Global Transitions Proceedings最新文献

An analysis of a unique compact planar antenna with a multiband microstrip square patch slotted in the form of Swastik on FR4 substrate is proposed in this paper. The proposed design has a Swastik shaped slot etched on the square radiating patch and the antenna is fed using a microstrip feed line. The FR4 substrate (ε_r = 4.4) is used for the simulation analysis. The current flow is altered by the Swastik shaped slot which resonates at the 5 bands (penta band), which are suitable for 5G sub-GHz applications. The antenna has a compact size of 32 × 32 × 1.6 mm³ and has a return loss, S₁₁ of less than -10dB for all resonant five frequencies. The analysis was performed taking into account S₁₁ (Return loss), directivity, antenna gain, and VSWR. In this proposed microstrip patch antenna design, patch is slotted in the shape of Swastik. In order to increase the number of resonant bands and to support multi band operation, the concept of DGS (Defective Ground System) is applied where purposefully the ground is etched out. This paper illustrates the proposed antenna design methodology and its results. The simulation work for the proposed design is carried out using HFSS (High Frequency Structure Simulator) tool.

The increasing accumulation of mess up plastic waste in natural environments creates a serious threat to our oceans, human health, flora and fauna. There is an urgent need to develop new approaches towards the disposal of non-biodegradable waste materials like plastics. It is now possible to develop novel biological treatment strategies concerning non-biodegradable waste (plastics) management because of the increasing literatures on the microbial degradation of the synthetic polymers like plastics. The valuable enzyme sources of microbes are capable of degrading synthetic polymers. The proposed waste segregator and decomposer (WSD) model focuses on the segregation of the non-biodegradable wastes automatically using AI techniques and also to frame an effective degradation strategy for commonly used synthetic plastics using novel microorganisms and associated enzymes.

Change is the only constant. In many sectors, a change is being witnessed that is getting increasingly rapid. This carries a plethora of new innovation possibilities with it. This necessitates well-founded data about trends, future developments and their consequences. This study seeks to catch the new directions, paradigms as predictors with an association of each topic which will be discovered through topic modeling techniques like LDA with BoW. For this, empirical analysis on 3269 research articles from the Journal of Applied Intelligence was done, which were gathered during a 30-year span. The inferred topics were then structured into a way suitable for performing predictive analysis. This is significant in the sense that it will help to predict what technology will be encountered in the future, as well as how far human's ability to innovate and discover things may lead this world to. The final model using TF-IDF scores has outperformed the baseline model by a margin of 41%.

Depression or stress is faced by most of the population throughout the world for multiple reasons and at different stages of life. Due to present busy life cycle, humans get into stress in their daily life, which leads to depression on long term. Stress is faced in education activity, competitive / challenging tasks, work pressure, family consequences, different types of human relation management, health disorders, old age etc. In this paper, a novel Artificial Intelligence therapy for depression analysis is proposed. This research is helpful for Psychologist to conduct counselling for their patients. Machine learning based Face Emotion techniques are used to detect depression level in any patient. This model can be tested for any age / category of patient, who faces depression due to any kind of problem or different sequences of life. To train machine learning algorithm, fer2013 open-source dataset is used. The algorithm was well trained and experiment were conducted on different age people. The results of this proposed algorithm were able to analyze depression more effectively.

In the subject of object detection using computer vision, image classification is becoming a prominent and promising aspect. However, studies have just scratched the surface. Till now, the superficials of food image classification in order to assess the nutritional abilities of people of different nationalities, The categorization of their traditional cuisine has a significant influence. Existing models categorize different sorts of foods. These models can only categorize a small number of meals at a given time. However, in a single model, the maximum number of foods must be recognized. This work focuses on the creation of a recognition model that uses transfer learning techniques to categorize various food products into their appropriate categories. Using Efficientnetb0, a transfer learning technique, the developed model classified 101 distinct food kinds with an accuracy of 80%. When compared to other state of art models, our model performed with best accuracy.

With the increase in social networks, more number of people are creating and sharing information than ever before, many of them have no relevance to reality. Due to this, fake news for various political and commercial purposes are spreading quickly. Online newspaper has made it challenging to identify trustworthy news sources. In this work, Hindi news articles from various news sources are collected. Preprocessing, feature extraction, classification and prediction processes are discussed in detail. Different machine learning algorithms such as Naïve Bayes, logistic regression and Long Short-Term Memory (LSTM) are used to detect the fake news. The preprocessing step includes data cleaning, stop words removal, tokenizing and stemming. Term frequency inverse document frequency(TF-IDF) is used for feature extraction. Naïve Bayes, logistic regression and LSTM classifiers are used and compared for fake news detection with probability of truth. It is observed that among these three classifiers, LSTM achieved best accuracy of 92.36%.

Natural language processing (NLP) components are responsible for analysing and contextualising human-like discussions between chatbots or any voice browser or with any live users are known as dialogue management systems (DMS). Dialog management systems, also known as plug-ins, allow the chatbot to complete this functionality with ease. The dialogue management system features a module called the agent for dialogue management that allows the DMS to contextualise information and deliver replies. Chatter-bots frequently employ dialogue management systems, such as ChatScript, to regulate the conversation structure based on themes. In the developed application which emulates the behaviour of a DMS, the functionalities like voice assisted navigation, functional keys implementation, language neutral search are implemented. The system is developed by taking into consideration of user experience as the primary factor. The system facilitates physically disabled users to perform all the above mentioned functionalities using voice commands with approx.90% of accuracy.

Optical Character Recognition (OCR) is an automatic identification technique which is applied in different application areas to translate documents or images into analysable and editable data. Printed or typed characters are easy to recognize as they have well defined shape and size, but this is not true in case of handwritten text. Handwriting of every individual is different so OCR face difficulty to recognize the characters. In past, researchers have been used different Machine Learning and Artificial Intelligence tools and techniques to analyse handwritten and printed documents and also worked to create an electronic format file from them. It is difficult to reuse this information as it is very difficult to search the content from these documents by lines or words. To solve this problem, OpenCV technique is used in this research work which focuses on training and testing of neural network model to conduct Document Image Analysis. The proposed model is named as J&M model for Text Detection from Hand written images. Implementation of research work is done in Python on MNIST database of handwritten digits. From this research work, 99.5% of training accuracy and 99% of testing accuracy was achieved along with training loss of 1.5%.

Image processing techniques have grown more important in the field of sericulture in the modern era, as the rapid growth of computer vision technology also provides a platform for image processing applications to obtain a better image. This review article provides an overview of the various types of algorithms used to count, classify, and detect silkworm eggs, whether the silworm eggs are fertilized (hatched) or unfertilized (unhatched), using image processing approaches. The literature review, analysis, and in-depth research explains the strengths and limits of the study and identify potential research problems. Modern tools and techniques for automatically counting, categorizing, and identifying silkworm eggs are being deployed, according to data gathered by previous researchers. A number of algorithms were used for automatic counting, categorizing, and detecting, however, the results were not accurate. As a result, in the field of sericulture, modern tools have proven essential to fully automatic counting, classifying, and detecting.

In today's world, a lot of processes are carried over the Internet to make our lives easier. But, on the other hand, many unauthorized and illegitimate activities that take place over it are causing major trouble for the growth of the economy. One of them being the fraud cases that misguide people and lead to financial losses. Major frauds reported recently occur through the malicious techniques that are made to work on Credit cards that are used for financial transactions over online platforms. Hence, it is the need of the hour to investigate this problem. Several companies have started their study in this regard and have formulated data driven models that use various Machine Learning algorithms and models on datasets to analyse false activity. Several techniques used are Support Vector Machine, Gradient Boost, Random Forest and their mixtures. In this comparative study, the anomaly of class imbalance and ways to implement its solutions are analysed to prove certain results. The effectiveness of the algorithms varies on the set of data and the instance in which it is used. They prove that all algorithms despite of all the calculations show certain imbalance at some point in the study The limitations have also been evaluated and highlighted to help in future. In this study, it is found that although logistic regression had more accuracy but when the learning curves were plotted it signified that the majority of the algorithm under fit while KNN has the ability only to learn. Hence KNN is better classifier for the credit card fraud detection.