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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Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting 6-15% of women worldwide. Its etiology involves insulin resistance, hyperandrogenism, obesity, hormonal imbalances, genetic factors, environmental exposures, stress, and ovarian follicular defects. PCOS significantly impacts women's quality of life and mental well-being. Management strategies should address these multifactorial aspects, including lifestyle interventions for obesity and hormonal balance. A better understanding of PCOS complexities can improve reproductive and metabolic outcomes for affected women.

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Hyperandrogenism, Follicular Defects, Endocrine, Multifactorial, Insulin resistance, obesity.

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Key Words: Ecocriticism, , Deep ecology , Environmental issues, etc

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Floods are among the most devastating natural disasters, causing extensive damage to both human lives and infrastructure worldwide. Timely and accurate flood prediction is crucial for mitigating the impacts of these events and enhancing community resilience. This abstract presents an overview of the current state of flood prediction, highlighting the growing importance of advanced forecasting models and data-driven approaches in improving prediction accuracy. Traditional flood prediction methods often rely on historical data and basic hydrological models, which may not adequately capture the complexities of modern climate patterns and urbanization. In recent years, advancements in technology, remote sensing, and computational power have revolutionized flood prediction by enabling the development of sophisticated predictive models. Integrating diverse datasets such as rainfall, river discharge, topography, land use, and weather forecasts to create a comprehensive understanding of the hydrological system. Leveraging machine learning and AI algorithms to process and analyze vast amounts of data, enabling the detection of patterns and trends that were previously challenging to identify. Utilizing satellite and remote sensing technologies to monitor environmental changes and provide real-time information on flood-prone areas.

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Flood Prediction, Machine Learning,Pytorch,Deep Learning

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Accurate forecasting of electrical appliance usage and peak demand is crucial for effective planning, maintenance, and the development of automation in electrical power systems. Discrepancies between actual appliance consumption and energy demand may arise from various factors, such as losses in lines and appliances, as well as mismanagement of energy demand. A groundbreaking approach for forecasting household electric appliance consumption and peak demand through a hybrid machine learning (ML) framework is presented here. To address these variations, a thorough examination of smart meter data is essential to pinpoint the key attributes and primary causes of differences between electrical appliance consumption and customers' peak demand. Understanding these intricacies is vital for optimizing power system operations and implementing strategies to enhance efficiency and reliability. The study employs a comprehensive dataset spanning multiple households, ensuring the generalizability of the proposed methodology. Results demonstrate superior predictive capabilities compared to traditional models, offering significant advancements in energy demand forecasting precision. This hybrid approach not only captures nuanced relationships within the data but also adapts dynamically to changing environmental and behavioral factors. As the energy landscape evolves, our innovative methodology stands poised to revolutionize how we understand and anticipate household electricity consumption, providing valuable insights for policymakers, utility providers, and consumers alike.

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Machine learning, Data filtering, Electrical appliance consumption, Peak demand, Smart meters

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Osteoporosis is a chronic skeletal disease that is characterised by reduced bone mineral density (BMD) with an increased risk of fractures. Its occurrence is being observed increasingly in post-menopausal women and the elderly population. Primary prevention is the best way to combat this problem. Medical intervention in the form of allopathic remedies and in severe cases, surgery is required and is the traditional approach. Pharmacological treatment includes antiresorptive and bone anabolic drugs that may help restore mass and quality. However, these techniques are accompanied by an array of undesired side effects due to less researched long-term issues and patient incompatibility in many scenarios. Mainstream medical practice is centralised around healing through allopathic medicine and has not yet fully acknowledged nutrient strategies to enhance skeletal health. For a holistic approach to the treatment of this rampant disorder, a balance between lifestyle, nutritional, and dietary factors is essential. This article discusses the effects of the intake of adequate amounts of nutrients in a prophylactic as well as therapeutic role. The significance of calcium, phosphorus, proteins, fatty acids, magnesium, vitamin D, and vitamin K is reviewed. Incorporation of these nutrients in daily diet is imperative to curb this medical pandemic along with amendments in lifestyle.

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Skeletal disease, Proteins, Fatty acids, Magnesium, Vitamin D, Vitamin K

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The article focuses on the government policies and initiatives that have played a vital role in shaping India's network infrastructure over the years. It mainly concentrates on the funding mechanisms, considering their feature and the impact on the development of roads and networks in India.

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BOT Models, Financial Structure, NHAI, PPP.

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Breast milk is the perfect food for normal neonate. It is the best gift a mother can give her baby. It contains all the nutrients for normal growth and development of a baby from the time of birth to first six months of life. Proper proportion and in a form that is easily digested and absorbed .Infants need to be given only exclusive breast feeding for the first six months of life."If the winter comes can the spring be for behind."Great poet says that the spring is followed by winter. That reveals that the joy after suffering. But labour does not come to end with child birth. The mothers do suffer much difficulty after childbirth. The design adopted for the study was quasi experimental pre- test and post -test control group design to assess the effectiveness of breast massage on reduction of breast engorgement among mothers undergone caesarian section. Purposive sampling was used to select 60 mothers in selected hospitals among that 30 samples were allotted for experimental group, 30 samples for control group. The data collection tools developed for generating the necessary data were standard scale was used to assess breast engorgement among mothers undergone caesarian sectionThe reliability of rating scale (r=0.9) was established by test retest technique method. The tool was found to be reliable. Pilot study was conducted to find out the feasibility of the study and to plan for data analysis. There was no significant difference between mean pretest level of breast engorgement among mothers undergone caesarian section in experimental and control group (t=0.86, p<0.05). There was a significant difference between mean post test level of breast engorgement among mothers undergone caesarian section in experimental and control group(t=4.88, p<0.05). There was a significant difference between mean pre and post test level of breast engorgement among mothers undergone caesarian section in experimental group(t=5.76, p<0.05). There was a no significant difference between mean pre and post test level of breast engorgement among mothers undergone caesarian section in control group(t=0.05, p<0.05). There was no significant association between post-test level of breast engorgement and experimental group demographic variables in age, education, occupation, postnatal day, feeding started, duration and frequency of feeding among mothers undergone caesarian section at ( p<0.05) level. There was a significant association between post-test level of breast engorgement and experimental group demographic variables in gravida, among mothers undergone caesarian section at (p<0.05) level. There was no significant association of post-test level of breast engorgement and control group demographic variables in age, education, occupation, postnatal day, feeding started, duration and frequency of feeding among mothers undergone caesarian section at ( p<0.05) level. There was a significant association between post-test level of breast engorgement and control group demographic variables gravida, among mothers undergone caesarian section at (p<0.05) level. Conclusion: The results of the study concluded that breast massage was effective on reduction of breast engorgement among mothers undergone cesarean section Breast massage is easy to practice, not painful and can enhance comfort to mother in the postnatal period, hence could easily be adopted as a regular intervention.

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Breast Engorgement, Mother

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Background: HIV and TB pose significant public health threats, but their combined impact has a much greater effect on epidemiological progression and the global health landscape. Having HIV is the primary factor that raises the risk of developing active TB. This not only makes individuals more prone to initial infection or reinfection but also increases the likelihood of TB reactivation in those with latent TB.This observational study was conducted with the objective to identify the prevalence and prescribing patterns of TB among PLHIV in a tertiary care teaching hospital. Methods: A prospective observational was conducted in 250 HIV patients on ART who attended an ART clinic over a 6-month period . Result: This study showed 250/100 (40%) prevalence of tuberculosis among people living with HIV, of which 68(68%) were males and 32(32%) were females. Low CD4 count (< 200/?l) had A strong statistical connection is observed between co-infection of HIV and TB compared to just having HIV infection.The treatment of HIV/TB Co-infection majority of the population received CAT 1 medications , for patients who were resistant to CAT 1 medications , Bedaquilline was recommended as CAT 2 medication. Conclusion: The increasing HIV prevalence is connected to more tuberculosis cases, demanding a reevaluation of control strategies. Integrated programs targeting behavior change and condom use can lower HIV transmission and vulnerability. Success hinges on public awareness and effective health education on tuberculosis and HIV spread.

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CD4+ cells, HIV, Tuberculosis, ART,ATT.

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It can be challenging to secure the cyber physical systems (CPS) that underpin the Internet of Things (IoT) since security measures taken into account for general information and technology operations (IT/OT) might not function well in a CPS environment. In this paper, we suggested an automatic cyber attack detection and classification framework based on machine learning. For the purpose of ensuring the safety of Internet of Things use cases, it employs a variety of machine learning models. Learning Based - Cyber Attack Detection(LB-CAD) is the approach we suggested. The input of the algorithm consists of an ML pipeline and an IoT integrated use case dataset.

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ML-Based Cyber Attack Identification in CP Networks

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^^Hkkjrh; laxhr esa dk"kh ds eafnjksa dh Hkwfedk^^

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