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.
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.
Copyright (c) 2026 JOURNAL UGC-CARE IJCRT (2349-3194) | ISSN Approved Journal https://creativecommons.org/licenses/by/4.0
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.
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.
Legacy of Lucknow: A Study of Four Famous Personalities of Cinema, Born and Raised in or Near the City
When inquired about the cultivation of maximalism by the City of Nawabs, Lucknow, Muzaffar Ali, the Indian maker, poet, artist, fashion designer, and cultural revivalist, replied, “It (Lucknow) has a deeper sense of understanding and feeling situations. The deeper the feeling the greater the sense of the maximal. Here one thing leads to another, till you create an anjuman, a congregation of people and ideas. These feelings are getting eroded by consumerism and film can be one way of recreating them for everybody.” (Shriti Das) Lucknow’s culture is marked by tolerance, courtesy and artistic refinement. The city is renowned for storytelling tradition popularly known as kissa goi, mushaira, kathak dance, poetry, theatre, literature, and is associated with the traditions of ghazals, and classical music. Lucknow, thus, provides quite a conducive environment for artistic growth and space for everything that constitutes the core components of cinema. Through Muzaffar Ali’s words a clear insight into the romantic sophistication of Lucknow city can be perceived in a subtler way: “The rational, historical, humanistic outlook of my father, and the compassionate, culturally-oriented life of my mother, Rani Kaniz Hyder, both were mirrors to the timeless types that existed in Awadh. Through them, these types found their way to the outer world and to the inner world of the sensitive, feminine feudal culture of Umrao Jaan” (Meera Ali). Some of the prominent cinema personalities from Uttar Pradesh are Kaifi Azmi, Shakeel Badayun, Muzaffar Ali and Naushad Ali. Kaifi Azmi, a noted lyricist of Bollywood, was from Azamgarh, Uttar Pradesh. Shakeel Ahmad was born in Badaun, Uttar Pradesh, while Naushad Ali and Muzaffar Ali both hailed from the City of Nawabs, Lucknow. The purpose of this study is to bring together the above mentioned four famous personalities of Hindi cinema born and raised in or near Lucknow, and study the impact that Uttar Pradesh, and Lucknow in particular, had on these eminent personalities so much that it shaped their lives to help them reach the heights and skies of fame and stardom.
Irretrievable Breakdown of Marriage: A Comparative Doctrinal Study of Institutional Ethics Review Frameworks in the United Kingdom of Great Britain and Northern Ireland, Canada, the Commonwealth of Australia, and the Republic of India
This study undertakes a doctrinal and comparative examination of institutional ethics review mechanisms governing research involving human participants, with particular reference to the United Kingdom, Canada, and Australia, and their relevance for India. The research proceeds on the premise that contemporary Institutional Review Boards and their functional equivalents have evolved into legally significant bodies responsible for safeguarding participant autonomy, dignity, welfare, and informational privacy. Relying exclusively on secondary sources, including statutory frameworks, regulatory guidance, and leading judicial pronouncements, the study analyses the substantive criteria, procedural architecture, approval timelines, and participant protections embedded within the selected jurisdictions. The findings reveal substantial normative convergence across the three foreign systems, particularly in relation to scientific validity, proportional risk assessment, meaningful informed consent, equitable participant selection, and confidentiality safeguards. However, important operational distinctions persist. The United Kingdom demonstrates the advantages of centralised digital integration and defined decision timelines; Canada reflects a principled yet decentralised institutional model; and Australia offers a hybrid framework combining national guidance with registered committee oversight. Across jurisdictions, proportionate review pathways and transparency mechanisms emerge as key determinants of regulatory efficiency and public trust. When evaluated against this comparative baseline, India’s ethics governance framework appears normatively robust but institutionally uneven. While the ICMR National Ethical Guidelines and the New Drugs and Clinical Trials Rules have strengthened the regulatory foundation, challenges remain in the areas of procedural uniformity, committee capacity, data governance assessment, and public transparency. The study concludes that India would benefit from calibrated institutional reforms, particularly the development of an interoperable national ethics submission platform, strengthened accreditation and training of ethics committees, culturally responsive consent processes, and tighter integration of privacy review within ethical scrutiny. The research ultimately affirms that effective ethics oversight enhances both participant protection and the legitimacy of scientific enterprise, and that India stands at a critical juncture in translating normative commitment into operational coherence.
The Principle of Inherent Human Dignity in International Human Rights Law: Philosophical Foundations, Legal Codification, and the Challenge of Cultural Relativism
The notion of human dignity serves as the foundational principle of modern international human rights law and acts as the normative cornerstone of global bioethics. This chapter explores the philosophical progression of human dignity, tracing its origins from ancient Greek and Stoic thought through Christian theology to Enlightenment philosophy, thereby illustrating how the concept has been articulated throughout various intellectual traditions. The analysis examines how dignity, defined as the inherent and inalienable worth that is equally possessed by all members of the human family, emerged as the organizing principle of the international legal order established post-World War II, first enshrined in the United Nations Charter (1945) and later in the Universal Declaration of Human Rights (1948). It is established in this chapter that human dignity serves as the ultimate source from which fundamental human rights are derived, rather than being classified as a right itself. This foundational relationship elucidates that rights exist due to individuals' intrinsic worth, independent of state endorsement. The inherent and universal nature of dignity necessitates equal treatment and prohibits discrimination, thus providing the normative framework for safeguarding human integrity against potentially detrimental biotechnological advancements. In addressing the critique of cultural relativism, the chapter illustrates that while human dignity has been systematically developed within Western philosophy, analogous concepts are present in non-Western traditions, including Confucianism and Islam. International human rights instruments, notably the Universal Declaration on Bioethics and Human Rights (2005), acknowledge cultural diversity while asserting that cultural practices cannot override fundamental human rights principles. The chapter concludes by affirming that human dignity functions as a unifying transcultural principle, facilitating the establishment of minimal common bioethical standards while honouring legitimate cultural variations in their application.
Experimental Investigation on Concrete 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. Nano Silica owing to its ultrafine particle size and high pozzolanic reactivity acts as a micro-filler and accelerates the formation of calcium silicate hydrate (C–S–H) gel thereby refining the pore structure and improving compressive strength. PVA fibers with their high tensile strength and excellent bonding properties, bridge micro-cracks and enhance ductility and toughness of the concrete matrix. In this study Nano Silica was used as a partial replacement for cement at 1%, 2%, and 3% by weight, while PVA fiber was added as 0.5% by volume of concrete. The specimens were tested for compressive strength, split tensile strength, and flexural strength at 7 and 28 days of curing.
Experimental Investigation on Concrete with Prosopis Juliflora Ash and Basalt Fiber Bar 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. The findings of this study highlight the potential of Prosopis juliflora ash as an eco-friendly and cost-effective alternative material for sustainable construction
Performance Analysis of PAPR Reduction in Multi-User OFDM Systems Using PTS, PSO-PTS AND DE-PTS Techniques
Orthogonal Frequency Division Multiplexing (OFDM) is widely employed in modern multi-user wireless systems due to its high spectral efficiency and robustness to multipath fading. However, the high Peak-to-Average Power Ratio (PAPR) of OFDM signals reduces power amplifier efficiency and degrades system performance. This paper presents a comparative analysis of Partial Transmit Sequence (PTS), Particle Swarm Optimization-based PTS (PSO-PTS), and Differential Evolution-based PTS (DE-PTS) techniques for PAPR reduction in multi-user OFDM systems. The proposed methods are implemented in MATLAB and evaluated in terms of PAPR, Bit Error Rate (BER), and spectral efficiency. Simulation results demonstrate that DE-PTS achieves the lowest PAPR of approximately 5.3 dB with minimal BER degradation and improved spectral efficiency, outperforming conventional PTS and PSO-PTS methods. These findings confirm the effectiveness of optimization-based PTS techniques for enhancing performance in next-generation wireless communication systems.
An Experimental Investigation on Geopolymer Concrete with Partial Replacement of M-Sand Using Iron Ore Tailings
This research work aims to study further sustainability to the less geopolymer concrete cement by partially replacing fine aggregate by Iron ore tailings. Geopolymer concrete is one of the building materials that has become more popular in recent years because it is significantly more environmentally friendly than standard concrete. Geopolymer concrete usually includes fly ash, Ground Granulated Blast Furnace slag, fine aggregate and coarse aggregate activated by means of alkaline liquids like sodium silicate and sodium hydroxide. Further the Iron ore tailings which is a byproduct obtained from Steel manufacturing industry can be used as a replacement to fine aggregate. By using this for construction purposes, it reduces environmental pollution. Geopolymer concrete of grade M30 with Iron ore tailings as a partial replacement to fine aggregate was studied for its compressive, tensile and flexural behavior and compared with conventional Geopolymer concrete. The study derived that in all stages, the performance of the geopolymer concrete with Iron ore tailings was marginally better than the conventional Geopolymer concrete with fine aggregate. In this study, the Iron ore tailings are used in various percentages such as 0%, 20%, 40% and 60% and found 40% was effective in terms of strength.
Green biosynthises of nanosilver using Gylcyrrhiza glabra extract -screening of its antioxidant, antimicrobial and antidiabetic activity
The current study aims to investigate the anti-bacterial, anti-fungal, antidiabetic and anti-oxidant properties of the silver nanoparticles synthesised by ecofriendly method of green biosynthesis by utilizing the aqueous stem extract of Glycyrrhiza glabra (Licorice Stem) as a reducing agent. The bio synthesised AgNPs are characterised in this study using visual colour change, FTIR study was used to detect the presence of functionals groups present in the AgNP, SEM analysis was used to characterise the average size of AgNPs, which were with a size range from 64.82 nm to 85.04 nm and UV-Vis spectroscopic analysis shows maximum absorbance band at 450 nm, respectively. Using the agar disc diffusion method, antibacterial and antifungal activity was evaluated against typical strains of bacteria and fungus. The antioxidant property was determined using DPPH assay and SOD assays. The antidiabetic property was tested using alpha amylase and alpha glucosidase inhibitory assays respectively and the results were compared with the results of acarbose standards. Due to its high concentration of plant-based phytoestrogens and/or natural antioxidants, the legume Glycyrrhiza glabra may be used in the long run to treat and/or prevent diseases linked to oxidative stress including diabetic nephropathy, ferroptosis and certain types of cancer and nanobiomedial applications.
AI-Driven Intrusion Detection System for Smart Agriculture in Remote and Disaster-Prone Areas
Smart agriculture integrates Internet of Things devices, sensors, and wireless communication technologies to enhance crop productivity and support sustainable farming practices. In India, climate change has significantly affected major crops, resulting in highly unpredictable yields over recent decades. Accurate prediction of crop yield before harvest is therefore essential for effective planning and efficient resource management. This study proposes an Artificial Intelligence driven Intrusion Detection System designed to secure smart agriculture networks while enabling intelligent crop yield prediction. The proposed framework combines agricultural data collected through Internet of Things technologies with machine learning techniques. A Random Forest algorithm is employed to predict crop yield with high accuracy. An interactive web based platform is developed to provide a user friendly interface for farmers and other stakeholders. Standard benchmark datasets such as the Network Security Laboratory Knowledge Discovery and Data Mining dataset are used to train and validate the Intrusion Detection System module. The system continuously monitors Internet of Things network traffic to detect potential cyber threats, thereby ensuring data integrity and reliable system operation. The integrated framework enhances network security and decision making capabilities, supports efficient resource allocation and farm planning, improves overall agricultural productivity, and strengthens cybersecurity in smart farming environments. The proposed approach promotes sustainable and data driven agriculture.
Research on Shaft and Tunnel Road Construction Technology Based on Solid Waste-based Cementitious Materials in Mining Areas
The large-scale accumulation of solid waste in mining areas and the high-carbon footprint of traditional cement-based materials present dual challenges for sustainable underground construction. This study investigates the preparation of high-performance cementitious materials from activated coal gangue and tailings for roadway pavement applications. Systematic experiments confirm the developed material exhibits superior mechanical strength, enhanced durability in humid/corrosive environments, and excellent environmental safety compliance. Furthermore, we propose an integrated technical solution encompassing optimized mix design, specialized construction techniques, and quality control protocols. This approach enables effective solid waste valorization while significantly reducing construction costs and environmental impact, providing practical support for green mine development and circular economy implementation in mining operations.
Solid waste-based cementitious materials
Advancing AI-Driven Tissue Engineering Constructs through Future Directions in Real-Time Adaptation, Multi-Modal Integration, and Personalized Scaffold Design
The integration of artificial intelligence (AI) in tissue engineering has emerged as a transformative approach to designing scaffolds that enhance tissue regeneration and integration. This paper presents the Adaptive Multi-Modal AI for Personalized Scaffold Design (AMAPS), a novel framework that addresses the limitations of existing AI models in tissue engineering by incorporating real-time adaptation, multi-modal data integration, and personalized scaffold design. The AMAPS framework employs advanced algorithms to analyze diverse biological data, allowing for the dynamic adjustment of scaffold properties in response to the evolving needs of regenerating tissue. Through a comprehensive review of recent literature, we highlight the current state of AI-driven tissue engineering and the challenges faced by traditional models, including their inability to provide personalized solutions and their reliance on static datasets. By contrasting AMAPS with existing methodologies, we demonstrate significant improvements in prediction accuracy, scaffold integration rates, and patient satisfaction. Our findings suggest that AMAPS not only enhances scaffold performance but also fosters a more patient-centric approach to regenerative medicine. This research paves the way for future developments in AI-driven tissue engineering, with the potential to revolutionize scaffold design and improve clinical outcomes in regenerative therapies.
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