Volume 14 | Issue 4 |

 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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 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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 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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 Keywords

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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 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

 Abstract

Native advertising has become a vital component of digital marketing strategies, offering a unique opportunity for brands to connect with their target audiences in a non-intrusive manner. By seamlessly integrating with surrounding content, native advertisements can enhance brand awareness, build credibility, and drive consumer engagement. This form of advertising also provides measurable returns on investment and can be tailored to specific audiences and platforms, making it an attractive option for marketers. However, native advertising raises important concerns regarding transparency, ethics, and regulatory compliance. To ensure effectiveness, such advertisements must be clearly labeled as sponsored content, provide relevant and valuable information to the audience, and comply with applicable regulations. When executed effectively, native advertising can create a mutually beneficial outcome for both brands and consumers by delivering meaningful content while achieving marketing objectives. This study examines the effectiveness of native advertising in digital marketing campaigns by analyzing its benefits, challenges, and best practices. It aims to provide insights into how businesses can leverage native advertising to enhance customer engagement, build brand loyalty, and achieve sustainable growth in a highly competitive digital environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Native Advertising, Digital Marketing, Digital Advertising, Content Marketing, Sponsored Content, Brand Awareness, Consumer Engagement, Targeted Advertising, Online Advertising, Marketing Effectiveness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sleep and anxiety disorders are extremely popular and co-morbid diseases that greatly deteriorate the quality of life, cognitive abilities, and health in general. The shortcomings of traditional pharmacotherapies, such as adverse effects, tolerance and dependence have resulted in the growing enthusiasm to explore herbal substitutes with superior safety profiles. The purpose of this review is to assess the therapeutic potential of the chosen herbal actives in treating anxiety and sleep disorders, their mechanisms of action, and formulation into form of convenient delivery systems. The databases used in a thorough literature search, including PubMed, Scopus, and Google Scholar, were searched in the period 2000-2025 with the priority on human clinical evidence with preclinical results. Pharmacological effects of key botanicals such as Withania somnifera, Nelumbo nucifera, Ocimum sanctum, Myristica fragrans, and Lavandula angustifolia were studied. These results suggest that these herbal actives have multi-targeted effects such as hypothalamic-pituitary-adrenal axis modulation, GABAergic neurotransmission, serotonergic pathway regulation, and antioxidant activity, which can lead to better sleep and less anxiety. Also, it has been shown that these actives can be incorporated into herbal oral dissolving films (HODFs), showing possible benefits in terms of bioavailability and patient compliance. To sum up, herbal actives are a promising multi-mechanistic treatment to sleep and anxiety disorders, but additional well-designed clinical trials are needed to confirm the effectiveness and standardization of multi-herbal preparations in the wider treatment options.

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 Keywords

: Sleep Disturbance; Anxiety Disorder; Herbal Extract; Nutraceuticals; Oral Dissolving Film

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid expansion of online shopping platforms has significantly increased the variety of products available to users, making the decision-making process more complex and time-consuming. Conventional recommendation systems, such as collaborative filtering and content-based approaches, often struggle to accurately interpret user intent expressed in natural language and typically lack transparency in their outputs. This paper presents a novel design of an AI-driven explainable recommendation system that integrates Large Language Models (LLMs), semantic similarity search using FAISS, and interpretable machine learning techniques such as SHAP. The proposed system is capable of processing user queries in natural language, extracting meaningful preferences, and retrieving contextually relevant products from large datasets. Additionally, it generates clear and human-understandable explanations for each recommendation. By combining advanced language understanding with explainability and efficient retrieval mechanisms, the system improves recommendation accuracy, enhances transparency, and increases user trust. The proposed framework demonstrates the potential of integrating modern AI techniques to build intelligent and usercentric recommendation systems. Index Terms--Explainable Artificial Intelligence (XAI), Recommendation Systems, Large Language Models (LLMs), Semantic Search, FAISS, SHAP, Natural Language Processing (NLP), E-commerce, Personalized Recommendations, Information Retrieval

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Explainable Artificial Intelligence (XAI), Recommendation Systems, Large Language Models (LLMs), Semantic Search, FAISS, SHAP, Natural Language Processing (NLP), E-commerce, Personalized Recommendations, Information Retrieval

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Communication between hearing-impaired individuals and people who do not understand sign language is often difficult. Sign language is the primary method used by deaf and mute individuals to express their thoughts and emotions, but many people are not familiar with it. This communication gap creates challenges in education, healthcare, and daily interactions. The Telugu Sign Language Translator using Deep Learning aims to bridge this gap by automatically recognizing hand gestures used in Telugu sign language and converting them into readable text or speech. The system uses computer vision techniques to capture hand gesture images through a webcam and processes them using a deep learning model. A Convolutional Neural Network (CNN) is trained to identify and classify different Telugu sign gestures accurately. Once the gesture is recognized, the system translates it into corresponding Telugu text and optionally generates voice output using text-to-speech technology. This allows normal users to understand the message conveyed by the sign language user in real time. The proposed system helps improve accessibility and communication for hearing-impaired individuals. By integrating deep learning, computer vision, and speech technologies, the model provides an intelligent and efficient solution for translating Telugu sign language. This technology can be further extended to mobile applications, real-time translation systems, and multilingual sign language recognition platforms. The system is designed to capture hand gestures using a webcam or camera device and process the input images through image preprocessing techniques such as resizing, normalization, and background removal. These processed images are then fed into a trained deep learning model that extracts important features of the hand gestures and classifies them into predefined Telugu sign language categories. The deep learning architecture, primarily based on Convolutional Neural Networks (CNN), is capable of learning complex patterns from gesture images and providing accurate predictions. The recognized gestures are mapped to their corresponding Telugu characters, words, or phrases. The output is displayed as Telugu text on the screen and can also be converted into speech using a text-to-speech module, allowing users to hear the translated message. This system can be highly beneficial in educational institutions, public service centers, hospitals, and workplaces where effective communication with hearing-impaired individuals is essential. By providing real-time gesture recognition and translation, the model reduces dependency on human interpreters and improves communication efficiency. Furthermore, the project highlights the potential of artificial intelligence in building inclusive technologies that support people with disabilities. Future improvements may include expanding the dataset to cover more gestures, improving model accuracy using advanced architectures, supporting continuous sign language sentences, and developing mobile or web-based applications for wider accessibility. Overall, the Telugu Sign Language Translator using Deep Learning represents an important step toward enhancing accessibility, promoting social inclusion, and leveraging modern AI technologies to solve real-world communication challenges.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Telugu Sign Language, Deep Learning, Computer Vision, Gesture Recognition, Convolutional Neural Network (CNN), Sign Language Translation, Image Processing, Machine Learning, Hand Gesture Detection, Text-to-Speech (TTS), Human-Computer Interaction, Accessibility Technology, Artificial Intelligence, Real-Time Gesture Recognition.

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Creative Commons Attribution 4.0 and The Open Definition