Global Transitions Proceedings最新文献

Standardization and technological advancements have contributed in the development of the IoT. The accessibility of ease IoT gadgets has likewise assumed a key job in facilitating IoT research, improvement, and deployment. IoT is worldview network that permits the virtual existence of physical objects throughout our life. The Internet of Things (IoT) is based on the idea of installing embedded devices in everyday objects. In the mean time, because of the low cost and high accessibility of sensor devices, wireless sensor networks (WSNs) have an extraordinary job in overspreading of IoT. To be clear, the function of such systems is completely unpredictable in terms of node heterogeneity and node failure. Continuous advancements in IoT systems have resulted in several of the new protocols designed specifically for sensor networks where energy saving is such a top priority. The routing protocols, on the other hand, have earned the most attention because they might change based on the application and network design. This study examines the most recent routing protocols for sensor networks and developing action plans for the various approaches pursued. One of the potential drawbacks in the IoT is the energy requirement. Furthermore, several directions to extend the network's life expectancy have attracted in an expanding level of attention. Recently, a number of a achievements have emerged. Designing routing protocols is one of the most encouraging of these mechanisms, as demonstrated by the significant amount of energy required for information transmission. This paper begins with a detailed description of the foundation and its associated works. In addition, this study introduces a new routing protocol to increase the energy efficiency of sensor devices in the Internet of Things.

Corona virus disease also acknowledged as COVID-19 outbreak, a worldwide pandemic is one of the most acute and severe viruses in recent time. The rate of COVID cases rise rapidly around the world. Although vaccines have been developed, deep learning (DL) techniques shown as a useful method for clinical diagnosis and other fields. Deep structured learning also known as Deep learning is method based on artificial neural network with interpretation learning. This paper aims to do a comparative analysis on medical images like computer tomography scans (CT scan) and X-ray by means of different deep learning systems. This analysis discusses about structures developed for COVID-19 analysis via deep learning performances on Inception, VGG, Xception, Resnet models and provide insights and on data sets to train these neural networks. A comparative analysis is done for considering the better deep learning model for detection. The main aim of this paper is to ease medical experts and help them to understand the ways of deep learning techniques and how they can be prospective used to combat COVID-19.

In India, half the population depends on agriculture for a livelihood. Microbial diseases are a significant threat to food security, but their rapid identification remains difficult due to limited infrastructure. With AI, automatic detection of plant diseases from raw images is possible using deep learning and transfer learning. This paper aims to detect and classify Grapes and Mango leaf diseases, employing a dataset of 8,438 images of diseased and healthy leaves collected from the Plant Village dataset and acquired locally. The deep convolutional neural network (CNN) is trained to identify diseases or their absence. A pre-trained CNN architecture called AlexNet is modeled for automatic feature extraction and classification. The system is developed with MATLAB achieves an accuracy rate of the detection of 99% and 89% for Grape leaves and Mango leaves respectively. An app named "JIT CROPFIX" is developed to implement the same on an Android Smartphone.

Facial Recognition is the biometric technique used in face detection. The task for validating or recognizing a face from the multi-media photographs is done using facial recognition technique. With the evolution of advanced society the requirement for face identification has been really important. Detection and identification of faces has been grown worldwide. It owes the demand for security such as authorization, national safety and other vital circumstances. There are number of algorithms for facial detection. This paper aspires to present the comparison of two face recognition techniques Haar Cascade and Local Binary Pattern edified for the classification. As a result the accuracy of Haar Cascade is more than the Local Binary Pattern but the execution time in Haar Cascade is more than Local Binary Pattern.

Commercial Software Engineering is a team based activity and therefore success is hugely dependent on whether the team has succeeded in building a cooperative environment and how well the team members get along together. Over the past years, team conflict has increasingly been viewed as a major factor that can cause the failure of a software project. Conflict must be properly managed in the best interest of the project's stakeholders. This research uses team conflict dynamics model to analyse different conflict types and team conflict profiles to produce a framework that can improve project success in software development. An eight stage framework was devised and was tested. From the data gathered it was found that the framework was successful. This framework can be studied by individuals, taught or applied by a mediator and also another benefit is that individuals are encouraged to express themselves and integrate emotional intelligence.

One's work can be done efficiently only if their mood is good. Emotions is the index of mood. Here model capture one's image as an input, predict their mood and play a video of opposite genre as an output, in order to change their mood, which is the main goal of this project. Hence taking them through an emotional roller coaster. The solution makes use of CNN (convolutional neutral networks) for detecting one's mood. It uses OpenCV (open-source computer vision library) in-order to get user's image using their respective web camera. It is done by importing modules like web-browser and requests in-order to get access to YouTube to play videos accordingly. The average accuracy rate of the system has increased to 98.53 percent. Eight primary emotion classes have been effectively classified by the method. As a result, the proposed strategy has been shown to be effective in recognizing emotions.

As the number of intrusions is increasing, intrusion detection of systems and network infrastructures Systems (IDS) is now an active research area to develop reliable and efficient detection and countering solutions. Finding the efficient methods for intrusion detection in information and network security is a crucial step and that in this study proposed an evolutionary approach for intrusion detection that is more efficient and effective. Evolutionary algorithms have been demonstrated in the IDS over the times, its maturity. Although most research is carried out on genetic algorithms which have their merits and demerits. In this paper, we present an optimized algorithm viz. Genetic-based Enhanced grey wolf optimization (GB-EGWO) Algorithm for intrusion detection. The number of feature selections for the proposed algorithm was selected from the new FS algorithm to increase IDS performance. In this study, the benchmark NSL-KDD network intrusion was applied to evaluate the proposed algorithm modified from the 99-data KDD cup to evaluate IDS issues. Simulation results prove its effectiveness over the existing work and have achieved better accuracy.

The use of non-conventional energy sources has increased in recent years due to the benefits of low power interruptions, unlimited power supply, and non-polluting power generation. Wind power generation has become one of the clean energies whose use will be a viable solution to global warming and power outages. One such proposed system consists and modelling of bladeless wind power generation, which uses wind as an energy source while producing power without the use of blades. Due to the fact that wind energy isn’t really constant, an MPPT with an artificial neural network is being intended to keep the voltage and current of a bladeless wind generator at their maximum peak values regardless of whether environments. The wind generator’s outcome has been fed into a single-phase induction motor that can be used for pumping stations application fields. The proposed wind generator’s architecture as well as findings has been modeled using MATLAB Simulink.

In today's day and age, there are billions of volumes of textual content being generated everywhere. In-apps messages like WhatsApp, Telegram, social media sites like Facebook, Instagram, news publishing sites, google searches and many other sources. All these sources are constantly generating huge volumes of text data every second. And because of these huge volumes of text data NLP becomes a vital resource in understanding the textual content. In this paper, the main focus is on the popular NLP task of Sentiment analysis. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. sentiment analysis proves to be an incredible asset for users to extract essential information and assists organizations with understanding the social sentiment of their brand, product or service while monitoring online conversations. This paper investigates the different approaches and classification models used in the task of sentiment analysis.

There are already many software tools present and many application software's that being developed. These is because the technology needs to be updated according to the requirements. During these busy times, Developing and updating technologies is itself a big challenge. If any bugs occur in future or to keep track of features are added are working properly according to requirements, it needs some tools to keep track of it. For these things to happen, it requires some methodology to improve debug time like ETL (Event Trace Log) for multiple display features. The other things are that, code that is written today has to be written in such a way that it could be used for at least next 10 years. This will reduce the time of adding the same code simultaneously. Collecting the ETL in the encoded form helps in securing the sensitive information present in the trace log. Adding more testing codes in Unit Test Framework (UTF) find more bugs at early stages which will reduce the code cost. Adding MMIO (Memory Mapped I/O) verifier rules as the part of UTF helps in register verification (Simulates real Hardware context). Addition of MMIO rules for existing and new display features in separate xml file for different platforms makes it easy to identify in which platform feature failure in happening. All these methods help in code quality improvements for Intel Windows Graphics Driver.