Volume 12 | Issue 3 |

 Abstract

Clustering, a fundamental technique in machine learning, plays a pivotal role in pattern recognition, data mining, and exploratory data analysis. This paper provides a comprehensive exploration of clustering algorithms, evaluation metrics, applications, challenges, and recent advancements in the field. We discuss various types of clustering algorithms including partitioning-based, density-based, hierarchical, and distribution-based methods, along with their strengths and limitations. Evaluation metrics for assessing the quality of clusters are examined, encompassing both internal and external measures. Real-world applications of clustering across diverse domains such as image segmentation, customer segmentation, anomaly detection, and document clustering are elucidated. Furthermore, we delve into the challenges faced by clustering algorithms including sensitivity to initialization, scalability issues, and interpretability concerns.

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 Keywords

Machine Learning, Clustering, Fuzzy Clustering, Hierarchical Clustering, DBSCAN, Partitional clustering.

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 Abstract

. Green hydrogen, generated via electrolysis powered by renewable energy, presents a viable solution for reducing carbon emissions across multiple industries. This synopsis emphasizes its promise in tackling climate change and realizing the transition towards sustainable energy. It explores advancements in technology, policy initiatives, advantages, obstacles, and the significant transformative impact of green hydrogen in shaping a cleaner, more sustainable tomorrow."

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 Keywords

Importance of Green Hydrogen, Latest Technology adaptation

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Creative Commons Attribution 4.0 and The Open Definition

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Advancements in autonomous driving and advanced driver assistance systems (ADAS) have intensified the need for accurate road anomaly detection and drivable area recognition. This paper puts forward the Dynamic Fusion Module, a new approach intended to improve these vital aspects of autonomous navigation. The module combines data from multiple sensors using a dynamic fusion technique to generate a thorough and consistent understanding of the environment. The proposed method aims to address the difficulties associated with road anomaly detection, in particular potholes and other road surface irregularities. This goal will be accomplished by harnessing state-of-the-art Convolutional Neural Networks (CNNs), specifically Yolov8 and ResNet architectures. By addressing the limitations of current systems, our project seeks to transform road anomaly detection, providing enhanced accuracy and robustness across diverse real-world conditions, while harnessing the power of deep learning with Yolov8 and ResNet as our selected backbone architectures. The overall fitness score of 0.96 reflects a well-trained model with the potential for fine-tuning.

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 Keywords

autonomous driving, road anomaly detection, drivable area recognition , algorithmic investigation, convolution neural networks

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 Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Data Engineering are three key technologies that are transforming industries and propelling previously unheard-of technological developments in the quickly changing field of technology.The management should take initiatives to make students ready for the industry exposure,how to use Artificial Intelligence(AI) in business process,study materials. In order to fully utilize the potential of large datasets, this research investigates the connections and synergies between these three fields. The basics of artificial intelligence (AI) are covered in the first section, along with how intelligent computers mimic human cognitive processes. It highlights the importance of algorithms in decision-making and problem-solving while discussing the revolutionary effects of AI in a variety of industries, including healthcare and finance. The author has tried to discuss in this study how to implement AI, ML and Big Data Engineering in the industry , how it can be useful in different sectors, and in their placement drives as well, the employability related to AI,ML and Big Data Engineering .

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 Keywords

Data Governance, Anomaly Detection, AI Systems

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Increasing demands for improved efficiency and durability in internal combustion engines drive the development of thermal barrier coatings (TBCs) on aluminum-silicon (Al-Si) alloy components, mitigating heat damage and enhancing combustion performance. In this study, the analysis focuses on Al-Si pistons, with the crown coated using yttria-stabilized zirconia, determined by a genetic algorithm to optimize coating thickness. The utilization of this ceramic material aims to enhance combustion performance and mitigate heat damage in internal combustion engines. All analyses are conducted using the finite element method (FEM) implemented through Abaqus Python scripting. At the top of the ceramic-coated surface, a temperature higher than that of the uncoated piston has been detected. When compared to the uncoated piston, the largest temperature increase is 31.45%. Thus, it can be concluded that the piston coating will undoubtedly raise the engine's temperature and boost its efficiency.

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 Keywords

Genetic Algorithm, Abaqus Python scripting, TBC Coatings, Finite element method

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Creative Commons Attribution 4.0 and The Open Definition

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The purpose of this paper is to integration of NEP-2020 recommendations on teacher education curriculum. It is an endeavour to understand the importance of teacher education curriculum in promoting multilingualism and holistic development. Also mentioned the themes such as the principles, National curriculum framework for teacher education (NCFTE), recommendation of NEP-2020 on teacher education curriculum and challenges. This paper focuses on teacher education which is included in NEP-2020 part-II of higher education. The present study is qualitative in nature. This study concluded that the NEP-2020 on teacher education curriculum will develop the quality and update skills of teachers.

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 Keywords

Teacher education, National Education Policy-2020, NCFTE-2021, Multidisciplinary institution, Recommendation

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Agriculture, a fundamental human necessity from ancient times, remains crucial for our sustenance. Throughout history, plants have been our primary food source. Today, agriculture continues to be a vital aspect of our lives, serving as the backbone of various economies, irrespective of their developmental stages. Agriculture faces a significant challenge in the form of plant diseases and their impact on trade. Detecting and managing these diseases in a timely manner is crucial for crop health. Plant diseases are defined as natural issues that afflict plants, impeding their growth and potentially causing plant death in severe cases. In modern farming, new technology like IoT and automation can be super helpful. It's really important to keep plants healthy and check their surroundings to catch diseases early and get a good crop. We use smart tools like artificial intelligence (AI) and deep learning to look at plant pictures and find diseases. AI makes the job faster and helps us spot sick plants and control the farm's conditions. Scientists did some research and found out that these technologies are great at finding plant diseases by looking at the leaves. In this project we propose a deep neural network that would be trained to classify a variety of plant diseases. We intend to use PLANT VILLAGE dataset to train the model for the purpose of classification. Plant village consists of almost 16,000 images of leaves(some sick, some healthy) covering 19 different plant diseases.

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 Keywords

PLANT VILLAGE; machine learning; disease detection; convolutional networks; modern farming.

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The increasing significance of remote control has spurred the development of Unmanned Aerial Vehicle (UAV) capable of flight, ranging from small insect sized drones to large conventional airplanes. These UAVs find diverse applications in agriculture, surveillance, environmental monitoring, search and rescue, aerial photography, infrastructure inspection and scientific research. This study aims to optimize the aerodynamic shape of a fixed wing UAV by enhancing lift-to-drag ratio at 0-degree angle of attack (AOA) using a completely automated workflow. Our research, including Genetic Algorithm (GA), mimics the evolutionary process of natural selection to discover optimal solutions within complex problem space and PyFluent, a robust Computational Fluid Dynamics (CFD) tool. The work is structured into three stages: Initial stage, Optimization stage and Simulation stage. The optimal airfoil configuration achieves a lift-to-drag ratio of 24.8 at 0-degree AOA, particularly at a velocity of 40 m/s.

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 Keywords

UAV, lift-to-drag ratio, 0-degree AOA, Genetic Algorithm (GA), PyFluent

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Down syndrome (DS), a genetic disorder brought on by a numerical alteration in single or multiple chromosomes affecting autosomes, sex chromosomes or both. that exhibits a variety of physical and medical features, a distinct appearance, and learning disability, has been a subject of multidisciplinary inquiry. This article presents a comprehensive review encompassing the aetiology, clinical manifestations, cognitive and behavioural features, medical implications, and societal perspectives associated with Down syndrome. By consolidating existing knowledge, this review aims to provide a valuable resource for healthcare professionals, special educators, policymakers, and the broader community to foster a more informed and supportive environment for individuals with Down syndrome.

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 Keywords

Down syndrome, chromosomal anomaly, and learning disability.

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Creative Commons Attribution 4.0 and The Open Definition

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Advertising plays an important role in the society. Marketers use different media to reach their target customers which may impacts them positively or negatively, but there are some advertisements which always educate, entertain, enrich. Empower and engage customers. These 5E's are important in advertising. This study focuses on positive impact of advertisement focusing on 5E's of advertising through some of the selected advertisement by ad agencies like Ogilvy India and Leo Burnett India.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Impact of Advertising, Social Advertising, Advertising Agency

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Creative Commons Attribution 4.0 and The Open Definition