Volume 12 | Issue 12 |

 Abstract

In the digital age, the task of detecting music plagiarism has become increasingly critical to preserving the integrity of creative works. Music plagiarism detection involves the identification of similarities between musical pieces that may indicate unauthorized copying or infringement. A key element of this challenge is the accurate extraction and comparison of audio features from different tracks. In this paper, we present a novel approach that employs Python's librosa library to extract essential audio features and utilizes Euclidean distance to quantify the similarity between these features. To further refine the accuracy of plagiarism detection, we have integrated the K-Nearest Neighbors (KNN) machine learning algorithm, which classifies the audio samples based on their extracted features. The culmination of our approach is a detailed plagiarism report generated using the reportlab library, which provides comprehensive insights into the similarities between analyzed tracks. This method offers a systematic and robust solution for identifying potential cases of music plagiarism, contributing to the protection of intellectual property in the music industry.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Music Plagiarism, Audio Features, Euclidean Distance, K-Nearest Neighbors, librosa, reportlab.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the state of Tripura, rural resource planning is heading towards web-based geospatial enablement. But, data-gap exists in case of clearly defined boundaries the Gram Panchayat (GP)/ Village Committee (VC) which are the basic planning units. Currently, GP/VC database are available in 1:50000 scale. For large scale mapping GP/VC boundaries were abstracted by the ground staff of Rural Development Department through surveying using Android Mobile Application and later fine-tuned with 30 cm spatial resolution ortho-rectified satellite imagery. Further, GP/VC boundaries were validated in the field by concerned Block Development Officers. The results show that the methodology is well suited for the precise demarcation of the territorial extents in a time-effective manner.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Satellite data, GIS, Administrative Boundaries, TSAC, GP/VC, MGNREGA, Developmental, Planning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Adolescence is a critical developmental stage marked by significant physical, emotional, and social changes. The ability to adjust effectively to these changes is essential for adolescents' overall well-being and success in various life domains, including academics. This study explores the adjustment levels of secondary school learners in the East Siang district of Arunachal Pradesh, focusing on emotional, social, and educational adjustment in relation to gender. A descriptive survey method was used, with a sample of 100 learners (50 boys and 50 girls) from Mirku Secondary School. The Adjustment Inventory standardized by A.K.P. Sinha and R.P. Singh was employed to measure the adjustment levels. The findings reveal that both boys and girls exhibit an average level of adjustment across all areas, with boys showing stronger social and educational adjustment, while girls demonstrate better emotional adjustment. The results highlight the need for gender-specific strategies to address the adjustment challenges faced by both boys and girls. The study emphasizes the importance of creating a supportive school environment, integrating activities like yoga, sports, and counselling services, and engaging parents to foster positive adjustment. The study concludes that targeted interventions can significantly improve the adjustment process, aiding adolescents in their transition from childhood to adulthood.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Adolescent, emotional, social and educational adjustment, Mirku secondary school.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality globally, highlighting the need for early and accurate detection systems. This project presents a comprehensive approach for predicting cardiovascular disease using ensemble machine learning techniques. The system comprises an image processing module that pre processes ECG images and a machine learning module to analyze the extracted features. The image processing phase involves converting ECG images to grayscale, applying Gaussian smoothing to reduce noise, and contour detection for waveform extraction. The next phase integrates Principal Component Analysis (PCA) for dimensionality reduction, aimed at enhancing the model's efficiency by minimizing redundant data while retaining crucial features. A VotingClassifier ensemble method is then employed, combining multiple machine learning classifiers including Support Vector Machine (SVM), k Nearest Neighbors (kNN), Random Forest, Gaussian Naive Bayes, and Logistic Regression. The use of a soft voting strategy aggregates predictions based on probability scores, improving robustness and prediction accuracy. This modular architecture leverages the power of ensemble learning, utilizing ECG data to make reliable predictions. Initial results demonstrate the effectiveness of the proposed model in identifying patterns associated with cardiovascular disease. The final system is implemented using the scikit-learn and Streamlit libraries, providing an intuitive front-end for medical professionals to upload ECG images and view predictive results. This work underscores the potential of combining image processing and ensemble learning for non-invasive diagnosis of heart conditions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cardiovascular disease prediction, ECG signal processing, Principal Component Analysis, Ensemble Learning, Voting Classifier, Machine Learning, Image Processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents the design and implementation of a smart card-based door access system with an LCD. The system ensures enhanced security for restricted areas, such as banks and offices, by allowing access only to authorized users. The design leverages a contact smart card reader interfaced with a microcontroller to validate user credentials. Unauthorized access triggers an alarm system, while valid users are granted entry through a motorized door. The system is designed to be cost-effective and user-friendly while maintaining high reliability. Additionally, the system demonstrates scalability and can be adapted to include features such as IoT integration and multi-factor authentication, making it suitable for modern security needs

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

The smart card-based door access system offers a practical, low-cost solution for secure access control. Its design addresses common limitations of existing systems, such as high costs and complexity, while maintaining robust security and user convenience. By leveraging smart card technology, the system provides a reliable alternative to biometric and RFID-based methods.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Background: The Quality Of Life Of Cancer Patients Undergoing Chemotherapy Includes Assessing The Quality Of Life In Different Domains I.E. Physical, Emotional, Sexual And Psychological. Objectives: To Assess The Quality Of Life Of Cancer Patients Undergoing Chemotherapy. Materials And Methods: The Research Design Was Descriptive Methodology With 68 Cancer Patients Who Had Undergone 4th,5th And 6th Cycle Of Chemotherapy Were Selected By Convenient Sampling. The Tools Used Were The Baseline Performa And EORTC QLQ C-30 Standardized Rating Scale. The Collected Data Was Analyzed By The Descriptive And Inferential Statistics. Result: The Study Result Shown That Majority Of The Cancer Survivors (64.70%) Had Neither Good Nor Poor QOL And 19.1%Had Good Quality Of Life. Study Result Shows That Place Of Residents And Type Of Cancer Had A Significant Association With QOL Of The Cancer Survivors. Conclusion: Findings Of The Study Imply That There Is Need To Assess The QOL Of Cancer Patients Who Is Receiving Chemotherapy. This Study Helps The Health Professionals To Understand The Need Of Creating Awareness On The Quality Of Life Of Cancer Patients.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

quality of life; cancer patients

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Let G = (V, E) be a simple connected graph of order p and size q. If {G1, G2,......,Gn} are edge disjoint subgraphs of G such that E(G) = E(G1) ? E(G2) ? E(G3) ?.............?E(Gn) then {G1, G2, ,Gn} is said to be a Decomposition of a graph G. A graph of size q = (n + 2) is said to have a Triangular 3 decomposition (TD) if G can be decomposed into n - subgraphs {G1, G2,.....,Gn} such that each subgraphs Gi is connected and |E(Gi)| = (i + 1) for 1 <= i <= n. In this paper we investigate Triangular decomposition of 2 graphs.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Triangular Decomposition, Octopus Graph, Wheel Graph, Crown Graph

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The era of 5G networks has arrived due to the quick development of broadband technology. These networks promise unmatched speed, low latency, and improved connectivity. This paper does a thorough analysis of 5G network performance, emphasising novel resource distribution techniques and important Quality of Service (QoS) indicators. According to our research, 5G networks often achieve ten times faster data transfer rates than their 4G counterparts. competitors through thorough analysis and real-world models..Furthermore, our results show a significant 30% decrease in latency, highlighting 5G technology's effectiveness and responsiveness. Furthermore, our research explores sophisticated resource allocation techniques, presenting a new method that maximises network resources and leads to a 15% rise in the overall effectiveness of the network. Empirical data from comprehensive field testing and simulations support these results, offering strong proof of the project's influence on 5G network performance. The study's findings might have a significant impact on the future of telecoms as 5G adoption picks up speed globally. They will provide network operators, legislators, and industry participants with useful advice on how to build a more dependable and efficient 5G ecosystem.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Resource Allocation, 5G, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The advent of Artificial Intelligence (AI) has significantly transformed the landscape of scientific writing, offering tools that enhance efficiency, accuracy, and accessibility. AI-powered platforms such as ChatGPT, Grammarly, and Quillbot provide researchers with capabilities ranging from grammar refinement to content generation, citation management, and even plagiarism detection. These tools streamline the writing process, enabling scientists to focus more on data interpretation and innovative thinking. However, the integration of AI in scientific writing also poses challenges. Over-reliance on AI may result in a lack of originality, ethical concerns over authorship, and potential propagation of misinformation if outputs are not critically evaluated. This paper explores the multifaceted role of AI in scientific writing, examining its benefits, limitations, and ethical implications. By analyzing the functionalities of various AI-based tools, this study aims to provide a balanced perspective on how AI can be optimally utilized to complement human expertise in producing high-quality scientific literature.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial intelligence, scientific writing, pros, cons

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Zinc is a trace element that has recognized biological activity, necessary for several fundamental body functions such as immune response, metabolism and particularly for wound healing. Nevertheless, zinc deficiency has become a significant public health concern to date and more so in the developing world. The following review aims at focusing on the physiological functions of zinc, especially as regards to immune system, oxidation and cell repair. Additionally, it analyses the correlation between the deficiencies of zinc and the various diseases such as; type 2 diabetes, age related macular degeneration and respiratory infections. Particular emphasis is placed on the importance of zinc in preventing childhood malnutrition and on possible uses of this element for syndrome treatment common illnesses such as diarrhea and the flu. For instance, zinc deficiency has been prevalent in India due to low level of zinc index in soils and poor consumption of zinc containing foods by children and pregnant women. This review emphasizes the necessity of generating more consistent and accurate data regarding zinc concentration in populations most affected; the necessity to increase the intake of zinc and the efficacy of supplementary doses with the goal of decreasing the effects of zinc deficiency.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Zinc, immune response, physiological functions, childhood malnutrition, deficiency, concentration, efficacy, supplementary doses.

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Creative Commons Attribution 4.0 and The Open Definition