Category Archives: Uncategorized

Authors: Soundrya Mallappa Biradar, Nikhil Gurudev Lonari, Aniket Ramesh Bhandare, Vishwaraj Pradip Pawar, Mrs. Pallavee Bavane-Patil

Abstract: Government subsidy programs play a crucial role in socio-economic development by supporting vulnerable populations in sectors such as agriculture, education, healthcare, energy, and food security. However, traditional subsidy management systems are often plagued by inefficiencies, fraud, leakage, lack of transparency, and poor targeting. The advent of digital governance and data-driven technologies has opened new avenues for reforming subsidy allocation and monitoring mechanisms. Machine learning (ML), in particular, offers powerful tools for automating eligibility assessment, predicting beneficiary behavior, detecting anomalies, and optimizing policy outcomes. This review paper presents a comprehensive analysis of online subsidy management systems integrated with machine learning techniques, with a specific focus on Logistic Regression, Decision Tree, and Random Forest algorithms. The paper discusses system architecture, data sources, preprocessing methods, algorithmic frameworks, evaluation metrics, real-world use cases, challenges, ethical considerations, and future research directions. The review aims to serve as a ready reference for researchers, policymakers, and system designers working toward intelligent, transparent, and efficient subsidy management platforms.

DOI: https://doi.org/10.5281/zenodo.19959139

Published by: vikaspatanker

Authors: Nirnayak Talukdar, Vulli Sai Rishika, Dr. Sadiya Nair. S

Abstract: Workforce restructuring has become a persistent feature of corporate life in the modern global economy. Driven by technological disruption, shifting market conditions, mergers, and competitive pressures, organizations regularly resort to workforce reductions and operational downsizing. The financial and strategic rationale behind such decisions has attracted considerable scholarly attention, but the way organizations communicate those decisions to employees has remained comparatively underexamined. This white paper examines internal corporate communication during workforce restructuring crises. The central argument is that the problem facing organizations in such circumstances is not the restructuring decision itself, but the quality, timing, tone, and ethical character of how that decision is communicated to employees. Evidence drawn from organizational communication theory, crisis communication scholarship, psychological contract research, and documented corporate case studies shows that poor internal communication during restructuring consistently produces measurable, lasting damage: trust erodes, rumours spread, morale falls, and voluntary turnover among retained employees rises substantially. The paper is organized around three concerns. First, it reviews and synthesizes the academic literature on internal communication, crisis communication, organizational trust, and psychological contracts. Second, it identifies research gaps that persist in the field, particularly the absence of structured, employee-centred communication frameworks and the limited empirical attention given to message tone, narrative framing, and listening mechanisms. Third, it proposes an original conceptual model, the Ethical Workforce Crisis Communication (EWCC) Model, offering a four-stage framework for guiding organizational communication through the full arc of a restructuring event. The paper concludes with ten targeted recommendations for corporate leaders, HR professionals, and communication practitioners. The core recommendation is that ethical, transparent, and empathetic communication is not merely a courtesy extended to departing employees; it is a strategic necessity for organizational continuity, survivor morale, and long-term institutional legitimacy.

DOI: https://doi.org/10.5281/zenodo.19956659

Published by: vikaspatanker

Authors: Sharvil M. Palvekar, Shreyas P. Jadhav, Ninad V. Sarpole, Soham B. Gharat, Dr. Sandeep B. Raskar

Abstract: Wildlife conservation increasingly relies on auto-mated monitoring systems to overcome the limitations of tradi-tional field-based observation methods, which are labor-intensive, subjective, and constrained in spatial and temporal coverage. This paper presents an AI-based animal monitoring and behavior analysis framework that integrates deep learning-based object detection, multi-object tracking, and spatio-temporal analytics for real-time wildlife surveillance. A YOLO26l detection model is employed to identify animal species from camera trap im-agery and video streams, followed by location-aware tracking to analyze movement patterns and population density. Heatmap-based visualization and statistical analysis are used to infer behavioral trends across different time intervals. Experimental results demonstrate robust detection accuracy and reliable species classification, supported by confusion matrix-based evaluation. The proposed system offers a scalable and interpretable solution for intelligent wildlife monitoring and conservation planning.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Mr. Harish B N, Prarthana S, Phalguni M H, Anushree T D, Sinchana K L, S N Meghana

Abstract: Plant diseases are also known to place huge burden on food security structure and agriculture to the global world; it is approximated that all plant diseases development costs a giant (an estimated 220 billion/year). To address this, the computer vision -specific and deep learning based automated disease detection systems are expandingly viewed as rather interesting as an option instead of the traditional forms of diagnosing that involve a significant amount of new employees . However, the literature screening is saturated with models that have been alleged to be super high in accuracy with regard to classifications when they are under some form of controlled conditions in the laboratory that must in no way imply any trustworthy depiction that they can be relayed over the situation in the real field. It can be said that such discrepancy in performance can stress the idea that there is a dire necessity to carry out more related and stiffer analysis of existing measures of data mining and optimization. This article has such an experimental alloy of which the plant disease variable models can be detected multi faceted in, which is discussed in detail on three axes parametric axis, combinatorial axis, computational axis. The rate of model performances to the hyperparameter options enshrined in the parametric assessment that may also be the optimizers are called counting. The combinatorial work involves the study of connections pertaining to the utility of various Convolutional Neural Network Convolutional designs, as well as the use of spectacular measures of data augmentation and fold up learning methods. The computational verification provided is a practical test of the feasibility of the model, comparison of statistics on the training time, model complexity, and speed of inference. According to the opinion that our experimental findings indicate, our individual models (as well as our EfficientNet) that come with the highest classification performance of about above 98 percent accuracy would always be the best trade off between accuracy and efficiency whereas ensemble models would adopt a combination of soft voting as the best trade off prerogative. The paper further estimates the radical performance augmentation with the generative data augmentation models against the conventional geometric transformations to apply the models in the truly competitive use. The primary accomplishment of this project is the system, which surpasses those pathetic signs of precision and rests upon the familiarization of scientists and performers with how to create, alter, and put to practical practice the scaleable, resilient, and effective plant disease detection methods used in the enhancement of the designated work in the agricultural forerunners.

Published by: vikaspatanker

Authors: Ansh Jena, Sujit Kakade, Arya Kedar

Abstract: Sentiment analysis can help track passengers’ per- ceptions and improve the service offered by an airline due to the increasing importance of social media, such as Twitter. It is about conducting a comparative analysis of three models of natural language processing, namely lexicon-based, machine learning, and transformer-based classification techniques for determining sentiments of airline tweets. Twitter US Airline Sentiment was chosen to be analyzed as it comprised labeled tweets from the major U.S. airlines. Data quality was improved by applying methods of text preprocessing, such as removing noise, tokeniz- ing, and eliminating stopwords. Lexicon-based sentiment analysis relied on VADER polarity baselines, machine-learning approach entailed extraction of TF-IDF features and further application of Random Forest classification technique while transformer model applied RoBERTa to identify the context of sentiment. As a result of the analysis, it was found out that while the lexicon model was faster and provided more easily understandable results, machine- learning model allowed identifying sentiments more accurately. Transformer-based RoBERTa performed the best in terms of handling more complex linguistic structures, such as negations and sarcasm.

DOI: https://doi.org/10.5281/zenodo.19948612

Published by: vikaspatanker

Authors: Dr. Dolley Srivastava

Abstract: The increasing problem of environmental pollution requires a new level of innovation going beyond the scope of existing monitoring systems. In this paper, we propose EnviroSense-ML – an end-to-end architecture leveraging IoT sensors together with machine learning algorithms for environmental monitoring and predictions. Our solution consists of a combination of inexpensive electrochemical sensors, LoRaWAN-based communication channels, and novel approaches in the field of hybrid machine learning techniques, which include the spatiotemporal GCN-LSTM model and CNN-BiGRU model using 8-bit quantization. The performance evaluations performed using the real-world dataset showed that our GCN-LSTM model demonstrated the highest interpolation accuracy (R² = 0.96), due to the inclusion of additional information about altitude and land cover into graph connections of the sensors. At the same time, 8-bit quantization resulted in 66% compression of the model's size with less than 1% degradation of its accuracy. Moreover, experiments showed that ML algorithms can improve sensor measurements' accuracy up to 46%. Also, our two-stage approach based on XGBoost reached near-perfect Air Quality Index prediction results (R² = 1.00, MAE = 0.35).

DOI: https://doi.org/10.5281/zenodo.19946681

Published by: vikaspatanker

Authors: Dr. Sharada H N, Dr. Sandhya S V

Abstract: Early-stage hiring processes continue to depend on resume-based and keyword-based filtering, which does not reliably capture a candidate’s actual abilities. This paper presents an AI-assisted skill evaluation system that prioritizes demonstrated performance over resume content. The system models candidate screening as a multi-stage pipeline: skill profiling, dynamic assessment delivery, automated rule-based and NLP evaluation, and weighted score aggregation. A competency model maps candidate skills to standardized assessment criteria, enabling objective cross-candidate comparison. Evaluation on simulated data (n=100) yields a Spearman rank correlation of 0.91, a false-positive shortlist rate of 12%, and a top-quintile precision of 78% — all substantially better than a conventional ATS baseline. The proposed framework is scalable, modular, and designed to reduce bias inherent in resume-centric screening.

DOI: https://doi.org/10.5281/zenodo.19946619

Published by: vikaspatanker

Authors: R.Thilakar, Dr.A.Venkatesh, Dr.M.Malarvizhi

Abstract: Maximum Power Point Tracking (MPPT) is one of the most important enablers in the field of grid-connected photovoltaic (PV) systems. This paper provides an extensive literature review on various MPPT methods used for grid-connected PV systems. The review includes both conventional approaches and advanced optimization algorithms like intelligent control schemes and metaheuristics. The article highlights some of the recent developments in the field of MPPT methods for grid-connected photovoltaic systems such as the utilization of HOA to tune fractional-order PI controllers, which can achieve a rise time of 0.0073 seconds and power generation capacity of 100.72 kW , using PSO to achieve power extraction up to 7.5% higher than P&O with only 1.54% THD, and Second Order Sliding Mode Control that achieved convergence in 0.009 seconds with 76.29% THD reduction . The comparative analysis demonstrates that although conventional methods have the advantage of ease of implementation, advanced optimization algorithms outperform in terms of faster dynamic response and global maximum point tracking.

DOI: https://doi.org/10.5281/zenodo.19946327

Published by: vikaspatanker

Authors: Dr. S. Thivyanathan, Dr. R. Anusha

Abstract: The unprecedented growth of conversational artificial intelligence agents has had a revolutionary impact on human-machine communication, but pragmatic competence—the capacity to understand and produce contextual meaning—is still an open problem for present-day technologies. This research provides a thorough linguistic study of pragmatics in human-AI interaction, which focuses on processing and producing meaning within the contexts of conversational agents' implicatures, presuppositions, speech acts, and common ground. Based on an empirical analysis of 50 transcripts of human-AI conversations, along with experimental work with 36 participants in the comparison of five conversational agents (ChatGPT-4, Google Bard, Microsoft Copilot, Claude 2, and LLaMA 2), the research concludes that although rule-based conversational agents stick to strict literal understanding, transformer models show emergent pragmatic competence through successful interpretation of indirect speech acts in 76% of the cases. However, Gricean implicatures remain difficult (recognized in only 34% of instances) and cross-turn common ground challenging (consistent in only 41% of examples).

DOI: https://doi.org/10.5281/zenodo.19946123

Published by: vikaspatanker

Authors: Mr. Abhishek Gadade, Ms. Priyanka Dharmul, Ms. Pratiksha Kamble, Prof. Krashna Rathi

Abstract: This project presents the development of a GSM-based health monitoring system using Arduino, designed to enhance patient care through real-time tracking and remote diagnostics. It integrates heart rate, temperature, and oxygen saturation sensors to continuously monitor vital signs, making it suitable for hospitals, elderly care, and home-based applications. The system displays readings on an LCD for local observation and transmits data via a GSM module to a mobile number or cloud server, ensuring remote accessibility. A buzzer alerts caregivers when any parameter exceeds safe thresholds, enabling prompt medical response. The GSM module serves as the communication backbone, facilitating SMS alerts and bridging the gap between patients and healthcare providers. The system’s modular design, centered around Arduino, allows for scalability and future upgrades such as cloud integration or mobile app support, highlighting the role of GSM technology in modern, accessible healthcare solutions.

DOI: https://doi.org/10.5281/zenodo.19942002

Published by: vikaspatanker