JOURNAL UGC-CARE IJCRT (2349-3194) | ISSN Approved Journal
Aim and Focus: The International Journal of Current Research and Techniques (IJCRT) is a prestigious peer-reviewed, refereed, and open-access journal that serves as a high-quality international platform for researchers, academicians, professionals, and students to present innovative, original, and impactful research across a wide range of disciplines. The journal is committed to promoting interdisciplinary research and bridging the gap between theory and practice by encouraging scholarly contributions that integrate knowledge across diverse academic domains. IJCRT Research Journal aims to foster global academic collaboration and disseminate cutting-edge research addressing contemporary challenges, emerging trends, and real-world applications. Indexed in Scopus, the journal ensures high visibility, global reach, and strong citation impact within the international research community. Scope of Coverage: The International Journal of Current Research and Techniques (IJCRT), as a truly multidisciplinary platform, invites submissions from a wide range of academic disciplines, including but not limited to Science and Technology such as Engineering, Computer Science, Information Technology, Artificial Intelligence, Data Science, Environmental Science, Applied Mathematics, and Physics; Health and Life Sciences including Medicine, Biotechnology, Pharmacy, Nursing, Public Health, and Agricultural Sciences; Business and Management covering Finance, Marketing, Human Resource Management, Operations, Entrepreneurship, and Economics; as well as Arts, Humanities, and Social Sciences including Education, Law, Political Science, Sociology, Psychology, Linguistics, and Cultural Studies. Ethics and Publishing Policies ◼ Journal Ethics: Strict adherence to ethical standards regarding plagiarism, authorship, data integrity, and conflicts of interest ◼ Peer Review Policy: Double-blind review ensuring unbiased and merit-based evaluation ◼ Open Access Policy: All articles are freely accessible without subscription barriers ◼ Repository Policy: Authors may archive their work in institutional repositories and personal platforms ◼ Licensing: Articles are published under Creative Commons CC BY-NC-SA 4.0, with authors retaining copyright ◼ Plagiarism Policy: Zero tolerance; all submissions are screened using advanced tools such as iThenticate ◼ AI Policy: Any use of AI tools must be transparently disclosed by authors ◼ Digital Archiving: Long-term preservation through recognized archiving systems such as Portico/CLOCKSS. ◼ Advertising Policy: Only relevant and ethical academic advertisements are accepted. ◼ Ahead of Print: Accepted manuscripts are published online with DOI prior to issue allocation.
Plurethosomal Nanocarriers As A Promising Platform For Arnebin-1 Delivery In Wound Healing Therapy: A Comprehensive Review
Wound healing is a complex physiological process that includes inflammation, tissue regeneration, angiogenesis, and extracellular matrix modification. Chronic wounds continue to pose a significant healthcare burden because to delayed healing, infection, and the limited efficacy of current therapy. Herbal bioactive chemicals are increasingly being investigated due to their medicinal potential and minimal toxicity. Arnebin-1, a natural naphthoquinone found in Arnebia species, has potent wound healing, anti-inflammatory, antioxidant, and antibacterial properties. However, its clinical applicability is limited due to weak water solubility, instability, and low skin permeability. Nanotechnology-based drug delivery systems, particularly plurethosomal nanocarriers, present a promising option by improving drug loading, stability, flexibility, and epidermal penetration. This review focuses on the therapeutic role of Arnebin-1, plurethosome formulation strategies, dermal delivery routes, characterisation methodologies, benefits, limits, and future opportunities in wound healing therapy.
Copyright (c) 2026 JOURNAL UGC-CARE IJCRT (2349-3194) | ISSN Approved Journal https://creativecommons.org/licenses/by/4.0
Experimental Investigation On Reinforced Concrete Beam With Nano Silica And Polyvinyl Alcohol Fiber
This experimental study investigates the combined effect of Nano Silica (NS) and Polyvinyl Alcohol (PVA) fiber in M30 grade concrete. In this study, the structural behaviour of reinforced concrete beams were studied through load–deflection testing. The behaviour of conventional concrete beam was compared with beam containing Nano Silica and Polyvinyl Alcohol fibers to evaluate the influence of the modified concrete on structural performance, crack development, and deformation characteristics. Nano Silica was used as a partial replacement for cement at 2% by weight, while Polyvinyl Alcohol fiber was added as 0.5% by volume of concrete. In addition, the performance of the modified concrete was evaluated through various durability tests to assess resistance against environmental effects. Microstructural characteristics of the material were examined using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analysis to understand the morphology and elemental composition.
Predicting Mental Health Risk Using Machine Learning Based on Instagram Usage Patterns
The rapid growth of social media platforms has significantly changed the way people communicate, interact, and share information in modern society. Among these platforms, Instagram has become one of the most widely used social networking applications, especially among young adults and students. Although social media offers benefits such as communication, entertainment, and self-expression, excessive usage has also been associated with mental health issues including stress, anxiety, depression, loneliness, and low self-esteem. Early identification of such mental health risks is important to provide timely support and preventive care. This research proposes a machine learning-based approach to predict mental health risk using Instagram usage behaviour. Important behavioural features such as daily Instagram usage time, posting frequency, average likes per post, follower count, and night-time activity are analysed to identify potential risk patterns among users. Several machine learning classification algorithms including Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest are implemented using the Scikit-learn library. The models are evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the Random Forest classifier achieved the highest prediction accuracy compared to other models. The findings demonstrate that machine learning techniques combined with social media behavioural analysis can contribute to early mental health risk detection and support the development of intelligent preventive healthcare systems.
Digital Pedagogies, Learning Engagement, and Integrated Assessment
ICT have taken the role of pedagogy in teaching and learning, and its variants had showed many difficulties to all the individuals of the society like adapting to online studies and schooling, work from home, shift to online or virtual world was indeed a difficult barrier for all the citizens. Last three years of education system had brought much difficulty to students or the learners including teachers and parents for adopting to technology for educational purpose as last three academic years had been on a virtual platform like online classes, online assignments, online presentations and online examinations, evaluation and assessment the education system had merely transferred from traditional teaching to online/ mobile teaching and learning process. Keeping this on mind this research paper tries to compare traditional teaching vs online teaching from learners’ perspective or students’ perspective. This research paper also looks into the positive and negative impacts of both traditional teaching and online teaching to draw the comparison. This research paper includes both qualitative and quantitative research methodology to draw out the conclusion.
Social Media Influencers and Pharmaceutical Products: A Review of Risks, Misuse, and Regulatory Challenges
The rise of social media has greatly changed how pharmaceutical and cosmetic products are promoted and used. Influencers have become the main people that Generation Z listens to when making decisions about skincare, over-the-counter medicines, and health supplements. However, since many influencers don’t have proper medical training, they often share incomplete or wrong information. This can lead to people using products incorrectly and experiencing side effects like skin irritation, acne, and allergies. To look into this issue, a study was done with 172 participants. It found that many consumers trust influencer advice without checking if it’s backed by scientific evidence. This gap between marketing and medical safety shows major problems in the enforcement of India’s regulations, including the Drugs and Cosmetics Act (1940), Consumer Protection Act (2019), and the Bharatiya Nyaya Sanhita (2023). The study concludes that protecting public health needs several steps: stronger regulation, holding influencers accountable, and raising awareness among the public. Most importantly, it suggests creating a digital platform with reliable, science-based information about drugs to connect digital marketing with safe consumer practices.
HALE-V2V: A Hybrid Adaptive Lightweight Authentication Engine for Secure Vehicle-to-Vehicle Communication
Vehicle-to-vehicle (V2V) communication is a cornerstone of modern intelligent transportation systems, enabling real-time exchange of Basic Safety Messages (BSMs) to prevent collisions and improve traffic efficiency. However, securing these messages demands authentication that is simultaneously fast, compact, energy-efficient, and cryptographically robust, requirements that no existing standardised scheme fully satisfies. This paper presents HALE-V2V, a Hybrid Adaptive Lightweight Authentication Engine for secure V2V communication. HALE-V2V separates authentication into two temporally distinct phases: an infrequent session-bootstrap phase using Elliptic Curve Diffie-Hellman (ECDH) key exchange, and a high-frequency per-message authentication phase using SPECK-64/128 encryption and HMAC-BLAKE3 tag generation. This stratified design achieves 128-bit security at 0.38 ms per message, 28 bytes of overhead, 72 µJ energy consumption, and 2631 messages/second throughput, outperforming ECDSA-P256 by 5.5×, 4.6×, 6.4×, and 5.5× respectively. Formal security proofs in the eCK model demonstrate resistance to replay, Sybil, and man-in-the-middle attacks. Scalability analysis confirms sub-5 ms authentication latency with up to 300 simultaneous vehicles, while hardware synthesis requires only 1650 gate equivalents and 320 bytes of RAM. A post-quantum migration pathway via CRYSTALS-Kyber key encapsulation is also discussed, requiring no modification to the per-message authentication phase.
MyColony: Design and Evaluation of a Colony-Based Digital Service Platform
This paper describes MyColony, a web platform we built to help residents of housing colonies find and hire local service providers in a verified, colony-specific environment. To improve trust and accountability, the system integrates JWT-based authentication, role-based access control, and administrator-led service approval. MyColony follows a three-tier architecture consisting of a React.js frontend, a Node.js and Express.js backend, and a MongoDB database. Cloudinary is used for handling media uploads and image optimization. The platform supports service listing, colony- specific access, and administrative monitoring within a secure environment. Functional testing and system evaluation show that the platform effectively supports multi-colony service management, secure user interaction, and moderation workflows. Usage observations also indicate improved service discoverability and stronger community engagement within residential boundaries. The proposed system demonstrates how a structured digital platform can improve hyperlocal service exchange while maintaining community-level trust and governance.
Formulation and Evaluation of Chia Based Health Drink
The present study focuses on the formulation and evaluation of a chia-based health drink utilizing Salvia hispanica L. as a functional ingredient. Chia seeds are widely recognized for their high nutritional value, including omega-3 fatty acids, dietary fiber, proteins, minerals, and bioactive phytochemicals with significant health-promoting properties. The objective of this research was to develop a palatable, stable, and nutritionally enriched beverage by incorporating varying concentrations of chia seeds along with suitable sweeteners, flavoring agents, and stabilizers. The health drink formulations (F1–F5) were prepared by dispersing pre-soaked chia seeds in a liquid medium under controlled conditions. The prepared formulations were evaluated for organoleptic properties, pH, viscosity, and sedimentation behavior to determine their quality and acceptability. The results indicated that an increase in chia seed concentration improved viscosity, texture, and physical stability due to the formation of a mucilaginous gel network. All formulations exhibited near-neutral pH, indicating compatibility and suitability for oral consumption. Among the developed formulations, F4 demonstrated optimal performance in terms of sensory attributes, viscosity, and stability, making it the most acceptable formulation. The study concludes that chia seeds can be effectively utilized in the development of functional beverages with enhanced nutritional and physicochemical properties. This formulation approach holds significant potential for application in the nutraceutical and functional food industry.
Machine Learning-Based Employee Performance Rating System
Employee performance evaluation is a critical component of organisational management, directly influencing productivity, promotion decisions, and workforce optimisation. Conventional appraisal systems are often subjective, inconsistent, and limited in their ability to process multidimensional data. This study proposes a machine learning-driven framework for predicting and classifying employee performance based on historical organisational datasets. The proposed system integrates structured data sources, including attendance records, task completion metrics, behavioural assessments, and project contributions, to develop predictive models capable of generating accurate performance ratings. Advanced classification algorithms, specifically Random Forest and Logistic Regression, are employed and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. To ensure practical applicability, the predictive model is deployed within a scalable web-based architecture, enabling real-time performance evaluation through an intuitive user interface. Experimental results demonstrate that the Random Forest model achieves superior performance with an accuracy of up to 88%, outperforming baseline methods. The proposed approach enhances objectivity, reduces human bias, and enables data-driven decision-making in human resource management. This research highlights the potential of machine learning techniques in transforming traditional performance appraisal systems into intelligent and automated evaluation frameworks.
Formulation and Evaluation of Boswellia serrata Gel for Topical Pain Management
The present study focuses on the formulation and evaluation of a topical herbal gel containing Boswellia serrata for effective pain management. Pain associated with conditions such as arthritis, muscle strain, and inflammation is commonly treated with synthetic drugs, which often produce adverse effects on long-term use. Hence, this study aims to develop a safer, natural, and cost-effective alternative using herbal ingredients. Boswellia serrata, known for its potent anti-inflammatory and analgesic properties due to boswellic acids, was selected as the primary active ingredient. The gel formulation also incorporated other herbal components such as curcumin, neem extract, menthol, and lavender oil to enhance therapeutic efficacy. The gel was prepared using Carbopol 940 as a gelling agent along with suitable excipients to ensure stability and patient acceptability. The formulated gel was evaluated for various physicochemical parameters including appearance, pH, viscosity, spreadability, homogeneity, washability, and skin irritation. The results indicated that the gel possessed a smooth texture, appropriate pH (5.2–6.1), good spreadability, and optimal viscosity, making it suitable for topical application. No signs of irritation were observed, confirming its safety. Additionally, the formulation demonstrated sustained drug release and significant anti-inflammatory activity, supporting its effectiveness in managing pain and inflammation. The study concludes that the developed Boswellia serrata gel is a promising herbal alternative to conventional analgesics, offering localized action with minimal side effects. Further clinical studies are recommended to validate its therapeutic potential and commercial applicability.
Experimental Investigation on Reinforced Concrete Beam with Prosopis Juliflora Ash and Basalt Fiber Bar as Reinforcement
The increasing environmental concerns caused by global warming and the rising costs of cement production due to quarrying have created a pressing need to explore alternative materials. To address this challenge, extensive research has been carried out to identify suitable replacements for cement. This research work is aimed to investigate the strength of concrete with partial replacement of cement with Prosopis juliflora ash (PJA) and compare it with conventional concrete. The grade of concrete is M30. Incorporating Prosopis juliflora ash can potentially reduce the overall consumption of cement in the country while promoting sustainable construction practices. The primary objective of this research is to evaluate the influence of Prosopis juliflora ash as a partial replacement for cement on the compressive, split tensile, and flexural strength of concrete. In this investigation, Prosopis juliflora ash (locally known as Seemaikaruvelam) was used to replace cement in varying proportions of 10%, 20% and 30%. Based on the experimental results, the mix containing 20% Prosopis juliflora ash exhibited optimal strength characteristics. Two beams (0% PJA and 20% PJA) were cast one with steel reinforcement and the other with partial replacement of steel with basalt fiber bars. Their flexural behavior and crack properties were studied in which PJA beam with basalt bars show high load bearing properties. The findings of this study highlight the potential of Prosopis juliflora ash as an eco-friendly and cost-effective alternative material for sustainable construction.
A Study on the Design and Development of AI Driven Student Performance and Recommendation Dashboard
This research proposes an intelligent system for analysing student academic performance and providing personalized recommendations using Artificial Intelligence and Data Mining techniques. Educational institutions generate large volumes of student data including attendance records, examination scores, and assignment results. Analysing this data manually is difficult and time-consuming. The proposed system uses machine learning algorithms to evaluate student performance and identify patterns in academic behaviour. The system processes academic records and predicts student performance using data mining techniques. A dashboard interface is designed to visualize performance trends, subject-wise scores, and attendance patterns. Based on the analysis results, the system generates personalized recommendations such as study strategies and subject improvement suggestions. The system helps teachers monitor student progress while enabling students to understand their academic strengths and weaknesses. The proposed solution demonstrates how Artificial Intelligence can enhance educational analytics and support data-driven decision making in modern education systems. The study highlights the importance of developing intelligent dashboards that assist teachers, administrators, and students in making data-driven decisions. The dashboard visualizes performance trends, attendance analysis, subject-wise performance, and engagement metrics in real time. The study concludes that implementing AI-driven student performance dashboards in higher education institutions can significantly improve learning outcomes, optimize resource utilization, and support personalized education strategies.
SHAKTISENSE – Smart Women Safety Pendant
Nowadays, cases of harassment, kidnapping, and unsafe situations for women are increasing day by day. Many times, women are not able to call for help during emergencies, and there is no proper system to quickly inform family members or police. (1) To solve this problem, this project presents a Smart Women Safety Pendant, a small wearable device that helps in danger situations. The pendant includes a camera, microphone, and GPS, which work together to provide quick help. (3) When the user presses the emergency button, the device immediately captures photos or videos, records surrounding sound, and finds the current location using GPS. (1)This information is sent instantly to family members and nearby police stations through a mobile network.(2) The main aim of this project is to provide fast help and also collect proof (image, video, audio) during emergencies. It is easy to use, portable, and can be worn like a normal pendant. This system can improve women’s safety and give them more confidence in daily life.
Hybrid Machine Learning Model for Breast Cancer Detection
Breast cancer is considered one of the major reasons for mortality in the female population around the world. It is highly important for medical practitioners to detect breast cancer as early as possible in order to provide adequate treatment and increase the chances for patients' survival. Thus, in the current study, the machine learning-based technique will be used for the prediction of breast cancer at an early stage using the Wisconsin Breast Cancer Dataset. It should be noted that the dataset consists of a set of different diagnostic features obtained from digitized image of fine needle aspirate (FNA) of breast mass. The supervised algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest, will be applied for the classification of the dataset into benign and malignant tumors. Pre-processing procedures are also implemented in the experiment in order to improve the effectiveness of machine learning algorithms. These procedures involve data cleansing, normalization, and feature selection. Performance of different models is evaluated on the basis of such metrics as accuracy, precision, recall, and F1-score. According to experimental data, Random Forest classifier demonstrated the highest accuracy.
Predicting Student Performance Using Demographic and Attendance Data with Machine Learning
This study investigates the influence of demographic, academic, and behavioral factors on student performance using advanced machine learning techniques. In modern education, success is shaped not only by classroom learning but also by background, habits, and daily behavior. The dataset included variables such as gender, family income, SSC and HSC scores, hometown, computer usage, preparation time, attendance, social media activity (watching short videos or “Reels”), and part time jobs. These features were analyzed alongside semester GPA and overall results to identify the strongest predictors of academic success. To evaluate these factors, both traditional classifiers (Random Forest, KNN, SVM, Naïve Bayes, Logistic Regression) and advanced boosting algorithms (CatBoost, LightGBM, XGBoost) were applied, along with ensemble methods such as Voting, Bagging, and Stacking. Among all models, Stacking Ensemble consistently achieved the highest accuracy of 95%, outperforming other approaches. Analysis showed that attendance, prior exam scores, and preparation time were the most influential predictors, while heavy social media use reduced concentration and performance. Visualizations including accuracy comparison charts, feature importance plots, and confusion matrices confirmed these findings. The CatBoost confusion matrix demonstrated reliable classification, with most predictions correctly aligned along the diagonal. The study highlights that while demographic factors such as income and hometown contribute to differences in performance, disciplined study habits and consistent attendance can help students overcome these challenges. These insights provide actionable guidance for educators and institutions. Schools can promote attendance, encourage effective study routines, and raise awareness about the impact of social media use. By leveraging machine learning, institutions can identify at risk students early and design targeted interventions to improve outcomes. Overall, this research demonstrates that student success depends on a combination of background, behavior, and daily habits. By integrating advanced machine learning methods, the study not only achieves high predictive accuracy but also offers practical strategies for supporting students. With proper support, motivation, and discipline, every student has the potential to enhance their performance and reach academic success.
Financial Performance Analysis of Major Oil & Gas Companies in India: A Comparative Study (2020–2025)
Economic growth and energy security of India is also reliant on the oil and gas industry. The Indian Oil Corporation Limited (IOCL), Bharat Petroleum Corporation Limited (BPCL), Hindustan Petroleum Corporation Limited (HPCL), Oil and Natural Gas Corporation (ONGC), and Reliance industries Limited (RIL), are the key Indian oil and gas companies that are considered in our research regarding the financial performance between 2020 and 2025. The performance measures employed in the study are profitability, liquidity, and solvency and efficiency. It involves statistical techniques like ratio analysis, ANOVA and regression (R2 = 0.5724) and it is based on five large companies. The sources of secondary data were financial databases and annual reports. The results suggest that Reliance industries is doing better than other companies because of diversification and operational efficiency and ONGC is well solvent. The profitability of businesses in the public sector is influenced by regulations. The article concludes that the operational efficiency is characterized by high impact on the financial performance.
Effectiveness of Mobilization with Movement Versus Muscle Energy Technique Along with Plyometric Exercises on Pain and Functional Performance in Subjects with Chronic Lateral Epicondylitis
Tennis elbow (lateral epicondylitis) is a painful overuse condition characterized by degeneration or micro-tearing of the tendons attaching forearm muscles to the lateral epicondyle, resulting in progressive pain and tenderness that worsens with forearm activity. This experimental study aimed to compare the effectiveness of Mobilization with Movement (MWM) versus Muscle Energy Technique combined with Plyometric Exercises (MET+PE) on pain and functional performance in subjects with chronic lateral epicondylitis. Sixty subjects meeting the selection criteria were randomly allocated into two groups of 30 each, Group A receiving MWM and Group B receiving MET combined with plyometric exercises, with both groups undergoing a four-week intervention protocol. Pain and functional performance were assessed using the Numerical Pain Rating Scale (NPRS) and the Patient-Rated Tennis Elbow Evaluation (PRTEE) questionnaire. Paired t-tests were applied to evaluate pre- and post-intervention differences within each group, while unpaired t-tests were used to compare mean differences between groups. Both interventions yielded statistically significant improvements in pain and functional performance; however, Group B demonstrated significantly greater gains across all measured parameters. These findings suggest that Muscle Energy Technique combined with Plyometric Exercises is more effective than Mobilization with Movement in reducing pain and improving functional performance in patients with chronic lateral epicondylitis.
Mobilization with Movement, Muscle Energy Technique
Patient Rated Tennis Elbow Evaluation Questionnaire
Detection and Analysis of Gas Emissions Based on Recycled Maintenance Technology
This study focuses on the green maintenance technology using 60% RAP plant-mix hot recycling, aiming to reveal the emission characteristics of asphalt mixtures with different gradations in road green maintenance and support low-carbon transportation infrastructure. Three asphalt mixtures, GAC-13, SMA-13 and GAC-20, were tested. Emissions of VOCs, inorganic gases and particulate matter during production and construction were monitored. Summa canisters with GC-MS, flue gas analyzers and filter membrane gravimetric methods were adopted for pollutant measurement. Results show that under 60% RAP hot recycling, SMA-13 emits more alkanes and amines than GAC-13 and GAC-20 at both construction sites and mixing plants. For CO₂ and particulate matter, emissions at construction sites and mixing plants both follow the order SMA-13 > GAC-13 > GAC-20. These findings clarify the gaseous emission differences among gradations and provide data support for low-carbon road maintenance technology selection and emission control.
AI-Based Predictive Market Pricing Model for Rural Farmers Using Machine Learning
Rural farmers in developing countries still struggle with wild price swings and unclear markets. These issues often leave them at the mercy of middlemen and facing serious financial stress. This paper introduces an AI-powered tool that predicts future crop prices using machine learning. By crunching historical market numbers together with local weather data, the tool gives farmers straightforward, useful pricing forecasts on their crops. But it’s not just a bunch of technical jargon, practical access matters. So, the model connects to a lightweight, easy-to-use web app that farmers can actually open and navigate. The backend runs on Node.js, built for speed with an asynchronous design, so price predictions pop up instantly. On the user side, the frontend is smooth and quick, built with React and TypeScript. The main goal? Close the information gap. Now, farmers have real market insights in their hands and can choose the best time and place to sell what they grow. Tests so far look promising, using XGBoost, the model’s price predictions land within 6% of actual prices for staple crops. All in all, this shows you can pair smart web tools with solid algorithms to actually help rural farmers, pushing for fairer markets and more sustainable agriculture.
Decoding The Financial Framework: Money Generation, Banking Dynamics and Investment Literacy
This research paper delves into the intricate fabric of the financial landscape, aiming to decode the multifaceted elements comprising money generation, banking dynamics, and investment literacy. The study examines the mechanisms underpinning money creation, exploring the roles of central banks, commercial banks, and the broader financial system in fostering economic growth. It scrutinizes the evolving dynamics within banking institutions, encompassing technological advancements, regulatory frameworks, and their implications on financial stability and inclusivity. Furthermore, this paper investigates the significance of investment literacy in empowering individuals to make informed financial decisions. It assesses the factors influencing investment behavior, such as risk perception, financial education, and market volatility. The research also elucidates the pivotal role of financial institutions and educational initiatives in enhancing investment literacy among diverse demographics. Drawing upon comprehensive analysis and empirical evidence, this research contributes to a deeper understanding of the intricate interplay between money generation, banking dynamics, and investment literacy. It underscores the need for accessible financial education, robust regulatory frameworks, and innovative strategies to foster a more resilient and informed financial ecosystem for individuals and economies worldwide.
Leaf Disease Detection Using Deep Learning and ML Techniques
Crop yield losses from plant diseases are estimated to be between 10% and 16% of total food production each year, making them one of the most enduring and economically devastating threats to global agricultural productivity. Particularly in rural and resource-constrained farming communities, conventional diagnosis methods rely on laborious, expert-led manual inspection that is limited in accuracy, scalability, and accessibility. In order to automatically classify plant diseases from digital leaf images without the need for specialized agronomist intervention, this paper presents a comprehensive AI-Assisted Plant Disease Detection System that uses Convolutional Neural Networks (CNNs). The system is implemented as a responsive Django web application with real-time inference, and it is trained on the popular PlantVillage benchmark dataset, which includes over 54,000 annotated leaf images covering 38 disease categories across 14 plant species. Four UML design artifacts—a system architecture diagram, a use-case diagram, an activity diagram, and a CNN layer architecture diagram—are used to formally document the entire system, offering a strict specification of both structural and behavioral system properties. Experimental results show that deep learning-based detection is a viable, low-cost substitute for traditional agronomic field inspection by confirming accurate real-time classification of several plant conditions, such as Pepper Bell Bacterial Spot, Tomato Early Blight, and Potato Late Blight.
Copyright (c) 2026 IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms https://creativecommons.org/licenses/by/4.0
Stock Price Prediction Using Machine Learning and Sentiment Analysis
The biggest challenge in computational finance is forecasting stock prices. The stock prices are always in a state of fluctuation due to various economic, geopolitical, and investor sentiment issues. None of the modeling techniques has been considered reliable in forecasting stock prices. The traditional models are based on past stock price movements; therefore, they are not able to consider major fluctuations in the stock market due to news and announcements. To overcome all these challenges, we are proposing our solution: "StockPredict AI." Our proposed system is a hybrid interactive application created using React. Our proposed system is "StockPrediction." Our system includes 38 major stocks in the NASDAQ stock exchange, including AAPL, NVDA, TSLA, AMZN, GOOGL, etc. In our proposed system, we are using two unique data sets: a quantitative data set and a qualitative data set. The quantitative data set includes financial ratios, stock performance, and stock price movements. The qualitative data set includes social media feed analysis. Historical prices and qualitative sentiment data from financial news headlines. Instead of using these data streams separately, we have proposed a unified approach to both data streams. For the data stream containing numerical data, we have proposed Long Short Term Memory (LSTM) networks with 50 units and 0.2 dropout. In addition to that, we have proposed Random the data stream containing sentiment signals, we have proposed a fine-tuned FinBERT model. The proposed FinBERT model can be used to classify eight different sentiment types: positive, negative, neutral, bullish, bearish, uncertain, fear, and greed. In addition to that, it can also be used to classify emotions based on Plutchik's wheel of emotions. Real-time macroeconomic data has been used in our proposed system. The data stream used in our system is VXN, USDX, and UNRATE. Our proposed system has achieved an accuracy of 80.5% along with an RMSE . This proposed system is an enhancement of 36.5% over the standalone LSTM system. It can be inferred that using a sequence of stock prices along with macroeconomic data and multi-class sentiment data can be used to achieve more accurate predictions.
Real-Time Medicine Availability Tracker for Rural Areas with Voice and Language Support
Challenges related to access to essential medicines create barriers to effective healthcare in the rural areas of many underdeveloped countries due to the limited supply chain infrastructure (i.e. limited access to pharmacies or pharmacies that do not have all medicines in stock) and other barriers such as lack of access to real-time pharmacy information, inability to read and understand prescriptions, use of different languages, etc.). In many cases, people living in rural areas are reliant on going from pharmacy to pharmacy (and potentially to multiple pharmacies) to find the medicine(s) they need, which may take several hours, potentially involve unnecessary travel and increased costs, and produce poor health outcomes. This research provides a solution to the problem by developing an easy-to-use system called a “real-time medicine availability tracker,” which is designed to function in rural areas, allowing individuals to search for medicines based on either their name or by describing symptoms, using voice or text in their native language, to find a pharmacy nearby that has the medicine(s) in stock. Our proposed system will enable access for illiterate people and elderly people through the speech-to-text (STT) processing and language translation components; therefore, the system will be able to support both of these user groups. The proposed system also includes a central database of pharmacies that continuously updates its inventory of available medicines at pharmacies. In addition, the Geographic Mapping feature will provide users with an immediate map showing nearby pharmacies where medicines are available. The tracker system will use Streamlit as the user interface, the backend instructions for processing will be written in Python, the inventory database is an SQLite database and the Speech, Translation and Location services will be supplied by external application programming interfaces (APIs).Experimental testing under typical rural usage scenario conditions reveals that voice recognition accuracy is high, language translation is reliable, the system response time is low, and user satisfaction with interaction has had high scores. These results prove that the proposed system can significantly enhance the accessibility of medicine, reduce search time, and minimize physical effort for rural populations, thus making an effective contribution to equitable healthcare delivery
Youth in Politics: Assessing the Impacts of Student Political Engagement in Arunachal Pradesh, India
The participation of students in politics particularly in Arunachal Pradesh has been crucially significant for socio-political strata of the state. The study is an attempt to explore and comprehend the impacts of student’s participation in politics of state, focusing on how such involvement shapes their civic engagement, political awareness and societal contributions. Drawing on existing literature and empirical studies, the paper examines the various ways of student’s engagement in politics that begins with voting, opinion building, activism, and advocacy. It also discusses the factors that influence students’ political participation such as education, social networks and political environment. Furthermore, the paper analyzes the consequences of their participation in politics both positive and negative political engagement, both at the individual and societal levels. Highlighting its potential to foster a more informed and participatory electorate. Accordingly, tools for data collection were administered to obtain relevant and significant information to comprehend the objectives of study undertaken. The study is primarily focused on relooking and researching outlook of student’s participation in politics. However, the study can be extended with new research design with larger scope in future. The subject has vast prospective for further searching and accomplishments. Various dimensions can be drawn from the study and carry forward with new research proposals.
Investigating Chishti Order Dargah and its Cultural Impact on Delhi During Medieval Period
Delhi has long been intertwined with Sufism and its rich cultural traditions. The arrival of Sufis in India during the eleventh century significantly reshaped the city’s cultural landscape. Central to Sufi architecture is the dargah a shrine constructed over the graves of revered saints which in Delhi often reflects the architectural influences of the Mughal and Sultanate periods. The Sufi tradition has had a profound impact on the cultural heritage of Delhi and its surrounding regions. This qualitative research examines the architectural styles of dargahs associated with the Chishti Sufi order in Delhi, particularly those built between the 11th and 14th centuries during the Sultanate period. It also explores the cultural influences and evolution of dargah architecture, assessing their role in shaping Delhi’s architectural and cultural identity. The findings offer insights and guidelines for preserving the indigenous heritage and cultural legacy of these sacred sites for future generations.
Enhancing Tissue Engineering Design through Interpretable AI and Machine Learning Models with Feature Engineering
Tissue engineering (TE) has emerged as a promising field for regenerating damaged tissues and organs, but traditional design approaches often rely on trial-and-error methods, leading to inefficiencies in scaffold development and biomaterial optimization. This paper explores the integration of interpretable artificial intelligence (AI) and machine learning (ML) models, enhanced by feature engineering techniques, to improve TE design. We review related work and existing systems, propose a novel framework utilizing XGBoost with SHapley Additive exPlanations (SHAP) for predicting scaffold biocompatibility, and present simulated results demonstrating improved accuracy and interpretability. The proposed system addresses key challenges such as data complexity and model transparency, paving the way for more efficient TE advancements. Our findings suggest that feature-engineered interpretable models can reduce development time by up to 30% while providing actionable insights into biomaterial properties.
Design, Develop and Evaluate a Sustainable Pharmaceutical Packaging Using Biodegradable Polymers
As a sustainable substitute for traditional petrochemical-based plastics, this study explores the creation and improvement of biodegradable PLA–PHB (polylactic acid–polyhydroxybutyrate) blends for pharmaceutical packaging applications. Solvent casting was used to create a variety of formulations (F1–F7) with varying compositional ratios, and their mechanical, thermal, barrier, biological, and environmental performance were assessed. According to the study, PLA-rich blends had minimal migration characteristics, excellent tensile strength, and thermal stability, which made them appropriate for rigid packaging with a long shelf life. PHB-rich blends, on the other hand, showed improved biodegradability, encouraging eco-friendly disposal. The addition of amber-modified formulations enhanced their antibacterial and UV protection qualities, making them perfect for medicinal goods that are sensitive to light. Extensive evaluation tests, such as DSC, TGA, antibacterial activity, and stability investigations, verified that optimized blends like F2, F4, and F6 satisfy crucial pharmaceutical standards of durability, safety, and sterility. Overall, the study shows that PLA–PHB blends are promising, environmentally friendly materials that can take the place of traditional polymers in pharmaceutical packaging while maintaining performance effectiveness and regulatory compliance.
From Traditional Formulation to Molecular Evidence: GC–MS Characterization of Lavangapattai Kudineer in Anti-Hemorrhoidal Therapeutics
According to the World Health Organization, nearly 70–80% of populations in several Asian and African countries depend on traditional medicine for their primary health care needs. Among the AYUSH systems of India, Siddha medicine has gained renewed global attention due to its holistic philosophy and therapeutic potential in managing chronic and lifestyle-related disorders. Bleeding hemorrhoids (Raththa Moolam) are a common anorectal condition characterized by painless rectal bleeding, vascular congestion, and inflammation, significantly impairing quality of life. Conventional treatment modalities often provide only symptomatic relief and are associated with recurrence, adverse effects, and financial burden, thereby emphasizing the need for safer and cost-effective alternative therapies. Lavangapattai Kudineer, a classical Siddha formulation indicated for bleeding disorders, has been traditionally prescribed for the management of Raththa Moolam. The present study aimed to scientifically validate this formulation using Gas Chromatography–Mass Spectrometry (GC–MS) analysis to identify its phytochemical constituents and evaluate its therapeutic relevance in bleeding hemorrhoids. GC–MS analysis revealed the presence of diverse bioactive compounds exhibiting anti-inflammatory, antioxidant, hemostatic, membrane-stabilizing, and tissue-protective activities. These pharmacological properties are closely aligned with the pathophysiological mechanisms of bleeding hemorrhoids, including oxidative stress, vascular fragility, venous congestion, and mucosal inflammation. The identified constituents may contribute to reducing inflammatory mediators, strengthening vascular integrity, decreasing capillary permeability, enhancing venous tone, and promoting mucosal healing, thereby controlling bleeding and minimizing recurrence.
Research on Splicing Technology of Expressway Reconstruction and Expansion Based on Finite Element Method
To address the mechanical performance issues of the splicing structure between new and old pavements in expressway reconstruction and expansion projects, this study employed ABAQUS software based on the three-dimensional continuum finite element method to systematically analyze the effects of load magnitude, layer modulus, and number of steps on the interlayer stress of the spliced pavement. The research findings can provide theoretical basis for the splicing design and construction control of highway widening and reconstruction projects
Multivariate Analysis of Mechanical Response and Rutting Performance in Asphalt Pavements Using 3D-Move Analysis
This study investigates the mechanical response and rutting performance of asphalt pavements using 3D-Move Analysis software based on the three-dimensional continuous finite layer method. A multivariate orthogonal experimental design was employed to simulate two pavement structures with different surface layer combinations (SBS-modified asphalt and SMA) under coupled thermal-mechanical loading. Key parameters including vehicle speed (35–77 km/h), temperature (0–40°C), and traffic volume (15,000–45,000 vehicles/day) were systematically analyzed. Results demonstrate that temperature significantly influences the viscoelastic behavior of asphalt mixtures, and optimal material selection effectively enhances long-term pavement performance. This research provides practical guidance for pavement structural design and material optimization.