Volume 14 | Issue 4

 Abstract

This paper introduces an innovative AI-powered platform designed to optimize and secure the candidate evaluation process in recruitment, education, and certification. The platform utilizes GPT-4's advanced natural language processing capabilities to analyze uploaded PDF resumes, generating personalized, concise questions that are specifically tailored to each candidate's qualifications, skills, and experiences. This approach ensures a more targeted and efficient evaluation, allowing recruiters and educators to focus on the most relevant aspects of the candidate's profile. In addition to automated resume parsing, the platform integrates real-time facial recognition technology for identity verification, preventing impersonation and ensuring the integrity of the process. In the event of any anomalies or suspicious behavior, the system sends instant alerts to recruiters, further enhancing security. The platform's dynamic questioning capability adapts to candidates' responses, offering instant feedback and refining subsequent questions based on previous answers. This creates an interactive and engaging assessment process that not only evaluates candidates more comprehensively but also improves their experience by providing personalized feedback. By combining these advanced features--automated resume parsing, dynamic questioning, and biometric security--the platform provides an efficient, secure, and user-friendly solution that enhances the accuracy and trustworthiness of candidate evaluations. This AI-powered system is highly suitable for applications in recruitment, education, and certification, offering a transformative approach to candidate assessment that benefits both candidates and recruiters alike

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 Keywords

This paper introduces an innovative AI-powered platform designed to optimize and secure the candidate evaluation process in recruitment, education, and certification. The platform utilizes GPT-4's advanced natural language processing capabilities to analyze uploaded PDF resumes, generating personalized, concise questions that are specifically tailored to each candidate's qualifications, skills, and experiences. This approach ensures a more targeted and efficient evaluation, a

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cloud computing environments face an escalating threat from Distributed Denial-of-Service (DDoS) attacks that exhaust computational resources, degrade service availability, and compromise sensitive data stored in shared infrastructure. Traditional centralized security architectures consolidate traffic analysis at a single point, creating single points of failure, scalability bottlenecks, and heightened privacy risks. This paper presents NetGuard, a scalable cloud-security framework that addresses these challenges without relying on artificial intelligence or machine-learning components. NetGuard combines three principal mechanisms: (i) a distributed, threshold-based DDoS detection engine that monitors per-node traffic metrics across multiple cloud nodes; (ii) a cryptographic key-based authentication system using Fernet symmetric encryption (AES-128-CBC with HMAC-SHA256) to enforce fine-grained file-access control; and (iii) an automated real-time alert and response subsystem that logs suspicious events, notifies data owners via e-mail, and triggers countermeasures such as IP-level access restriction. The framework is built on Python and Flask, uses MySQL for persistent storage, and integrates seamlessly with standard cloud-object-storage backends. Experimental evaluation on a multi-node testbed demonstrates that NetGuard achieves attack-detection latency below 3.2 seconds, sustains throughput above 1,500 legitimate requests per second under volumetric flooding, and reduces false-positive rates to 1.8% compared with 6.4% for signature only baselines. The modular design allows horizontal scaling across heterogeneous cloud environments, making NetGuard suitable for enterprise, academic, and government deployments that require robust, yet operationally transparent, cloud-file security.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cloud Security, DDoS Detection, Key-Based Authentication, Fernet Encryption, Threshold-Based Anomaly Detection, Secure File Access, Automated Alert System, Flask, Distributed Architecture

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents the design and implementation of a secure, transparent, and decentralized voting system based on blockchain technology. Traditional voting systems suffer from issues such as lack of transparency, vote tampering, and delays in result processing. The proposed system utilizes blockchain to store votes as immutable transactions, ensuring data integrity and security. Smart contracts are used to automate voter authentication, vote validation, and result computation, eliminating the need for centralized control. Additionally, cryptographic techniques ensure voter anonymity and prevent duplicate voting. The system enhances transparency by allowing verification of transactions while maintaining confidentiality. Experimental results demonstrate improved reliability and efficiency compared to traditional methods. Overall, the proposed system provides a secure and scalable solution for modern digital voting applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Voting System, Smart Contracts, Ethereum, Security, Transparency, Cryptography

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of e-commerce platforms has generated massive volumes of consumer interaction data, including clickstreams, browsing histories, transaction logs, and online reviews. Accurately analyzing this data to understand consumer behaviour and predict purchase intention is crucial for improving recommendation systems, customer retention, and revenue optimization. Traditional statistical approaches struggle to model the non-linear, high-dimensional, and temporal nature of consumer behaviour data. Machine learning (ML) and deep learning (DL) techniques--such as Random Forest, Gradient Boosting, LightGBM, neural networks, transformers, and graph neural networks--have emerged as powerful data-driven solutions [2]. This review systematically surveys recent research (2020-2025) on ML-based consumer behaviour analysis and purchase intention prediction in e-commerce. We examine datasets, modeling techniques, performance metrics, and key findings across representative studies. The review highlights that advanced models frequently achieve high predictive performance (often exceeding 90% accuracy or AUC) [4]. However, challenges related to generalization, scalability, real-time deployment, and interpretability remain. This paper identifies critical research gaps and outlines future research directions toward robust, scalable, and explainable e-commerce intelligence systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer Behaviour Analysis, Purchase Intention Prediction, E-commerce Analytics, Machine Learning, Deep Learning, Clickstream Data, Recommendation Systems, Graph Neural Networks, Transformer Models, Explainable AI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study focus on the formulation and evaluation of a herbal face scrub prepared from Oryza sativa (rice). Oryza sativa is rich in bioactive compounds such as vitamins, minerals, and antioxidants that help nourish the skin, remove dead cells, and promote a natural glow. The rice powder acts as a gentle exfoliating agent, effectively cleansing the skin without causing irritation. To enhance the scrub's efficacy, natural ingredients like honey, aloe vera gel, and turmeric may be incorporated for their moisturizing, healing, and antimicrobial properties. The herbal formulation is safe, eco-friendly, and free from synthetic chemicals, making it suitable for all skin types. The study aims to develop a cost-effective and natural alternative to synthetic exfoliants, providing a smooth, radiant, and healthy skin appearance.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Oryza sativa, herbal face scrub, rice powder, natural exfoliant, antioxidant, skin nourishment, turmeric, honey, aloe vera, eco-friendly formulation, chemical-free, skincare, cost- effective.

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Creative Commons Attribution 4.0 and The Open Definition

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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MDD(Major Depressive Disorder), Social Media,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents the design and implementation of an AI-based Resume Analyzer and Job Description Matcher system that automates candidate evaluation by comparing resumes with job requirements. The system leverages Natural Language Processing (NLP) and Machine Learning (ML) techniques to extract relevant information such as skills, experience, and education from resumes and match them against job descriptions. The proposed system calculates a match score, highlights missing skills, and provides improvement suggestions. The solution uses tools such as Python, spaCy, TF-IDF, and cosine similarity, along with optional Large Language Models (LLMs) for intelligent feedback. Experimental results show improved efficiency in recruitment processes and accurate candidate-job matching. The system reduces manual screening effort and enhances decision-making. Index Terms -- Resume Analysis, NLP, Machine Learning, Job Matching, TF-IDF, Cosine Similarity, AI Recruitment

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI Resume Analyzer, Natural Language Processing (NLP), Machine Learning, Resume Screening, Job Description Matching, TF-IDF, Cosine Similarity, Recruitment Automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Maternal nutrition is a pivotal determinant of maternal health, fetal growth, and long-term offspring outcomes . Adequate nutritional status before conception, during pregnancy, and throughout lactation underpins the complex physiological adaptations required to sustain maternal-fetal homeostasis . Deficiencies in essential macro- and micronutrients are strongly associated with adverse outcomes, including maternal anemia, preterm birth, low birth weight, impaired neurodevelopment, and heightened susceptibility to chronic metabolic disorders later in life . This review synthesizes contemporary biomedical evidence alongside classical Ayurvedic insights to provide a comprehensive understanding of maternal nutritional requirements and their systemic implications. Key nutrients--iron, folic acid, calcium, iodine, vitamin D, high-quality protein, and omega-3 fatty acids--play indispensable roles in hematopoiesis, skeletal formation, thyroid regulation, placental function, and fetal neurogenesis . Pregnancy-induced alterations across cardiovascular, respiratory, renal, endocrine, and metabolic systems further amplify nutritional demands, necessitating trimester-specific dietary optimization. However, socioeconomic inequities, food insecurity, cultural dietary restrictions, adolescent pregnancies, and inadequate antenatal care remain substantial barriers to optimal maternal nourishment, particularly in resource-constrained settings. Evidence-based interventions, including targeted micronutrient supplementation, staple food fortification, community-based nutrition education, and strengthened antenatal services, have demonstrated cost-effectiveness in reducing maternal and neonatal morbidity . In parallel, the Ayurvedic framework of Garbhini Poshan and Masanumasika Pathya underscores individualized dietary regimens, psychosomatic balance, and holistic gestational care. Integrating modern nutritional science with culturally contextualized traditional approaches may enhance maternal health strategies and foster sustainable intergenerational well-being. Strengthening maternal nutrition across the reproductive continuum is imperative for advancing safe motherhood and long-term population health equity.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Maternal nutrition; Pregnancy; Micronutrient deficiency; Fetal development; Antenatal care; Public health nutrition; Garbhini Poshan; Intergenerational health

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Oil Palm Boiler Clinker, Structural Lightweight Concrete, Lightweight Aggregate, Strength-to-Weight Ratio, Sustainable Construction Materials

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid spread of digital credentials across aca-demic institutions, recruitment organisations, and online ser-vice platforms has created a pressing demand for dependable certificate authentication tools. Traditional methods of manual verification are inherently slow, subject to human error, and increasingly ineffective against sophisticated document forgeries and tampered QR codes. This paper presents an AI-powered web-based system that addresses these shortcomings by automatically analysing uploaded certificate images and QR code images to determine their authenticity

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake certificate detection, QR code tampering, convolutional neural network, deep learning, document forgery, image classification, fake-probability score, Django, web-based verification

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of electric vehicle (EV) adoption has introduced significant power quality challenges in modern distribution networks. Grid-connected EV charging stations, particularly fast chargers, behave as nonlinear loads that inject harmonic currents, increase Total Harmonic Distortion (THD), and degrade voltage quality. With increasing EV penetration, aggregated harmonic effects can lead to transformer overheating, additional power losses, and malfunction of sensitive equipment. This review paper examines harmonic generation in EV charging infrastructures and evaluates advanced mitigation techniques to maintain grid stability. Existing solutions including passive filters, active power filters (APFs), smart charging coordination, and renewable energy-assisted compensation are critically analyzed. Special attention is given to photovoltaic (PV)-assisted shunt active power filters and multilevel inverter-based compensation strategies that simultaneously supply clean energy and reduce harmonics. The review highlights control techniques such as synchronous reference frame theory, instantaneous power theory, and model predictive control for real-time harmonic cancellation. Key challenges including DC-link voltage stability, fluctuating renewable generation, and regulatory limitations are also discussed. The study concludes that integrated PV-storage active filtering solutions offer an effective and sustainable approach for harmonic mitigation in future high-density EV charging networks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Electric Vehicle Charging Stations, Harmonic Mitigation, Shunt Active Power Filter, Photovoltaic Integration, Power Quality etc.

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Creative Commons Attribution 4.0 and The Open Definition