Proceeding ICSEMT June 2026

An Arduino-Based Integrated System For Environmental Control And Intrusion Detection In Smart Agriculture

Authors: Kulkarni Mrunmayee Sanjay, Prof. Dr. Shirish Kulkarni

Abstract: This research provides the design, development, and evaluation of an affordable smart agriculture system using an Arduino Uno microcontroller. The system includes real-time environmental monitoring—temperature, light intensity, and soil moisture—coupled with a local security aspect. A digital temperature sensor, e.g., the DHT11, and a Light Dependent Resistor (LDR) measure the ambient conditions, while a soil moisture sensor measures the moisture levels in the soil. An automatic cooling fan, regulated by a relay module, is powered to mitigate excessive temperature, thereby providing an ideal microclimate. In the security aspects, an Infrared (IR) sensor detects illegal access, which causes an alarm through a buzzer. Experimental verification verifies the system performance in providing favorable environmental conditions while issuing timely alerts for illegal access. This combined approach offers a realistic and general solution for small to medium-scale agribusinesses, thus improving crop management and farm security.

DOI: https://doi.org/10.5281/zenodo.20132972

Virtual Canvas: A Dual-Pipeline Benchmark Of MediaPipe And YOLOv11-Pose

Authors: Jatin Jain, Dr. Sakshi Indolia

Abstract: This paper presents Virtual Canvas, a real-time touchless drawing application that simultaneously executes two hand-pose estimation pipelines — Google MediaPipe Hands and a pre-trained YOLOv11-Pose model — on every captured webcam frame. The dual-pipeline architecture eliminates input variance between models, enabling a controlled side-by-side experimen- tal comparison under identical real-world conditions. Across 11 sessions spanning 10 days of live evaluation on CPU-only hardware, 1,391 performance samples were captured at 500 ms intervals via automated CSV logging, covering inference latency, frames per second, CPU utilisation, hand detection counts, and lighting conditions. Results demonstrate that MediaPipe achieves a 2.35× lower mean inference latency than YOLOv11-Pose (t = −120.68, p < 0.0001, Cohen’s d = 4.58). Under dim lighting, YOLOv11-Pose inference variance increased by 133% while MediaPipe remained stable, though MediaPipe latency itself rose by 18.2%. YOLOv11-Pose exhibited systematic over- detection, reporting two hands in 81.6% of single-hand frames. Exponential Moving Average (EMA) smoothing (α = 0.35) and 5-frame gesture debouncing enabled fluid drawing interaction despite sub-5 FPS dual-pipeline throughput. The system provides a practical, data-driven benchmarking framework for selecting between lightweight pre-trained detectors and heavier single- stage models in human-computer interaction applications.

DOI: https://doi.org/10.5281/zenodo.20135316

FairScan: A Dual-Stage Bias Detection And Mitigation Framework For Machine Learning Classification Models

Authors: Shubhi Bhardwaj, Dr. Yatu Rani

Abstract: Bias in machine learning models is one of the biggest concerns in today’s AI-driven world. When models are trained on data that reflects real-world inequalities, they end up making unfair predictions that can harm people based on their gender, race, or age. This paper introduces FairScan, a two-stage framework designed to first detect and then actively reduce bias in classification models. The detection stage uses a new metric called the Statistical Parity Divergence Score (SPDS), which measures bias not just across individual groups but also at the intersections of multiple sensitive attributes. The mitigation stage applies a custom training strategy called Reweighted Fair Gradient Descent (RFGD), which adjusts how much the model learns from different groups during training to push it toward fairer outcomes. We tested our approach on the UCI Adult Income dataset and found that FairScan reduced the Demographic Parity Difference by up to 79.4% while maintaining a classification accuracy of 86.7%. Our results show that it is genuinely possible to build models that are both accurate and fair, which is a step forward for responsible AI development.

DOI: http://doi.org/10.5281/zenodo.20226534

AegisIDS: An Adaptive Hybrid Intrusion Detection System For Intelligent Cyber Defense

Authors: Muskan, Dr. Yatu Rani

Abstract: The evolution of cyber threats requires security methods that are smarter, more adaptive, and tailored to the unique properties of web technology beyond the capabilities of traditional IDS. AegisIDS [10] – An adaptive hybrid intru-sion detection system combining signature based and machine learning-driven anomaly detection for greater accuracy and responsiveness. The new system has been proposed using several techniques such as dynamic data sampling technique, optimized feature selection, and ensemble learning to solve problems related to class imbalance, false positive rate and detection latency. AegisIDS is built to work well for today environments including Cloud, Internet of Things (IoT), and enterprise network. Ex-perimental insights from recent hybrid IDS studies demonstrate that combining adaptive learning with hybrid architectures significantly improves detection rates and reduces false alarms. This paper discusses the architecture, methodology, performance considerations, and future scope of AegisIDS.

DOI: https://doi.org/10.5281/zenodo.20227328

Animator – Ai-Powered Text-To-Video Animation System

Authors: Prof. Sachin Dhawas, Dhruv Gonnade, Chetan Parate, Gaurav Madavi, Vighnesh Durge, Girish Charpe

Abstract: In today’s digital age, video content plays a major role in communication, learning, and marketing. However, creating animated videos is still a complex and time-consuming task that requires technical skills, expensive software, and professional expertise. Because of this, many students, educators, and small businesses find it difficult to create high-quality animation content. To solve this problem, we developed “Animator”, an AI-powered text-to-video animation system that makes video creation simple and accessible for everyone. The system allows users to generate complete animated videos just by entering a text prompt. It uses advanced technologies such as natural language processing for script generation, Stable Diffusion for image creation, AnimateDiff for animation, and text-to-speech models for voiceover generation. It also automatically generates subtitles and combines all elements into a final video. The system is designed using multiple modules, including input processing, script generation, visual creation, audio generation, subtitle generation, and video rendering. By automating these steps, the system reduces manual effort, saves time, and lowers the cost of video production. The results show that the proposed system can generate quality animated videos efficiently with minimal user input. This project demonstrates how artificial intelligence can act as a powerful creative tool and make content creation easier, faster, and more accessible for everyone.

DOI: https://doi.org/10.5281/zenodo.20324353

Artificial Intelligence, IoV, And Security In Modern Intelligent Systems: A Holistic Study

Authors: Mustaq Kunnur

Abstract: Recent breakthroughs in Artificial Intelligence (AI), Internet of Vehicles (IoV), Brain–Computer Interfaces (BCI), blockchain security, autonomous driving, and speech processing are reshaping intelligent communication and automation systems. This review synthesizes 60 contemporary research contributions across secure vehicular networks, interpretable transfer learning for BCI, LLM-assisted 6G IoV communication, federated edge learning, digital twins, and post-quantum blockchain frameworks. We highlight the paradigm shift from performance-driven AI toward trust-centric, interpretable, and quantum-resilient architectures. While state-of-the-art systems demonstrate remarkable gains—such as 89.7% accuracy in BCI applications and an 80% reduction in IoV verification overhead—the transition to pervasive edge-cloud environments exposes persistent challenges. Computational complexity, thermal throttling, data poisoning, and hardware dependencies remain critical barriers to scalable real-world deployment. Our analysis underscores both the promise and the unresolved hurdles of next-generation intelligent systems.

DOI: http://doi.org/10.5281/zenodo.20523052

Transforming Visual Intelligence: Advances In Image Processing And Smart Vision Systems

Authors: Siddalingesha G R

Abstract: Image processing has become one of the most important research areas in electronics, artificial intelligence, computer vision, healthcare, and industrial automation. Modern intelligent systems require accurate and real-time image analysis for applications such as medical diagnosis, autonomous vehicles, surveillance systems, robotics, agriculture, and remote sensing [1][4]. Recent developments in deep learning, Edge AI, AIoT, and quantum computing have significantly improved the performance of image processing systems [9][11][14]. This conference paper presents a comprehensive overview of modern image processing techniques including image enhancement, image restoration, segmentation, compression, feature extraction, and object recognition [1][2]. The paper also discusses recent advancements in deep learning-based vision systems, Edge AI architectures, intelligent surveillance, agricultural image analytics, and medical imaging applications [5][6][12][13]. Furthermore, the paper highlights current challenges, emerging trends, and future research directions in intelligent image processing systems.

DOI: http://doi.org/10.5281/zenodo.20507707

A Privacy Preserving Hybrid Deep Learning Framework With Block Chain Anchored Federated Training And Explainable Reasoning For Predictive Analytics In Industrial IoT

Authors: Asha Rani, Mukesh Singla

Abstract: Industrial Internet of Things (IIoT) deployments now stream terabyte-scale telemetry from programmable controllers, vibration sensors, smart meters and edge gateways every day. Predictive analytics on this data for fault diagnosis remaining useful life estimation, energy optimisation and anomaly screening has become a core operational requirement rather than a research curiosity. Yet the prevailing pattern of shipping raw plant data to a central cloud for model training exposes operators to data exfiltration, model-poisoning, regulatory penalties under GDPR and emerging EU AI Act obligations, and the growing class of adversarial perturbation attacks documented in 2024–2025 IIoT security literature. This paper proposes a layered framework that pairs a CNN–LSTM feature extractor with an Adaptive Neuro-Fuzzy Inference System (ANFIS) decision module, distributes training across edge nodes through a FedProx-based federated protocol with client-side differential privacy, anchors model-update integrity on a permissioned blockchain (Hyperledger Fabric with PBFT consensus), and surfaces decision rationale through SHAP attributions and ANFIS rule traces. The architecture targets the four properties that recent IIoT studies identify as gating industrial adoption: predictive accuracy, data confidentiality, tamper-evident auditability, and human-readable explanations. The paper articulates the design rationale, layer-wise responsibilities, expected performance envelope, and the trade-offs that practitioners must weigh between privacy guarantees, communication overhead, and latency on resource-constrained edge hardware.

DOI: http://doi.org/10.5281/zenodo.20536923

A Review On Security In The 6G-Enabled Internet Of Vehicles

Authors: Bhagyashri M

Abstract: Advertising has evolved from a mere transactional tool to a powerful emotional conduit that shapes consumer perceptions and behaviors. In the fast-moving consumer goods (FMCG) sector, emotional advertising has become a cornerstone strategy for building brand loyalty and enhancing brand recall. This study examines the effectiveness of emotional advertising in fostering brand recall, with a focused analysis on Cadbury Dairy Milk, one of India's most iconic chocolate brands. The research investigates how emotional cues embedded in advertisements — including nostalgia, joy, love, and familial warmth — influence the depth and durability of brand memory among consumers. A descriptive and analytical research design was adopted, employing a structured questionnaire administered to 150 respondents across urban demographics. The findings reveal that emotionally charged advertisements not only outperform rational or information-based advertisements in recall but also strengthen brand identity and consumer affinity over time. Cadbury's long-running campaigns such as 'Kuch Meetha Ho Jaaye' and 'Real Taste of Life' were found to have exceptionally high spontaneous recall rates, suggesting that consistent emotional messaging creates enduring mental imprints. The study further explores the mediating role of emotional intensity, repetition frequency, and storytelling quality in shaping recall outcomes. Managerial implications are discussed for advertising professionals and brand strategists aiming to leverage emotional intelligence in campaign design. The study contributes to the growing body of literature on affective advertising and consumer neuroscience, offering actionable insights for achieving sustainable competitive advantage through emotional brand equity.

DOI: http://doi.org/10.5281/zenodo.20557249

Digital Twin In The 6G Internet Of Vehicles: A Concise Review Of Channel Modelling, Learning, And Security

Authors: Rashmi Vanajakar

Abstract: The Internet of Vehicles is being recast around two technologies that are reaching deployment maturity in the same window. On the cellular side, sixth-generation networks open terahertz spectrum, integrated sensing and communication, and a space-air-ground integrated fabric in which roadside units, unmanned aerial vehicles, and low-earth-orbit satellites all serve as edge servers. Digital twin networks add a continuously synchronised virtual counterpart for every vehicle, road segment, and radio environment, on which learning algorithms can operate as if it were the physical network itself. Each technology has a substantial literature on its own; the joint deployment they are now becoming raises four questions that no single paper resolves: how a radio-frequency twin is grounded in the physics of a millimetre-wave channel, how vehicle twins migrate across heterogeneous edge servers, how learning is split between vehicles and their twins without leaking data, and how the resulting pipeline is defended against active falsification. This review reads fifteen recent peer-reviewed frameworks against those questions and treats them as components of a single deployed pipeline rather than as isolated proposals. The 3D ray-tracing RF digital twin scheme of Liu et al. is given a dedicated discussion because most of the learning-oriented works reviewed here rest on a channel abstraction that, in deployment, would have to be served by some form of RF twin. Two comparison tables consolidate the readings, and five research gaps are identified: unverifiable twin fidelity, fragmented benchmark practice, opaque synchronisation cost, weak active-adversary threat models, and absent end-to-end energy accounting. Each gap is paired with an incremental, testable next step.

DOI: http://doi.org/10.5281/zenodo.20557590

Work–Life Balance and Sustainable Career Well-Being Among Working Women: In the Rayagada District, Odisha

Authors: Ms. Kaniti Monica, Dr. Subhasish Das, Dr. Debasis Pani

Abstract: The growing participation of women in the labour force has significantly influenced organizational practices and family relationships (Greenhaus & Allen, 2011; Voydanoff, 2005). Despite notable professional accomplishments, working women frequently face difficulties in managing occupational demands and personal responsibilities, often leading to work–family conflict, role overload, and psychological strain (Clark, 2000; Kossek et al., 2011). The present study examines the association between work–life balance and sustainable career well-being among working women in Rayagada District, Odisha. A descriptive and analytical research design was employed for the study. Primary data were gathered from 250 working women working in government, private, educational, banking, healthcare, and service sectors through a structured questionnaire. Statistical techniques such as descriptive statistics, reliability analysis, and one-way ANOVA were utilized to analyse the data. The findings indicate that work–life balance has a significant impact on career fulfilment, psychological health, organizational engagement, and long-term career sustainability (Haar et al., 2014; Kelliher & Anderson, 2010). Factors such as flexible work practices, family support, effective workload management, and organizational assistance were identified as key determinants of sustainable career well-being (Allen et al., 2013; Medina-Garrido et al., 2023). The study suggests the adoption of employee-centric policies, wellness initiatives, counselling services, and flexible work arrangements to enhance the overall quality of work and life among working women (Kossek & Ozeki, 1998; Greenhaus et al., 2003).

DOI: http://doi.org/10.5281/zenodo.20688400

Drivers Of Lean Marketing Readiness Among E-Grocery Stores In Telangana: Insights From Awareness And Perceptual Factors

Authors: Mrs. Ashita, Dr. Saumendra Das, Prof. Shehbaz Ahmed

Abstract: The rapid digitalization of retail and the growth of e-grocery platforms have increased the need for efficient and innovation-driven marketing practices. Lean Marketing focuses on value creation, process efficiency, waste reduction, and continuous improvement to enhance competitiveness (Womack & Jones, 2003; Kotler et al., 2017). Organizational readiness for Lean Marketing is influenced by awareness and perceptions, which shape adoption behavior (Rogers, 2003; Ajzen, 1991). This study examines Lean Marketing Readiness among e-grocery stores in Telangana using a descriptive and analytical design. Primary data were collected from 500 respondents using a structured questionnaire, and a pilot study of 135 respondents ensured instrument validity. Chi-square analysis was used to test associations between awareness, perceptions, and readiness. The study highlights that awareness and perceptions are key determinants of Lean Marketing Readiness and provides insights for improving digital retail competitiveness in emerging markets.

DOI: http://doi.org/10.5281/zenodo.20692061

Road Safety Enhancement Using Deep Learning For Pothole Detection

Authors: Roshan P

Abstract: Vehicular safety on public roads stands as a pressing societal concern worldwide, with pavement craters ranking among the foremost contributors to traffic collisions and automobile damage. An autonomous pothole recognition framework has been engineered and deployed, employing the YOLOv8 deep learning architecture to locate pavement voids in continuous video streams acquired from onboard cameras. Capable of analyzing footage from dashboard-mounted cameras or handheld devices, the framework delivers instantaneous audible hazard notifications to vehicle operators upon each confirmed detection event. The architectural design integrates a browser-accessible front end developed with HTML, CSS, and Bootstrap, while Python augmented by the Flask microframework governs all computational processing and model execution at the server side. The YOLOv8 detection engine was trained on a meticulously curated, annotated corpus spanning heterogeneous road surfaces, illumination regimes, and meteorological conditions, ultimately attaining a mean Average Precision (mAP@0.5) exceeding 89% on withheld evaluation data. Empirical assessments demonstrate that the framework operates with sustained reliability, sustaining throughput at a mean rate of 40 frames per second on commodity computing platforms. Extending beyond individual driver protection, the framework simultaneously archives detection telemetry for use by pavement management agencies in scheduling remediation campaigns. The work illustrates how operationally deployable deep learning technologies can advance the aspirations of smarter road governance and more resilient transport networks. Specifically, the framework attains a precision of 94.2% under unobstructed daytime lighting and an aggregate mAP@0.5 of 89.1% across all evaluation scenarios, accompanied by a mean inference throughput of 40.3 frames per second, corroborating its readiness for real-time driver-assist deployment on widely accessible consumer platforms.

DOI: http://doi.org/10.5281/zenodo.20721532

QEFFE:Quantum Entropy-based Feature Forward Elimination For AdversariallyAware Feature Selection In Deep Neural Networks

Authors: Manoj Raosaheb Gaikwad, Dr. A. B. Pawar

Abstract: Feature selection is a well-studied problem, yet established criteria—variance (PCA), statistical dependency (mutual information), neighbour distance (ReliefF), and recursive elimination (RFE)—are all blind to the adversarial threat model: they optimise for representational fidelity, not for robustness against malicious perturbation. This paper introduces QEFFE (Quantum Entropy-based Feature Forward Elimination), a feature-selection method whose selection criterion is the symmetric Kullback–Leibler divergence between a feature's clean-input and adversarial-input activation distributions, combined with a redundancy penalty inside a greedy forward-selection loop. The “quantum” label denotes an entropy-divergence design metaphor and not quantum hardware. Evaluated on a 512-dimensional deep-residual feature space derived from MNIST, QEFFE attains the highest dimensionality reduction among five compared methods (81.25%, 512→96) and, as an isolated component, raises projected-gradient-descent (PGD, ε=0.20) accuracy by 33.0 percentage points—the single largest contributor in a full-pipeline ablation. The method adds zero inference-time parameters, operating as an index lookup. Results on EMNIST Balanced (47 classes) confirm that the criterion transfers beyond the binary-scale digit task.

DOI: http://doi.org/10.5281/zenodo.20721721

Banking, Fintech, And AI Adaption In Japan’s Aging Economy

Authors: Rajeew Vishvakarma, Sooraj Jacob

Abstract: Japan is undergoing a demographic transition marked by population aging, declining fertility, workforce reduction, and regional depopulation. These changes increasingly affect the structure, economics, and technological strategies of banking and fintech. Traditional banking models reliant on branch networks, household formation, lending growth, and labor-intensive service delivery face sustainability challenges in an aging society. This paper investigates how Japan’s demographic crisis is transforming banking, fintech, and AI-enabled financial services. Using a case study based on secondary research, it examines pressures on traditional and regional banks, payment ecosystems, behavior, and workforce availability. It assesses responses through digital banking, cashless payments, artificial intelligence, robotic process automation, cloud modernization, open banking, embedded finance, and hybrid service models. The paper introduces a five-layer framework linking demographic pressures to banking stress, technology responses, governance adaptation, and long-term transformation, identifying demographic decline as both constraint and catalyst.

DOI: http://doi.org/10.5281/zenodo.20785745

Is Memory The Criterium Of Personal Identity On David Hume’s Conceptions?

Authors: Dr. Nargish Afroza

Abstract: In this paper, it will try to clarify why David Hume did not refer to memory as the only source in providing a theory of personal identity whereas the eminent 17th century philosopher John Locke called that in his illustrious work “An Essay concerning Human understanding”, the memory is the only source of personal identity. Hume, in his prosperous work “A Treatise of Human Nature” (THN/T), stated that the necessary relations are only source of personal identity because in this case causality and analogy must be recognized as the fundamental principles. Because the individual can imagine his/her personal identity based on fundamental principles. Therefore, according to Hume, the identity of person is nothing more than the imagination of fiction. However, we will analyse here, how Hume can refute John Locke’s theory of personal identity by the beliefs of necessary relations.

DOI: http://doi.org/10.5281/zenodo.20956632

Automatic Keytone Detection System For Harmonium Using AI/ML

Authors: Ishwari Gadewar, Mitali Umbarje, Vedika Thakur, Prof. S. R. Warhade

Abstract: Accurate identification of musical key tones in har-monium performance is essential for effective learning, tuning, and real-time feedback in Indian classical music. However, existing pitch detection tools are primarily designed for Western musical scales and fail to capture the microtonal nuances and timbre characteristics of the harmonium. Additionally, manual tone identification is time-consuming and prone to human error, especially for novice learners. This paper proposes a novel Auto-matic Key Tone Detection System for Harmonium using Artificial Intelligence (AI) and Machine Learning (ML) techniques. The system captures live audio input and processes it through a real-time pipeline involving signal preprocessing, feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) and log-Mel spectrograms, and classification using a lightweight Convo-lutional Neural Network (CNN) model. A key design aspect is the system’s low-latency architecture, enabling real-time detection with high accuracy while maintaining computational efficiency suitable for standard desktop environments. The proposed model is trained on a curated dataset of harmonium tones across multiple shrutis and octaves, augmented to improve robustness under varying acoustic conditions. The system provides real-time output displaying the detected swara, octave, and confidence score through a user-friendly interface. By bridging the gap between traditional musical practice and modern AI-driven tools, this solution aims to enhance learning efficiency, improve tuning accuracy, and support musicians in real-time performance environments.

DOI: http://doi.org/10.5281/zenodo.20957304

Analysis Of Sliding Mode Triboelectric Energy Harvester Under Various Simulation Conditions

Authors: Satish Kumar, Geetanjali Kale

Abstract: Low-cost and easy to manufacture energy-harvesting triboelectric device is developed to transform mechanical energy into electrical energy. A lateral sliding mode triboelectric energy harvester (SM-TEH) is fabricated based on a double-dielectric-layered structure. Dielectric layers are comprised of Nylon sheets and PTFE sheets attached to copper (Cu) electrodes. A slider-crank mechanism is considered to provide mechanical movement that has been designed and to understand the effect of varying operating conditions on the output performance of SM-TEH. The experimental results are compared with simulation results at various rotational speeds from 40 to 70 rpm and at separation-distance varied from 7 to 10 cm to evaluate the model's reliability in predicting SM-TEH's output performance. The stability and durability test is performed for SM-TEH. The feasibility of the developed SM-TEH is proved by lighting up 40 red Light-Emitting Diodes (LEDs). SM-TEH has vast applications in self-powered electronic devices and systems.

DOI: http://doi.org/10.5281/zenodo.20961671

Traditional Leadership Vs Modern Leadership

Authors: Vandana M, Dr.Maheswari.GS

Abstract: Leadership plays a crucial role in determining organizational effectiveness, employee performance, and long-term sustainability. Over the years, leadership approaches have evolved significantly, transitioning from traditional models based on authority, hierarchy, and centralized decision-making to modern approaches that emphasize collaboration, innovation, employee empowerment, and adaptability. This paper compares traditional and modern leadership styles by examining their characteristics, advantages, limitations, and impact on organizational performance. Traditional leadership provides clear structures, discipline, and efficient decision-making in stable environments but often limits employee participation and creativity. In contrast, modern leadership fosters open communication, teamwork, continuous learning, and innovation, making it more suitable for today's rapidly changing and technology-driven business environment. The study also highlights the influence of digital transformation, globalization, and changing workforce expectations on leadership practices. The findings suggest that while neither leadership style is universally superior, organizations can achieve greater effectiveness by adopting a balanced approach that integrates the stability of traditional leadership with the flexibility and inclusiveness of modern leadership. Such a hybrid leadership model enhances employee engagement, organizational resilience, and sustainable competitive advantage.

Ecological Association Of Medicinal Plants In Different Seasons In And Around Delhi NCR

Authors: Ms. Susmita Gupta, Dr.Ajay Prakash

Abstract: The current research explores the biodiversity, medicinal uses, ecological affinities, seasonal variations, and conservation status of medicinal plants found in the rapidly developing Delhi National Capital Region (NCR). As a result of botanical surveys and ethnobotanical interviews, 342 plant species were identified belonging to 87 families; out of these, 189 (55.3%) species of plants have been reported for their medicinal properties. The main characteristics of the medicinal plants include that of herbs and belong to various families including Fabaceae, Asteraceae, and Lamiaceae. According to the ecological surveys, scrub forests and grasslands are considered to be the most biodiverse places in terms of medicinal plants. The significant seasonal pattern in regard to the use of medicinal plants has been detected, particularly high in summer and monsoon season when used for gastrointestinal and respiratory diseases treatment. At last, it should be noted that according to the threat analysis, there are 57 endangered (30.2%) species of medicinal plants in the area.

DOI: http://doi.org/10.5281/zenodo.21336209