Volume 13 | Issue 3 |

 Abstract

The Industrial Internet of Things (IIoT) represents a transformative evolution in industrial operations, integrating advanced communication technologies, data analytics, and automation to enhance efficiency, productivity, and decision-making. However, the increasing connectivity and reliance on IIoT systems also expose them to significant cybersecurity threats. This paper explores the architecture of IIoT systems with a focus on enabling seamless communication and robust network attack detection mechanisms. We discuss the challenges associated with IIoT communication, including latency, reliability, and scalability, and propose solutions to mitigate these issues. Furthermore, we delve into the various types of network attacks that IIoT systems are vulnerable to, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and ransomware attacks.

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 Keywords

IIoT, Network, DDos, Time, Realibility

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 Abstract

Accurate energy demand forecasting is crucial for optimizing power distribution, reducing energy waste, and improving resource efficiency. Traditional forecasting methods often suffer from limitations in accuracy and scalability. Using a variety of algorithms, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbours (kNN), and XGBoost, this study suggests a machine learning-based method to forecast energy consumption. Real-world data from commercial buildings in Malaysia is utilized, considering the influence of environmental factors such as temperature. The dataset is pre-processed to deal with missing values and outliers in order to guarantee dependable model training. The predictive performance of different models is evaluated using metrics such as RMSE, NRMSE, MAE, and MAPE. The results indicate that machine learning models provide significant improvements in forecast accuracy, with ANN and KNN outperforming traditional approaches. This study contributes to energy management by offering an interpretable and scalable solution for demand prediction in smart grids.

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 Keywords

Energy Demand Forecasting, Machine Learning, Artificial Neural Networks, k-Nearest Neighbors, Support Vector Machine, Predictive Modeling, RMSE.

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 Abstract

For energy management, policy planning, and guaranteeing a steady supply of electricity, accurate forecasting of electricity demand is essential. Forecasting models have been greatly enhanced by recent developments in artificial intelligence (AI) and machine learning (ML), which incorporate a variety of independent variables such as weather, economic factors, and historical consumption data. In order to improve forecasting accuracy, this study investigates the combination of machine learning models, specifically artificial neural networks (ANN), and multi-criteria decision-making techniques. A key challenge in electricity demand forecasting lies in selecting relevant input variables. This research employs a systematic approach to identify significant factors impacting demand and applies advanced ML techniques, such as Random Forest, LSTM, and Extreme Learning Machines, to develop robust predictive models. Comparative analysis using performance metrics like RMSE and MAPE demonstrates the effectiveness of AI-driven forecasting approaches. The findings contribute to the evolving landscape of energy demand prediction, aiding in efficient power system planning and optimization.

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 Keywords

Electricity demand forecasting, machine learning, artificial neural networks, multi-criteria decision-making, energy consumption prediction, time-series forecasting, energy management.

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The following paper illustrates the various resemblances in the narrative elements and discourse of Indian and Greek mythologies. With a Jungian understanding of the psychoanalytical importance of myths and its archetypes, the paper will dive deep into the aforementioned mythologies and fish out some interesting parallels between the plot, characters, conflict, setting and theme of the two. The paper will discuss how the underlying tone of various myths and mythical characters is the same and how despite the different language the essence of the mythologies remains the same. Monica Fludernik's essay- "The Structure of Narrative" would be used to understand the narrative discourse, structure and elements of the myths. An endeavor to perform a comparative analysis of the mythological epics- Homer's Iliad and Valmiki's Ramayana would be made. The paper would also attempt to explore the process of stories transcending into history and history transcending into mythology. Rahul Sankrityayan's Volga Se Ganga and the Parry-Lord theory will be used to understand the significance of the similarities between these mythologies and the possibility of a combined narrative and a collective narrator in context of migration and orature.

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 Keywords

archetypes, epic, gods, history, orality, narrative, narrative discourse, narrative elements, narrator, myth, mythology, storytelling

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Creative Commons Attribution 4.0 and The Open Definition

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The development of effective antimicrobial agents is critical in combating bacterial infections, particularly in the face of increasing antibiotic resistance. In this study, we explore the formulation of fraxidin-loaded antimicrobial nanoemulsion gel as a promising therapeutic approach. Fraxidin, a bioactive compound with known antimicrobial properties, has been encapsulated in a nanoemulsion system to enhance its stability, solubility, and bioavailability. The gel formulation was developed by incorporating the fraxidin-loaded nanoemulsion into a suitable gel matrix, designed for topical application. Characterization of the nanoemulsion gel, including particle size, zeta potential, encapsulation efficiency, and in vitro release studies, demonstrated the successful encapsulation of fraxidin and its sustained release profile. Antimicrobial efficacy was evaluated against common pathogens, with the fraxidin-loaded nanoemulsion gel showing significant inhibitory effects, as compared to free fraxidin, indicating an enhanced antimicrobial activity due to the nanoencapsulation. Furthermore, the gel formulation exhibited desirable rheological properties, stability, and skin compatibility, making it suitable for topical application. This research highlights the potential of fraxidin-loaded nanoemulsion gel as a novel, effective, and safe alternative for the treatment of skin infections and other microbial-related ailments. The results of this study contribute to the growing field of nanomedicine, demonstrating the therapeutic advantages of nanoemulsions in drug delivery systems for enhanced antimicrobial action.

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Nanoemulsion, Antimicrobial study, Transdermal flux, Stability study, Skin irritation study

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Organic fertilizers maintain the soil structure and increase its nutrient holding capacity. They are easily biodegradable and free from environmental pollution. On the other hand, chemical fertilizers contaminate land and water resulting in the extinction of a number living organisms. Now a day's most of the people in rural India depend directly or indirectly on farming for their livelihood. They are using some traditional methods to prepare the liquid bio fertilizers for their crop land to enhance the soil quality, growth and yield of the plants. In this study the organic liquid fertilizers, amirtha karaisal and vermiwash effects were analyzed by their application to the plants Solanum lycopersicum (Tomato) and Abelmoschus esculentus (Lady Finger). And the results were compared to the control and inorganic fertilizer applied plants. The growth parameters such as seed germination, plants' height, internode, leaf length, leaf area index and number of leaves were significantly increased in organic liquid fertilizers applied plants (P<0.05).

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Amirtha karaisal, Vermiwash, Solanum lycopersicum, Abelmoschus esculentus , seed germination, height, internode, leaf length and leaf area index etc.,.

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Creative Commons Attribution 4.0 and The Open Definition

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This study examines the financial literacy and investment habits of women investors in Dakshina Kannada, India, focusing on the relationship between financial knowledge, self-efficacy, and investment decisions. Employing a quantitative research approach, the study uses a survey-based descriptive research design with a sample size of 200 women investors across all 8 taluks in Dakshina Kannada. The methodology included stratified random sampling and a structured questionnaire survey covering financial literacy levels, investment behaviours, and psychological influences. The major findings reveal moderate financial literacy among Women investors, with a preference for safer investment options, such as stocks, mutual funds, and fixed deposits. Most respondents demonstrated moderate risk tolerance and engaged in regular portfolio review. Wealth accumulation and retirement planning have emerged as primary investment goals. The study identifies a gap between financial knowledge and active investment practices influenced by socioeconomic and psychological factors. Suggestions include implementing targeted financial education programs to address behavioural biases, encouraging diverse investment strategies, and promoting gender equality in financial decision-making. This research contributes to the understanding of the challenges faced by women investors and highlights the need for tailored interventions to enhance their financial participation and decision-making capabilities.

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 Keywords

Financial literacy, Women investors, Investment behaviour, Dakshina Kannada, Financial decision-making

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Handwritten digit recognition is a fundamental problem in the field of computer vision, with applications in automated postal sorting, bank check processing, and more. This paper presents an approach to recognizing handwritten digits using Convolutional Neural Networks (CNNs), a powerful deep learning technique known for its ability to automatically learn spatial hierarchies of features. We employ a CNN architecture that includes multiple layers of convolution, pooling, and fully connected layers to achieve high accuracy in digit classification. The model is trained on the MNIST dataset, a well-established benchmark in the machine learning community. The effectiveness of the CNN model is evaluated through various performance metrics, including accuracy, precision, and recall, demonstrating its superiority over traditional machine learning methods. Our results show that CNNs significantly outperform other classifiers in terms of accuracy, achieving near-human-level recognition rates. This study highlights the potential of deep learning techniques in automated handwritten digit recognition tasks.

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 Keywords

: Handwritten Digit Recognition, Convolutional Neural Networks, Deep Learning, MNIST Dataset, Image Classification,Character recognition, ReLU function, Feature extraction

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 Abstract

The lower classes are the poor working class people who eat the least and then take care of everyone else. They work the hardest and are the most disrespected--the superiors very muchBullying them easily. Deprived of the comforts of life, they are brought up on the surplus of the country's resources. Sadhan Chatterjee's 'Jaltimir'The picture of this crisis of the lower class is embodied in the novel. This crisis is related to globalization, because of globalization the lower class people are affected.

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 Keywords

Lower class, globalization, arsenic

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Creative Commons Attribution 4.0 and The Open Definition

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The purpose of this research was to examine the effectiveness of SHGs in assisting women in the villages in the Darrang District and the neighbouring district of Morigaon to improve their economic standing. The primary data used to compile this study spans only a six-month time frame. From 18 SHGs across 3 villages, 200 participants were selected using a simple random sampling method. When people feel empowered, it's because they've gained a greater voice in decision-making and believe they have the authority to make their own choices. One way to gauge women's autonomy is by how much say they have in household decisions. Sixty-six percent of beneficiaries said their husbands make all the decisions, but more than a third of those surveyed said they had some say in the matter. This shows that women's economic status has increased as a result of their participation in SHGs and access to microfinance. Chi-square analysis of data from the Darrang district found that female SHG members were less likely to participate in decision-making compared to their female counterparts in nuclear families.

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 Keywords

Self help groups, Women empowerment, Socio economic study, Entrepreneurship

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