Volume 14 | Issue 4 |

 Abstract

Financial Literacy as a Tool for Inclusion: A Study of Economically Disadvantaged Communities in Karnataka's Backward Districts

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Financial Literacy, Financial Inclusion, Economically Disadvantaged Communities, Digital Financial Services, Economic Empowerment

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This research paper examines the historical and cultural background surrounding the publication of Things Fall Apart (1958) by Chinua Achebe. Prior to the appearance of this novel, most literary representations of Africa and its people were shaped by Western authors, many of whom portrayed the continent through a distorted and reductive lens. In response to such misrepresentations in colonial literature, Achebe and other African writers emerged as powerful voices, offering narratives rooted in African experiences and perspectives. These nationalist writers sought to challenge and dismantle the stereotypical and dehumanizing images that had long defined Africa and its inhabitants. This study argues that, although colonial novels propagated misleading portrayals of Africa, they inadvertently contributed to a growing awareness among Africans of the importance of reclaiming and narrating their own histories. This realization played a significant role in inspiring the creation of Things Fall Apart and other contemporary literary works that aimed to assert the richness and complexity of African cultures and traditions on a global stage. Employing a critical postcolonial framework, the paper places Achebe at the center of this intellectual and cultural movement, highlighting his dissatisfaction with colonial discourse and his commitment to representing Igbo and broader African traditions authentically. Ultimately, Things Fall Apart is interpreted as an Afrocentric narrative designed to reshape European perceptions and foster a more accurate understanding of African cultural identity.

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Keywords: colonial novel - racism - biased description - Africa - rehabilitation novel

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Forensic Face Recognition, Sketch-to-Photo Matching, InsightFace, FaceNet, Test-Time Augmentation, FAISS, Multi-Region Analysis, Canny Edge Detection, Case Management System

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The increasing energy demand in commercial buildings and growing environmental concerns have accelerated the adoption of renewable energy technologies. Solar photovoltaic (PV) systems integrated with microgrids offer a sustainable solution for reliable and efficient power supply. This study proposes a Solar PV-based microgrid system equipped with an Energy Management System (EMS) to ensure continuous and optimized electricity supply in commercial buildings. The system incorporates Maximum Power Point Tracking (MPPT) using the hill-climbing algorithm to maximize PV energy extraction under varying irradiance conditions. A Battery Energy Storage System (BESS) is integrated to store surplus energy and provide backup during nighttime or low solar availability. Loads are categorized into secured and non-secured types, with an Uninterruptible Power Supply (UPS) ensuring uninterrupted power to critical loads during grid outages. The EMS intelligently manages energy flow between PV generation, battery storage, and grid supply under grid-connected and standalone modes. Simulation results demonstrate improved energy efficiency, enhanced reliability, reduced dependence on fossil fuels, and sustainable energy utilization in commercial building applications.

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Solar Photovoltaic (PV) System, Microgrid Energy Management System (EMS), Maximum Power Point Tracking (MPPT), Battery Energy Storage System (BESS), Sustainable Commercial Building Energy etc.

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Wheelchair users and elderly individuals are highly vulnerable to fall-related injuries. Existing systems often rely on basic alert mechanisms and lack real-time communication capabilities. This paper proposes an IoT-based fall detection system designed specifically for wheelchair users. The system uses gyroscope sensors, ultrasonic sensors, and GPS modules integrated with an ESP32 microcontroller to monitor posture and detect falls. In case of an emergency, alerts are sent instantly to caregivers through a cloud-based platform. Additionally, a manual emergency button allows users to request assistance for situations such as medical needs or mobility support. The proposed system aims to improve response time, enhance user safety, and enable effective communication between users and caregivers.

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Fall Detection based on IoT, Wheelchair users, Emergency alerts, Real Time monitoring, cloud based Notifications, Posture Sensing, GPS Tracking, Emergency button inter- face, ESP32 Microcontroller, Elderly Care, Assistive technology, Sensor fusion, Care giver communications, Location based alerts

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The high rate of social media penetration in the lives of young adults and adolescents has created the need to re-examine the etiology and persistence of the Major Depressive Disorder (MDD) through a digital lens. The report offers a comprehensive overview of the existing literature, which is synthesized through the prism of clinical psychology, regarding the complex association of social media use with depressive symptoms. The analysis can be used to clarify the interaction between digital surroundings and developmental susceptibility through the assessment of Diagnostic and Statistical Manual of Mental Disorders (DSM-5-TR) criteria and existing global and Indian patterns of use. The key themes are psychological differentiation of active and passive use, the mediating effect of the fear of missing out (FoMO) and social comparison, social media-related cyber bullying, and Neuro-cognitive effects of platform-specific designs, as seen in TikTok infinite scroll and Instagram aesthetic duration. Practical focus is made on the Indian setting, discussing the gap in urban and rural areas and mental health treatment gap.

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MDD(Major Depressive Disorder), Social Media,

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Dalit literature, rooted in the lived realities of India's historically oppressed communities, stands as a testament to the enduring struggle for dignity, self-expression, and social justice. These works, often autobiographical and intensely personal, bring to light the day-to-day experiences of discrimination, exclusion, and resistance that define Dalit identity. As Dalit voices have gained prominence in the literary world, the need to translate these narratives into English and other Indian languages has grown. However, the process of translation is fraught with significant challenges. One of the primary concerns is the risk of eroding the socio-political context that is integral to Dalit writing. The intricate and culturally-loaded vocabulary--terms such as "paraiyar," "joothan," or "chamar"--resist easy translation, carrying with them histories and meanings that are deeply embedded in specific social realities. Moreover, the act of translation can sometimes blur or even erase the distinctive voice of the Dalit author, which is inseparable from the community's collective experience. Translators must make difficult choices regarding which elements to retain, adapt, or explain for new audiences. Through a focused examination of seminal Dalit texts such as Bama's Karukku, Omprakash Valmiki's Joothan, and Urmila Pawar's The Weave of My Life, this article investigates how these challenges manifest in practice. The analysis explores how translation decisions affect the representation, authenticity, and accessibility of Dalit literature, while also considering ethical responsibilities. Ultimately, this research aims to enrich the discourse around caste, identity, and resistance by foregrounding the complex interplay between language, power, and representation in the translation of Dalit voices.

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Dalit literature, translation, caste, representation, authenticity, resistance

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India constitutes a part of the top three producers of several different crops worldwide and serves as a well-known agricultural center. While Indian farmers play a crucial role in agriculture industry, a large proportion of them are still at the lower end of the socioeconomic scale. Even with a few technological fixes, farmers still struggle to recognize the most lucrative and viable crops for their soil given the diversity of soil types in different parts of the world. This investigation introduces a crop recommendation system that forecasts the best crop derived from a thorough examination of several criteria, such as geography, soil type, yield, selling price, and more. It does this by using both a Convolutional Neural Network (CNN) architecture and a Random Forest Model. It is anticipated that the CNN design would provide an accuracy rate of 95.21%, while the Random Forest Algorithm may produce an accuracy rate of 75%

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Random Forest, Image classification, Deep learning,Convolutional Neural Network, MobileNet v2

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The growing complexity of cyber threats in today's digital landscape has exposed critical weaknesses in conventional, rule-based security systems that depend on passive detection without active mitigation. This paper presents the design and development of a unified, web-based Cyber Threat Intelligence Platform built with Role-Based Access Control (RBAC) to serve two distinct user tiers -- Enterprise administrators and Public consumers -- each with dedicated detection and prevention environments. At the Enterprise Tier, an Intrusion Detection and Prevention System (IDPS) is powered by the XGBoost (Extreme Gradient Boosting) algorithm and validated against three widely accepted benchmark datasets: NSL-KDD, CIC-IDS 2017, and UNSW-NB15. Upon detecting malicious network traffic, the system automatically generates Snort IDS rules and Linux IPtables scripts to block attacking IP addresses in real time. At the Public Tier, three heuristic forensic engines defend individual users: a Lexical Phishing Analyzer for detecting suspicious URLs, a Digital Media Forensics module using Error Level Analysis (ELA) and pixel variance to identify AI-generated synthetic media, and a Static Malware Sandbox that performs byte-level inspection to detect extension-spoofed malicious files, triggering memory-level quarantine upon detection. All forensic events are secured through an automated reporting pipeline that computes cryptographic checksums (SHA-256 and MD5) to preserve evidence integrity. The proposed framework successfully bridges passive anomaly detection with proactive cyber threat prevention across both enterprise and consumer attack surfaces.

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Cyber Threat Intelligence, XGBoost, Intrusion Detection and Prevention System, Role-Based Access Control, Phishing Detection, Error Level Analysis, Malware Sandbox, NSL-KDD, CIC-IDS 2017, UNSW-NB15, Cryptographic Check-sums, Digital Forensics

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India's economy relies heavily on agriculture, with approximately 58 percent of the rural population being employed within this sector. Despite these figures, small and marginal farmer's have difficulty obtaining timely agricultural advice regarding disease diagnosis, weather-based recommendations, market prices, and government schemes. This paper introduces a Smart Crop Advisory System called "KrishiSahayak" (Krishi Mitra) that aims to provide a comprehensive and integrated resource for small and marginal farmers. The KrishiSahayak (Krishi Mitra) Smart Crop Advisory System leverages AI, ML, Cloud Computing, and Mobile Technologies to create an integrated platform for crop disease detection, hyper-local weather-based recommendations, Mandi price updates in real-time, and government scheme information. The system also provides multiple channels of access (Flutter mobile app, SMS, IVR, AI chatbots) to allow farmers of different levels of digital literacy to access the information they need. The system was built using a cloud native architecture with the help of Django, TensorFlow/PyTorch, and Rasa to promote high levels of scalability and reliability. Based on our experimental simulations, we believe AI-based Advisory Systems have the potential to reduce crop losses due to disease by 15-25 percent, enable farmers to utilize renewable resources more effectively, and increase farmer income through access to improved market awareness.

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 Keywords

Smart Agriculture, Crop Disease Detection, Weather-Based Advisory, Machine Learning, Cloud Computing, Marginal Farmers, Precision Agriculture, Mobile Application, Digital Agriculture, Chatbot

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