Volume 12 | Issue 5 |

 Abstract

The rise of deepfake videos in recent years has raised significant concerns about the potential misuse of manipulated content for malicious purposes. To address this growing threat, this research introduces an enhanced Convolutional Neural Network (CNN) architecture specifically tailored for the detection of deepfake videos. The proposed model builds upon the DenseNet architecture, incorporating novel modifications to improve its performance in distinguishing authentic from manipulated visual content. The key enhancements include the integration of attention mechanisms and feature fusion strategies to capture complex patterns and subtle anomalies indicative of deepfake manipulations. Furthermore, a comprehensive dataset comprising a diverse range of authentic and manipulated videos is utilized for training and evaluation, ensuring the robustness and generalization of the proposed model. Experimental results demonstrate the superior performance of the proposed Dense CNN architecture compared to state-of-the-art deepfake detection models.

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 Keywords

D-CNN, GAN, Computer vision, Deep Learning, LSTM, ResNet

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 Abstract

ABSTRACT A study to assess the effectiveness of planned teaching program (PTP) on knowledge regarding self-awareness among nurses was conducted in selected psychiatric hospital in Goa with the objectives, to assess the level of knowledge regarding self- awareness among nurses using a structured knowledge questionnaire, to find out association between knowledge score of nurses with socio demographic variables and to develop and validate planned teaching program on self-awareness and to assess the effectiveness of Planned Teaching Program (PTP) regarding self-awareness among nurses by comparing the mean pre and post test knowledge scores. One group Pre-test Post-test pre-experimental research design was used. Convenience sampling technique was used for obtaining 30 nurses. The tools used for the study were self-structured questionnaire consisting of 36 items. Data was analyzed using inferential and descriptive statistics. The major findings of the study were: Majority of nurses were in the age group of 51-60 years (36%), 1male and 29 females, majority were from Hindu community 20 (66%), In relation to qualification, majority were GNM 21(70%), 30% BSc./PBBSc, majority of the nurses were permanent (28) 93.3%, around 9(30%) had 21 years of experience. Majority of the nurses 29(96.7%) had not attended any in-service education program. In the pre test majority of the nurses had average knowledge scores 25(83%) whereas 5(16.6%) had poor knowledge and none had good knowledge. While in post-test there was increase the level of knowledge. Around 83% (25) had good knowledge scores and 5(16.6%) had average knowledge scores. It was found that the planned teaching program effective as there was significant difference in knowledge scores of nurses pre and post- test. The study has implication for mental health nurses as self awareness is an important aspect of psychiatric nursing.

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KEY WORDS: Psychiatry, Self-awareness, Nurses, Planned Teaching Program, Knowledge.

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This paper presents a method for measuring strains and deflections using strain gauges. An Arduino Uno board is used for data acquisition due to its low cost and easy programming. The measured signals are conditioned using a Wheatstone bridge and then converted to digital form by an external Analog-Digital Converter. To validate the measurement system, experiments were conducted on beams, and the results were compared with analytical and finite element simulation findings. The results align with existing literature and demonstrate that the proposed system has satisfactory accuracy

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Strain Gauges, Arduino, Data acquisition, Wheatstone-Bridge, Beam

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Abstention rates are still rising today, partly because voters must travel to cast their ballots. For this reason, everyone will be able to vote remotely via electronic voting, increasing turnout by eliminating the need for travel. In addition, it will yield results more quickly and with less risk than a typical paper ballot vote. The advantages that these systems offer, such as the ability to vote remotely and expedited vote counting, can be used to explain this tendency. In fact, a remote e-voting system needs to be the best in terms of security, dependability, and transparency because elections carry such high stakes. Only then can citizens trust such a system. The immutable vote storage mechanism of blockchain technology increases the voting process's resilience, lowering the possibility of vote tampering and preserving the validity of elections. Several nations, including Germany, Russia, Estonia, and Switzerland, have included this technology into their electronic voting systems. This paper offers a thorough analysis of the blockchain-based electronic voting methods that are being adopted by numerous nations and businesses and that have been suggested for scholarly investigation.

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 Keywords

Blockchain, Electronic voting, Authentication, Privacy, Security, Transparency.

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Recent IT infrastructure now includes cloud computing heavily since it offers scalability and flexibility. However, the increasing reliance on cloud services also attracts malicious activities and cyber threats. To protect cloud environments in this situation, an efficient Intrusion Detection System (IDS) is important. So, In this proposed System a better Cloud Intrusion Detection System design that makes use of a machine learning classifier and a hybrid feature selection method. The suggested system improves both the accuracy and effectiveness of intrusion detection by utilizing label encoding, correlation analysis, and the Extra Tree algorithm. The suggested system has been verified by the UNSW-NB15 dataset Gather a diverse and representative dataset of cloud security incidents and non-incidents. Understand the characteristics of the dataset, including feature types (categorical, numerical) and potential challenges Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and list their primary attributes, frequently using data visualization. The UNSW-NB15 datasets have validated the proposed method, yielding accuracies of more than 98% and 99% in the multi-class classification scenario, respectively. It was found that an intrusion detection system would function better if it had less informative features.

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Intrusion Detection System, Machine Learning, Extra Tree Algorithm, UNSW-NB15 Dataset, Exploratory Data Analysis (EDA)

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This research paper investigates the relationship between parenting styles and two crucial psychological factors affecting the development of adolescents: self-esteem and academic motivation. A sample of 150 adolescents aged between 13 and 19 years participated in this study. The study employed three standardized psychometric tools to measure parenting styles, self-esteem, and academic motivation: the Parental Authority Questionnaire, Rosenberg's Self-Esteem Scale, and the Academic Motivation Scale. The findings revealed significant associations between parenting styles and adolescents self-esteem and academic motivation. Specifically, authoritative parenting demonstrated a positive correlation with higher levels of self-esteem and intrinsic academic motivation, while authoritarian and permissive parenting styles showed negative correlations. Adolescents raised in authoritative households showed to have greater self-esteem and intrinsic motivation to excel academically, attributed to the balance of warmth and structure provided by authoritative parents. Conversely, authoritarian parenting, characterized by high control and low warmth, was associated with lower self-esteem and motivation, potentially due to restricted autonomy and harsh disciplinary practices. Similarly, permissive parenting, marked by low control and high warmth, exhibited negative effects on adolescents self-esteem and academic motivation, possibly stemming from the lack of clear boundaries and expectations. These findings underscore the critical role of parenting style in shaping adolescents psychological well-being and academic outcomes. Understanding the influence of parenting practices on self-esteem and academic motivation is vital for designing effective interventions and support systems aimed at promoting positive youth development. Future research should explore additional contextual factors and longitudinal effects to further understand the complex interplay between parenting, self-esteem, and academic motivation among adolescents.

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PARENTING STYLE, SELF-ESTEEM, AND ACADEMIC MOTIVATION

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This research paper analyzes how ESG ratings impact the market capitalisation of the top firms in India. Thus, the purposes of the study are to assess the relationship between ESG performance and the stock market value and determine leaders and laggards in ESG companies. In this case, the companies under consideration are five selected and operate in the FMCG industry, namely Hindustan Unilever, ITC, Tata Consumer Products, Nestle India, and Britannia Industries. In addition, the research uses both quantitative and qualitative data whereby the ESG score from the companies is evaluated based on the environmental, social and Governance indicators as well as comparing the three sets of ESG scores to the market capitalization of the respective companies. Classification of the variables Multiple linear regression analysis is carried out to compare the performance of the ESG scores (IV's) and market capitalization (DV). Based on these results, it may be seen that some firms excel in particular ESG issues that have to do with their business activities, however, the authors do not disclose the statistically significant correlation between ESG scores and market capitalization for the analyzed sample. Accordingly the paper concludes CSR scores are insignificant in this dataset insofar as an impact on market capitalization is concerned.

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Environmental, Social, and Governance (ESG), Market Capitalization, Corporate Social Responsibility (CSR), Sustainability, Regression Analysis, FMCG Industry.

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In recent months, free software tools based on deep learning have made it easier to create reliable facial transformations in videos that appear out of nowhere without control effects, so-called "DeepFake" (DF) videos. Digital video management has been proven for many years using the view.Recent advances in deep learning have led to major improvements in the accuracy and accessibility of false content where possible . created. These are called AI Synthetic Media (often called DF). However, this represents a significant challenge when it comes to the discovery of these DFs. Because training the algorithm to find DF is not easy. We take a step forward in DF detection by using neural networks and recurrent neural networks. The system uses a convolutional neural network (CNN) to extract phase models. These features are used to train a Recurrent Neural Network (RNN) that can detect inconsistencies between, which learns to determine whether videos have been modified and physical disparities between frames represented by DF Design. tool. Expected results for many fake videos collected from the dataset. We show that our system achieves competitive results on this task using a simple reference model.

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 Keywords

Deepfake video detection, recurrent neural network (RNN), convolutional neural network (CNN).

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Education is the fundamental right of every children. But this fundamental right cannot be achieved without availability of quality education. Schooling has directed effects on children's Psycho social development. According to Solomon (1960), the school classroom has the second important place after home Foso human development. School provides first face to face interaction with reality to children. In school child get format education as well as learns so many informal things. This happens because teachers, peons and other students affect their personality in many ways. There are so many stimulating materials present in school premises that affect children's personality intelligence, emotions, motivation, sociolisation etc.

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Impact of Private and Government School's Education on Children's Psycho-Socio Development

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Efficient project management is indispensable in today's digital age, especially in educational institutions. This paper introduces TRIAD, a comprehensive project management system designed specifically for higher education. TRIAD inte grates project planning, task assignment, progress tracking, and communication features tailored for academia. Moreover, it incor porates advanced plagiarism detection algorithms and meeting facility coordination to ensure academic integrity and streamline collaboration. This paper outlines the system's architecture, func tionalities, related works, proposed enhancements, and future scope, highlighting its transformative potential in revolutionizing education and preparing students for the professional world.

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 Keywords

Project Management system, Plagiarism Detection, Meeting Facility Coordination, Academic Integrity, Higher Education

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