Authors: Ashwajit Kamble, Utkarsha Lodha, Rushabh Dhakane, Prof. Kiran Khedkar

Abstract: To develop and operate an IoT-based Smart Greenhouse Monitoring and Control System, first install environmental sensors such as DHT22 for temperature and humidity, soil moisture probes, and LDRs for light intensity inside the greenhouse to continuously collect data on growing conditions. Connect these sensors to a microcontroller like Arduino Uno and integrate a WiFi module such as ESP8266 or NodeMCU to enable real-time wireless data transmission to an IoT cloud platform for remote monitoring and storage. Once data is available online, analyze it through dashboards or mobile apps to observe trends and make informed decisions. When environmental parameters deviate from optimal levels, the system should automatically trigger actuators—such as fans, sprinklers, or grow lights—to maintain ideal conditions. Throughout the cultivation cycle, data logging and analysis help identify patterns for predictive control and resource optimization, reducing manual intervention and improving crop yield and quality. The system should be operated continuously to maintain stability and can be enhanced over time by adding AI algorithms for predictive adjustments, renewable power sources for sustainability, and scalability to hydroponic or commercial setups, ensuring consistent productivity and energy-efficient farming year-round.

DOI: https://doi.org/10.5281/zenodo.19936180

Published by: vikaspatanker

Authors: Er. Sanju Surendran Girija

Abstract: Residential construction projects demand the coordinated integration of engineering execution, financial planning, architecture, and long-term usability. In many emerging economies, homeowners frequently prioritize full completion of structural, architectural, and interior works prior to occupancy. Although this approach offers immediate convenience and aesthetic satisfaction, it often imposes substantial financial pressure, accelerates decision-making under time constraints, and limits adaptability to future technological or lifestyle changes. This paper critically examines two dominant residential completion strategies: full pre-occupancy completion and phased post-occupancy development. Through engineering-economic analysis and practical construction management perspectives, the study evaluates their impacts on capital expenditure, lifecycle cost, material efficiency, flexibility, and occupant satisfaction. Findings indicate that phased completion—where essential functional systems are completed first and non-critical enhancements are deferred—can significantly improve cash flow management, reduce debt exposure, and enable future integration of advanced materials and smart technologies. The paper concludes that a hybrid strategy, combining immediate structural readiness with planned incremental enhancements, provides the most sustainable and economically rational solution for modern homeowners.

DOI: https://doi.org/10.5281/zenodo.19934779

Published by: vikaspatanker

Authors: Dr. Satish Chandra, Kritika Tyagi , Ritesh Kumar

Abstract: The digital age has placed data as an extraordinary intangible asset in the insolvency domain, yet its monetization sharply collides with the protective measures that provide privacy. The Insolvency and Bankruptcy Code (IBC) of 2016 has made it mandatory for corporate debtors to maximize the value of the asset, usually through asset-wise sale under CIRP Regulation 29 or liquidation. This would include digital assets such as customer databases and proprietary user data. High-profile cases like Jet Airways have shocked the international community with the lifeblood of the company in question, viz. JetPrivilege: Passenger Data; eventually, such information is furnished for sale, raising questions on how it could be misused. Valuing such data is a Herculean task, given the varied methodologies followed–be it market, income, or cost approach–emulating the peculiar difficulties in IP asset valuation. Concurrently, the Digital Personal Data Protection Act (DPDP) of India 2023 provides wide-ranging rights to data principals and sets out obligations for data fiduciaries regarding consent, purpose limitation, and cross-border transfers. Enforcement will fall upon the newly set-up Data Protection Board. Some insolvency-related data processing (for example, through NeSL) might be spared from the full reach of the Act's legitimate-uses carve-out. The conflict between the creditor-oriented goal of maximizing asset value and the demands of data privacy creates a regulatory dilemma. This study proposes a synchronized legal framework, consisting of valuations standardized uniformly, specifications setting out IBC interfaces with DPDP, and procedural safeguards enabling speedy insolvency resolutions while safeguarding individual privacy rights.

DOI: https://doi.org/10.5281/zenodo.19927399

Published by: vikaspatanker

Authors: G Adithya Kumar Dubey, S Gokul, A Daarshan, Dr. R. Bharathi

Abstract: The demand for managing sports facilities efficiently is. We need better digital tools to handle bookings. Traditional methods often lead to scheduling conflicts. Are not very efficient. This paper talks about Arenabook a web-based platform for booking and scheduling sports facilities. It was built using the Django framework. With Arenabook users can see what's available in time make reservations and manage their bookings. Administrators can control scheduling, pricing and resource allocation. Here's how Arenabook works to prevent bookings: it uses a check-lock-confirm-update mechanism. This ensures that everything runs smoothly and consistently. The platform has secure user authentication and role-based access control. It works well on devices. The backend of Arenabook uses a database. This helps with handling data and processing queries. Arenabook makes managing sports facilities easier reduces the need for work and improves the user experience. Tests show that Arenabook is scalable, reliable and suitable for sports facility management. Arenabook can handle a lot of users and data making it a great solution, for sports facilities. Arenabook is a platform that can improve the way sports facilities are managed.

DOI: https://doi.org/10.5281/zenodo.19926939

Published by: vikaspatanker

Authors: Rajat Takkar, Disha Sharma, Hridyesh Sharma

Abstract: The rapid growth of edge computing has changed how artificial intelligence is deployed on devices with limited resources such as smartphones, embedded systems, and IoT devices. In such environments, constraints related to memory, power, and storage make it difficult to use traditional deep learning models directly. Although modern neural networks perform well in tasks like computer vision, they often need high computational resources, which limits their practical use on edge devices. In this work, we focus on lightweight deep learning architectures that are designed to operate efficiently under these constraints. Specifically, we examine three widely used models—MobileNetV2, SqueezeNet, and EfficientNet-B0—for real-time inference on edge devices. The CIFAR-10 dataset is used as a benchmark to evaluate model performance. To improve training efficiency, we also apply transfer learning by utilizing features from pre-trained models. In addition, optimization techniques such as structured pruning and dynamic quantization are used to reduce unnecessary parameters and improve computational efficiency without significantly affecting performance. These methods help in lowering model size and speeding up inference, making deployment more feasible in resource-limited environments. The experimental results show noticeable differences in performance across the selected models. EfficientNet-B0 achieves the highest classification accuracy of 92.06%, while SqueezeNet provides faster inference due to its compact architecture and fewer parameters. MobileNetV2 offers a balanced trade-off between accuracy and latency, making it suitable for practical applications. Overall, the findings highlight the importance of selecting appropriate lightweight architectures along with effective optimization strategies when deploying deep learning models on edge devices. This work provides useful insights into balancing accuracy, model size, and inference speed, which are key factors in real-world edge computing scenarios.

DOI: https://doi.org/10.5281/zenodo.19926432

Published by: vikaspatanker

Authors: Dr. Shrikant V. Sonekar, Professor Rohan B Kokate, Miss. Vaishnavi R Tandulkar

Abstract: The rapid growth in global energy demand, coupled with increasing environmental concerns, has accelerated the transition toward renewable energy sources, with solar power emerging as one of the most promising and sustainable alternatives. Despite its advantages, the efficiency and performance of solar power plants are significantly influenced by dynamic environmental conditions such as solar irradiance, temperature variations, dust accumulation, cloud cover, and equipment degradation over time. Traditional monitoring and control mechanisms are often reactive, manual, and incapable of handling large-scale data, resulting in suboptimal performance and increased operational costs. In this context, Machine Learning (ML) has gained considerable attention as a powerful tool for enhancing the efficiency and reliability of solar energy systems This paper presents a comprehensive study on the application of Machine Learning techniques to improve the efficiency performance of solar power plants. The proposed approach utilizes data-driven models to analyze historical and real-time data collected from solar panels, sensors, and weather forecasting systems. Various supervised learning algorithms, including Linear Regression, Random Forest, and Support Vector Machines (SVM), are employed for accurate prediction of solar power generation and identification of performance patterns. Furthermore, advanced deep learning models such as Artificial Neural Networks (ANN) are implemented to handle complex nonlinear relationships between environmental variables and energy output. In addition to energy prediction, the system incorporates intelligent fault detection and predictive maintenance mechanisms. Machine Learning algorithms continuously monitor system parameters to detect anomalies such as panel degradation, inverter malfunctions, shading effects, and wiring faults. Early detection of such issues enables timely maintenance, reducing downtime and improving overall system reliability. The integration of predictive analytics also allows operators to optimize panel orientation, tilt angles, and tracking mechanisms, thereby maximizing energy capture throughout the day. The proposed ML-based framework is evaluated using a dataset comprising solar irradiance, temperature, humidity, and historical power output records. Experimental results demonstrate a significant improvement in prediction accuracy and operational efficiency compared to conventional methods. The system achieves up to 20–30% enhancement in energy output efficiency, along with a considerable reduction in maintenance costs and system failures. Additionally, real-time monitoring and automated decision- making contribute to improved scalability and adaptability of solar power plants.

DOI: https://zenodo.org/records/19925745

Published by: vikaspatanker

Authors: Dr. Shrikant V. Sonekar, Professor Rohan B Kokate, Miss. Samiksha S Raut

Abstract: Cloud computing has revolutionized the way data is stored, processed, and shared by providing scalable, flexible, and on-demand access to computational resources over the internet. It has enabled individuals, enterprises, and government organizations to efficiently manage large volumes of data without investing heavily in physical infrastructure. Despite these advantages, the rapid adoption of cloud platforms has introduced significant challenges related to data security, privacy preservation, and fine-grained access control. Since data is stored on third-party servers, users lose direct control over their sensitive information, increasing the risk of unauthorized access, insider threats, and data breaches. Traditional encryption techniques such as symmetric and asymmetric cryptography ensure data confidentiality but fail to provide flexible and scalable access control mechanisms in dynamic, multi-user cloud environments. These methods rely heavily on complex key management systems and are not suitable for scenarios where access permissions need to be defined based on user roles, attributes, or contextual conditions. To address these limitations, Attribute-Based Encryption (ABE) has emerged as a powerful cryptographic approach that enables secure and flexible data sharing by enforcing access policies based on user attributes rather than identities. In particular, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows data owners to define access structures directly within the encrypted data, ensuring that only users whose attributes satisfy the defined policies can decrypt and access the information. This paper presents the design and implementation of a secure and privacy-preserving data-sharing framework based on CP-ABE in cloud computing environments. The proposed system incorporates advanced security features such as fine-grained access control, secure key generation and distribution, user authentication, and protection against common attacks including collusion attacks and unauthorized data access. Additionally, privacy-preserving mechanisms are integrated to ensure that sensitive user attributes and data remain protected even from cloud service providers. The system architecture includes key components such as data owners, attribute authorities, cloud servers, and data users, working together to provide a secure and efficient data-sharing environment. Experimental evaluation demonstrates that the proposed framework significantly improves data security, reduces the risk of data breaches, and enhances access control efficiency compared to traditional encryption-based systems.

DOI: https://zenodo.org/records/19924638

Published by: vikaspatanker

Authors: Sourabh Vijay Patil, Vaishnav Maruti Kadam, Ajay Angad Ahir, Altaf Yasin Mahat

Abstract: Missing person cases are a global concern that cause emotional distress for families and challenges for law enforcement agencies. Traditional search methods such as posters, manual surveillance, and public announcements are slow and inefficient. This paper proposes FaceTrace, an artificial intelligence based missing person detection system that uses deep learning facial recognition to identify individuals from images and surveillance streams. The system leverages ArcFace embeddings, computer vision techniques, and a centralized MySQL database to match uploaded images with stored records. The proposed system enables faster identification and improves accuracy compared to manual methods.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Manish S. Bansode, Tejas S. Mokal, Saad S. Pathan, Karan K. Rathod, Professor Yash S. Shet, Professor Hemanth K.Thakur, D. N. Jaiswal

Abstract: The rapid increase in construction activities has significantly increased the demand for water used in concrete curing. Conventional curing methods require continuous external water supply, which is often impractical in regions with water scarcity. This research focuses on self-curing concrete using Polyethylene Glycol (PEG-400) and Polyvinyl Alcohol (PVA) as internal curing agents. The study evaluates the mechanical properties of concrete, particularly compressive strength, by varying the percentage of these agents. The results demonstrate that self-curing concrete improves hydration, reduces shrinkage, enhances durability, and minimizes water consumption. The study concludes that PVA shows better performance compared to PEG in terms of strength and water retention.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Udit Tripathi

Abstract: SocioMind AI is an AI-powered analytical framework that quantifies psychological states and personality traits through the automated processing of heterogeneous social media data. Unlike traditional sentiment analysis — which reduces complex human communication to a single positive/negative polarity score — SocioMind AI employs a multi-dimensional approach to construct a comprehensive "Linguistic DNA" profile of an individual, correlating public persona signals with private aspirational data to deliver a 360-degree behavioral footprint. The system operationalizes a novel concept: the Digital Behavioral Footprint (DBF) — the aggregate, cross-contextual trace that an individual leaves across multiple social media channels, each reflecting a different facet of their psychological identity. By processing and cross-referencing Primary Content, Interactional Tone, Interest Graphs, and Aspirational Signals simultaneously, SocioMind AI achieves what single-channel sentiment tools cannot: a holistic, internally-validated psychological portrait. At its inference core, SocioMind AI leverages the Gemini 3 Flash large language model architecture, optimized for structured JSON output to ensure deterministic, research-grade data handling. The analytical output spans Big Five personality trait quantification, Emotional Density Mapping, and derived psychological indicators including Social Stress Levels, Behavioral Consistency scores, and Mood Trajectory projections. The system is implemented as a React-based web application with Recharts-powered radar and bar chart visualizations, making complex psychological matrices accessible to both researchers and non-specialist users. Validation experiments across 300 profiles demonstrate Cohen's kappa = 0.74 for Big Five dimensions and Pearson r = 0.81 for emotional valence detection, establishing SocioMind AI as a viable zero-knowledge psychological proxy for research-grade personality inference.

DOI: http://doi.org/

Published by: vikaspatanker