Volume 13 | Issue 1 |

 Abstract

The Purpose of this study was to compare the achievement level in science subject among the students of Government Prishadiya Upper Primary Schools and students of Aided Junior High School. The sample size group is 300 students for Government Parishadiya Upper Primary Schools and 300 students of Aided Junior High School. A test of 20 marks is applied on each student . Stratified random sampling method used for collection of data.Then after calculating the answer and analysing the result by statistical tools and t-test ,the result of the study shows that there is no significant difference in the achievement level in science subject among the students of Parishadiya Upper Primary Schools and students of Aided Junior High School. It is Possible that the teachers of Government Parishadiya Upper Primary Schools and Aided Junior High School have similar interest in teaching or the learning level of the students of Government Parishadiya Upper Primary Schools and students of Aided Junior High School is same.

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 Keywords

Comparative study of achievement level in science subjects among the students of Government Parishadiya Upper Primary Schools and students of Aided Junior High Schools of district Pilibhit.

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Facial expression recognition (FER) is an important component in improving human computer interaction through the ability of machines to recognize human emotions. The new improved FER systems have been using deep learning techniques, especially Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Real time emotion recognition is something that CNNs excel at as their spatial feature extraction powers them to be very good at extracting spatial features from static images. On the other hand, RNNs are aptly suited for temporal sequences, that is, for capturing of emotion progression with time that is crucial for video based FER. In this paper, we offer a comparison of CNNs and RNNs, with respect to their strengths, limitations, and where we think they are best applied. It also examines hybrid models that combine both structures, and thus presents a complete approach to exploiting the combined capabilities of both architectures. Our findings suggest that CNNs are well suited for fast, static image recognition, while RNNs are better suited for dynamic, sequence based tasks. The next step in future research should extend to making models more robust and integrating multimodal data to make more adaptive and accurate FER systems.

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 Keywords

Facial Expression Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Deep Learning, Emotion Recognition

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Abstract Ecofeminism is a critical theory and movement that examines the relationship between the exploitation of the environment and the oppression of gender. Drawing on feminist and ecological thought, they emphasize the interconnections between patriarchal systems and human domination of nature. In this paper, we discuss the historical trajectory, a short list of principles, along with the contributions of ecofeminism in literature, activism, and contributions to sustainable development. Through the lens of prominent ecofeminist theorists and authors, the research highlights its applicability in tackling contemporary environmental problems and gender inequalities about land ownership, environmental protection, and climate change.

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: Ecofeminism, Gender, Environment, Patriarchy, Climate Justice, Environmental Literature

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The Banarasi saree, a culturally significant and beautiful garment, faces challenges due to technological changes and global fast fashion. This has affected local artisans, craftsmen, and entrepreneurs, who must adapt to current market trends to promote handlooms and handicrafts. The proposed paper aims to identify traditional aesthetics and the need for contemporary design in the Banaras saree, highlighting the gap between traditional and modern design aesthetics. The paper evaluates whether design intervention can revive the craft and explores innovative ways to generate livelihoods in this struggling craft. The paper will be a review article, analyzing secondary data and suggesting an innovative approach for craftspeople to expand their craft vocabulary and access contemporary market trends.

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 Keywords

Indian culture, Banaras handloom, Banaras saree, Design aesthetics, Materials & Techniques, Trends In design, Contemporary design, Sustainability, Design intervention.

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The integration of artificial intelligence (AI) and machine learning into fuzz testing, termed AI Fuzzing, presents a promising approach to enhancing software security by automating the identification of vulnerabilities through systematic input generation. However, traditional fuzz testing methods face significant limitations, particularly in their inability to encompass various attack types, leading to a critical gap in software protection prior to deployment. This study aims to explore the concept of AI Fuzzing, specifically focusing on the combination of coverage-guided and behavioral fuzzing techniques. The research employs a structured methodology utilizing OSS-Fuzz's Fuzz Introspector tool to identify under-fuzzed code segments, followed by an evaluation framework that generates and tests new fuzz targets using a large language model (LLM). The framework iteratively adjusts the fuzz targets based on observed outcomes, enhancing code coverage. The findings reveal prevalent software vulnerabilities, including memory leaks, injections, sensitive data exposure, insecure deserialization, buffer overflows, use-after-free errors, data races, and software crashes. These results underscore the effectiveness of AI-driven fuzz testing in revealing security flaws that traditional methods may overlook. The implications of this research highlight the potential for developers to leverage a variety of open-source fuzz testing tools, as well as enterprise solutions, to improve software security in diverse environments, particularly within larger development teams and DevOps settings. This study emphasizes the complementary nature of static code analysis and dynamic fuzz testing in identifying vulnerabilities, advocating for a holistic approach to software security.

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AI Fuzzing, software security, vulnerability detection, fuzz testing, machine learning, dynamic analysis code coverage

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Machine learning (ML) a type of Artificial Intelligence (AI) that allows systems to learn and improve from data without being explicitly programmed. It uses artificial neural networks and deep learning to analyse large amounts of data, identify patterns, and make predictions. ML is used in many areas of our lives, including banking, online shopping, social media, healthcare, and entertainment. A pituitary tumour is an abnormal growth of cells in the pituitary gland, a small gland located in the brain that produces hormones that regulate many bodily functions. Classifying brain tumours accurately is crucial for treatment and prediction. In the proposed work, the ML models, including k-nearest neighbour (KNN), decision trees, logistic regression and Support Vector Machine (SVM algorithms, are used to classify pituitary brain tumours. MRI dataset of Brain Tumour Classification, available in Kaggle, is used. It contains 105 images of no tumour and 74 images of pituitary tumour. From the empirical analysis, it is found that the CNN Classifier has exhibited an accuracy of 88%. ML algorithms exhibit strong performance in classifying brain tumors, with near-maximum area under the curves, sensitivity, and specificity.

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Brain Tumour, machine learning, Convolutional Neural Networks, Decision Tree Algorithms, KNN, SVM

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Brain-Based Learning (BBL) is an innovative educational approach rooted in neuroscience, emphasizing teaching strategies that align with the brain's natural learning processes. This paper explores the theoretical underpinnings, practical applications, and challenges of implementing BBL in diverse educational contexts. Key components, such as neuroplasticity, emotional regulation, multisensory engagement, and memory enhancement, are analyzed to illustrate how BBL promotes critical thinking, creativity, and academic achievement. The integration of technology, including AI, is discussed as a means of personalizing learning experiences and addressing diverse student needs. Additionally, the study examines emotional engagement's role in fostering a supportive classroom environment, enhancing memory retention, and building resilience. Practical strategies for implementing BBL, supported by evidence-based examples, are presented to provide actionable insights for educators. The study concludes by highlighting the benefits, limitations, and future directions of BBL in transforming education.

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Brain-Based Learning (BBL) Neuroscience in Education Emotional Engagement Neuroplasticity Multisensory Learning Memory Retention Educational Technology Critical Thinking Personalized Learning Classroom Strategies

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This research proposal looks into the impact of AI on Indian businesses, focusing on its applications, benefits, challenges, and future potential. It analyses 10 studies on AI adoption across sectors like e-commerce, banking, manufacturing, and hospitality and identifies key themes such as AI's role in enhancing operational efficiency, the challenges of infrastructure and skills shortages, and the opportunities for growth in AI-driven industries. A comparative analysis depicts the contrast of AI adoption in various sectors. It reveals that banking and e-commerce lead, while others like education take a longer time in adopting due to infrastructural and resource limitations. It provides a good source for businesses planning to incorporate AI into their processes. The analysis proposes that AI integration is the practice in banking and e-commerce, while all other sectors are somehow still behind in line due to infrastructural and resource constraints. Future advancements in AI, together with the intersection of AI and many other growing technologies such as blockchain and IoT, are promising enough to transform the Indian industries. However, an organized effort involving government and the private sector in approaching issues on how to overcome the barriers and arrange robust digital infrastructure is very important. This research highlights essential sector-specific AI applications, thereby enabling industries to make informed decisions and optimize their processes to secure a competitive advantage in the changing digital front.

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Artificial Intelligence, India, E-commerce, Banking, Manufacturing, Hospitality, AI Adoption, Operational Efficiency, Challenges, Future Potential.

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Rainfall prediction plays a vital role in agriculture, disaster preparedness, and water management. Conventional methods, such as statistical and physics-based models, often struggle to achieve the desired accuracy due to the nonlinear nature of weather patterns. Long Short-Term Memory (LSTM) networks, which are a form of Recurrent Neural Network (RNN), have emerged as an effective machine learning technique for solving these difficulties. This paper reviews various studies focusing on the application of LSTM networks in rainfall prediction. Key methodologies, case studies, and model comparisons are presented, emphasizing the strengths of LSTM in capturing temporal dependencies. Challenges, future directions, and the potential of hybrid models are also discussed. The review underscores LSTM's transformative role in improving prediction accuracy and reliability.

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Rainfall Prediction, Long Short-Term Memory, Machine Learning, Time-Series Analysis, Hybrid Models

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Gas chromatography (GC), also known as gas-liquid chromatography (GLC), is a widely utilized analytical technique in both academic and industrial research settings, particularly for quality control and the identification and quantification of components within mixtures. This paper aims to evaluate and describe the various stages involved in developing and validating GC methods. GC is recognized for its sensitivity, accuracy, repeatability, and flexibility, making it an essential tool for analyzing complex mixtures, including pharmaceuticals and medicinal compounds. However, its application is limited to molecules that can be derivatized into volatile, thermally stable compounds. The development and validation of GC methods are critical to discovering, developing, and producing pharmaceuticals. Method development involves demonstrating that an analytical technique is suitable for accurately measuring the concentration of an active pharmaceutical ingredient (API) in a specific dosage form. This allows for streamlined procedures to confirm that the method will consistently deliver precise measurements of the API in pharmaceutical preparations. The validation of these methods is crucial and is rigorously assessed for various parameters, including robustness, system suitability, linearity, accuracy, precision, range, and detection limits.

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 Keywords

Gas Chromatography (GC), Gas-Liquid Chromatography (GLC)Analytical Method Development, Analytical Chemistry, Quality Control.

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Creative Commons Attribution 4.0 and The Open Definition