Volume 11 | Issue 2 |

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Slow learners, personal problems, academic problems and English subject

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One of the main farming industries where the process of automating plants based on illnesses may be carried out is the agricultural industry. It's important to maintain track of healthy and diseased plant leaves in such an agricultural setting so that they may be further separated to produce exponential crop yields and returns. Utilizing picture categorization techniques, a variety of cutting-edge technologies have been combined for this goal, including machine learning, deep learning, and artificial intelligence. To accurately identify the presence of illness in plant leaves, deep learning-based models' working theories have been continually improving. We suggest using Convolutional Neural Network, ResNet-50, Efficient-B2, and VGG-16 for this purpose in order to identify and confirm the existence of plant illnesses in the relevant leaves. Gathering a dataset of 87k plant photos from the Kaggle library is how the paper is put into action. This library runs on 38 different categories and includes photographs of both healthy and sick plants. However, 250 photos from each class are used in the final implementation.

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CNN, Efficient-B2, machine learning, deep learning, ResNet-50, VGG-16.

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This study aimed to investigate the association between college students' emotional intelligence and academic stress. For data collection, the researcher chose 210 college students from Kurukshetra district in the state of Haryana. The Academic Stress Scale, developed by Abha Rani Bisht in 1987, and the Emotional Intelligence Inventory, developed by Dr. S. K. Mangal (2004), was the instruments used in the study. Using the relevant statistical methods, including mean, SD, t-test, and product-moment correlation, the data thusly acquired was examined. The study's findings showed a significant difference in academic stress experienced by male and female college students, as well as a significant difference in emotional intelligence. Academic stress and emotional intelligence did not significantly correlate.

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Academic Stress, Emotional Intelligence, College Students

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Absurdism is a philosophical theory that is based on the belief that human life has no meaning, no existence of life, and this feeling of the situation of human being leads toward alienation, frustration, loneliness, and futility in life. Absurdism is a philosophical movement that came out after the devastation of World War II. During this war, a lot of bloodshed and killed people and their fellow beings created a terrible situation in the lives of human beings. This situation pursuit to think about their existence, the existence of these internal changes began to seem absurd and the plight of the individual futile. So, the present paper focused on the theory of absurdism, its meaning, and its existence in human life. Moreover, it also discussed the pre-absurdism movement which existed before the absurdism movement. However, this paper is an endeavor to shed light on a philosophical theory of absurdism.

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Absurdism, Absurdity, Human Existence, Irrational, human being

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Buddhism, Modern World, Nirvana and Noble Truths.

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In this report,The purpose of this smart wheelchair project is to develop a regular powered wheelchair by combining sensors to sense the environment around the wheelchair and a speech interface to give commands. In the future welfare society, intelligent wheelchairs will play a critical role

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Artificial intelligence, IR and ultrasonic sensors, Arduino microcontroller, and Bluetooth.

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Blockchain technology is one of the most significant discoveries and inventive advances that is currently playing a crucial part in the professional world. The trajectory of blockchain technology is toward constant revolution and development. No matter how far apart two people are from one another, this chain of blocks protects their information and upholds their mutual confidence. The growth of blockchain technology over the past several years has compelled academics and industry experts to consider novel ways to integrate blockchain technology into a variety of fields. The exponential growth of blockchain technology has opened up a wide range of new application possibilities, including those in the field of healthcare. An in-depth analysis of developing blockchain-based healthcare technology and related applications is provided by this survey. In this investigation, we draw attention to the unresolved issues in this rapidly expanding sector by briefly outlining them. We also demonstrate how blockchain technology has the potential to revolutionize the healthcare sector.

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blockchain,PBFT,DPOS,DEMATEL,FUZZY SET THEORY

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Phytoremediation is the process of removing soil contaminants by suitable plants. It is a very cheap and economical way to decontaminate soil and water by simple process of absorption. The present work has been designed to choose a suitable plant which can efficiently absorb heavy metals without being affected. The Phyto-remedial property of mustard and maize was compared for lead contaminated soil. Results confirmed Mustard to be more suitable for lead uptake as compared with Maize. Fresh and dry weight of plants gradually decreased with increase in concentration of lead in the soil. Duration of the observation was from August to October 2022. Concentration of lead varied from 5 mg kg-1 to 20 mg kg-1. After treatment, the amount of lead absorbed in Mustard and Maize was estimated by the Atomic Absorption Spectrophotometry method.

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Lead, Phytoremediation, Indian Mustard, Maize, Atomic Adsorption Spectroscopy (AAS)

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Effectiveness, SIM, Knowledge, Peptic ulcer disease, Middle adult

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The goal of this study is to investigate how authors ability to recognise and convey emotions through words and expressions changes in an online, adaptive learning environment. The effectiveness of a method developed to improve a convolution neural network's performance using Bidirectional Long Short-Term Memory is verified through a simulated exercise (CNN-BiLSTM). It was decided to do this after carefully examining the deep learning neural network methods already available. Based on the experimental results, the CNN-BiLSTM method provided in this article achieves an accuracy of up to 98.75%, which is at least 3.15 percentage points more than the accuracy of other algorithms. The book "Deep Learning-Based Artificial Emotional Intelligence" delves at the ways in which AI has been used to emotional intelligence, specifically to the goals of accelerating and augmenting emotional intelligence. Some examples of artificial intelligence applications that are investigated include machine learning and deep learning. It offers research on tools that may be utilised by system architects and designers to ease and streamline the process of developing deep learning systems. Specifically, we employ a deep learning approach for labelling feelings in our research. In this method, a huge number of sensor inputs from different modalities are added and subtracted in an iterative process. Our dataset was derived from information collected by multiple smartphones and wearables in a real-world investigation. It does so by displaying signal dynamics and the temporal correlations between the various sensor modalities, all of which are gathered from a unified model that incorporates on-body, ambient, and location sensors. The raw sensor data is fed into numerous learning methods, including a hybrid that employs both convolutional neural networks and long short-term memory recurrent neural networks (CNN-LSTM). Thus, feature engineering and extraction can be carried out automatically.

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Deep learning, Emotion recognition, Convolutional neural network, Long short-term memory mobile sensing

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Creative Commons Attribution 4.0 and The Open Definition