Volume 12 | Issue 7 |

 Abstract

Abstract: This paper explores the intricate interplay between fantasy and children's literature through an in-depth analysis of C.S. Lewis's The Chronicles of Narnia and J.K. Rowling's Harry Potter series. By examining the historical and cultural contexts of both series, it delves into how significant events, such as World War II, shaped their narratives. The thematic exploration highlights the portrayal of the battle between good and evil, with key characters like Voldemort and Harry, and the White Witch and Aslan, embodying these struggles. The discussion on the depiction of magic reveals stark contrasts between the two series and addresses criticisms, particularly from religious groups. A detailed character analysis underscores the protagonists' growth, with Harry Potter and the Pevensie siblings undergoing profound personal journeys. The roles of supporting characters further illustrate the narrative depth and thematic complexity of both series. Additionally, the paper examines the rich symbolism and allegory, focusing on Christian symbolism in Narnia and the moral lessons in Harry Potter. The use of animals and mythical creatures, such as Aslan and Buckbeak, is analyzed for their narrative significance. Finally, the reception and impact of both series are discussed, noting the critical acclaim, controversies, and their enduring cultural legacies. This comparative study aims to highlight the enduring appeal and literary significance of these cornerstone works in fantasy and children's literature.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fantasy Literature, Children's Literature, Harry Potter, the Chronicles of Narnia, Symbolism and Allegory.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: In our modern era where the internet is ubiquitous, everyone relies on online resources for news. Along with the increase in the use of social media platforms like Facebook, Twitter, etc. news spread rapidly among millions of users within a very short span of time. The spread of fake news has far-reaching consequences like the creation of biased opinions to swaying election outcomes for the benefit of certain candidates. Moreover, spammers use appealing news headlines to generate revenue using advertisements via click baits. It is harmful for the society to believe on the rumours and pretend to be a news. The need of an hour is to stop the rumours especially in the developing countries like India, and focus on the correct, authenticated news articles. Fake news detection is used to avoid rumours from spreading across various platforms, such as social media and messaging platforms. so, we aim to perform binary classification of various news articles available online with the help of concepts pertaining to Artificial Intelligence, Natural Language Processing and Machine Learning. We aim to provide the user with the ability to classify the news as fake or real and also check the authenticity of the website publishing the news. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data is Logistic Regression. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake news detection, Logistic Regression classification, machine learning, web scraping, Feature Extraction, TF-IDF vectorization.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Communication is a life-long process. We can't live without communication. There are different types of communication. But intra-personal communication or spiritual communication is the most important type for our lives. This research paper explores concept of spiritual communication to achieve a successful life through the teachings of Bhagavad Gita. The Bhagavad Gita is a Hindu scripture that provides deep insight into self-awareness, mindfulness, and detachment, which is an important element of spiritual communication. Efforts will be made to know the main concepts of Bhagavad Gita regarding spiritual communication. The aim of this paper is to demonstrate how spiritual communication can be meaningful in personal development, better decision making and building better relationships. Secondary data analyzed for this paper.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bhagavad Gita, Spiritual Communication, Trust, Mindfulness, Detachment, Devotion.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper seeks to examine the relevance of The Metamorphosis by Franz Kafka as it relates to alienation, identity, and existential angst in order to connect it with some of the main contemporary social problems of mental health, digital isolation, and the gig economy. In synthesizing these classic literary analyses with modern standpoints, it brought out precisely the potential of this novella for enlightening people of today regarding the stake of complexities and psychological issues at stake. The study underlines the timelessness of Kafka's work by showing how its themes still echo and give critical insight into contemporary problems, thus deepening the understanding of the human condition and the impact of societal structures on personal identity and well-being.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Franz Kafka, The Metamorphosis, Alienation, Identity, Existential Angst, Mental Health, Digital Isolation

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The theoretical underscores the potential perils presented by programmers and gatecrashers, as well as the inborn security weaknesses associated with IoT hardware. IoT gadgets might be mishandled in light of their interconnectedness, especially through abnormality assaults. To distinguish strange assaults in IoT gadgets, the venture proposes utilizing AI techniques, including SVM and Random Forest (RF), stacking classifier, and voting classifier [2, 7, 8, 9, 10, 11, 12]. These strategies were utilized on the grounds that they function admirably for both element determination and discovery. The NSL-KDD dataset in arff design is utilized in the review for trial and error. The picked techniques, stacking classifier and RF, have magnificent precision paces of around. Accentuation is put on bogus positive rates, which show a low rate in all cases. The uplifting aftereffects of the proposed approach are featured, particularly the expanded exactness accomplished utilizing random forests when stood out from past exploration. The expected adequacy of the stacking classifier and Random Forest in identifying and relieving abnormal attacks in IoT gadgets is exhibited by their reassuring exactness, review, and accuracy. Furthermore, there are troupe moves toward that join the forecasts of a few unique models to get a last expectation that is more dependable and exact. These incorporate the Voting Classifier (RF + AB) and the Stacking Classifier (RF + MLP with LightGBM). Whereas the Voting Classifier achieved 100% accuracy and the Stacking Classifier 100% accuracy, we likewise developed the front end with client confirmation for IoT abnormality discovery and client testing using the flask system.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

IOT devices, Support Vector Machine (SVM) and Random Forest (RF).

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The development and maintenance of institutional repositories are crucial for academic and research institutions to manage, preserve, and disseminate their digital content. Free and open-access software options provide cost-effective solutions while offering robust features and community support. This research study provides an overview and examines of several prominent open-source software platforms used for developing institutional repositories, including DSpace, EPrints, Fedora Commons, Invenio, Islandora, Greenstone, Samvera, DSpace-CRIS, and Archivematica. The Each software has various pros and cons, In the present research article is discussed for DSpace software in terms of its key features, customization and extensibility, community support, scalability, metadata and standards support, and integration capabilities. The selection of an appropriate platform depends on the institution's specific needs, technical capabilities, and long-term goals. The study aims to explore about technical need and support for implementation of the DSpace Open Source Software which supports and guide institutions, library and information science professionals in making informed decisions about the software that best fits their repository requirements.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Institutional Repository, Open-Source Software, Digital Content Management, Digital Preservation, Metadata Standards, Digital Asset Management, Hardware and Software Requirement.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Music genre is a classification system that classifies music into different types. The classification of music genre is very important to make a selection of songs from a large collection of data. Different features have been extracted as it is essential for the genre classification. We use convolution neural networks to classify music genres. The project aims to create an automated system for classification models of music genres. We use the most publicly used data set which is GTZAN for evaluation of music genres. It contains 10 genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock). The audio file is sent as input and the classification is done. CNN is used to classify the data furthermore.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Music Genre Classification, Machine Learning, Audio Processing, Feature Extraction, Convolutional Neural Networks (CNN), Genre Detection, Audio Signal, Music Dataset (e.g., GTZAN, FMA), Audio File Formats (e.g., MP3, WAV), Signal Processing.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research paper conducts a thorough analysis of the opportunities and challenges associated with online businesses in the context of the rapidly advancing technology and widespread use of the internet (Smith, 2021). The study explores various opportunities, including global market reach, cost-effectiveness, enhanced customer engagement, and increased flexibility, while also addressing challenges such as intense competition, security risks, trust and reputation management, regulatory compliance, and technological dependencies (Jones et al., 2020). Through a systematic review of literature and empirical data, the paper provides valuable insights for entrepreneurs, policymakers, and academics interested in understanding the dynamic landscape of online business.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

online business, e-commerce, digital marketing, challenges, opportunities

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Now a days we are facing the serious problem of global climate change, ozone layer depletion, acidic raining, air, water and noise pollution, soil erosion, deforestation, loss of biodiversity etc. it is indeed necessary to conserve our biological diversity for peaceful living and for further fair and equitable share in benefit of natural resources. To overcome the serious issues stated above we need ethical conduct and moral behaviour in our personal life as well as in our business life too. Especially in a business ethics like accountability and transparency, respect to human dignity and privacy, truthfulness, producing and selling healthy and hygienic product, avoiding discrimination among people on the basis of gender, caste and religion, charging unfair prices, avoiding artificial scarcity of product, providing healthy and hygienic atmosphere at workplace avoiding all kind of pollution etc. are very helpful to all the stakeholders of business word.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Business Ethics, Business Stakeholders, Human Behaviour, Moral Slandered, Environmental Issues, Health and Hygiene. Human Wellbeing.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Weeds are one of the most important factors affecting agricultural production. Farmers often experience a bad agricultural yield as a result of weeds. It is becoming more and more clear that full-coverage chemical pesticide spraying pollutes and wastes farming ecosystems. Accurately identifying crops from weeds and obtaining precise spraying exclusively for weeds are crucial given the ongoing increase in agricultural production levels. With the continuous improvement in the agricultural production level, accurately distinguishing weeds from crops and achieving precise spraying only for weeds is important. However, precise spraying depends on accurately identifying and locating weeds and crops. Scholars have employed a variety of techniques to do this in recent years. In this project YOLO (You Only Look Once) v5 model was used to train the data set of the crop images along with the weed. The data set consists of images along with the labeled data of the weed. After the training was finished and the model was formed, we used StreamLit to establish a website where users could post pictures and videos to help identify weeds.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Yolov5, CNN algorithm, weed detection, object detection, agriculture automation, machine learning, dataset preparation, precision farming, image analysis, autonomous farming, computer vision

  License

Creative Commons Attribution 4.0 and The Open Definition