The hybridization of renewable energy resources is a known topic in sustainable technology. Many projects are going on based on the topic. The use of Photovoltaic, wind energy, and other renewable resources can be helpful to optimize the load in the utility grid. Countries like Europe and other western countries have electricity storage, whether the developing countries are still struggling to make sure the stable utility grid connection to the distribution network system. In this research, we would like to discuss the different energy production processes sustainably. As we know, the energy sources are volatile and cannot always assure stable production to keep the requirements or demand properly. We want to use the combination of the sources in a way so that we can make the balance between the demand and the supply system. This research will be an overview in terms of technical and financial sites. Also, by using the different combinations of the Internet of things and data analysis method, we will see the correlation between the different sources and their production. Based on the production data, we can determine the financial feasibility and the outcome of the system. The main problem of renewable energy sources is uncertainty. In terms of wind energy, the velocity is also not stable according to the location. We want to show a predictive model by using the intelligent formula by which we can maintain the hybrid system. The production data from different sources will tell us their contribution to the system. This contribution will help us monitor the system and control which sources have more contribution on the demand side. The predictive model will have consisted of renewable sources such as photovoltaic, wind, utility grid, and inverter systems. In the research, the tool such as Artificial Intelligence can be implemented by sustainable management. The arrangement information is prepared to extricate data and based on resources. The renewable sources data are variant according to their location and it has an impact in terms of energy production. Data acquisition and analysis could help the current technologies such as smart grid, microgrid, and their control systems. This exploration aims to introduce a predictive foundation for the management of enormous volumes of data through large Information instruments (sensors) to help the coordination of environmentally friendly power. The main difference between the conventional electricity system and the renewable energy system is the variability of sources, with conventional sources such as utility grids and diesel generators and renewable sources consisting of photovoltaic (PV), wind, etc.
Open-source technologies (OSINT) are becoming increasingly popular with investigative and government agencies, intelligence services, media companies, and corporations. These OSINT technologies use sophisticated techniques and special tools to analyze the continually growing sources of information efficiently. There is a great need for professional training and further education in this field worldwide. After having already presented the overall structure of a professional training concept in this field in a previous paper [25], this series of articles offers individual further training modules for the worldwide standard state-of-the-art OSINT tools. The modules presented here are suitable for a professional training program and an OSINT course in a bachelor’s or master’s computer science or cybersecurity study at a university. In this part 1 of a series of 4 articles, the OSINT tool RiskIQ PassivTotal [26] is introduced, and its application possibilities are explained using concrete examples. In part 2 the OSINT tool Censys is explained [27]. Part 3 deals with Maltego [28] and Part 4 compares the 3 different tools of Part 1-3 [29].
Open-source technologies (OSINT) are becoming increasingly popular with investigative and government agencies, intelligence services, media companies, and corporations. These OSINT technologies use sophisticated techniques and special tools to analyze the continually growing sources of information efficiently. There is a great need for professional training and further education in this field worldwide. After having already presented the overall structure of a professional training concept in this field in a previous paper [25], this series of articles offers individual further training modules for the worldwide standard state-of-the-art OSINT tools. The modules presented here are suitable for a professional training program and an OSINT course in a bachelor’s or master’s computer science or cybersecurity study at a university. In part 1 of a series of 4 articles, the OSINT tool RiskIQ PassivTotal [26] is introduced, and its application possibilities are explained using concrete examples. In this part 2 the OSINT tool Censys is explained [27]. Part 3 deals with Maltego [28] and Part 4 compares the 3 different tools of Part 1-3 [29].
Open-source technologies (OSINT) are becoming increasingly popular with investigative and government agencies, intelligence services, media companies, and corporations [22]. These OSINT technologies use sophisticated techniques and special tools to analyze the continually growing sources of information efficiently [17]. There is a great need for professional training and further education in this field worldwide. After having already presented the overall structure of a professional training concept in this field in a previous paper [25], this series of articles offers individual further training modules for the worldwide standard state-of-the-art OSINT tools. The modules presented here are suitable for a professional training program and an OSINT course in a bachelor’s or master’s computer science or cybersecurity study at a university. In part 1 of a series of 4 articles, the OSINT tool RiskIQ Passiv-Total [26] is introduced, and its application possibilities are explained using concrete examples. In part 2 the OSINT tool Censys is explained [27]. This part 3 deals with Maltego [28] and Part 4 compares the 3 different tools of Part 1-3 [29].
As the development of interactive robots and machines, studies to understand and reproduce facial emotions by computers have become important research areas. For achieving this goal, several deep learning-based facial image analysis and synthesis techniques recently have been proposed. However, there are difficulties in the construction of facial image dataset having accurate emotion tags (annotations, metadata), because such emotion tags significantly depend on human perception and cognition. In this study, we constructed facial image dataset having accurate emotion tags through subjective experiments. First, based on image retrieval using the emotion terms, we collected more than 1,600,000 facial images from SNS. Next, based on a face detection image processing, we obtained approximately 380,000 facial region images as “big data.” Then, through subjective experiments, we manually checked the facial expression and the corresponding emotion tags of the facial regions. Finally, we achieved approximately 5,500 facial images having accurate emotion tags as “good data.” For validating our facial image dataset in deep learning-based facial image analysis and synthesis, we applied our dataset to CNN-based facial emotion recognition and GAN-based facial emotion reconstruction. Through these experiments, we confirmed the feasibility of our facial image dataset in deep learning-based emotion recognition and reconstruction.