Volume 14 | Issue 3

 Abstract

The era of the internet has seen social media emerged a powerful platform of political communication. The fast development of the internet connection and smartphone devices has largely increased the scope of social media marketing, such as Facebook, WhatsApp, Instagram, and X are some of the most commonly used social media platform tools in India. Political parties, leaders, and citizens have become a common user of these platforms to disseminate political information, discuss issues, as well as shape popular opinion. This paper will look at how social media can impact on electoral behaviour in India. It discusses the role of social media platforms in enhancing awareness about politics, voter turnout, and campaigning. Another aspect brought out in the study is the influence of social media in forming the opinions of politics and the dissemination of information as well as misinformation via the election times. The study is founded on secondary data that was gathered by use of books, academic journals, articles and reputable sources on the internet. The results show that social media has a considerable effect on the political involvement and the voting patterns to a large extent among young voters. Nevertheless, such problems as the misinformation, political polarization, and digital manipulation are critical issues. The research concludes that, although social media has enhanced political communications, there is need to ensure it is used responsibly and one must be digitally literate to ensure healthy democratic process.

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 Keywords

Social Media, Electoral Behaviour, Political Communication, Political Awareness, Digital Platforms and Elections.

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Customer reviews play a major role in e-commerce platforms' ability to assist customers in making selections. However, one of the biggest problems facinginternet markets is the increasing number of fraudulent or deceptive evaluations. These fraudulent reviews can mislead consumers anderodetheirfaithintheplatformbecause they are frequently created to falsely promote or harm the reputation of goods and merchants. The enormous amount of onlinefeedbackcreateddailymakesit challenging to manually identify such bogus reviews. The capacity of e-commerce platforms to help customers make decisions is greatly influenced by user feedback. However, the rise in fake or misleading ratings is one of the major issues facing online markets. Because these fraudulent evaluations are often made to falsely promote or damage the reputation of products and merchants, they have the potential to deceive customers and undermine their trust in the platform.Itisdifficulttomanuallyidentify suchfraudulentevaluationsduetothe massive volume of online comments generated every day.

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Fake Review Detection, Deep Learning, NaturalLanguageProcessing,E-commerce Platforms, Sentiment Analysis, Machine Learning

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Intrusion detection systems are very useful in protecting the contemporary networks against all types of cyber attacks which are becoming more complex and dynamic. This paper provides the development of a dynamic intrusion detection system that combines swarm intelligence-based optimization with the latest machine learning and deep learning architectures to allow real-time dynamic thresholds and efficient attack response. A powerful preprocessing pipeline was created, and it includes feature encoding, YeoJohnson transformation, mutual informationbased feature selection and a hybrid approach to class imbalance dealing with SMOTE and ADASYN. Differentiation Evolution was utilized to optimize the important hyper parameters so that global search is made to be efficient and better model generalization is given in case of imbalance. Classical machine learning as well as deep learning models were assessed using the macro-averaged performance measures so as to have a fair evaluation across the traffic classes. The results of experiments show that ensemble and distance-based machine learning models obtained almost perfect detection performance. The 1D Convolutional Neural Network has shown superiority over temporal models, making it a good choice of deep learning techniques, with accuracy of 99.98 percent, precision of 99.99, recall of 99.98 and F1 -score of 99.98. The results prove that swarm intelligence augmented with optimized learning architectures has been able to offer a scalable, robust, and highly accurate means of detecting network intrusions in real-time in the dynamic cybersecurity challenges.

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Intrusion Detection System, Swarm Intelligence, Differential Evolution, Adaptive Thresholds, Feature Selection and Network Security

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Non-commutative quantum mechanics (NCQM) arises naturally in various frameworks related to quantum gravity, string theory, and deformation quantization, where the coordinates of space-time no longer commute at the Planck scale. This research offers a thorough examination of the fundamental Lie algebraic structures and symmetries that define non-commutative quantum phase spaces. We develop the modified Heisenberg-Weyl algebra on non-commutative manifolds and analyze its deformation using star-product formalism and operator techniques. The study investigates how non-commutativity alters classical symmetries, including rotational invariance and gauge transformations, as well as how representations of Lie algebras adjust in response to these deformations. Additionally, we explore the connection between non-commutative parameters and Lie algebra cohomology, demonstrating that specific algebraic deformations relate to central extensions of the conventional Heisenberg algebra. This analysis yields fresh perspectives on the relationship between algebraic structures, geometric symmetries, and physical observables within the quantum domain.

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Non commutative Geometry; Lie Algebra; Quantum Phase Space; Symmetry; Deformation Quantization; Star Product; Heisenberg-Weyl Algebra.

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The Energy Management System is developed to monitor and optimize power consumption and generation in an industrial setup. The system allows users to input multiple consumption parameters along with renewable energy sources such as solar and wind, and automatically calculates total consumption, generation, and energy balance. It also includes electricity cost estimation using slab-based tariff calculation and demand charge analysis based on sanctioned and maximum demand. Additionally, the system stores historical data for future reference, enabling efficient energy tracking and decision-making. This project helps in improving energy efficiency, reducing costs, and providing a practical solution for real-world energy management.

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Energy Management System, Power Consumption, Energy Generation, Demand Charges, Renewable Energy, Cost Calculation, Industrial Energy Monitoring, Data Analysis

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The rising use of digital devices had created many challenges in marital relationships due to phubbing. The present study was carried out to examine the relationship and predictive effects of phubbing and emotional availability on marital satisfaction among married couples in Kerala. Using a quantitative correlational research design, sample of 160 married couples aged 25-40 years from Thrissur district, were selected using purposive sampling. Standardized questionnaires were used to assess phubbing, emotional availability, and marital satisfaction. After analyzing the data using Pearson correlation and multiple regression analysis, results revealed that phubbing showed a weak but significant negative relationship with marital satisfaction, while emotional availability showed a strong positive relationship with marital satisfaction. Regression analysis indicated that phubbing negatively predicted marital satisfaction, whereas emotional availability was a strong positive predictor, jointly explaining 71.8% of the variance. In conclusion, Emotional availability significantly enhances marital satisfaction, while phubbing adversely affects marital quality. Promoting emotional communication and reducing digital distractions may improve marital well-being.

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Phubbing, Emotional Availability, Marital Satisfaction, Married Couples

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Total quality management (TQM), a management philosophy developed for industrial purposes, is now attracting increasing attention in the field of education. In recent years total quality management (TQM) has emerged as a viable solution to achieve the goal of quality. Although business organizations have applied TQM principles with considerable success, its use in teacher training institutes is largely limited and has received little attention. The indomitable spirit of higher education paves the way for the growth of a nation in the several dimensions. Teacher education is one of the areas in higher education which trains student-teachers in pedagogy, which in turn helps them to train the young minds of educational institutions. Teacher education institutions should maintain quality to ensure the academic excellence of trainees who come into the teaching profession. Quality assurance in teacher education reflects on the high profile of the institution and the competency of student-teachers. The present article focuses on the need concept and thrust areas of TQM in teacher education in India. It has exposed the perception of teachers on quality indicators, such as teacher quality, linkage and interface, students, co-curricular activities, innovative strategies in teaching learning practices, pedagogy, curriculum development, value inculcation, Teacher Education Institutions and Schools linkages, use of information and communicative technology and governance. The present paper wants to highlight the TQM (Total quality management), TQM (Total quality management) in Teacher Education.

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Total quality management, Teacher Education, TQM in Teacher Education.

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 Abstract

Excessive vehicle speed has emerged as a significant contributor to accidents, resulting in the loss of numerous lives. In response to this pressing issue, we have developed a novel system to address the problem. The primary objective of this project is to offer a user-friendly and dependable solution for speed detection in vehicles utilizing IR sensors. The system not only calculates the vehicle speed but also presents it on an LCD (Liquid Crystal Display). Moreover, a built-in alert mechanism, incorporating a buzzer, is activated when the system identifies instances of over-speeding. This comprehensive approach aims to enhance road safety by providing real-time speed information to drivers and issuing immediate warnings in cases of potential over-speeding incidents.

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Power supply, Arduino UNO, IR sensors, LCD, Buzzer, ESP32 camera.

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The increasing demand for natural skincare products has led to the development of a herbal antioxidant lotion incorporating Moringa oleifera (moringa) leaf extracts. The native tree Moringa oleifera Lam. (Marantaceae) is widely used as an antioxidant, anti-inflammatory, detoxifying and nutrient. Apart from its therapeutic qualities, it has a potential function in cleansing and hydrating the skin. Body lotion is a cosmetic product used to keep hands and body moisturised. Numerous free radical inhibitor compounds and a number of additional naturally occurring metabolites with high antioxidant activity can be found in moringa leaves (Moringa Oleifera Lam). different formulations of the lotion were developed using different concentrations of the extracts and evaluated for key physicochemical parameters, including pH, viscosity, spread ability, and texture. The optimized formulation exhibited a pH range of 5.8-6.5, smooth texture, and ideal viscosity for easy application. Stability testing over 30 days under various temperature conditions. In study's findings and product testing, moringa leaves extract is essential for boosting skin elasticity, decreasing dryness, and improving skin texture

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Moringa leaves extract, Antioxidant activity, Nourishment, Body lotion.

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 Abstract

Nyay Darshane Jati Padarth?????? ?????? ??????????????? ???????? ???????? ???????? ?????????, ??????? ??????????? ??????????????? ????????????? ?? ????????? ????????????, ?????? ???????????? ??????????? ???????????? ??????????? ????????? ? ?????, ????? ??????????? ??????????????? ??????? ????????????? ??????????????????????? ??????, ??? ??????????? ??????????? ?????????????????? ????? ??????????, ???? ?????? ?????????????????? ??????????????? ? '?????????????' ??? ??????, ???????? ??????????? ??? ?????????????????????, ???? ??????????????????????????? ?????????????????????????? ??????? ??????????? ?????????????????? ????????????? ? ?? ??????????? ??? ?? ??????????? ????????????????????????????? ??????????????????? ?? ?? ??????????? , ??? ???????????????????????????????? ??????????? ???????????? ?????????????????? ?????? ? ?????? ???????

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Nyay Darshane Jati Padarth

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Embroidery, as a textile tradition, transcends mere ornamentation--it embod- ies history, identity, and human expression. This paper presents a comparative exploration of Kashida embroidery from the Kashmir Valley of India and Per- sian embroidery from Iran, investigating how these two intricate traditions reflect the ethos of their respective cultures. Through an interdisciplinary method- ology involving visual analysis, historical context, and artisan narratives, the research reveals how motifs, materials, and stitching techniques are shaped by geography, religion, gender roles, and socio-political influences. The study finds that while both traditions share Indo-Persian artistic heritage, they have evolved uniquely--Kashida emphasizing nature and regional simplicity, and Per- sian embroidery reflecting structured opulence and spiritual symbolism. This research underscores the living legacy of embroidery and its potential in con- temporary cultural preservation and economic empowerment. Embroidery, as a textile tradition, transcends mere ornamentation--it embodies history, identity, and human expression. This paper presents a comparative exploration of Kashida embroidery from the Kashmir Valley of India and Persian embroidery from Iran, investigating how these two intricate traditions reflect the ethos of their respec- tive cultures. Through an interdisciplinary methodology involving visual analysis, historical context, and artisan narratives, the research reveals how motifs, mate- rials, and stitching techniques are shaped by geography, religion, gender roles, and socio-political influences. The study finds that while both traditions share Indo-Persian artistic heritage, they have evolved uniquely--Kashida emphasiz- ing nature and regional simplicity, and Persian embroidery reflecting structured opulence and spiritual symbolism. This research underscores the living legacy of embroidery and its potential in contemporary cultural preservation and economic empowerment. .

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Embroidery, Traditional, Cultural, Kashida

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Early and effective identification of retinal pathologies plays a very crucial role in the prevention of irreversible vision loss and facilitating the process of large-scale retinal screenings. This study focuses on the implementation of an explainable and Vision Transformer-based multi-class retinal disease classifiers using color fundus photographs, focusing on the proposed work around the Swin Transformer-Base architecture. A selectively compiled dataset of 4,217 retinal fundus photographs was assembled by combining various publicly accessible sources, followed by the classification of the compiled set into four retinal disease classes: Normal, Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Branch Retinal Vein Occlusion (BRVO) diseased retinas. The previously mentioned Swin Transformer-Base network, pretrained on large-scale image datasets, was further adapted by the use of transfer learning to achieve more accurate results through standardized preprocessing and optimizing settings during the training process to avoid any potential bias in the derived results. From the experimental analysis, the proposed work showcases the potential of the proposed Swin Transformer-Base architecture in successfully maximizing the classification accuracy to 99.54%, and to further provide more robustness and explainability to the proposed results, the implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) visualization techniques has been accomplished, focusing on the identification of critical retinal areas in retinal pathologies, thereby facilitating more enhanced clinical usability. Furthermore, to provide more utility value to the proposed clinical approach, a lightweight and easy-to-use diagnostic tool has been provided to upload the images in real time and to access the results in a more facile graphical format.

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Retinal disease classification, Vision Transformer, Fundus imaging, Computer-aided diagnosis, Explainable AI, Telemedicine.

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 Abstract

Vitamin deficiencies represent one of the most common health problems in many countries of the world, especially where access to medical test facilities is poor. These deficiencies could take the form of impaired immunity, a sight problem, skin diseases, anemia, or poor physical development. Conventional techniques for its diagnosis primarily rely on blood tests, which are very accurate but sometimes invasive, expensive, and ill-suited for mass screening or screening in remote areas. This demands the development of other methods of diagnosis that are non-invasive in nature and yet would facilitate early detection. This project is aimed at the development of an AI-driven system for the detection of vitamin deficiencies using images with visible symptoms from eyes, nails, skin, tongue, and hair by deep learning. In this approach, transfer learning applies the ResNet152V2 model for feature extraction, which uses the attention mechanism to help the model focus on most of the significant parts of the image that are associated with deficiency-related visual patterns, improving the detection of very small manifestations of symptoms. A deep learning approach using a custom dataset of images of different body areas and corresponding labels is used to train and test the proposed system. The proposed model will be able to detect multiple vitamin deficiencies and give accurate predictions. The performance of the proposed model tested using common evaluation metrics such as accuracy(91.4%), precision(91%), recall(90.7%), and F1-score(90.8%), which will show the efficiency of the proposed classification model. The proposed model is expected to be used as a supporting diagnostic tool and will work to decrease the dependence on laboratory work. The proposed model will also be non-invasive and will be easily deployable on a large scale.

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Vitamin Deficiency Detection, Deep Learning, ResNet152V2, Transfer Learning, Image Classification, Visible Symptom Analysis, Remote Healthcare.

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Banking institutions increasingly rely on digital document submissions for services such as account creation, loan approval, and customer verification. Rapid digitalization has resulted in increased image-based fraud using forged or manipulated banking documents. This paper proposes an AI-powered image authenticity verification system designed to detect fake document images using deep learning techniques. The system analyzes document images such as Aadhaar cards, PAN cards, and bank statements. Input images undergo preprocessing steps including resizing, normalization, and noise removal. A Convolutional Neural Network (CNN) is used to extract visual features such as texture inconsistencies, edge distortions, and compression artifacts. The system classifies images into genuine or fake categories with high accuracy. Public datasets such as CASIA and Deepfake datasets are used for training and evaluation. The system improves banking security by reducing fraud and automating verification processes .Banking institutions increasingly rely on digital document submissions for services such as account creation, loan approval, and customer verification. Rapid digitalization has resulted in increased image-based fraud using forged or manipulated banking documents. This paper proposes an AI-powered image authenticity verification system designed to detect fake document images using deep learning techniques. The system analyzes document images such as Aadhaar cards, PAN cards, and bank statements. Input images undergo preprocessing steps including resizing, normalization, and noise removal. A Convolutional Neural Network (CNN) is used to extract visual features such as texture inconsistencies, edge distortions, and compression artifacts. The system classifies images into genuine or fake categories with high accuracy. Public datasets such as CASIA and Deepfake datasets are used for training and evaluation. The system improves banking security by reducing fraud and automating verification processes.

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Fake Image Detection, CNN, Banking Security, Image Forensics, Deep Learning

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"Reversing Lens: Contemporary Gender Biases against Men in Society and Social Media," explores the evolving dynamics of gender inequality by shifting focus toward the often overlooked challenges faced by men. While acknowledging the historical and on-going struggles of women, the study argues that contemporary social, legal, and digital environments have generated new forms of bias that disproportionately affect men.

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Gender bias, Male marginalization, Legal discrimination, False accusations, Social media trials, Masculinity stereotypes, Mental health stigma, Patriarchy, Gender norms, Inclusive gender equality

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: This study examines that I am relationship between green Bond issuance and key and environmental and macroeconomic indicators including renewable energy consumption GDP per capita and CO2 emissions using a multi country data sets for the post 2015 period. The analysis employees a combination of descriptive statistical techniques ,Pearson correlation analysis and multiple linear regression models to access the magnitude and significance of the relationships among these variables. Empirical findings indicate that green bond issuance exhibits statistically significance positive effect on renewable energy consumption. However the impact on co2 emission is found to be moderate suggesting the influence of additional macroeconomic and structural factors. The findings of the study highlight the growing importance of green bonds as a financial instrument for financing environmentally sustainable projects and emphasize the need for strengthening policy support to enhance their effectiveness in achieving long term environmental and economic objectives.

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Green bonds issuance, Renewable energy, Sustainable development, Carbon emissions

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In this paper, a stochastic fractional solid transportation model is developed by considering demand as a random variable following multiple probability distributions to capture uncertainty in logistics systems. The model integrates a fractional objective function within a three-dimensional framework involving sources, destinations, and conveyance modes. Using distributional properties, the stochastic model is transformed into a deterministic equivalent form. The optimal solution is obtained using Karush-Kuhn-Tucker (KKT) optimality conditions. A numerical example is provided to demonstrate the effectiveness and applicability of the proposed approach in transportation decision-making under uncertainty.

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Keywords: Stochastic fractional solid transportation; KKT multipliers; Demand uncertainty; Probability distribution modeling; Optimization under uncertainty.

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Psychological well-being is an essential component of overall mental health and plays a significant role in individuals' ability to cope with daily challenges and stress. Women in their thirties often experience multiple responsibilities related to career, family, and personal development, which may increase psychological stress and affect emotional well-being. The present study aimed to examine the effectiveness of a journaling intervention in enhancing psychological well-being and reducing perceived stress among women aged 30-39 years. The study employed a quantitative pre-test-post-test research design with a sample of 30 participants selected through purposive sampling. Standardized psychological measures were used to assess participants' levels of psychological well-being and perceived stress before and after the intervention. Participants engaged in a structured journaling practice for a specified period, during which they recorded personal reflections and positive experiences. Statistical analysis using descriptive statistics and paired sample t-tests revealed a significant increase in psychological well-being scores and a reduction in perceived stress levels following the intervention. The findings suggest that journaling can serve as an effective and accessible psychological strategy for promoting emotional well-being and managing stress. The study highlights the potential role of journaling-based interventions in mental health promotion among adult women.

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journaling, psychological well-being, perceived stress, women's mental health, positive psychology

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This paper explores the philosophical ideas, practical similarities and differences between Mahatma Gandhi and Sonam Wangchuk as transformative figures in Indian society. While Gandhi's methods are rooted in non-violent resistance and ethical politics, Wangchuk employs innovation and sustainable technology to address contemporary challenges. Through a comparative analysis of their ideas on education, sustainability, modernity, and social reform, this paper argues that Wangchuk represents a modern adaptation of Gandhian principles in a technologically driven world. The study highlights how both figures emphasize self-reliance, local empowerment, and ethical responsibility, though their methods differ significantly due to historical and contextual changes.

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Satyagraha, Ahimsa (Non-violence), Sustainable Innovation, Social Change, Self-reliance, Grassroots Development.

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Antibiotics are often called "wonder drugs" because they have been used for decades to fight infections caused by microbes. These drugs are not only used for treating illnesses but are also widely used in agriculture and animal farming to prevent disease. However, a serious problem has emerged: microbes are becoming resistant to common antibiotics, and many people aren't aware that this resistance is developing. The purpose of this review is to look into where antibiotic resistance comes from, how it has developed, and the current challenges in fighting it. Our findings show that antibiotic resistance is growing rapidly. Many infections, like pneumonia, tuberculosis, and gonorrhoea, are becoming harder to treat, and in some cases, antibiotics no longer work at all. This is because the more antibiotics are used, the more likely it is that resistance will develop. The overuse and misuse of antibiotics, especially outside of medical settings (such as in farming), is a major factor in making microbes resistant. Currently, there are very few treatment options for hard-to-treat bacterial infections caused by multidrug-resistant bacteria, leading to higher rates of illness and death. This review emphasizes the need to use antibiotics more carefully in both humans and animals to slow down antibiotic resistance. It also highlights that the public's understanding of antibiotic resistance is still very limited. For this reason, educating both patients and the general public is crucial in the fight against antibiotic resistance.

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Antibiotic Resistance , Antimicrobial Resistance Multi drug Resistance bacteria, Antibiotic misuse and overuse, Superbugs, One health Approach.

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