Volume 12 | Issue 5 |

 Abstract

We are living in the digital era, technological advancements have impacted human life in various ways. In India rapid digitalization and penetration of smart phones have expended the digital space and exposed the marginalised children towards the cyber exploitation. In recent past there has been a concerning raise in cyber crime against the vulnerable population, specifically children. Since the time of pandemic, children are spending more time online for education, games, entertainment, shopping and for other regular activities the same has exposed the children to wide range of cyber crime like- cyber bulling, sexual abuse and exploitation, identity theft, online grooming etc. In India as a legislative measure to deal with the cyber crime several has been enacted for protecting the children from crime. The Information Technology Act 2000 and The Protection of Children from Sexual Offences(POCSO) Act 2012, Indian Penal Code 1860 provides the legal framework to deal with the Cyber attacks. This research work is an endeavour towards identifying the key pattern of cyber crimes against children and its recent treads, what the existing legal mechanisms available to combat, to discuss the key strategies to mitigate the risk in cyberspace and to ensure the rights of Childs in digital space.

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 Keywords

Digital Exploitation, Law Enforcement, Vulnerability, Digital Literacy, Child Protection, identity theft, Online Safety, Cyber Crime.

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In conventional approaches, Magnetic Resonance Imaging (MRI) stands as a cornerstone for brain tumor diagnosis. It generates intricate pictures of brain anatomy, assisting in pinpointing and defining the tumor.Traditional methods frequently depend on features crafted by hand from MRI scans. These features encompass statistics based on intensity, descriptions of texture, and characteristics related to shape. Machine learning algorithms such as Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN) have been implemented to categorize brain tumors based on extracted features. There are inherent complexities and restrictions. Brain tumors exhibit substantial variation in terms of their location, shape, and size. This variation presents challenges for precise detection and segmentation. Finding small lesions remains a challenge, especially when the affected area after a stroke or the size of the lesion itself varies. Access to high-quality datasets with labels is essential for training reliable models. The availability of such datasets can be limited. There can be inconsistencies in interpretations of MRI scans by different radiologists, leading to variations in ground truth annotations.

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 Keywords

Brain tumor prediction, convolutional neural network, CNN, MRI scans, deep learning, medical

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Deep learning is enhancing the medical industry by enabling academics and healthcare practitioners find opportunities concealed in data. In addition, it helps physicians accurately diagnose any type of illness and improve patient medication, leading to better medical decisions. Medical conditions like Polycystic Ovarian Syndrome (PCOS) need appropriate diagnosis and treatment other options. It is a common endocrine condition that triggers ovarian cysts to form in women who are trying to conceive, which leads to in infertility. Deep learning techniques using the VGG 16 convolution neural network design can be used to aid in the diagnosis of PCOS. These techniques yield effective solutions in problems involving picture classification. The current study compares the accuracy and other performance measures of previously described deep learning techniques and discusses the issue of over-fitting and the accuracy was achieved in this proposed work was 94%

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polycystic ovarian syndrome (PCOS), Deep learning, VGG 16, convolution neural network.

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Abstract: Memory, in scientific terms, implies a bioelectric action, while in philosophy, its repercussions are vast and interdisciplinary. Memories are inevitable for individuals and society as they revolve around objects, are shared among people, and are woven into the fabric of culture. History and memory lean toward the past, viewing it from the same angle but with different perspectives. Early academia relied on evidence and traces to write history, formulating narratives by connecting the dots. The history of Kerala was not much different in this regard; it documented the ruling dynasties, culture, religion, wars, foreign transactions, and caste-based discrimination. However, some people's struggles are absent in the records. Poikayil Appachan, a renaissance leader in the nineteenth century, from the remnants of the past, recollected the forgotten history of his community through his songs. This paper discusses how his songs were able to bridge the gap between history and memory.

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Key Words: History, Memory, Oral History, Collective Memory, Slavery.

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The Help Center response website is the main platform for community support and provides essential services such as rapid cycling support and rapid health support. This research paper examines the design, development, and analysis of the user experience in the service center, focusing on user-friendly design principles, usability considerations, and the service provider. This study uses a mixed method combining qualitative and quantitative research to better understand the platform's effectiveness in meeting society's needs. Research methods include user interviews, usability tests, surveys and benchmarks to measure the platform's ease of use, service delivery and user satisfaction. The results show that the user-centered design of the service center, combined with easy access and continuous improvement, helps ensure a good user experience and a high level of customer satisfaction. Provide recommendations to improve the usability, usability and functionality of the platform based on user feedback and data analysis. Overall, the Service Center is a great asset to the community, providing timely assistance and support to those experiencing cycling issues and medical needs.

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 Keywords

Helping Hub, Responsive Website, Community Support, Bike Support, Health Support, User-Centered Design, Accessibility, User Satisfaction, Efficiency, Continuous Improvement.

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Identifying and neutralizing malicious activities within network traffic is crucial in cybersecurity, and botnet detection is key in this effort. This study assesses the performance of various machine learning algorithms in detecting botnets, using the CTU-13 dataset. This dataset is notably imbalanced, with background and normal traffic significantly outweighing botnet traffic. To address this challenge, algorithms such as CNN-LSTM, XGBoost, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were tested. Results show that XGBoost achieved the highest accuracy among the tested models. By employing undersampling techniques, accuracy improved to 80%, despite the data imbalance. In contrast, oversampling methods were less effective. This research underscores XGBoost's potential for handling imbalanced datasets and its superior performance in botnet detection. Future studies might aim to enhance the model's robustness and explore further methods to manage data imbalance effectively.

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 Keywords

Botnet Detection, CTU-13 Dataset, Machine Learning, XGBoost, Data Imbalance, Cybersecurity.

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ABSTRACT In recent months, unfastened deep studying-based totally software program tools have facilitated the creation of credible face exchanges in photographs that depart few traces of manipulation, in what they may be referred to as deep faux face detection. Manipulations of digital pics were validated for numerous decades through the coolest use of visual consequences, latest advances in deep studying have caused a drastic increase in the realism of fake content and the accessibility in which it could be created. Those so-called AI-synthesized media (popularly known as fake). Creating fake photographs using Artificially clever gear are easy undertaking. But, with regards to the detection of those fake pictures, it's far a first-rate task. Because education the set of rules to identify the faux pics isn't always simple. We've got taken a step forward in detecting the fake images using a Convolutional Neural network. The system uses a convolutional Neural network (CNN) to extract functions at the body level. These capabilities are used to teach a CNN model which learns to classify if a given photograph has been challenged to manipulation or no longer. Anticipated result against a massive set of faux pix collected from the same old records set. We display how our system can be competitive and bring about this undertaking effect by using an easy architecture. Deep faux and real picture classification. The use of Deep studying is an emerging discipline that specializes in distinguishing between laptop-generated (deep fake) and authentic pictures. The rapid advancement in the era has made it increasingly more difficult to detect these deep fakes, as they come to be greater practical. This undertaking employs deep learning techniques to address this challenge, aiming to increase an effective and efficient model for accurate classification. This topic is selected due to the increasing occurrence and dangers related to deep faux technology. As deep fakes grow to be extra commonplace and convincing, the potential for misuse in various sectors turns into a widespread situation. Growing powerful detection methods is crucial to mitigate these risks and ensure the integrity and trustworthiness of virtual media.

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 Keywords

Deep Learning, Image Classification, Deep Fake Detection, Convolutional Neural, Networks (CNN), Image Processing, Xception, MobileNet, Face Forensics++.

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Power is the ability to hold and control others and regulate their actions. The zealous desire for power is innate in human nature. All fights and conflicts in humanity are to gain power over others. Hence, in the present scenario, the study of power relations is significant and relevant. Aravind Adiga's The White Tiger attempts to invent the various kinds of power strategies that function in society. It shows what are its impacts and effects on the life of an ordinary man. Michel Foucault is one of the most influential of all the French critics who brought about a revolution in the late 1960s with the interpretation of power. Foucauldian theory of power interacts with institutions among groups and individuals. The fundamental idea emerging from the power theory of Foucault is that holding power is nothing but the association between man and his social surroundings. His notion is that power is exercised by an institution or a group of people. It is fundamentally implicated only with suppression and inhibitions. The present study analyzes select novel in the light of the Foucauldian theory of power as oppression and foregrounding resistance to power can be seen in this book. A thorough reading of The White Tiger makes a reader accepts the availability of Foucault in terms of power and resistance to power throughout the novel.

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 Keywords

Control, Institution, Oppression, Power, Resistance

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Skin is the largest organ in the body that covers the whole body externally. It is made up of three layers viz. epidermis, dermis, and hypodermis. Cells present in the epidermis are keratinocytes, melanocytes, Langerhans' cells and Merkel's cells. Due to external or internal factors, the skin gets affected and causes skin diseases like albinism etc. According to Ayurveda, albinism is a lifestyle and hereditary disease and many concepts show that modern science also considers it. The most common presentation of albinism is oculocutaneous albinism (OCA). The pathophysiology of albinism is also similar in both (ayurveda and modern science). There are 867 known genes on the X chromosome and the majority of these genes are involved in the development of tissues such as bone, neural, blood, hepatic, renal, retina, ears, cardiac, skin, and teeth. The skin, hair, exocrine glands, and anterior pituitary are formed by the ectoderm. A rare group of diseases known as ectodermal dysplasia are caused by the aberrant development of tissues derived from the ectoderm which is the one reason for albinism. Albinism is an X-linked recessive disease. In Ayurveda, albinism is a disease of kustha (skin disease) and all kustha are caused by lifestyle and hereditary factors. If bhrajak pitta is affected it can cause albinism.

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 Keywords

Albinism, Gene, Bhrajak Pitta, Kustha

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 Abstract

Meningitis poses a significant health challenge worldwide, with its multifaceted forms caused by various pathogens. Addressing its complexity requires innovative therapeutic approaches beyond conventional treatments. Hence, I propose employing network pharmacology to identify promising phytochemical compounds derived from plants as potential therapeutics for meningitis and its associated risk factors. In this study, we aim to leverage bioinformatics tools to analyze gene expression data obtained from patients with meningitis and individuals at risk of developing the disease. By conducting co-expression analysis, we anticipate identifying genes exhibiting altered expression patterns across both contexts. These genes are likely to play crucial roles in the development and susceptibility to meningitis. Subsequently, we will construct a network where co-expressed genes serve as nodes, interconnected by known interactions. This network will provide insights into the molecular mechanisms underlying meningitis and its risk factors. Integrating information on phytochemical compounds with relevant properties, we will employ computational tools to predict interactions between co-expressed genes and phytochemicals. Through network analysis, we will prioritize lead phytochemical candidates based on their interactions with key genes implicated in the disease process. We anticipate that this study will unveil co-expressed genes that are pivotal in both meningitis and its risk factors. By establishing a network-based approach, we aim to identify promising phytochemical-based therapies for treating meningitis. In conclusion, this investigation amalgamates co-expression analysis and network pharmacology to pave the way for novel phytochemical-based therapies against meningitis. Targeting co-expressed genes involved in both the disease and its risk factors holds promise for developing more effective and broadly therapeutic treatment strategies.

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 Keywords

Meningitis, Network Pharmacology, Co-expressed Genes, Phytochemicals, Drug Discovery

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