Global Transitions Proceedings最新文献

Polycystic ovary syndrome is a disorder involving prolonged menstrual cycle, and often excess androgen level normally occurs in several women at the time of their reproductive age. This causes impotence along with gynaecomastia and hirsutism. Studying these kinds of condition in women is a major problem which can be resolved by analysing ultrasound images which have the necessary details like number of follicles, size, and position. However, there is a lack of solid objective test that can provide absolute affirmative to diagnose and understand PCOS. This motivates us to think about finding a method to diagnose PCOS at early stages preventing further complications. An automatic PCOS diagnosing tool would help to save the actual time spent on manual tracing of follicles and measuring the geometric features of every follicle. The proposed method was able to achieve classification with accuracy greater than 97% using a KNN classifier. The classifier will improve the time spent on diagonising PCOS and improve its accuracy, reducing the risk of the fatal complications that can be caused by delayed diagnosis.

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.

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.

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.

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.

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.

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.

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.

With every business process and organization being more concerned about adopting the latest technology towards understanding the success rate and risk associated with the product/service launch, they need to understand the intention and review of their prospective customer. Sentiment Analysis is one such advanced technology to analyze and perceive the behavior of a consumer. However, many challenges hinder analyzing precise sense of sentiments and locating the appropriate sentiment divisions. There has been a significant amount of work being carried out in this direction since the last decade. Furthermore, with the evolution of big data technologies, new methodologies have been introduced to improve sentiment analysis with various evolving applications. This paper provides a comprehensive study on sentiment analysis to provide valuable insight into sentiment analysis approaches and related fields. The paper discusses various essential information associated with the dataset, a new arena of application and methodologies, upcoming research methods, study findings, and further contributing to the ultimate study and research gap.

A range of influences, including solar insolation, temperature, shading, deterioration, incompatibility losses, dirtying, etc., all have an impact on the solar cells' performance and reliability. Shading, whether total or fractional, can also have a substantial influence on power output, including things in the PV-array, shading trends, and the presence of bypass diodes encapsulated in the PV segment configuration. It's indeed important to determine the significant impacts of shading, mostly on P-V and I-V curvature, to obtain the desired energy from a PV array.