Volume 14 | Issue 4

 Abstract

Every year, plant diseases cause large losses in crop yield, posing a serious threat to global food security. Effective intervention depends on a prompt and precise diagnosis of the disease, but farmers often lack access to specialists in plant pathology, particularly in rural areas. In order to help with plant disease diagnosis, this paper introduces PlantDocBot, a multimodal AI-driven chatbot that can accept both textual symptom descriptions and leaf images. The system combines Natural Language Processing (NLP) for symptom-based diagnosis and a Convolutional Neural Network (CNN) for image-based disease identification in an intuitive chat interface. The use of transfer learning (MobileNet/ResNet-based models) for effective image classification and a fine-tuned transformer (BERT-based) to understand symptoms reported by farmers are two important innovations. Once a disease has been identified, a treatment recommendation module suggests evidence-based treatments based on a carefully curated knowledge base. With a Streamkit-built frontend prototype that allows image upload and real-time communication, we deployed PlantDocBot using HuggingFace Transformers for natural language processing and TensorFlow for CNN model training. A brief user study reveals the chatbot's responses are pertinent and useful, and evaluation on benchmark datasets demonstrates high disease detection accuracy (up to 95% top-1 accuracy). Small-scale farmers can now access real-time, affordable diagnosis thanks to PlantDocBot's scalable approach to democratizing plant disease expertise. To further close the knowledge gap in agriculture, future work will investigate voice interfaces, multilingual support, and increased coverage of disease.

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PlantDocBot, Plant disease classification, Computer vision in agriculture, BERT, Transfer learning, Smart farming

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The rapid evolution of digital agriculture had necessitated the development of intel-ligent systems that could bridge the in-formation gap for smallholder farmers. This project presents an AI-Based Farmer Query Support and Advisory System, a combined framework designed to provide real-time and data-driven agricultural guidance through advanced computational techniques. To leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), this system ensures that queries related to crop management, pest control, and soil health are answered with high factual accuracy, minimizing the risks of AI hallucinations often found in standard generative models [1], [2], [11]. A core challenge in global agriculture is the linguistic barrier; as a result, this sys-tem implements multilingual and cross-lingual architectures to support low-resource languages, ensuring that farmers can interact in their native dialects while the system retrieves expert knowledge from scientifically validated English cor-pora [3], [8], [21]. To enhance the robust-ness of its recommendations, the frame-work utilizes Multi-Modal Fusion and IoT-based real-time data integration [9], [15]. This allows the "Advisory and Rec-ommendation Engine" to process diverse inputs , ranging from voice and text que-ries to real-time weather forecasts and soil sensor data , to generate site-specific ad-vice [4], [7], [22]. Furthermore, the system incorporates Deep Learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to provide accu-rate agricultural commodity price fore-casting and yield predictions [5], [12]. This predictive capability empowers farmers with market intelligence and poli-cy scheme optimization, facilitating better financial decision-making [15], [19]. By ensuring accessibility in remote regions with limited connectivity, this architecture includes an Offline Edge AI framework, enabling localized processing of sensor data and immediate advisory delivery [6], [22]. The integration of a Feedback and Learn-ing Module creates a closed-loop system where user interactions and expert-vetted "Golden Q&A" datasets are used to itera-tively improve the model's performance [4], [11]. By combining state-of-the-art AI innovation with practical agricultural ex-tension needs, this system serves as a scalable roadmap for the digital transfor-mation of global agrifood systems, ulti-mately enhancing productivity, sustaina-bility, and food security for smallholder farmers [18], [20], [23].

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 Keywords

Large Language Models (LLMs) [1, 21], Retrieval-Augmented Generation (RAG) [3, 23], Random Forest Classifier [2, 12], Nearest Neighbor [3, 21], Multi-Modal Fusion [15, 17], Agricultural Advisory System [7, 10], Voice-based Farmer Sup-port [8, 15], Smallholder Farmers [4, 11, 20], and Market Intelligence [5, 15].

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Jaunsar Bavar kshtra Main Prachalit Khumdi Vyavastha : Ek Sameekshatmak Adhyan

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Jaunsar Bavar kshtra Main Prachalit Khumdi Vyavastha : Ek Sameekshatmak Adhyan

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This study explores school-community partnerships to promote inclusive and quality education and to make inclusion more meaningfully successful as successful inclusive education requires mobilizing the cooperation of families, as well as the support of the community, voluntary organizations, and the general public. Leveraging community resources and local partnerships fosters high-quality educational and enrichment opportunities; this is because it broadens the scope of experiences typically offered to all students particularly those with special needs while simultaneously enhancing their access to local expertise (Kaushik, 2019). Parents should also play a role in formulating policies related to inclusion. They should be involved in supporting the changes necessary to develop inclusive schools. Teachers holding senior positions bear a responsibility toward the wider community as well; they should not only strive to improve or develop their own schools but also collaborate with stakeholders outside the school premises (Sharma, 2021). When families and communities stand united with educational institutions, they ensure that inclusion is not merely a policy mandate but a lived reality, providing every child, regardless of ability, with the equal opportunity to learn, grow, and thrive.

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Inclusive education, Inclusive practices, School-community partnerships, Community resources, Family support

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Bereavement, Conversational AI, Retrieval-Augmented Generation, Financial Guidance, Digital Asset Management, Natural Language Processing, India.

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The safety and reliability of highway bridges are critical for modern transportation systems, as structural failures can lead to serious economic losses and safety hazards. This work proposes an advanced monitoring and maintenance framework based on Wireless Sensor Networks (WSNs) to continuously observe the structural health of highway bridges. In the proposed system, multiple sensors such as strain gauges, vibration sensors, temperature sensors, and displacement sensors are strategically installed on different parts of the bridge to collect real-time structural data. The sensed information is transmitted wirelessly to a central monitoring unit where embedded processing and data analysis techniques evaluate the bridge condition and detect abnormal structural behaviour at an early stage. The system enables continuous condition assessment, early fault detection, and predictive maintenance, thereby reducing the risk of sudden structural failure and minimizing maintenance costs. By integrating wireless communication, embedded processing, and real-time monitoring, the proposed approach significantly improves bridge safety, operational efficiency, and long-term infrastructure management.

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Wireless Sensor Networks (WSN), Structural Health Monitoring, Bridge Safety, Embedded Monitoring System, Real-Time Data Acquisition, Vibration Sensors, Strain Measurement, Predictive Maintenance, Infrastructure Monitoring, Remote Bridge Monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Yuva aur maanasik svaasthya

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Yuva aur maanasik svaasthya

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In the present paper, it has been argued how the law on Geographical Indications (GI) provides the best way of protecting Traditional Knowledge (TK). It has been illustrated with reference to the Indian legislation enacted to provide domestic protection to the GIs and its provisions which recognize GIs as a sui generis form of IPRs. Unlike any other Intellectual Property Right (IPR), the protection granted by GIs to an object rests upon a whole community instead of an individual. The evolution of the law on GIs starting from the Uruguay Round of GATT till the Doha Work Programme has been discussed in brief in order to demonstrate the ongoing conflict between the developed and developing countries over the extent of protection accorded to a GI object under the law. Further, criteria for the protection of TK under the GI have also been analyzed. The discussion is based on several aspects like equity and justice, protection of biological diversity, avoidance of biopiracy, etc. The interrelation between GI and TK has been established with respect to two types of protection, namely, constructive and defensive. Examples have been taken from the state of Assam (India) to illustrate how the registration of certain objects under GIs have not only provided economic incentives to the locals but also facilitated the transmission of traditional skills within generations as well as ensured community identification and self-governance. However, it must be pointed out that while GI provides a label or reputation to a product, it is not able to protect the knowledge base and the skill sets used in manufacturing the goods. Finally, it has been concluded that even though GI may not replace TK completely, the former should be treated as an important complement to the latter in ensuring adequate protection. In fact, GI law must be made more robust in consonance with the overall intellectual property rights regime.

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Geographical Indications, Traditional Knowledge, TRIPS Agreement, Intellectual Property Rights, Biopiracy, Cultural Heritage, GI Act 1999, Muga Silk, Assam, Sui Generis Protection

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Creative Commons Attribution 4.0 and The Open Definition

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This paper examines whether Plato's theory of class, as presented in The Republic, can be used to justify the Indian caste system. Plato divides society into three classes--rulers, auxiliaries, and producers--based on natural ability and the principle of justice, where each person performs the role best suited to their nature. Similarly, the Indian caste system (varna system) also organizes society into hierarchical groups with specific duties. At a superficial level, both systems appear similar because they emphasize division of labour and social order. However, this paper argues that the philosophical foundations of the two are significantly different. Plato's model allows for mobility based on merit and education, whereas the caste system is largely hereditary and rigid. Moreover, Plato's idea of justice is rooted in harmony and efficiency, while the caste system has historically been associated with social inequality and exclusion. By revisiting Plato in the Indian context, this study critically evaluates whether his class theory can genuinely justify caste. It concludes that although there are structural similarities, Plato's theory cannot be used to defend the caste system in its traditional form.

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Key Words: Ambedkar,caste, class, justice, Plato

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Rabindranath Tagore, India's most celebrated literary figure, was far more than a poet and novelist. He was a visionary thinker who championed women's empowerment in colonial India when such advocacy was revolutionary. This paper examines Tagore's philosophy on women's rights and development through an analysis of his writings. The paper traces the introduction of the concept of true liberation for women through education and independence, showing how Tagore worked toward challenging societal norms that confined women to domestic roles. It explores how Tagore envisioned building women's strength and identity beyond traditional expectations. The paper analyzes his concept of the new woman, educated, independent, and socially conscious, as a transformative force for Indian society. Through examination of notable quotes on women, the paper demonstrates how Tagore articulated a vision grounded in voices of strength and autonomy. The paper presents women's empowerment in the words of Rabindranath Tagore, showing his multifaceted approach to gender equality. The discussion section synthesizes Tagore's interconnected ideas about education, economic independence, and intellectual freedom. The paper concludes with suggestions that Tagore's nineteenth-century vision remains deeply relevant to contemporary struggles for women's empowerment.

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Rabindranath Tagore, women's empowerment, true liberation, education, challenging societal norms, gender equality.

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Creative Commons Attribution 4.0 and The Open Definition

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Twisted wire jewellery represents a unique intersection of cultural heritage and mathematical reasoning. Across civilizations, ornaments have embodied geometric principles such as symmetry, proportion, torsion, and periodicity, revealing how aesthetic beauty is deeply tied to mathematical logic. Building on earlier computational design frameworks , this study develops a unified methodology for modeling and classifying twisted wire motifs through parametric equations and shape grammar transformations. A structured taxonomy is introduced, encompassing both fundamental motifs--spiral, heart, petal, knot, and braid--and compound forms such as spiral-heart, petal-spiral, and triple configurations. Unlike prior work that focused on isolated replication, this research demonstrates how designs can expand into generative families, each carrying cultural meaning and mathematical coherence. The findings establish twisted wire ornaments as "wearable mathematics," offering a foundation for algorithmic design, digital fabrication, and interdisciplinary exploration in ethnomathematics and computational design.

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Twisted wire jewellery, Ethnomathematics, Parametric modeling, Shape grammar, Motif taxonomy, Compound motifs, Generative design, Geometric invariants, Wearable mathematics, Computational jewellery design, Cultural heritage preservation, Algorithmic design, Digital fabrication

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In today's digital age, technology has become an essential part of everyday life. While it has made communication, business, and education easier, it has also created new opportunities for crime. Cyber crime is one of the fastest-growing forms of criminal activity in the modern world. This paper explains what cyber crime is, why it is increasing, the different forms it takes, and how it affects individuals and society. It also discusses the difficulties in controlling cyber crime and suggests practical ways to prevent it. The goal is to present the topic in a clear and relatable way so that readers can better understand the risks and take steps to protect themselves.

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Cyber Crime, Cyber Security, Phishing, Hacking, Ransomware, Identity Theft, Online Fraud, Social Engineering, Data Breach, Digital Payments, UPI Fraud, Malware, Internet Security, Cyber Law,

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Creative Commons Attribution 4.0 and The Open Definition

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Maternal mortality remains a major global health concern, particularly in developing countries like India. One of the effective tools for reducing maternal and neonatal complications during labor is the partograph. The partograph is a simple, cost-effective graphical tool used to monitor the progress of labor and the condition of the mother and fetus. Despite its proven benefits, its utilization is often inadequate due to lack of knowledge and skills among healthcare providers. This study aims to assess the knowledge regarding the use of partograph among staff nurses and GNM students and to improve their understanding through educational interventions. The findings of the study are expected to contribute towards improving maternal healthcare services and reducing complications associated with labor.

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Partograph, Maternal Mortality, Labor Monitoring, Staff Nurses, GNM Students

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: Background: Commando surgery (combined mandibulectomy and neck dissection) is a radical procedure for advanced head and neck cancers, requiring specialized postoperative care. Nursing officers play a critical role in recovery, yet structured training and standardized protocols are often lacking, particularly in resource-limited settings. Objectives: To assess the baseline knowledge of nursing officers regarding post-Commando surgery care, implement a structured Care Bundle, evaluate its effectiveness on knowledge, compare pre- and post-test scores, and determine associations with demographic variables. Methods: A quasi-experimental one-group pre-test post-test design was used with 60 nursing officers at Apollo Sage Hospital, Bhopal, selected via convenience sampling. A structured knowledge questionnaire (27 items) was administered pre-intervention. The intervention consisted of a validated self-instructional booklet and structured teaching session on post-Commando surgery care. A post-test was conducted after 7 days. Data were analyzed using descriptive statistics, paired t-test, and ANOVA. Results: Pre-test knowledge was inadequate in 51.7% of participants, moderate in 38.3%, and adequate in only 10%. Post-test scores improved dramatically: 90% achieved adequate knowledge, 8.3% moderate, and only 1.7% remained inadequate. The paired t-test confirmed a highly significant knowledge gain (p < 0.001). ANOVA revealed a significant association between knowledge gain and professional designation (p = 0.03), but not with age or gender. Conclusion: The structured Care Bundle significantly enhanced nursing officers' knowledge regarding post-Commando surgery care. Integrating such evidence-based bundles into nursing education and clinical protocols is recommended to standardize care, reduce complications, and improve patient outcomes.

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Keywords: Care Bundle, Commando Surgery, Head and Neck Cancer, Nursing Knowledge, Quasi-Experimental Study, Postoperative Care

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For indigenous communities, forests are not only habitats but living pharmacies where traditional medicine evolves through sustained interaction among humans, animals, and ecological systems. However, the growing intensity of human-animal conflict has profoundly altered this relationship. Expanding conservation controls, habitat fragmentation, and conflict-driven forest restrictions have increasingly limited indigenous access to medicinal resources, placing traditional healing practices at risk of extinction. This paper examines the impact of human-animal conflict regimes on indigenous systems of traditional medicine, situating the analysis within the broader framework of forest governance, conservation law, and indigenous rights. It argues that contemporary conflict management strategies often designed with an exclusive focus on wildlife protection and human safety frequently overlook the healthcare needs and cultural practices of forest-dependent communities. As a result, indigenous healers face marginalization, criminalization of customary practices, and erosion of intergenerational medicinal knowledge. Adopting a socio-legal and rights-based approach, the study critically analyses relevant legal frameworks, including wildlife conservation laws, forest rights legislation, and international instruments such as the UN Declaration on the Rights of Indigenous Peoples and the Convention on Biological Diversity. The paper highlights the tension between conservation imperatives and indigenous health systems, particularly in conflict-prone forest landscapes where access to medicinal plants and traditional spaces is increasingly regulated or denied. The paper contends that the silencing of forest healers represents not merely a cultural loss but a violation of the right to health, livelihood, and cultural identity of indigenous communities. It concludes by advocating for inclusive human-animal conflict management frameworks that recognize traditional medicine as an essential component of sustainable coexistence. Integrating indigenous knowledge into conservation and conflict mitigation policies is crucial for ensuring ethical, equitable, and sustainable solutions.

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Indigenous Peoples, Traditional Medicine, Human-Animal Conflict, Indigenous Rights, Forest Rights.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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?????? ????????, ???? ???????, ????? ????????, ???? ???????, ????????, ??????? ???, ???? ????, ???????, ???? ???????

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Creative Commons Attribution 4.0 and The Open Definition

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Human-animal conflict has emerged as one of the defining governance dilemmas of the Anthropocene, an epoch marked by profound human alteration of ecological systems. As expanding cities, linear infrastructure, extractive economies, and intensive land-use practices encroach upon natural habitats, the boundaries between human and non-human worlds increasingly blur. The consequences are often violent and unequal. It manifests in loss of human life, erosion of livelihoods, ecological fragmentation, and creates deep ethical questions about coexistence in a shared landscape. This paper approaches human-animal conflict not merely as a problem of wildlife conservation or management, but as a complex legal and human rights issue embedded in development choices and institutional failures. It critically examines the legal and policy frameworks governing conflict mitigation, with reference to environmental law, forest governance, wildlife protection regimes, and disaster management responses. Particular attention is paid to the lived experiences of indigenous peoples, forest-dependent communities, and other marginalised groups who bear a disproportionate share of conflict-related harms. These often persists without adequate legal recognition, protection, or remedy. At the same time, the paper explores the evolving normative space within environmental governance that increasingly acknowledges animal welfare, ecological integrity, and interspecies ethics. It interrogates the scope of criminal and civil liability arising from human-animal conflict, assessing state responsibility, administrative accountability, and the limitations of individualised, fault-based legal approaches in addressing structurally produced ecological harm. Compensation and rehabilitation mechanisms are analysed through the lens of environmental justice, revealing persistent gaps in accessibility, adequacy, and effective implementation. The paper further evaluates the role of technology, such as, early warning systems, geospatial mapping, and data-driven monitoring, as both a tool of innovation and a site of new governance challenges, including concerns relating to surveillance, exclusion, and distributive equity. Situating domestic responses within the broader architecture of international environmental law, biodiversity conservation treaties, and cross-border wildlife governance, the paper ultimately argues for a paradigm shift. It calls for a move away from reactive, compensation-centric models towards rights-based, community-centred, and preventive governance frameworks capable of fostering sustainable and ethically grounded coexistence between humans and the more-than-human world.

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Human-Animal conflict, Anthropocene Governance, Human Rights, ESG, Surveillance Technology.

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Creative Commons Attribution 4.0 and The Open Definition

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Smart cities, GPS, web technologies, complaint redressal, spatial analytics, e-governance, Spring Boot.

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The purpose of this paper is to introduced an encryption and decryption algorithm that uses Fuzzy logic to improve data communication security and efficiency. The technique adds complexity and randomness to the cryptographic process by combining Spiral rotation transformations with fuzzification and intensification operations because it transforms crisp input (a precise number) into a fuzzy linguistic value. that bears no relation to the primary data. This fact leads to considerable ambiguity on the part of attackers trying to guess the value of the key or the plain text, making the system more robust against security attacks.

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Fuzzy logic, Spiral rotation, Encryption and Decryption, fuzzification and intensification.

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Among the factors that have caused agriculture to have low productivity are climate variability, imbalance of nutrients in soils, and poor choice of crops, this paper presents a Smart Crop Advisory System that employs machine learning with real-time environmental information to make agricultural decisions. The system analyses such parameters as nitrogen (N), phosphorus (P), potassium (K), pH, temperature, humidity, and rainfall and recommends appropriate crops based on a Random Forest machine learning model. The OpenWeatherMap API provides weather data in real-time to improve the quality of forecasts and provide an understanding of the climate. The system also has the capability of yield prediction, irrigation instructions and soil analysis features, though smart agricultural technologies are developing fast. Many studies have explored mainly single functionalities like crop prediction with machine learning model, soil nutrient analysis, or environmental monitoring with the help of IoT devices [1], [4]. Even though these strategies have been performing well under controlled environments, the strategies normally lack real-time data, which is critical in controlling dynamical agricultural settings, as affected by climate change. In addition, other systems related to the IoT may constantly monitor the environmental conditions and parameters, such as soil moisture, temperature, and humidity, but they do not tend to provide predictive analytics and smart decision-support systems that can further transform raw data into farm-indicative information [9]. which are an interactive Flask-based dashboard. Experimental testing of this system has achieved an accuracy of approximately 94% confirming the effectiveness of the system in precision agriculture.

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Creative Commons Attribution 4.0 and The Open Definition

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Precision agriculture, Machine learning, Crop recommendation, Smart farming, Random Forest.

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Creative Commons Attribution 4.0 and The Open Definition