Communication Technology provides many solutions. The issue of so far developed disaster management systems is their inefficiency in semantics that causes failure in producing dynamic inferences. Here comes the role of semantic web technology that helps to retrieve useful information. Semantic web-based intelligent and self-administered framework utilizes XML, RDF, and ontologies for a semantic presentation of data. The ontology establishes fundamental rules for data searching from the unstructured world, i.e., the World Wide Web. Afterward, these rules are utilized for data extraction and reasoning purposes. Many disaster-related ontologies have been studied; however, none conceptualizes the domain comprehensively. Some of the domain ontologies intend for the precise end goal like the disaster plans. Others have been developed for the emergency operation center or the recognition and characterization of the objects in a calamity scene. A few ontologies depend on upper ontologies that are excessively abstract and are exceptionally difficult to grasp by the individuals who are not conversant with theories of the upper ontologies. The present developed semantic web-based disaster trail management ontology almost covers all vital facets of disasters like disaster type, disaster location, disaster time, misfortunes including the causalities and the infrastructure loss, services, service providers, relief items, and so forth. The objectives of this research were to identify the requirements of a disaster ontology, to construct the ontology, and to evaluate the ontology developed for Disaster Trail Management. The ontology was assessed efficaciously via competency questions; externally by the domain experts and internally with the help of SPARQL queries.
A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.
Management of land records includes actions such as registration and transfer of property ownership. For many nations, land ownership and management are important sources of income. Corrupted spans from small-scale payments to large-scale cause an abuse for government. In the literature, a number of concerns have been raised about Land Record Management. There are several problems with Land Record Management in developing nations, such as tampering with land records and no methods of retrieving a full property ownership record, operating multiple linked Land Record Management Systems independently, etc. Traditional land record management solutions do not solve these challenges. We propose a Blockchain-based Land Record Management system for Pakistan to solve these concerns. It has been decided to use the suggested system, and the specifics of its implementation are described in this thesis.
Since its appearance in late 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become a significant threat to human health and public safety. Machine learning has been extensively exploited in the past to solve a range of problems in everyday life. It has also played its role in virtually all aspects of pandemic management, ranging from early detection and contact tracing to vaccine and drugs development and treatment. This chapter discusses some of the ways in which machine learning-based solutions have helped. In this regard, computer vision approaches have been used for the early detection of disease. Contact tracing has been enhanced by machine learning models to improve distance estimation techniques. Similarly, machine learning techniques have been used to accurately predict mortality rates to optimize resource management. These techniques have also helped in the otherwise tedious processes of vaccine and drugs development in numerous ways, such as providing insights into drug target interactions and possibilities of repurposing the existing drugs.
Abstract-Disasters affect human lives severely. Due to these disasters, hundreds and thousands of human beings lost their lives and gracious properties. Government agencies, nongovernment organization and individual volunteers act to rescue the affected people and to mitigate the disaster effects. These teams require real time information about the nature, severity, area and number of affectees. Their efforts can be supported by providing timely, effective and specific information so that the rescuers can get better idea about the available routes to reach the affectees, urgency and mass of loss. People share huge amount of data through blogs and social media that can be utilized to help rescue operations. This information can electronically be filtered, arranged and formatted in a proper manner. Thus, semantic web technologies can play a vital role in providing timeliness information. Purpose of this research is to capture explicit knowledge of the domain in form of ontologies, automatic information extraction, generation of implicit knowledge and then disseminating this information to various stakeholders. Collection of implicit and explicit knowledge will help improve decision making for disaster trail management.
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