Blockchain in Big Data for Agriculture Supply Chain

Agricultural food supply chain is a complex system starting from the production of food on a farm to the table of the consumer involving multiple stakeholders and a variety of processes. In recent years, the food supply chain has grown rapidly across nations, with customers demanding fresh, exotic foods all year round. The global shutdown due to the COVID pandemic has further complicated the food supply chain which has become prone to various contaminations and adulterations. Adulterated food is highly toxic to human health leading to several health issues, nutritional deficiencies, kidney disorders, and failure of vital organs. The existing systems used in the food supply chain do not provide enough transparency, traceability, food safety, or consumer trust. With today's Big Data integrated supply chains, such technologies are highly ineffective. In order to ensure food safety and consumer satisfaction, this chapter proposes using blockchain as an efficient technology to provide transparency, traceability, and trust in food supply chains. The chapter discusses the significance of smart agriculture and how blockchain might help agricultural supply chains that have Big Data incorporated overcome their difficulties. A thorough description of exclusive applications of blockchain in Big Data integrated food supply chains is provided. The chapter also describes how blockchain is integrated into each stage of the food supply chain management process and explores the challenges in implementing blockchain in Big Data integrated food supply chain systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abhigrith: Central Adoption System

Over the years, the number of orphans in India have been increasing and the adoptions taking place have not seen significant growth in them. The current system provided by the Central Adoption Resource Authority (also known as CARA) is inefficient and backward. Due to this, the parents get frustrated and give up on the idea of adopting a child. With the recent introduction of COVID-19 pandemic, the problem has become more relevant. There are also various other issues such as child trafficking, illegal adoption, child labor, etc. that have come into the picture due the lack of security in the existing system. To solve this problem, there is a need to develop a system that will replace the current parent-centric process to a child-centric process. The system will be at a centralized location so that it is accessible to all the stakeholder. The system will enable the adoption procedure which will be faster and more responsive. © 2022 IEEE.

A Comparative Study of Question Answering over Knowledge Bases

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at https://github.com/tamlhp/kbqa. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

IoT-Based Home Automation System with Covid-19 Detector

As the advancement in technology continues to increase, systems also need to be upgraded to meet new trends and solve developing problems. Existing systems of home automation generally focus on energy management, security, and comfort. These smart homes would be more efficient if they can also perform health diagnoses. In this paper, a cost-effective IoT-based home automation system that performs the function of energy conservation, smart security, and disease detection is designed. The system uses the NodeMCU ESP8266 IoT board with its in-built Wi-Fi shield, eliminating the need to acquire a separate Wi-Fi module. The NodeMCU is interfaced with the user's android device where the connected loads on the relay modules are controlled thus enabling the system to control home appliances via an android application. The ESP8266 is also connected to a PIR sensor that checks for motion and triggers an alarm when motion is detected and connected to a contactless temperature sensor that measures the body temperature of anyone and sends the readings to the display while also triggering an alarm if the temperature is above normal indicating the sign of illness or diseases such as coronavirus, high fever, influenza. This paper presents a design of a simple system that not only assists the user to control his home appliances, conserve energy, and improve home security but also helps to protect the user from allowing covid-19-infected persons into their home. © 2022 IEEE.

Reimagining “Space”: An Approach to Increase Participants’ Social Aspects During Online Conferencing

Online conferencing has become a new normal after the COVID-19 pandemic. However, existing systems like ZOOM fall short of facilitating informal social aspects like chats, talks, discussions, dialogues, gatherings, secrets, or even gossip or quarrel, which often occur spontaneously during physical meetings. In this study, we design and prototype a novel system considering key spatial features that influence social interactions offline. The proposed system consists of three typical meeting modes: square mode for free social, room mode for split group discussion, stage mode for speech and presentation. Through Wizard-of-Oz testing with 10 participants, we summarize the design features that contribute to the richness of ambiance, the flexibility of distance, the serendipity of interaction of online conferences, and the effect of these aspects on social interaction. Together with the limitations and suggestions for future work, we hope this paper can inspire the design of spatial interaction on screen, with the aim to improve informal social aspects of online conferencing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Aspect-Level Drug Reviews Sentiment Analysis and COVID-19 Drug prediction using PPI & Deep Learning

The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems. © 2021 IEEE.

Capacity Estimation from Environmental Audio Signals Using Deep Learning

Estimating the capacity of a room or venue is essential to avoid overcrowding that could compromise people’s safety. Having enough free space to guarantee a minimal safety distance between people is also essential for health reasons, as in the current COVID-19 pandemic. Already existing systems for automatic crowd counting are mostly based on image or video data, and some of them, using deep learning architectures. In this paper, we study the viability of already existing Deep Learning Crowd Counting systems and propose new alternatives based on new network architectures containing convolutional layers, exclusively based on the use of environmental audio signals. The proposed architecture is able to infer the actual capacity with a higher accuracy in comparison to previous proposals. Consequently, conclusions from the accuracy obtained with out approach are drawn and the possible scope of deep learning based crowd counting systems is discussed. © 2022, Springer Nature Switzerland AG.

Coping with Changing Contexts: A Healthcare Security Perspective

With the fourth industrial revolution, there is a digitization wave going on for the transformation of existing systems into modern digital systems. This has opened the window for many opportunities, but at the same time, there is a multitude of cyber-security threats that need to be addressed. This paper considers one such threat posed by phishing and ransomware attacks to the healthcare infrastructures. Phishing has also been the most prevalent attack mechanism on the healthcare infrastructures during the ongoing COVID-19 pandemic. The paper proposes two intervention strategies as a step towards catering to the challenges posed by phishing and ransomware attacks in the context of healthcare infrastructures. © 2022, IFIP International Federation for Information Processing.

Ensuring Social Distancing using Artificial Intelligence: Distancia-the new normal

COVID-19 has impacted the lifestyle worldwide. Health, economy, daily routines, work culture, research, mode of communications etc. all have been adversely affected and a new normal has been defined. Until an effective and reliable vaccine is developed, social distancing is one of the key solutions to prevent the virus from affecting the community as lockdown is never a permanent solution to this problem. In order to ensure proper social distancing at any organization, a system is developed that is able to detect whether the people are following social distancing norms by using the proposed 'Distancia-the new normal Algorithm'. The system is designed such that it gives an alert whenever the social distancing norms are violated. It can be deployed with a CCTV camera, so that the live footage fetched by the CCTV can be directly used by the system for checking that the social distancing is being followed or not. Any organization regardless of the number of employees can use this system. Existing mobile applications designed for this purpose cannot be used at schools, educational institutes or any other organization where the use of mobile phones is prohibited. Also, since the effect of rain is not considered in any of the existing system, therefore the present applications can only be deployed at limited places where there is no hindrance caused by rain/snow etc. Thus, the proposed system can prove to be of great importance for organizations where keeping a check of the social distancing is difficult. © 2021 IEEE.

Autonomous identification of high-contact surfaces from convolutional neural networks

The rapid spread of the SARS-CoV-2 virus has highlighted many social interaction problems that favor the spread of disease, particularly airborne spread, which can be addressed by adjusting existing systems. Of particular interest are places where large numbers of people interact, as they become a focus for the spread of these diseases. This paper proposes and evaluates an autonomous identification scheme for certain surfaces considered high risk due to their continuous handling. These high-contact surfaces can be identified by an autonomous system to apply specific cleaning tasks to them. We evaluate three convolutional models from a proprietary dataset with a total of 2000 images ranging from wall switches to water dispensers. The objective is to identify the ideal architecture for the system. The ResNet (Residual Neural Network), DenseNet (Dense Convolutional Network), and NASNet (Neural Architecture Search Network) models were selected due to their high performance reported in the literature. The models are evaluated with specialized metrics in non-binary classification problems, and the best scheme is selected for prototype development. © 2021 Institute of Physics Publishing. All rights reserved.