bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated LSTM Based Deep Learning Model for Handwritten Telugu Answer Script Analysis
Authors: Padmavathi Pragada
Published: February 2025

The growing demand for automated evaluation systems in educational environments, especially for languages with complex scripts like Telugu, drives the motivation for this research. Traditional handwriting recognition methods for Telugu have faced challenges with limited accuracy and adaptability, particularly in real-world educational scenarios. These limitations often result in reduced precision in character and sentence recognition, along with increased processing delays. This study proposes a novel system for the automated evaluation of handwritten Telugu answer scripts. The model incorporates advanced preprocessing techniques such as adaptive thresholding for binarization and Gaussian blurring for noise reduction, enhancing the readability of diverse handwriting styles. Robust feature extraction is achieved using Convolutional Neural Networks (CNNs) like ResNet101 and Inception networks. To capture the contextual flow of Telugu scripts, Quad Long Short-Term Memory (LSTM) networks are utilized, with Attention Mechanisms improving focus on intricate character sequences. Additionally, Transformer-based models like BERT, trained on Telugu text, enable the system to better understand the syntax and semantics of the language. For evaluation, Visual BERT embeddings and cosine similarity metrics are employed to ensure precise semantic analysis and answer matching. Testing across multiple datasets demonstrates a significant improvement over existing approaches, with higher precision and accuracy in character recognition and notable enhancements in area under the curve (AUC). Sentence recognition also shows marked improvements in precision, accuracy, and AUC. This work represents a significant advancement in automated evaluation systems for languages with intricate scripts, improving both efficiency and accuracy in educational assessments. Beyond its primary application, the system offers potential for broader uses in document processing and language technologies. This research makes a valuable contribution to the fields of automated handwriting recognition and natural language processing for Indic scripts.
Fractional Difference Equations with Initial Time Difference
Authors: D. Naga Purnima
Published: February 2025

In this paper, we consider non-linear fractional difference equations at different initial times and establish the existence of solutions using monotone iterative technique.
An operations and relations on the Cartesian Product of Interval Valued Intuitionistic Fuzzy Matrices
Authors: R. Amutha,
Published: February 2025

In this paper, we study an operations and relations on the cartesian product over interval valued intuitionistic fuzzy matrices are introduced and its some properties are explored. We prove some equality based on the operation and the relation over interval valued intuitionistic fuzzy matrices. Finally, we introducing some cartesian formulas 1, 2, 3, 4, 5 in cartesian product of interval valued intuitionistic fuzzy matrices.
Leukemia Cancer Gene Identification Through Genetic Optimization Technique and Prediction with LSTM
Authors: M.Kalaivani ,
Published: February 2025

Cancer disease persists as one of the major causes of mortality worldwide, emphasizing the critical necessity for accurate and early diagnosis.  Identifying genes associated with cancer can facilitate effective early-stage treatment. DNA Microarray data plays a vital role in detecting and diagnosing cancer. Microarray analysis allows the examination of gene expression levels in specific cell samples, enabling the simultaneous analysis of thousands of genes. However, microarray gene data is characterized by high dimensionality and contains redundant genes. The gene (feature) selection process is essential in microarray gene expression data analysis for selecting highly relevant genes with low redundancy that may cause improved prediction results. This research article presents a model designed to reduce the high-dimensional gene data to low-dimensional space, eliminate redundant genes, and improve early-stage disease prediction. The proposed model is implemented in the following phases. The initial process involves checking the multicollinearity between genes and identifying the low intercorrelated genes using the Karl Pearson correlation coefficient on the cancer dataset. Next, the Genetic Optimization Algorithm integrated with the Signal to Noise Ratio method is applied to determine an optimal set of genes, focusing on reducing dimensionality while filtering out noisy and redundant genes.   In the final phase, the selected genes are classified using a Deep learning classification method namely the Long Short Term Memory classifier is utilized.  The model’s performance is evaluated with various optimizers, including Adam, Adagrad, RMSProp, and SGD by comparing accuracy and loss values. Also calculate the other classification parameters such as Precision, Recall, and F1-Score value. Finally, comparative analyses of these metrics across the different optimizers are performed. The experimental results demonstrate that the research model Cor-GA_SNR-LSTM significantly improves the detection analysis by reducing the number of features in the gene data, enhancing the accuracy value, and minimizing the loss value.  The Adam optimizer yielded the optimal performance with an accuracy of 88.45% and a loss value of 0.1612. The proposed method effectively identifies the most relevant genes responsible for detecting and predicting Leukemia cancer disease.
GRA Approach for the Solution of Mitigating Air Pollutant Emissions from Animal Barns with the Help of fuzzy Knowledge Measure
Authors: Anirudh,
Published: February 2025

The air pollutants emissions from concentrated animal husbandry contribute to environmental pollution and global warming problems. A number of air pollutants, including NH3, H2S, CH4 and CO2, can be produced via land application, animal housing, and manure storage. It is necessary to reduce these contaminants while maintaining the supply of animal protein. In addition to safeguarding the environment, this mitigation will enhance indoor air quality, which is vital for the welfare and health of animals as well as the workers’ safety. For this problem various methods are compared that can resolve this problem and a knowledge measure based on fuzzy sets for managing ambiguity and uncertainty in real life problems. This work uses fuzzy sets to present a knowledge measure to handle these type of ambiguity and uncertainty. The fuzzy sets that satisfies the Knowledge measure has accumulated significant focus yet remains pending due to its importance in evaluating fuzzy information. This paper presents a knowledge measure under fuzzy sets and rigorously evaluates its validity through a systematic, axiomatic analysis. The effectiveness of the suggested measure for ambiguity and linguistic comprise is explained via quantitative example, depend upon the discussed measure. Any fuzzy set that is co-relate to Knowledge is assess by a knowledge measure. Initiation of a new knowledge measure has been introduced for the current problems to get more effective results. The prime target of this study is to get foremost ideal solution which is much better than the orthodox fuzzy approach. The validity and usefulness of knowledge measure can be evaluated by numerical examples. Additionally, with the aid of Multi Criteria decision-making and GRA approach this knowledge measure becomes a useful methodology for comparison and can be applied to improve a solution to a problem. We present the suggested approach’s real-world implementation especially by reducing CO2 (Carbon Dioxide) and NH3 (Ammonia) gas. A case study on the existence of pollution and how can a producer reduce CO2 and NH3 concentration in animal barns with this approach. Throughout the entire study a comparison is made between the enlisted approach and current approach in order to assess the proposed technique’s efficacy and competence.
A Comprehensive study on the Women Disparities in the society using the Methodology of Multi-Criteria Decision Making
Authors: S. Shafeena Miraclinl
Published: February 2025

MCDMs are the unique tool for making decisions and variety of MCDM methods are used halfway around the world aiding in choosing the best option among the multiple alternatives based on various criteria. In this work we have applied the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method that represents a solution methodology for Fuzzy TOPSIS with Heptagonal Fuzzy numbers and each alternative's rating is indicated by Heptagonal Fuzzy Numbers. So, when compared to TOPSIS using triangular numbers we obtain improved and more accurate results. For application and verification, a numerical instance is provided that deals with the Disparities of Women faced in the community
Optimal Resource Planning Using Homer PRO with Monthly Predictions from TCN-based Bidirectional-GRU-LSTM Models
Authors: Anuku Arjuna Rao
Published: February 2025

This paper introduces an innovative approach for optimizing resource allocation in hybrid energy systems by utilizing HOMER Pro, which incorporates monthly load forecasts produced by a Temporal Convolutional Network (TCN)-based Bidirectional-GRU-LSTM (BiGRU-LSTM) model. The proposed technique seeks to overcome the shortcomings of traditional load forecasting methods by employing deep learning strategies to effectively capture both local and long-term dependencies within time-series data. The forecasted load serves as an input for HOMER simulations, facilitating a comprehensive analysis of costs and performance for renewable energy systems. Significant challenges have been identified, such as the static nature of HOMER’s load assumptions and the potential for misrepresentation in renewable energy penetration during specific periods. Our findings reveal that although the TCN-BiGRU-LSTM model greatly enhances the accuracy of load forecasting, discrepancies in HOMER’s simulations may arise when relying on monthly averaged data, particularly in scenarios with high renewable penetration. The work emphasizes the necessity for more sophisticated simulation frameworks that take into account real-time variations in load and generation data. This research offers important insights into the improvement of hybrid energy system design and optimization through the application of advanced forecasting techniques.
Quasi D-Limits Of Bounded Sequences And Regular Matrices
Authors: Sakambari Mishra
Published: February 2025

In this paper, we explore the concept of quasi-D-limits for bounded sequences through the nonnegative regular matrix transformation D. Quasi D-limits extend spaces of D-limits notions, providing a framework to analyse the convergence properties of sequences that exhibit specific bounded behaviour. We establish criteria for quasi-D-limits, demonstrate their existence via regular matrix transformations, and apply these results to various mathematical and applied contexts.
Solving the Type 2 Trapezoidal Intuitionistic Fuzzy Fractional Transportation Problem Using the Diagonal Optimal Approach
Authors: K. Senbagam,
Published: February 2025

This research develops a diagonal optimal technique for solving the Type 2 Trapezoidal Intuitionistic Fuzzy Fractional Transportation Problem (T2TIFFTP), aiming to minimize transportation costs within an optimal solution framework. By incorporating the intuitionistic fuzzy fractional approach, the method effectively addresses the uncertainty and imprecision commonly encountered in real-world transportation scenarios. Numerical results demonstrate the technique’s efficiency and effectiveness. This highlighting its potential to significantly enhance the accuracy and quality of solutions in complex transportation situations. The diagonal optimal technique provides a robust framework for tackling fuzzy transportation problems, paving the way for more reliable and cost-efficient decision-making in uncertain environments
Smart Automation Techniques for Enhancing the Efficiency of Space Exploration Missions
Authors: Arvind Dewangan
Published: February 2025

Smart automation enables a range of various space exploration missions; this paper aims to review how smart automation improves efficiency when used in space exploration. Measuring the impact of enhance state of arts like artificial intelligence (AI), machine learning (ML), robotics, edge computing, and the autonomy system, the study explores the potential of cutting cost, enhancing operational efficiency, improving system reliability, and achieving high-level mission autonomy. By reviewing each element of the AI and ML technology’s application, it is shown that the integration improves the resource allocation process and reduces the human interference needed to complete the missions, thus cutting down the expenditures. Robotics and the use of an anticipatory maintenance system improve system dependability by preventing failure where possible. Furthermore, edge computing and autonomous navigation enhance the amount of interaction within specific locations and aspects as well as the real-time decision-making. The outcomes underscore the possibility of these technologies to change the way, space missions are conducted by enhancing their effectiveness, reliability and perhaps most critically self-sufficiency and opening the door for other revolutions in the field of space expedition.
Federated Learning: A Comparative Survey on Privacy-Preserving Approach to Medical Intelligence Models
Authors: Manjunath N
Published: February 2025

Federated Learning (FL) is a transformative approach to machine learning that enables collaborative model training across multiple entities without compromising data privacy. In healthcare, where patient data is highly sensitive and governed by stringent regulations such as The Health Insurance Portability and Accountability Act (HIPAA) in the United States and General Data Protection Regulation (GDPR) in the European Union, FL offers a privacy-preserving solution. This study investigates the application of FL in predicting cancer outcomes, comparing its performance against traditional machine learning algorithms, including Logistic Regression, based on key metrics such as accuracy, precision, recall, F1-score, and training time. The results demonstrate that FL outperforms Logistic Regression with an accuracy of 89%, precision of 88.67%, recall of 86%, and an F1-score of 89.7%, while maintaining competitive training efficiency. This paper also provides practical implementations of FL in real-world healthcare scenarios, showcasing its potential to address privacy challenges and enable robust medical data analysis. By leveraging FL, healthcare institutions can achieve enhanced collaboration, improved predictive accuracy, and compliance with data protection regulations, paving the way for advancements in privacy-sensitive medical machine learning applications.
The Role of Design Thinking, Critical Thinking and Innovation in Apple's Market Leadership
Authors: Supreet Kaur Sahi
Published: February 2025

Apple Inc. applies innovation for improving different features like blending iPod and phone features to create the iPhone, replacing Touch ID with Face ID for better security, changing its chip architecture from Intel to Apple Silicon, eradicating headphone jacks to push wireless technology, and reversing traditional product interactions with gesture-based navigation. This innovation has helped Apple in maintaining its market leadership and user-centric design approach.This research paper surveys the way Apple Inc. has maintained its competitive edge after applying design thinking, critical thinking, and innovation. This work will also analyze and present the impact of Apple's ecosystem, the use of SCAMPER model, and how strategic decisions are shaping different industry trends by reviewing Apple's user-centric methodology. The findings provide understanding about Apple's product expansion mechanism, customer retention, and future innovation developments. A case study methodology is used in this paper by presenting comparison of Apple's innovation strategy with its competitors.
Revolutionizing Network Security with Hybrid Deep Learning Models for Intrusion Detection
Authors: Shrikant Telang
Published: February 2025

Metaphorically speaking, network security in this fast-moving and ever-changing digital sea has become a necessity. In this work, we provide an overview of the use of hybrid deep learning models for intrusion detection, covering some benchmark datasets (including NSL-KDD, CICIDS2019, BoT-IoT and KDDCup99). DSSTE and Conditional GANs help to overcome the data imbalance problem, while Swin Transformers and Seq2Seq models use sophisticated feature extraction methods for accurate spatial and temporal analysis. The hybrid CNN Transformer network which we propose achieves state of the art detection rate for all kinds of attack, namely DoS, DdoS with minimal precision, recall and F1-scores. Besides, issues such as false positives also exist; however, the research shows that hybrid deep learning models have the potential to reformulate intrusion detection systems from passive entities to adaptive, scalable, and proactive units for next-generation network-level attacks
Hybrid Forecasting Framework for Risk Analysis in Cystic Fibrosis
Authors: Ashwini A. Pandagale, Lalit V. Patil
Published: February 2025

For efficient clinical decision support and prompt intervention in the setting of cystic fibrosis (CF), it is imperative to properly forecast fast decreases in lung function. However, the variability in lung function deterioration and the limitations of current forecasting methods, particularly those relying on Forced Expiratory Volume in 1 second (FEV1) criteria, pose significant challenges. Traditional survival models often fail to incorporate longitudinal biomarker and risk factor observations, leading to inadequate mortality predictions. To address these limitations, this work introduce a Hybrid Forecasting Framework utilizing deep learning algorithms to enhance survival prediction for CF patients. This proposed approach, termed the Temporal Fusion Transformer (TFT) for Competing Risk Prediction, integrates longitudinal data including FEV1 %, Lung Allocation Score, age, gender, weight, and pancreatic sufficiency to dynamically update survival predictions for multiple competing risks. Employing D-SMOTE for data balancing and multi-head attention to capture dynamic temporal connections, the TFT model effectively manages complex temporal relationships and dependencies. This advanced framework surpasses traditional methodologies, offering clinicians a powerful tool for making informed decisions regarding the timing of lung transplantation for CF patients. The TFT for Competing Risk Prediction significantly outperforms current survival models, with an accuracy, precision, and Mean Absolute Error (MAE) of 97%, 91%, and 0.2, respectively. This presents a highly accurate and precise method for predicting rapid declines in lung function in patients with CF.
AI-Powered Crop Care: Transforming Farming with Disease Detection and Sustainable Practices
Authors: Prachi Janrao
Published: February 2025

introduction: This paper offers an AI-powered platform designed to transform agricultural practices thru superior crop disease detection. the usage of deep learning models, the platform identifies plant illnesses with high accuracy, enabling well timed interventions to limit yield losses. It features a user-pleasant interface and multilingual guide to make sure accessibility for diverse farming groups. by delivering actual-time crop health insights, the platform empowers farmers to make knowledgeable decisions, enhancing productivity, sustainability, and profitability. This study highlights the platform's ability to revolutionize sickness control and sell resilient farming practices.
Targets: This study develops a deep getting to know-primarily based machine for forecasting, detecting, and classifying plant leaf sicknesses. It predicts outbreaks by way of figuring out early signs and utilizes ResNet-50, VGG16, and VGG19 for correct category. switch studying, hyperparameter tuning, and rigorous assessment beautify performance, contributing to progressed crop protection and food security.
Strategies: This research integrates advanced device gaining knowledge of and deep getting to know techniques to decorate agricultural selection-making via crop disease detection, crop pointers, and market charge forecasting. The proposed platform follows a established pipeline: information series, version development, machine integration, and deployment. Publicly to be had datasets, which include PlantVillage, undergo preprocessing to improve version generalization. For disease detection, a ResNet-50, VGG16 & VGG19 primarily based CNN, satisfactory-tuned via transfer learning and hyperparameter optimization, ensures high classification accuracy.
Outcomes: The 3-degree detection version performed excessive accuracy in identifying plant illnesses, with the exceptional-appearing approach achieving 99% checking out accuracy and 94% validation accuracy. The gadget tested robust generalization, reducing overfitting whilst improving precision and bear in mind.
Conclusions: The AI crop disorder detection platform offers a transformative solution for sustainable farming by using enabling early disease detection, decreasing pesticide use, and enhancing productivity. Its actual-time insights aid knowledgeable selection-making, and destiny integration with precision agriculture technology ought to further enlarge its effect. This study highlights AI's potential in revolutionizing present-day farming with scalable and reachable solutions.
Hybrid Model Compression Pipeline for Efficient Object Detection using Parallel Recurrent Convolutional Neural Networks
Authors: Shraddha S. More, Rajesh Bansode
Published: February 2025

Several Models of Deep Learning (DL) have demonstrated impressive performance across multiple object detection problems. Large object detection methods based on DL are typically computationally and memory-intensive. Hence, this paper presents model compression strategies for object identification with Parallel Recurrent Convolutional Neural Networks (MCS-OD-PRCNN). Initially, the input photos come from the Common Objects in Context (COCO) 2017 dataset. Next, using Improved Bilateral Texture Filtering (IBTF), the input images are pre-processed. The pre-processed images are then given to the suggested deep-learning model, Parallel Recurrent Convolutional Neural Networks (PRCNNs), which identifies and localizes the objects in the image. After training and validating the PRCNN model on the pre-processed dataset, compression model precision is decreased with the application of strategies like quantization and pruning, eliminating redundant weights and connections, and training a smaller, more efficient student model based on the larger PRCNN model. To ensure optimal performance, hybrid fox and chimp optimization algorithms (Hyb-FCOA) are employed for the compression model’s parameter tuning. The suggested methodology is carried out in Python environment, and fundamental evaluation metrics such as Accuracy, Precision, Recall, F-Measure, mean Average Precision (mAP), Matthew’s Correlation Coefficient (MCC), Intersection over Union (IoU), and Positive Predictive Value (PPV) are employed to evaluate the strategy's performance. The proposed method attains 20.08%, 23.35%, and 27.79% higher accuracy compared to existing techniques such as using one-to-one instruction and guided hybrid quantization for remote sensing object detection (GHOST-GQSD), Fast Region-Based Convolutional Neural Network (Fast-RCNN), and You Only Look Once version 4 (YOLOv4), respectively.
Developing a Lightweight CNN-Based Model for Enhanced Automatic White Blood Cell Classification Using Deep Learning Techniques
Authors: P. P. Mahale
Published: February 2025

Finding and monitoring many diseases, including infections and leukaemia, depend on the classification of white blood cells (WBCs). Conventional techniques mostly rely on seeing samples under a microscope and calling for an expert to evaluate the outcomes. This method takes a lot of time and human mistake might create errors. This work uses a lightweight convolutional neural network (CNN) to provide a basic model to enhance the automated categorisation of white blood cells (WBCs) via deep learning. The proposed paradigm maximises computational efficiency, hence it is perfect for real-time applications and environments with limited resources. We kept the model correct while still designing a simplified form that would make using it easier. Trained and validated on a whole dataset including diverse microscopy images of WBCs, the model guarantees stability and generalizability throughout many imaging environments. Our findings reveal that, in terms of sorting accuracy, speed of processing, and operational efficiency, we have achieved significant progress over conventional techniques. The minimal weight of the device makes it simple to use in internet services and mobile health applications, therefore enabling more individuals to get necessary medical attention. By means of concepts on building efficient deep learning models for healthcare, this work provides a beneficial tool for better categorising white blood cells and also helps enhance medical photographs.
Developing Advanced Deep Learning Layers for Enhanced Automatic Feature Extraction and Higher Accuracy in Alzheimer's Disease Classification
Authors: Minal A. Zope
Published: February 2025

Alzheimer's Disease (AD) classification through medical imaging is important for early detection and planning treatment, but it's still hard to do because neuropathological changes are so minor and complicated. This work presents new layers of advanced deep learning (DL), intended to enhance automated feature extraction, hence improving the accuracy of categorisation. Conventional approaches for AD diagnosis may rely on manual identification of brain biomarkers, which can take a lot of time and vary for every observer. Our work developed a novel set of convolutional neural network (CNN) layers able to automatically identify and rank structural MRI features connected to AD, hence addressing these issues. Three well-known deep learning architectures—density network, inception network, and ResNet—were employed in this work. Every model was trained and tested using a set of MRI images from AD patients and healthy controls made accessible for public use. The upgraded layers were meant to highlight early AD symptoms like hippocampus atrophy and loss of cortical thickness. The accuracy of categorisation is shown to be much greater than with standard DL models. Showing a 12% increase above the average, our enhanced layers included into the DenseNet architecture made it the most accurate. Our models were also robust in feature extraction; they could routinely identify significant AD-related changes in photos with different sizes and brightness levels. This approach not only accelerates the classification of AD but also provides the foundation for future research on accurate and automated neurodegenerative disease testing, which can significantly influence patient outcomes and therapeutic practices.
The Implementation of Artificial Intelligence by Judges in Law Enforcement Reviewed From Legal Convergence Theory
Authors: Dharma Setiawan Negara
Published: February 2025

Laws often do not regulate phenomena in society, especially those related to technology, especially in Indonesia. One thing related to technology that is not yet regulated in Indonesia is artificial intelligence (AI). This of course has the juridical consequence of creating dualism in the legality of the use of AI, including by the Panel of Judges in law enforcement. Regarding this problem, the problem formulation in this article is first, the characteristics of the use of artificial intelligence in law enforcement and second, the urgency of using artificial intelligence by the panel of judges in law enforcement. This research is legal research with statutory, conceptual and comparative approaches. The data in this article is secondary data in the form of primary and secondary legal materials. The results of this article are first, the characteristics of the use of artificial intelligence in law enforcement vary according to the qualifications of the law enforcer. There are law enforcers in the world who are already using AI in law enforcement, Police, Prosecutors, Judges and Advocates. Second, the urgency of using artificial intelligence by the panel of judges in law enforcement is that AI can be used as a tool to make it easier for judges to make decisions. With judges being able to make decisions faster, it will increasingly bring about fast, simple and low-cost justice, as mandated in Article 4 paragraph (1) Law Number 48 of 2009 concerning Judicial Power.
Securing the Internet of Things (IoT) against possible intrusions based on Channel State Information (CSI) and Peak to Average Power (PAPR) Reduction
Authors: Navashish Kaur
Published: February 2025

Conventional computer networks are being replaced with more pervasive networks such as the Internet of Things (IoT) and fog computing networks. Pervasive networks lay a constraint on the amount of processing power and memory for the diverse type of connected devices. This leads to investigating security measures which can be applied to the pervasive network structure so that it secures the network at multiple levels of the OSI model. This paper presents a proactive approach which combines leveraging the channel state information (CSI) as well as minimizing the PAPR of the system so as to enhance the security in both aspects of security aware channel assignment as well as introducing imperceptibility in data transmission over the security aware channel assignment protocol. The effectiveness of leveraging security aware channel assignment is evaluated in terms of the BER and sum secrecy rate of the system while the imperceptibility is evaluated in terms of the PAPR or crest factor value. The paper also combines MIMO-equalization so as to enhance the spectral efficiency of the system. A comparison with existing work in the domain clearly demonstrates the improvement of the proposed approach in terms of the performance metrics.
Exploring the Impact of Social Entrepreneurship on Sustainable Economic Development
Authors: Sagar Uttam Shinde
Published: February 2025

Social entrepreneurship has emerged as a powerful force in driving sustainable economic development by integrating innovative business models with social impact. This  paper explores the role of social entrepreneurship in fostering economic sustainability by addressing societal challenges such as poverty, unemployment, and environmental degradation. Social entrepreneurs leverage business strategies to create scalable and self-sustaining solutions, thereby contributing to long-term economic stability.
The study examines key factors influencing social entrepreneurship, including access to finance, government policies, stakeholder engagement, and technological advancements. It also highlights successful case studies demonstrating how social enterprises have fostered job creation, community empowerment, and inclusive growth. The interplay between social entrepreneurship and the United Nations’ Sustainable Development Goals (SDGs) is analyzed to assess its contribution to global economic resilience.
Challenges faced by social entrepreneurs, such as regulatory barriers, limited funding opportunities, and scalability constraints, are also discussed. The study explores potential solutions, including impact investing, public-private partnerships, and supportive policy frameworks, to enhance the effectiveness of social entrepreneurship in driving sustainable economic growth.
By synthesizing existing literature, this paper provides a comprehensive understanding of how social entrepreneurship serves as a catalyst for economic transformation. The findings suggest that fostering a supportive ecosystem for social enterprises can significantly enhance their ability to address societal issues while ensuring economic sustainability. Future research directions are suggested to explore innovative financing models, cross-sector collaborations, and policy interventions that can further strengthen the impact of social entrepreneurship on sustainable economic development.
This paper contributes to the academic discourse on sustainable development by emphasizing the need for a holistic approach that integrates social innovation, business acumen, and policy support to achieve lasting economic and social progress.
Intellectual Fertilizer Prediction System for Precision Agriculture Using Optimized Artificial Networks
Authors: Bhagwan Dinkar Thorat
Published: February 2025

In agriculture, effective fertilizer management is essential to increasing crop yields and developing sustainable agricultural practices. Devices with artificial intelligence (AI) have produced agricultural tools that help farmers achieve controlled and accurate farming. To meet farmer expectations, increase yield production, and oversee agricultural operations, it is more crucial to anticipate fertilizer correctly. A unique Automated Fertilizer Management System (AFMS) using an Optimized Artificial Neural Network (OANN) model, augmented by back propagation and chain rule techniques, is presented in this study using deep learning (DL) models. The proposed method refines the back propagation process and increases learning efficiency by utilizing advanced optimization techniques such as regularization and hyper parameter modifying. During training, the model guarantees precise and effective weight updates by utilizing the chain rule for gradient calculation. A wide range of data, including soil nutrient levels and climate parameters unique to the Indian agricultural context, are used to train the ANN. These attributes are then sent into an "Optimized Artificial Neural Network (OANN)" that uses previously acquired data to forecast fertilizer results. Specifically, back propagation and chain rule techniques are used to modify the OANN weights in order to increase the prediction accuracy of the classifier.
Gesturely: A Conversation AI based Indian Sign Language Model
Authors: Nupur Giri
Published: February 2025

The project, Gesturely, aims to improve communication in educational settings for people with hearing impairments. Sign language, notably Indian Sign Language (ISL) in India, serves as a primary mode of expression for the deaf community. The form of expression among the deaf relies on a rich vocabulary of gestures involving fingers, hands, arms, eyes, head, and face. The research endeavors to develop an algorithm capable of translating ISL into English, initially focusing on words within the education domain. Through the integration of advanced computer vision and deep learning methodologies, the objective is to create a system capable of interpreting ISL gestures and converting them into written text. The project involves the creation of a comprehensive dataset, with 50 words and more than 2500 videos. The vision is to empower the deaf community with real-time translation capabilities, promoting inclusivity and accessibility in communication
Structural Equation Modeling of Factors Influencing the Success of the Wearable Device Industry in Wenzhou
Authors: Chengsi Wang
Published: February 2025

This research investigates the key success factors (KSFs) of the wearable device industry in Wenzhou, China, by analyzing the impact of technological, market-related, and sociocultural variables on industry success. Using structural equation modeling (SEM), the study develops a framework to quantify these relationships and provides actionable insights for companies in the wearable technology sector. The research population includes stakeholders such as companies, consumers, industry experts, and government agencies. A sample size of 400 respondents was determined using the Taro Yamane formula, with stratified random sampling ensuring representation across diverse groups. Additionally, qualitative data was collected through interviews with nine small, medium, and large executive officers from three wearable device companies in Wenzhou. The findings reveal that technological, market-related, and sociocultural factors significantly influence the success of wearable devices, with statistical significance at the 0.05 level. The SEM model, KSF = 0.572SF + 0.547MF + 0.324*TF, demonstrates that sociocultural factors (SF) have the strongest impact (0.572), followed by market-related factors (MF) at 0.547 and technological factors (TF) at 0.324. This highlights the importance of aligning products and marketing strategies with Chinese consumer preferences and cultural trends. The model explains 79.40% of the variance in success, with a square multiple correlation of 0.794, confirming its reliability as a forecasting tool. The study provides a valuable framework for companies to guide strategic decision-making, allocate resources effectively, and predict success. By understanding the interconnectedness of these factors, companies can foster collaboration between departments and government agencies to drive innovation and adapt to societal changes. Ultimately, applying the insights from this SEM enables companies to enhance their competitiveness in the dynamic wearable device market through product differentiation, catering to local preferences, and building strong brand reputations. This research contributes to academic understanding and practical applications in the wearable technology industry.
A Study on Portfolio Construction using Sharpe Single Index Model
Authors: Ashwini A
Published: February 2025

Making wise decisions is essential to getting good returns on investments. These choices assist investors in deciding whether to invest at all and which stocks to include in their intended balanced investing strategy. Investors may use the Sharpe Index Model to build an ideal portfolio if they believe that stock volatility and market volatility are almost the same. Tools like Standard Deviation (S.D.), Variance (S.D.²), Beta-volatility, and Cut-off-Rate (ci) are used in the analysis. A number of variables, including beta, excess returns in relation to beta, unsystematic risk, and the cut-off point, are taken into account when determining the percentage of investment for each chosen securities. With less risk, investors can achieve higher profits thanks to this analysis.
Assessing Information Security Risks in an Interconnected System using Octave Allegro, NIST Privacy Framework and ISO 27010:2015
Authors: Fauzan, Benfano Soewito
Published: February 2025

The increasing adoption of information systems (IS) within government organizations necessitates the seamless integration of these systems to improve efficiency and transparency in service delivery. While the operation of interconnected government information systems enhances efficiency, it also introduces unknown risks to information security and privacy. The purpose of this study is to identify and assess the potential risks that may arise from the nature of interconnected systems and to propose measures to mitigate these risks. The study employs the OCTAVE Allegro method to address information security risks based on data gathered from the day-to-day operations of the interconnected systems. A new framework is then developed by integrating two well-established frameworks— the NIST Privacy Framework and ISO/IEC 27010:2015— with the risk assessment findings. This new risk-based information security framework is subsequently used to evaluate the current operations of three IS within the Ministry of Public Works and Housing, based on organizational, technical, and personnel indicators. The results reveal that the average score of the current controls in place is 2.58, which is considered fairly good although the organizational indicator received the lowest score. To address this, specific recommendations are provided for each control in the new framework to close the gap between the ideal and current conditions. These insights are then utilized by the government organization to enhance overall infrastructure security and privacy practices, ultimately contributing to a more resilient and interconnected ecosystem for public services.
A Comparative Assessment for Examining the Performance of Reconfigurable Multiband MIMO Antennas for Communication Systems
Authors: S. N. V. J. Devi Kosuru
Published: February 2025

This overview discusses various MIMO Antennas used in wireless communications. MIMO Antennas play an important role in day-to-day communications in the current age scenario. Many research activities in the field of MIMO antennas have already been proposed within the last decade. The reconfigurability of reconfigurable antennas is used to meet the ever-changing scenarios and operating requirements. Reconfigurability in terms of polarization is critical in MIMO systems because they require multiple inputs to reduce fading. In this survey, we explain distinct MIMO Antennas and about their parameters. This research article will also cover the various MIMO antennas with specifically artificial components and their influence on reducing The majority of MIMO antennas suffer from the unfortunate results of Mutual Coupling (MC), low radiation gain, and insufficient information taking care of limit because of restricted range. We will examine mutual coupling and cross polarisation between antenna parts as well as address surface currents. ECC and MIMo antenna competence are also important in dissecting the presentation of an antenna.
Age Estimation using Lightweight Convolution Neural Network
Authors: A.Annie Micheal
Published: February 2025

Introduction: Currently, age estimation is significant in sectors such as age-specific human-computer interaction, security control, surveillance monitoring, Electronic customer relationship management, and forensic art. Nowadays makeup is used by all groups of people. Makeup is now commonly used to boost an individual's confidence and self-esteem. Makeup can give a person a younger or older appearance depending on their makeup choice. This transformation is caused due to varied makeup products such as eyeliner, lipstick, foundation, compact powder, mascara, and eye pencil. These products impact the real face of the individual by concealing the age spots, scars, pimples, and wrinkles, highlighting the cheeks, and brightening the facial parts. Due to the transformation in the look, the apparent age varies from the real age. The estimation of real age is essential in various real-time applications.
Objectives: The application of makeup is a great challenge in estimating the age of the person in vision-based technology. This manuscript deals with deep learning-based age estimation with makeup.
Methods: The proposed framework is composed of data augmentation, face detection, face alignment, and age estimation. The data augmentation techniques used in our methodology are average blur, gaussian blur, histogram equalization, color jittering, bilateral filtering, detexturization, unsharp filtering, and gamma contrast. Multi-task Cascaded Convolutional Network (MTCNN) is adopted for face detection and alignment. Light CNN is used for age estimation with makeup.
Results: The methodology attains an MAE of 5.58 on the self-built Facial Makeup for Male and Female (FMMF) database.
Conclusions: This manuscript presents a deep learning based-age estimation with facial make-up and its results are also evaluated. The developed mythologies will improve the quality of applications such as human-computer interaction, surveillance systems, commercial development, content-based indexing, and searching, demographic studies systems, targeted advertising, and biometric system.
Value System in the Work of Youth Civil Servants: Survey in Hanoi city, Vietnam
Authors: Do Thi Tuyet
Published: February 2025

Building a civil service culture, focusing on ensuring discipline and administrative order; improving the effectiveness and efficiency of agencies and organizations; building a team of officials and civil servants loyal to the State; respecting and wholeheartedly serving the People is an important factor contributing to promoting administrative reform, building and perfecting the socialist rule-of-law State of Vietnam; at the same time, arousing the trust, dedication, talent and intelligence of the team of officials and civil servants. The value system of young civil servants is a component of the Vietnamese value system. The study was conducted in March 2024 using a survey method using a questionnaire with 656 young civil servants and public employees (from 18 to 30 years old) in 6 districts of the city including: Dong Da, Cau Giay, Bac Tu Liem, Hoai Duc, Dong Anh, Gia Lam showing: “Sense of responsibility; Filial piety; Kindness; Honesty” are the values that young civil servants consider most important to themselves. Young people expect civil servants to have both “virtue” and “talent”, the most prominent of which is the model of “Honesty” civil servants; duty; culture”. Research results show that when young people value a value, they also tend to expect that value and quality from those working in public service
Data-Driven Periodic and Nonlinear Trends in Option Pricing: A Curve-Fitting Perspective
Authors: Sheena scaria
Published: February 2025

This study presents a novel curve-fitting tuned methodology to model dynamic trends in data-driven option prices using various fitting models such as polynomial, exponential, Gaussian, and Fourier. It addresses the challenges of pricing options through mathematical modelling, analysing stock price uncertainties, and identifying the best trend fit. The proposed model captures trends and variability in the data, enabling the extraction of critical insights such as the rate of stock price changes over time and the ability to forecast future option prices. Using traded data from various sectors for stock prices between 13.11.2017 and 9.8.2019, statistical measures such as R-squared error, Sum of Squares Error (SSE), Degrees of Freedom Error (DFE), Adjusted R-squared, Root Mean Squared Error (RMSE), and parameter coefficients were evaluated for each model.
Analysis on the Integration of Henan Traditional Art Intangible Cultural Heritage into Public Art Education in Local Colleges and Universities
Authors: Yihan Zhao
Published: February 2025

Public art education in local colleges and universities provides fertile ground for integrating Intangible Cultural Heritage (ICH) into contemporary educational practices. By incorporating traditional art into the curriculum, educators enhance students' understanding of cultural heritage, inspire creativity, and cultivate a sense of belonging to their cultural roots. However, despite the potential benefits, the integration process faces significant challenges, such as limited awareness among students, insufficient resources, and a lack of collaboration between educational institutions and local artisans. By integrating cultural heritage into creative practice and theory, the study aims to foster students' appreciation for local traditions, enhance their creative skills, and strengthen cultural continuity. This study analyses the role, challenges, and preservation of ICH in colleges and universities. This investigation proposes a technique to maximize universities and colleges of fine arts in the area of Henan in terms of their professional benefits, focusing on preserving and advancing the ICH of arts. It also emphasizes the value of incorporating traditional ICH into university instruction and presents a pathway for achieving this integration through the lens of the cultural ecosystem. This study concludes that embedding Henan’s ICH in public art education not only promotes cultural preservation but also enriches the learning environment by connecting students to their cultural roots and fostering innovation through tradition.