SMART FARMING 4.0 UNTUK MEWUJUDKAN PERTANIAN INDONESIA MAJU, MANDIRI, DAN MODERN

Smart farming 4.0 based on artificial intelligence is a flagship launched by the Ministry of Agriculture. Smart farming 4.0 encourages the farmers to work more efficient, measurable, and integrated. Through technology, farmers are able to carry out farm practice by relying on mechanization, not on the planting season, from planting to harvesting accurately. Several smart farming technologies such as blockchain for modern off farm agriculture, agri drone sprayer, drone surveillance (drone for land mapping), soil and weather sensors, intelligent irrigation systems, Agriculture War Room (AWR), siscrop (information systems) 1.0 have been implemented in some areas. However, farmers deal with various educational backgrounds, aging farmers phenomenon, and high cost of smart farming technology tools to implement smart farming. This paper aims to analyze the huge opportunities of smart farming by utilizing the potential of millennial farmers as actors and analyzing various government policies to support smart farming 4.0. The Ministry of PDTT has carried out pilot projects to implement smart farming in several locations. The Ministry of Agriculture also needs to play a role by creating a smart farming roadmap. The Government's Strategic Project 2020–2024 through food estate based on farmer corporations may support massive smart farming applications

Towards Self-Adaptive Software for Wildfire Monitoring with Unmanned Air Vehicles.

Wildfires have evolved significantly over the last decades, burning increasingly large forest areas every year. Smart cyber-physical systems like small Unmanned Air Vehicles (UAVs) can help to monitor, predict, and mitigate wildfires. In this paper, we present an approach to build control software for UAVs that allows autonomous monitoring of wildfires. Our proposal is underpinned by an ensemble of artificial intelligence techniques that include: (i) Recurrent Neural Networks (RNNs) to make local UAV predictions about how the fire will spread over its surrounding area; and (ii) Deep Reinforcement Learning (DRL) to learn policies that will optimize the operation of the UAV team.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

SMART CITY MANAGEMENT USING MACHINE LEARNING TECHNIQUES

In response to the growing urban population, smart cities are designed to improve people\u27s quality of life by implementing cutting-edge technologies. The concept of a smart city refers to an effort to enhance a city\u27s residents\u27 economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people\u27s quality of life and design cities\u27 services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) and the Internet of Things (IoT) play a far more prominent role in developing smart cities when it comes to making choices, designing policies, and executing different methods. Smart city applications have made great strides thanks to recent advances in artificial intelligence (AI), especially machine learning (ML) and deep learning (DL). The applications of ML and DL have significantly increased the accuracy aspect of decision-making in smart cities, especially in analyzing the captured data using IoT-based devices and sensors. Smart cities employ algorithms that use unlabeled and labeled data to manage resources and deliver individualized services effectively. It has instantaneous practical use in many crucial areas, including smart health, smart environment, smart transportation system, energy management, and smart water distribution system in a smart city. Hence, ML and DL have become hot research topics in AI techniques in recent years and are proving to be accurate optimization techniques in smart cities. In addition, artificial intelligence algorithms enable the processing massive datasets and identify patterns and characteristics that would otherwise go unnoticed. Despite these advantages, researchers\u27 skepticism of AI\u27s sometimes mysterious inner workings has prevented it from being widely used for smart cities. This thesis\u27s primary intent is to explore the value of employing diverse AI and ML techniques in developing smart city-centric domains and investigate the efficacy of these proposed approaches in four different aspects of the smart city such as smart energy, smart transportation system, smart water distribution system and smart environment. In addition, we use these machine learning approaches to make a data analytics and visualization unit module for the smart city testbed. Internet-of-Things-based machine learning approaches in diverse aspects have repeatedly demonstrated greater accuracy, sensitivity, cost-effectiveness, and productivity, used in the built-in Virginia Commonwealth University\u27s real-time testbed

Technology, governance, and a sustainability model for small and medium-sized towns in Europe

New and cutting-edge technologies causing deep changes in societies, playing the role of game modifiers, and having a significant impact on global markets in small and medium-sized towns in Europe (SMSTEs) are the focus of this research. In this context, an analysis was carried out to identify the main dimensions of a model for promoting innovation in SMSTEs. The literature review on the main dimensions boosting the innovation in SMSTEs and the methodological approach was the application of a survey directed to experts on this issue. The findings from the literature review reflect that technologies, governance, and sustainability dimensions are enablers of SMSTEs’ innovation, and based on the results of the survey, a model was implemented to boost innovation, being this the major add-on of this research.info:eu-repo/semantics/publishedVersio

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Application of data science for controlling energy crises: A case study of Pakistan

© 2019 Association for Computing Machinery. Today Pakistan is facing numerous challenges for the interconnection of local energy resources and for balanced energy policies. Data Science, Big Data, Artificial Intelligence (AI), IoT and Cloud computing draws our focus towards controlling energy crises in terms of smart energy generation, consumption and to overcome causes of energy crises. To make a conclusion valuable we have to extract significant value from a large amount of data that‟s why data management plays a significant role. This Paper presents a review of energy sectors, energy resources, energy crises in Pakistan. It also presents the possible solution of energy crises with the help of data science application and the involvement of Big Data, Cloud computing, IoT and AI

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator