Volume 12 | Issue 5

 Abstract

Motivational Factors Being Important In determining the successes In any form of enterprises. This paper attempts to study the factors that motivates small Farmers toward entrepreneurships. The study is based on sample survey of 50 samples. 5 point Likert scale is used to analyse the motivational Factors. Being independent and desire to earn income were found to be the most important factors motivating the Entrepreneurs. It was found that Entrepreneurs were more motivated by internal factors.

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Motivational Factors, Entrepreneurship , Likert Scale

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This paper enunciates the suppression of the girl children in India. The focus of this paper is to light on the wounded spirit of the girl children in the Indian sociocultural structure concerning Anuradha Roy's Sleeping on Jupiter. Roy's protagonist 'Nomi' is the collective voice of the unrecognized Wounded spirit of millions of girl children in India who lost their identity, voice, rights, and status in the explosion of rich Indian culture. Indian culture is simply a hegemonical culture where women lost their all-inclusive rights. This paper aims to explore the impact of patriarchal hegemony and exploitation on the girl children and how it affects their spirit and alters their entire lives. Children are the world's most valuable resource and its best hope for the future but acts of violence like rape committed against these potential nation-builders or undermining the country. It is extremely difficult to accept that the perpetrators are frequently the big teams of the girl children who underwent the violence by the close ones. They are the ones that the children anticipate up to trust and expect love. When the children weakness to the crime against them, they lose their innocence and their irredeemable childhood. The only thing they could recall from the childhood harassment and the betrayal of their loved ones.

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Girl Children, Anuradha Roy, Sleeping on Jupiter, Indian Culture, Patriarchal Hegemony, Nation-Builders, Innocence, And Childhood Harassment

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This paper presents a comprehensive literature review on Interpretive Structural Modeling (ISM), focusing on its application across various sectors to enhance operational efficiency. ISM is a powerful methodological tool used to analyze complex systems by identifying and modeling relationships among specific variables. Through an extensive examination of existing literature, this review aims to provide a detailed understanding of ISM's theoretical underpinnings, methodological advancements, and practical implementations. The review synthesizes key findings from a diverse range of studies, highlighting the versatility and efficacy of ISM in addressing multifaceted challenges across different industries. By exploring case studies, scholarly articles, and empirical research, this paper seeks to map out the evolution of ISM, its current applications, and future potential. The review emphasizes the role of ISM in dissecting complex problems, facilitating strategic decision-making, and improving operational processes. This literature review aspires to serve as a valuable resource for researchers and practitioners, offering insights into the methodological nuances of ISM and its significant contributions to enhancing operational efficiency in various contexts.

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ISM, ISM-based model, Driving power- dependence analysis.

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Concrete is extensively used material in construction industry because of good workability and ability to be moulded into any shape and size. Steel fibres are generally used as resistance of cracking and strengthening of concrete. This paper deals with comprehensive and detailed study on the behavior of the concrete mixed with steel fiber in place of the traditional reinforcements. In the recent times steel and poly fiber has emerged as the member playing key role in the modern-day concrete in place of the conventional concrete. Steel/ polyfiber is playing a vital role in tunnels (shotcretes, lining), Structure (concrete girder, crash barrier, beams, columns and slab as a replacement to the of concrete to the traditionally used reinforcement

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Steel fibre reinforced concrete, Compressive Strength, Crack resisting, flexural strength

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The study is conducted on educational status of Schedule Cast Community with special reference to Narayanpur Block in Lakhimpur District, Assam. The objective of the study is to find out the present educational status of the SC community and identify the educational problems faced by them in accessing and completing the education. For this study, the investigator used survey method under descriptive research. The data needed for the study has been collected with the help of a self designed questionnaire. There are 270 Schedule Caste households of two selected villages. Out of these households, 40 households are taken as a sample of the study by using random sampling techniques. The study found several challenges have faced by the SC students in accessing quality education such as poverty, parents' illiteracy, poor socio-economic background, poor communication etc.

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Educational Status, Schedule Caste, Community, Households, Education etc.

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Agriculture requires technologies that are easier for farmers to understand, implement, anduse. It is a device that costs less money and requires less human effort and time. Implementation is very necessary for the success of the agricultural industry. That's why we developed an IoT controlled seeding system that helps farmers plant seeds in the right place and save time and money. A machine used in small-scale farming to perform tasks such as plowing, seeding, and picking. Sowing seeds is one of the main processes in agricultural activities. This requires a lot of human effort and time investment. These are the main requirements of growers. Therefore, we have created an automatic operated seeder when the farmer has to do less work, increasing seeding efficiency and reducing the problems associated with automatic seeding.

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Sowing Machine, IOT, Seed and Automatic.

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The model is able to discriminate between damaged and healthy leaves.The intend is to identify realistic techniques to plant protection and support our farmers as an alternative to the old time-consuming process.Using the deep convolutional neural network that we will train, test, and validate.

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Model using ML

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Forensic dentistry is crucial for identifying deceased individuals when visual or other identification methods are not feasible, especially after mass disasters or criminal activities. Accurate and timely identification of the deceased and injured is essential in the aftermath of any disaster. Establishing a person's identity often relies on common methods such as dental, DNA, and fingerprint analyses. Forensic odontology, a specialized branch of dentistry, plays a significant role in identifying individuals involved in accidents, civil unrest, natural and mass disasters, and crimes related to genocide. When natural teeth are absent, marking or labelling dentures becomes vital for personal identification. Incorporating a paper or embossing patient data on dentures can be difficult to read due to the opacity of the acrylic resin. Therefore, using a laser for auto-identification is especially beneficial as it is cost-effective, time-efficient, and less cumbersome.

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Forensic Dentistry, Odontology, Prosthodontics, Lasers, Denture Marking

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Emotion detection and sentiment analysis in textual data have garnered significant attention in recent years due to their wide-ranging applications in fields such as marketing, customer feedback analysis, and mental health monitoring. This paper provides a comprehensive review of various methodologies and techniques employed in the realm of emotion detection and sentiment analysis on text. It explores the evolution of approaches, ranging from traditional machine learning algo- rithms to state-of-the-art deep learning models, highlighting their strengths, limitations, and areas of application. Additionally, this review discusses the challenges associated with accurately identifying emotions and sentiments in text, including ambiguity, sarcasm, and cultural nuances. Furthermore, it examines the impact of domain-specific data and pre-processing techniques on the performance of emotion detection and sentiment analysis models. Finally, this paper concludes with a discussion on future research directions and potential advancements in the field, emphasizing the need for robust and interpretable models to enhance the understanding and utilization of emotional signals in text data.

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sentiments, emotions, machine learning, reviews , API,Text classification

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Abstract:- The word 'LEADER' consists of so many characteristics. But in the case of a political leadership, mass people assume the particular leader only to be eloquent orally. Especially in the recent scenario of political periphery in our country, if would not be wrong to say that the assumption is totally inappropriate. Along with the communication expertise, a leader should be a visionary, resilient, confident, courageous , a prompt decision maker etc. Besides these skills, if a leader does have passion and compassion, it would be a perfect combination for being a high-statured human being. One of the finest states in the north-eastern part of India, Tripura gave birth so many leaders in her lap. The most relevant name in the recent political arena of the state is Sudip Roy Barman, who is an engineer and an advocate in his academics, having a well known family background of high portfolio. An unbeatable MLA for six times in the prestigious seat of 6 No. Agartala, Sudip babu is embellished with some rare qualities which should be ascertained and also be hailed publicly.

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Keywords:- Communication, Passion, people, politics, resilience, compassion, eloquencey, peoples leader, youth icon, legend, Super hero, The emperor of heart.

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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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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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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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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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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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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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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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Albinism, Gene, Bhrajak Pitta, Kustha

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

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