Volume 11 | Issue 11 |

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This study compares Machine Learning techniques like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Deep Learning for COVID-19 detection. It presents a comprehensive comparative analysis of these three distinct approaches in the context of COVID-19 detection, without limiting the scope to a specific data source as well as assesses the performance, accuracy, precision, recall, and F1-score of these models across various datasets and types, revealing their strengths and weaknesses. The findings provide insights into their suitability for different applications, offering guidance for researchers and practitioners in improving COVID-19 diagnostics and monitoring.

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ANN

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Society always needs psychosocially competent individuals who can function effectively in any life situation, manage their lives, get along with others, and contribute meaningfully to society. But it can be argued that the idea of performing well in the 21st century is way too different than what it was in the 20th century or earlier. Adapting to a world characterized by globalization, mutual interdependency among nations, fast-changing technology, culturally diverse global workplaces, and changing market - place demand for the acquisition of competencies that empower today's individuals. So, there is a need to think of essential competencies and skills that 21st century learners need to cultivate to deal with the challenges posed by uncertainties and complexities of present society. In this paper, the researchers have attempted to explore the concept of psychosocial competence. In this regard, relevant international frameworks on skills and competencies were reviewed and findings were discussed.

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psychosocial competencies, life skills, socio-emotional skills, 21st century skills.

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development of effective therapeutic strategies against cancer remains a significant challenge in modern medicine. This study aimed to evaluate the anticancer efficacy of a novel drug molecule on human cancer cell lines in order to assess its potential as a promising treatment option. In this in vitro study, various human cancer cell lines representing different types of cancer were employed, including breast, lung, colon, prostate, and ovarian cancer. The drug molecule under investigation was administered to these cell lines at varying concentrations, and its effects on cell viability, proliferation, and apoptosis were assessed using established experimental protocols. The results demonstrated a dose-dependent inhibition of cell viability in all tested cancer cell lines following treatment with the drug molecule. Additionally, a significant reduction in cell proliferation was observed, indicating the potential of the drug molecule to impede cancer cell growth. Furthermore, the drug molecule induced apoptosis in the cancer cells, as evidenced by morphological changes and activation of apoptotic markers. Notably, the drug molecule exhibited selectivity towards cancer cells, sparing the viability of normal human cells tested in parallel experiments. This selective anticancer activity suggests a potentially favorable therapeutic index, minimizing the risk of adverse effects on healthy tissues. Overall, our findings suggest that the novel drug molecule holds promise as an effective anticancer agent. Further investigations, including in vivo studies and clinical trials, are warranted to validate its efficacy, safety, and clinical applicability. If successful, this drug molecule could contribute significantly to the development of targeted therapies for various types of cancer, improving patient outcomes and survival rates.

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Cancer

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Thalassemia, a common monogenic disorder, encompasses a spectrum of conditions from mild to severe. Beta-thalassemia, characterized by reduced or absent beta-globin chain synthesis, leads to ineffective erythropoiesis and anemia. Severe cases require regular blood transfusions, causing iron overload and complications. Endocrine issues are common. Understanding the clinical and hematological aspects is vital for effective management and complication prevention.

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Thalassemia, Monogenic Disorder, Beta- Thalassemia, Complications, Anemia.

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The integration of electric vehicles (EVs) into the modern transportation network necessitates advanced charging control and management systems to enhance efficiency and sustainability. In this study, we proposed a Hybrid Deep Learning (Hybrid DL) Mechanism for Charging Control and Management of EVs, which combines Recurrent Neural Networks (RNN) and Gated Recurrent Unit (GRU) techniques. In pursuit of robust feature extraction for battery information, we propose the utilization of RNNs. This approach aims to acquire comprehensive feature insights crucial for understanding the state of the EV battery. To further enhance predictive capabilities, we introduce a bidirectional GRU. The RNN-GRU hybrid model is designed to capture the temporal dependencies in EV charging patterns, offering improved prediction accuracy and real-time control capabilities. The benefits of this model include enhanced charging scheduling accuracy, faster response times to dynamic charging demands, and efficient energy utilization. The RNN component enables the model to learn from historical charging data, while the GRU component enhances the model's ability to adapt to changing EV usage patterns. By leveraging this hybrid approach, our model aims to make charging infrastructure more intelligent and adaptable, contributing to reduced energy costs, minimized grid impact, and a more sustainable EV ecosystem.

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EVs, Hybrid Deep Learning, GRU, Battery State Prediction, Charging Infrastructure Optimization.

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The increasing integration of renewable energy sources in smart grids necessitates intelligent decision-making systems for optimal utilization. This paper presents an autonomous decision-making system leveraging deep learning (DL)techniques to enhance the efficiency of renewable energy resource utilization in smart grids. The proposed DeepGrid system employs advanced neural network models to analyze real-time data, predict energy production patterns, and dynamically optimize grid operations by optimized DL. By autonomously adapting to changing environmental conditions and energy demand, DeepGrid ensures a reliable and sustainable power supply. The model's DL architecture enables it to learn complex relationships within the data, facilitating accurate decision-making for grid management. Through simulation studies, we demonstrate the efficacy of DeepGrid in improving grid stability, minimizing reliance on non-renewable sources, and ultimately contributing to a more sustainable and resilient energy infrastructure.

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Smart Grids, Renewable Energy, Deep Neural Networks, Optimization, Machine Learning

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Emotions are one of the important factors that enhance the process of human communication. The facial expressions and gestures convey nonverbal communication cues that play in vital role in interpersonal relations. There is significant variability making facial recognition a challenging research area. Features like Histogram of Oriented Gradient (HOG) and Scale Invariant Feature Transform (SIFT) have been considered for pattern recognition. These features are extracted from images according to manual predefined algorithms. In recent years, Machine Learning (ML) and Neural Networks (NNs) have been used for emotion recognition. In this research, a Convolutional Neural Network (CNN) is used to extract features from images to detect emotions. The Python is used for implementation. A CNN model is trained with grayscale images from the MMI Face dataset to classify expressions into five emotions, namely happy, sad, neutral, fear and angry. To improve the accuracy and avoid overfitting of the model, batch normalization and dropout are used. The best model parameters are determined considering the training results.

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Facial Action Coding System, Convolutional Neural Network, MMI Face dataset, Graphical Processing Units

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Waste management has become a major issue in cities due to tremendous increase in the quantity of waste generated every day. Municipal solid waste includes flower waste which has high moisture content and is biodegradable. Improper disposal of flower waste cause environmental pollution. Composting of flower waste is an environment friendly way of converting the flower waste into biofertilizer. The present study focuses on composting flower waste and checking the stability and maturity of the compost by analysing the physical and chemical parameters. The results of these parameters were found to be within the acceptable limits of standard values. The mature compost can be used for enriching the soil and can support the growth of plants. This method is affordable, pollution free and can be promoted for better waste management.

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MANAGEMENT OF FLORAL WASTE BY COMPOSTING

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Partnership" is the relation between persons who have agreed. to share the profits of a business carried on by all or any of them acting for all.India cannot be pinpointed. However, many believe that investment by high-net-worth individuals and companies in the railroad project implemented by the British government in India was one of India's first instances of public-private partnerships.

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Partnership fund, partner, estoppel,agreement and partnership deed

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