Global Transitions Proceedings最新文献

The world saw a health crisis with the onset of the COVID-19 virus outbreak. The mask has been identified as the most efficient way to prevent the spread of virus [1]. This has driven the necessity for a face mask recogniser that not only detects the presence of a mask but also gives the accuracy to which a person is wearing the face mask. Also, the face mask should be recognised in all angles as well. The goal of this study is to create a new and improved real time face mask recogniser using image processing and computer vision approach. A Kaggle dataset which consisted of images with and without masks was used. For the purpose of this study a pre-trained convolutional neural network Mobile Net V2 was used. The performance of the given model was assessed. The model presented in this paper can detect the face mask with 98% precision. This Face mask recogniser can efficiently detect the face mask in side wise direction which makes it more useful. A comparison of the performance metrics of the existing algorithms is also presented. Now with the spread of the infectious variant OMICRON, it is necessary to implement such a robust face mask recogniser which can help control the spread.

One of the most life-threatening disease is cardiovascular disease. Its high mortality rate contributes to nearly 17 million deaths all over the world. Early diagnosis helps to treat the disease in timely manner to prevent mortality. There are several machine and deep learning techniques available to classify the presence and absence of the disease. In this research, Logistic Regression (LR) techniques is applied to UCI dataset to classify the cardiac disease. To improve the performance of the model, pre-processing of data by Cleaning the dataset, finding the missing values are done and features selection were performed by correlation with the target value for all the feature. The highly positive correlated features were selected. Then classification is performed by dividing the dataset into training. testing in the ratio of 90:10, 80:20, 70:30, 40:60 and 50:50. The splitting ratio of 90:10 gives best accuracy as listed below. The LR model obtained 87.10% accuracy.

With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. In this paper, an approach for Deepfake detection has been provided. ResNext, a Convolutional Neural Network (CNN) algorithm and Long Short-Term Memory (LSTM) is used as an approach to detect the Deepfake videos. The approach and its steps are discussed in this paper. The accuracy obtained for the developed Deep-Learning (DL) model over the Celeb-Df dataset is 91%.

The broad spectrum of optical wireless communication meets the needs of high-speed wireless communication, which is optical wireless communication's primary advantage over traditional wireless communication technologies. Optical fiber communications, as significant use of laser technology, are vital facilitators for the contemporary information era. With the rise of new technologies such as the Internet of Things, big data, cloud computing, virtual reality, and artificial intelligence, there is an increasing need in society for high-capacity data transmission, raising the bar for optical fiber communication technology. Many new technologies are coming our way, which has made our lives a lot simpler. But now that this new technology has arrived, we've run out of patience. To do whatever in the shortest possible period. Furthermore, in today's fast-paced society, sluggish walkers are quickly left behind while the rest of the world keeps moving forward. Many innovative methods for speeding up and simplifying our work have been identified. With optical fiber technology, our scientists have achieved a breakthrough, allowing us to go from one place to another in a matter of seconds. Wireless optical fiber communication networks are discussed in this research. This study also illustrates the many difficulties that optical fiber installation and processing face.

The Wireless Sensor Network (WSN)is applied in several networking situations. It suffers from dissimilar types of attack because of its meagre security mechanisms. The Sinkhole attack is the most destructive attack of WSN. A Reliable Self Reconfiguration (RSR) mechanism has been suggested in this work to eliminate the malicious sinkhole attack from the network. The proposed reliable reconfiguration (RSR)) system consists of two steps. The malicious node is detected and after detection it is corrected without resource loss by using the reconfiguration mechanism. In this paper, the reconfiguration mechanism for correcting sinkhole attack is applied using the C++ built simulator and factors such as Packet Delivery ratio and energy consumption are obtained for estimation The differences in the energy level have been calculated for the three scenarios i.e., Network without attack, Network with sinkhole attack and Network after Reconfiguration. The proposed Reliable Self-Reconfiguration (RSR) method outperforms the various detection mechanisms in finding and eliminating the sinkhole attack.

Sunspots are known to be the most prominent feature of the solar photosphere. Solar activities play a vital role in Space weather which greatly affects the Earth's environment. The appearance of sunspots determines the solar activities and being observed from early eighteenth century. In this work, we have implemented a deep learning model which automatically detects sunspots from MDI and HMI image datasets. Proposed model uses Alexnet based deep convolutional networks to generate promising deep hierarchical features and proposed deep learning approach achieved excellent classification accuracies. Also, model has shown the improved result with MDI data set which is equal to 99.71%, 100%, 100%, and 100 for accuracy, precision, recall, and F-score respectively. This is to construct and build robust and reliable event recognition system to monitor solar activities which are crucial to understanding space weather and for physicists it is an aid for their research.

COVID 19 pandemic is causing a global health epidemic. The most powerful safety tool is wearing a face mask in public places and everywhere else. The COVID 19 outbreak forced governments around the world to implement lockdowns to deter virus transmission. According to survey reports, wearing a face mask at public places reduces the risk of transmission significantly. In this paper, an IoT-enabled smart door that uses a machine learning model for monitoring body temperature and face mask detection. The proposed model can be used for any shopping mall, hotel, apartment entrance, etc. As an outcome a cost-effective and reliable method of using AI and sensors to build a healthy environment. Evaluation of the proposed framework is done by the Face Mask Detection algorithm using the TensorFlow software library. Besides, the body temperature of the individual is monitored using a non-contact temperature sensor. This proposed system can detect the users from COVID 19 by enabling the Internet of Things (IoT) technology.

The paper develops an efficient and accurate method for detecting fresh aromatic coconuts. Coconuts have a nearly cosmopolitan distribution due to human action in using them for agriculture. At present, the only way to determine whether a coconut is aromatic or not is by tasting it. By implementing the IAC (Identification of Aromatic Coconuts) method as proposed in this research, it is possible to identify the aromacy through non-invasive mechanisms with the help of image-processing techniques. The brightness of the image has to be adjusted accordingly for actual implementation. The underlying principle is that the color of the region of interest at the bottom part of the coconut shell is correlated to its age. Segmentation is done on the image via K-Means. The region of interest in RGB color is converted in to HSV and the Threshold is applied to it. After that the amount of white pixels in each layer on the image are measured using Polynomial Regression to obtain the predicted value of aromacy.

Network forensic tools enable security professionals to monitor network performance and compromises. These tools are used to monitor internal and external network attacks. Technological improvements have enabled criminals to wipe out tracks of cybercrime to elude alterations. Network forensics procedures use processes to expedite investigation by tracking each original packet and event that is generated in the network. There are many network forensic tools, both open source and commercial versions available in the market. In this work, the result of a survey participated by different experts in open source network forensic tools have been presented. The advantages, challenges, and necessities have been identified for network forensic investigation of such tools. A few open source network forensic tools have been studied and performed a comparative analysis based on six key parameters. Further, two malware datasets are analyzed using open source tools to perform investigation and present a comprehensive network forensic analysis comprising IO graphs, Flow graphs, TCP stream, UDP multicast stream, mac-based analysis, and operating system analysis.

Smart cities are an important domain that is reaching great heights nowadays. IoT plays an important role in the implementation of smart cities. The people of the country which contains smart cities will be well developed socially, economically and the quality of their knowledge and living will also be developed a lot. The human efforts and time that are spent on doing the works manually will be reduced by bringing up smart cities. The people can be protected from any disaster, natural calamities, and any difficult situations by smart city ecosystem. The construction of smart cities will avoid time wastage in our day-to-day lives by all means. This paper mainly describes on what a smart city is, how it is created, uses, challenges, real-time applications, future scope for smart cities, etc. The IoT technologies used in implementing smart cities, the devices used to implement them are also discussed.