VOLUME 2, ISSUE 10, OCTOBER 2025

Abstract: In this paper, we design a digital DSSS System using Cyclone II EP2C20F484C7 FPGA from ALTERA placed on education and development board DE-1 for the DSSS system with the transmitting and receiving sections according to the following parameters: -Clock frequency of the system 10 KHz.
-Length of spreading pseudo-noise code (PNC): is 16 chips.

Type generation of spreading code: Walsh codes (H16).
-Length of data bit: is 2bits with 16 chips for every one bit.
-spread operation: X -Number of users: is four -User 1 with Data (00) and pseudo-noise code:

Code 1=[ 1111111111111111]

1 → +1 , 0 → −1 ⟹ Code 1=[ 1111111111111111]

-User 2 with Data (01) and pseudo-noise code:

Code 2=[ 1010101010101010]

1 → +1 , 0 → −1 ⟹ Code 2=[ 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1]

-User 3 with Data (10) and pseudo-noise code:

Code 3=[ 1100110011001100]

1 → +1 , 0 → −1 ⟹ Code 3=[ 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1]

-User 4 with Data (11) and pseudo-noise code:

Code 4=[ 1001100110011001]

1 → +1 , 0 → −1 ⟹ Code 4=[ 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1]

These codes must be orthogonal to each other, so they are chosen according to Welch's codes via the HADAMARD matrix, for a 16-bit codes, the H16 array should be chosen.

Keywords: CDMA, DSSS, Walsh Codes, PNC, PNCG, FPGA.

Abstract: This research paper investigates the consumer purchasing patterns for branded and traditional jewellery within Rajkot City, Gujarat, India. Through a combination of surveys, interviews, and observational data, the study aims to analyse the preferences, behaviours, and motivations of consumers when it comes to purchasing jewellery. By exploring the dynamics between branded and traditional jewellery markets, this research sheds light on the factors influencing consumer choices in Rajkot City's jewellery sector. 100 samples collected by using survey method employed by questionnaire. Chi square and percentage analysis have been used for testing hypotheses and finding suggest that There is a significant difference in consumer awareness toward branded jewellery compared to traditional jewellery and Consumer purchasing decisions for jewellery are significantly influenced by convenience over brand preference.

Keywords: Jewellery, Buying Behaviour, Consumer, Brand, Traditional

Abstract: This paper explores the effects of nanofluid, namely Aluminium Oxide (Al₂O₃), Titanium Oxide (TiO₂), and Magnesium Oxide (MgO) as working fluids in a compact shell and tube heat exchanger. The study focuses on assessing Thermal efficiency, namely Heat transfer coefficient and Nusselt number, under different conditions. Nanofluid is used on the tube side as cold fluid, while water serves as the hot fluid on the shell side. Parameters such as nanofluid concentration (0.8%, 1.5%, and 2% by volume) and inlet flow velocity of cold fluid (0.4 m/s, 0.7 m/s and 1 m/s) are varied. Study uses a single-pass, counter-flow heat exchanger, modelled using ANSYS Fluent for CFD simulation. This research explores the change in heat transfer with varying velocity and concentration and also studies the potential of compact heat exchangers, which are increasingly preferred for their efficiency and space-saving design.

Keywords: CFD, Heat Transfer Coefficient, Nusselt Number, Flow Velocity, Nanofluid, Shell & Tube Heat Exchanger (STHE).

Abstract: The lore and legends of indigenous people are embedded with survival stories- against nature including wild animals, diseases, and even fellow human beings. Even in this advanced world full of enhanced technology in the areas of medical and administrative systems the basis of survival still remains the same- adaptability, resistance, and an urge to leave. These instincts amplify manifold when the degree of vulnerability increases. This is also true for every group of indigenous people all over the world. They have been struggling to survive against various agencies such as invasion elimination colonization and modernization their survival always involves a strong protest against any force which tries to modify their original way of living and threaten their existence. This concept has been given name by Gerald Vizenor, a Native American scholar as “survivance”. This is a word made by combining survival and resistance. They face constant struggles globally. They adapt and protest by staying purely connected to their ancestral lands and predictions. They emphasize on their cultural continuity through generational transfer and communal togetherness. This article aims to explore visionary’s concept of survivals in the novels of Mamang Dai set in the northeast Indian tribal regions. Her Legends of Pensam and The Black Hill novels describe the ways of resistance- through oral storytelling and even claiming cultural identity. Their survival aims to provide continuity across generations. It gets transferred through the word-of- mouth oral narratives, folklores, myths, and traditional stories. The interconnected stories in Legends of Pensam provide a non-linear perspective as opposed to the linear and coherent narrative of the colonial historians. The Black Hill presents Kajinsha, the protagonist who is an embodiment of the tribals and their ideology. He makes sure that they do not become the victims of colonial amnesia and their traditions make a significant mark on the tapestry of history.

Keywords: Survivance, Survival, Resistance, Erasure, Cultural Identity

Abstract: Nowadays, cloud computing is rapidly growing across the IT industry, government sector, and various other domains. Security remains one of the biggest challenges in the cloud. Among the most critical threats is the Distributed Denial of Service (DDoS) attack, which can disrupt secure data communication, generate excessive network traffic, and increase response times. A DDoS attack works by sending a massive number of false or malicious requests to a cloud server, resulting in heavy network congestion. This survey paper examines a range of robust security measures, including hybrid cryptography techniques and machine learning algorithms, aimed at addressing security issues and achieving end- to-end protection in hybrid cloud environments. It also highlights key security challenges and outlines future research directions for mitigating DDoS attacks. The goal is to provide a clear understanding of recent mechanisms and advancements in cybersecurity, serving as a valuable reference for both practitioners and researchers.

Keywords: Intrusion Detection System, Enhanced Cryptography Techniques, Deep Learning Model, Fuzzy Logic, Clustering Techniques

Abstract: One of the prominent programming languages in data visualization and statistics is R. The author deployed R programming tools to analyze the air quality across the United States from 1980 to 2022, which provided valuable insights into the patterns of the national Air Quality Index (AQI). The findings indicate that some states have improved air quality through stringent environmental regulations. In contrast, others struggle with high AQI levels due to industrial activities, urbanization, and natural factors such as wildfires. The study identified that ozone and PM2.5 are the most persistent pollutants, significantly contributing to poor air quality. States such as California and Texas exhibited persistently high AQI values, while others, such as Vermont and Maine, showed relatively stable or improving air quality.

Keywords: R Programming Tools; Exploratory Data Analysis; Data Visualization; Air Quality Index; Particulate Matter

Abstract: Academic performance prediction has become a crucial area in educational data mining, enabling early intervention for at-risk students and improving institutional strategies. This study provides a comparative analysis of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) approaches in predicting academic performance by exploring a range of models, including linear regression, random forests, support vector machines, multilayer perceptrons, and convolutional neural networks. The analysis leverages demographic, socioeconomic, and historical academic data to evaluate model effectiveness using metrics such as accuracy, mean absolute error, and root mean squared error. While ML algorithms like Support Vector Regressors offer robust predictive results, the incorporation of deep learning models (especially CNNs and Bi-LSTM), demonstrates improved performance -- albeit with increased computational complexity and data requirements. The review identifies the suitability of each method depending on the context, scale, and features of the educational dataset. The findings suggest that deep learning models excel in handling complex, high-dimensional data, whereas traditional ML models perform reliably with structured and tabular information. Ultimately, integrating comprehensive preprocessing and feature engineering enhances results, signalling the need for tailored approaches in educational settings.

Keywords: Academic Performance Prediction, Machine Learning, Deep Learning, Artificial Intelligence, Educational Data Mining