Volume 12 | Issue 12 |

 Abstract

Combining the interpretability and uncertainty management of fuzzy logic with the hierarchical feature extraction capabilities of deep neural networks creates a potent hybrid framework. This synergy is especially useful in complex decision-making and prediction problems since it tackles difficulties with interpreting imprecise, noisy, or ambiguous input. Applications are found in many different fields, including autonomous systems, natural language processing, and medical diagnostics. New developments include using deep models to automatically construct fuzzy membership functions, optimizing fuzzy rules with learning algorithms, and integrating fuzzy layers in deep networks. By improving the scalability and openness of AI models, this hybrid method opens the door for reliable and understandable answers to practical issues.

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 Keywords

Deep Learning, Fuzzy Systems, Hybrid Models, Uncertainty Management

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 Abstract

Bioactive materials have had a great impact on restorative dentistry, favoring the longevity of restorations, cell stimulation for dentin repair, increasing adhesive resistance, and reducing the recurrence of cavities and bacterial microfiltration. This article focuses on different bioactive materials and their significance in pediatric dentistry because these materials are crucial to pediatric dentistry. By speeding up the regeneration and repair of damaged tissues, tissue bioengineering helps science create and expand new treatments and/or new biomaterials that restore, enhance, or stop the decline of compromised tissues' function. To develop bioactive materials that mimic and replace the natural structure of teeth and surrounding bone, more research is needed. Newer concepts of adhesion and incorporation may change dentistry and dental care in the future.

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 Keywords

Dental Materials, Bioactive materials, Regeneration, Remineralization

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Creative Commons Attribution 4.0 and The Open Definition

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Excessive exposure to ultraviolet (UV) radiation from the sun is a major cause of skin damage, including sunburn, premature aging, and skin cancer. While traditional sunscreens offer protection, they often contain synthetic chemicals that may have adverse effects on the environment and human health. In this study, we explore the development of UV protection fabrics using natural extracts derived from plants known for their antioxidant and photoprotective properties.

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V-blocking fabrics ,Sun protective fabrics ,Natural dyes , Natural pigments ,Plant extracts

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in recent years, the field of biomedical and life sciences has witnessed significant advancements in the realm of automatic disease detection systems, particularly with a focus on respiratory diseases. Among these, the detection of cough and pulmonary conditions has emerged as a crucial area of interest. This study presents a groundbreaking voice analysis approach designed to detect such respiratory ailments swiftly, costeffectively, and reliably, providing a non-invasive alternative to conventional diagnostic methods. The proposed methodology leverages a Gradient Boosting Machine based classifier as the core component of a novel speech-based respiratory disease detection scheme. A comprehensive set of features, including spectral, cepstral, and periodicity features, alongside spectral descriptors, are extracted from recorded speech samples. These diverse features are seamlessly fused to generate relevant statistical features, serving as inputs to the Gradient Boosting Machine. The performance of the proposed model is rigorously evaluated using speech data spanning thirteen sound categories, sourced from over 50 countries through the incorporation of five standard datasets. This extensive dataset ensures the robustness and generalizability of the model, leading to accurate diagnoses of respiratory diseases, with a particular emphasis on cough and pulmonary conditions. Remarkably, the overall average accuracy of the proposed model, as demonstrated in stratified k-fold crossvalidation tests, exceeds an impressive 97%. The analysis of various performance matrices further underscores the potential and reliability of this innovative approach, emphasizing its practical utility for physicians engaged in the diagnosis of cough and pulmonary diseases. This research significantly contributes to the evolving landscape of disease prediction by introducing cutting-edge voice analysis techniques. The demonstrated success of the proposed model suggests its potential as a valuable tool in the hands of healthcare professionals, offering a streamlined and effective means of identifying respiratory diseases. As technology continues to advance, the integration of such automated systems could play a pivotal role in enhancing the efficiency and accuracy of medical diagnoses, particularly in the context of respiratory health.

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 Keywords

Pulmonary Disease Detection, Cough Sound Analysis, Speech Classification, Gradient Boosting Machine, Respiratory Disease Diagnosis Audio Feature Combination Feature Fusion Non-Invasive Diagnosis, Speech-based Health Monitoring

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 Abstract

A closer look at the world around us reveals that in this 21st century, mankind is facing various challenges, ranging from individual mental crises to environmental crises and ethical value degradation to social inequality. For all these unprecedented challenges, the wisdom of Veda can illuminate our path towards a sustainable life and harmonious evolution of mankind. Vedic teaching focuses. on holistic living, sustainable practice, and society with ethics and values. This study highlights the role of education in reviving Vedic knowledge for developing a healthy society. Through educational institutions, Vedic knowledge can be integrated with the contemporary curriculum that will foster a sense of moral responsibility and values in young minds. A case study was conducted on Viswatma Vidyamandir, a school situated in the Balangir district of Odisha, that integrates Vedic knowledge into its curriculum with modern pedagogical practice which imparts experiential learning for students that not only enriches the educational experience but also inculcates a sense of purpose in the young learner. The physical, mental, psychological, scientific, and spiritual development of students is the basic approach of this Vidya mandir. Objectives of the study are (i) to explore the integration of Vedic knowledge within the modern education system to address contemporary challenges (ii) to analyze its impact on students and the community, and (iii) to find out the challenges in integrating Vedic knowledge within a contemporary educational framework. The findings of the study indicate that the revival of Vedic knowledge fosters a sense of responsibility and ethical awareness in students. It encourages the students to realize their duty towards the community and nation. Although the institution faces various challenges but still, it able to shape its students into responsible citizens who can lead a harmonious society. By reconnecting with the ancient teachings, society can cultivate holistic living as well as sustainable practices which make a great impact on not only the development of individuals but also the whole society. This study calls for a concerted effort to integrate Vedic knowledge into contemporary practices, ensuring its relevance and vitality for future generations.

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Vedic knowledge, sustainable practice, holistic development, 21st century challenges.

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The effectiveness of teachers in secondary schools is a critical factor in students' academic success and overall development. However, the pressures and challenges teachers face in their professional and personal lives can significantly impact their performance in the classroom. Family stress, in particular, has emerged as a crucial factor influencing teacher effectiveness. This article explores the relationship between family stress and teacher effectiveness among secondary school teachers, drawing on existing literature and expert opinions to provide a comprehensive understanding of this issue. Family stress can significantly impact the professional performance of individuals, particularly female teachers, who often juggle the dual responsibilities of work and home. This study investigates the relationship between family stress and teaching effectiveness among female teachers in private schools using a mixed-method approach. The study also examines how these stressors affect their ability to deliver quality education The sample consists of 50 female teachers and quantitative data analysis found that family stress significantly affects teacher effectiveness.

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Teacher effectiveness, family stress, female teachers, private schools

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Electroencephalography (EEG) is a vital non-invasive tool for recording electrical activity in the brain. It offers significant insights into the neural processes and is widely employed in clinical diagnostics, cognitive neuroscience, and brain-computer interface (BCI) applications. However, analyzing EEG signals is a challenging task due to their complex, non-linear, and dynamic nature. Traditional signal processing techniques often fall short in fully capturing the rich information embedded in EEG data. In recent years, Machine Learning (ML) and Deep Learning (DL) algorithms have emerged as powerful alternatives, capable of uncovering patterns and features that are difficult to detect through conventional methods. These advanced techniques offer automated and scalable approaches to EEG signal analysis, enabling applications such as mental state monitoring, epilepsy detection, sleep stage classification, and emotion recognition. Machine learning algorithms, such as Support Vector Machines (SVM), Decision Trees, and k-Nearest Neighbors (k-NN), have demonstrated success in EEG classification tasks. On the other hand, deep learning architectures, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have revolutionized the field by offering enhanced feature extraction capabilities, leading to more accurate and robust EEG signal interpretation.

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EEG signal analysis, Cognitive neuroscience, Signal processing, Machine learning (ML), Support Vector Machines (SVM), Convolutional Neural Networks (CNNs),

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The complex link between student motivation and school culture is examined in this study report. The significance of creating a welcoming and inclusive learning environment is becoming more and more apparent to educators and legislators, thus it is imperative to comprehend how school culture affects students' motivation. In order to present a thorough examination of the influence of school culture on student motivation, the paper looks at previous research, integrates empirical data, and applies educational psychology concepts. With an emphasis on leadership, connections between teachers and students, and the overall learning environment, this research attempts to further the continuing conversation about successful teaching strategies.

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Motivation, Culture, Educational psychology, child-Behaviour

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India's fast industrialization has led to an increase in environmental deterioration, including soil, water, and air pollution. Consequently, it is now crucial to consider corporate accountability for environmental damage. The Water (Prevention and Control of Pollution) Act of 1974 and the Environmental Protection Act of 1986 are the two main pieces of legislation that hold businesses accountable for environmental harm. In India, even with a legal framework, it is still challenging to enforce corporate convictions for environmental damage. Pollution control agencies occasionally lack the funds required to carry out the Water (Prevention and Control of Pollution) Act, and public awareness of environmental issues and the legal foundation for environmental protection are both deficient. Citizens find it challenging to hold corporations responsible for environmental harm because there are insufficient enforcement resources and little public awareness. This paper looks at corporate accountability for environmental harm in India, the judiciary's role in the legal system, and the challenges of upholding the law.

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industrialization, environmental damage, Environmental Protection Act, enforcement resources, public awareness

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Mathematics education is facing a typical problem due to anxiety and phobia of the students. Various studies show that Gender of the students is a significant factor of anxiety in mathematics. This paper is an attempt to compare between boys and girls of higher secondary schools in mathematics anxiety and phobia. To carry out this study, 184 boys and 184 girl students of Purba Bardhaman district of West Bengal were selected by adopting proper method of sampling to compare the relationship among the students over the gender in the degrees of anxiety & phobia in Mathematics. Data were analyzed by appropriate method with inferential statistics. It was observed from the findings that the boys & girls do not differ significantly in their anxiety & phobia in mathematics. The results lead to conclude that boys and girls are having similar degrees of anxiety in mathematics, though the trend of anxiety indicates that the girls are little more anxious than boys in mathematics. Moreover it was found that the effects of different strategies on the achievement of boys and girls in mathematics do not differ significantly. However, the achievement of boys and girls treated through Strategy-1 (Guided Concept Attainment) were significantly better than the achievement of boys and girls treated through Strategy-2 (Programmed Instruction).

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 Keywords

Mathematics, Gender, Anxiety & phobia, Achievement.

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