bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness Of Intravitreal Bevacizumab In The Treatment Of Neovascular Age-Related Macular Degeneration: Visual And Anatomical Results - A Real-Life Retrospective Study.
Authors: Sampaio Tom Back Maria Isabel , Souto Gandra Ana Luisa , Carvalho Tom Back Bernardo Franco de , Abreu Gabriel , Nazareth Soares Aleida , Braga Pereira Frederico , Hubner de Souza Alessandra ,   Faculdade de Ciências Médicas, Brasil.
Published: February 2025

Objective: To evaluate the effectiveness intravitreal
bevacizumab in the treatment of neovascular macular
degeneration (n-AMD) in a real-life scenario.
Method: Retrospective, longitudinal study that evaluated the
medical records of 101 eyes (89 patients) with n-AMD who
received intravitreal bevacizumab, in a Pro Re Nata (PRN)
treatment regimen, between July 2021 and March 2024.
Results: After one year of follow-up, 41 eyes (41.4%) showed
improvement in VA, 32 (32.3%) showed a decline in VA, and
26 (26.3%) maintained stable VA. There was a significant
reduction in median central macular thickness, with a
decrease from 317 µm at baseline to 271 µm (p-value <
0.001; ∆ = 46 µm). Of all variables evaluated, the number of
visits was the only modulator significantly correlated with VA
improvement (p = 0.008).
Conclusion: In our study, the majority of patients treated
with Bevacizumab improved or maintained their CCVA
status. Therefore, bevacizumab in a PRN regimen was
effective in the treatment of n-AMD, supporting its use in a
high-throughput public service. Our study also reinforced
the clinical application of OCT-based anatomical results
to individualize and optimize therapeutic strategies and
protocols for patients
Pemenuhan Hak Pendidikan Anak Berkebutuhan Khusus di SD Negeri 30 Palembang
Authors: Damayanti Novi
Published: February 2025

This study aims to describe the fulfillment of the rights to education for children with special needs (ABK) at the SD Negeri 30 Palembang. Type of qualitative descriptive research. Data collection techniques use questionnaires, interviews and observation methods. The research subjects were teachers and principals. The research results show that the school has been able to implement the inclusive school concept quite well. In general, the rights to education for ABK have been fulfilled amidst facilities and infrastructure that are not yet inclusive. This study provides a real picture of how inclusive education is implemented in elementary schools in Indonesia, especially in Palembang City. This case study can be used as an example for other schools trying to fulfill ABK's educational rights. It is hoped that the results of this research can contribute to the development of inclusive education policies at the local and national level. Apart from that, this research can also provide input for teachers, schools and other stakeholders in improving the quality of education for ABK.
Quality of Life and its Associated Factors among Patients with Lower Limb Osteoarthritis
Authors: V S Anjana , Mathew A Preemy , Raveendran Remmya
Published: February 2025

Osteoarthritis is a degenerative joint condition. Osteoarthritis is one of the significant contributors to years lived with disability among the musculoskeletal conditions. In 2019, about 528 million people worldwide were living with osteoarthritis; an increase of 113% since 1990. About 73% of people living with osteoarthritis are older than 55 years, and 60% were female.1 Around 23.46 million individuals in India had osteoarthritis in 1990; this increased to 62.35 million in 2019. The age-standardized prevalence of osteoarthritis has increased from 4,895 in 2019, per 100,000 persons. Similarly, disability-adjusted life years due to osteoarthritis increased from 0.79 million to 2.12 million. While age-standardized disability-adjusted life years increased from 164 to 180 per 100,000 persons from 1990 to 2019. Osteoarthritis was the 20th most common cause of years lived with disabilities in India in 2019, accounting for 1.48 % of all years lived with disability; increasing from 23rd most common cause in 1990.2 The present study was intended to find out the quality of life (QOL) and associated factors among patients with lower limb Osteoarthritis.The study was aimed to assess the QOL of patients with lower limb Osteoarthritis and to find the associated factors of QOL of patients with lower limb Osteoarthritis. A descriptive cross-sectional design was adopted for the study. Every consecutive case of lower limb Osteoarthritis, who meet inclusion criteria, attending Orthopaedics Out Patient Department, T.D Medical College Hospital, Alappuzha were selected as sample in this study. Data were collected by interview, biophysiological measurements and clinical record review. The study finding showed the mean age of patients with lower limb Osteoarthritis was 60.08 years and majority (83.5%) of patients were females. Majority (88.8 %) of patients with lower limb Osteoarthritis had primary Osteoarthritis and in 72.4 % patients knee joints were involved. More than three quarters (81.2 %) of patients with lower limb Osteoarthritis had moderate pain on lower limbs, and 18.8 % of patients had severe pain on lower limbs. Majority (87.64 %) of patients had poor QOL and 12.35 % of patients had good QOL. Most of (95.9 %) patients with lower limb Osteoarthritis had poor QOL in physical domain and in mental domain more than half (57.6 %) of patients with lower limb Osteoarthritis had good QOL. Linear logistic regression analysis revealed that, the QOL of patients with lower limb Osteoarthritis is 3.06 times poor among patients with severe pain on lower limb when compared to patients with moderate pain.
A Dual-Architecture Framework for Context-Aware Video Recommendations with Dynamic Resource Optimization
Authors: Suneetha Davuluri
Published: February 2025

This paper introduces a dual-architecture framework that optimizes video recommendations through dynamic switching between MobileNetV2 and YOLOv8 models. Evaluation on 2.3 million user interactions demonstrates that MobileNetV2 achieves 94.5% model size reduction (14.2MB vs 257.8MB) and 56.3% lower GPU memory usage (2.1GB vs 4.8GB) while maintaining 87.3% accuracy in mobile environments. The YOLOv8 component excels in complex scenarios with 89.2% accuracy, notably in crowded scenes (+8.9%) and low-light conditions (+5.3%). Our dynamic switching mechanism improves system throughput by 21.4% (3,400 vs 2,800 users/second) and reduces cold-start latency by 18.8%. The framework demonstrates robust performance across content types, achieving 89.2% accuracy for short-form videos and 84.5% for live streams, while maintaining performance stability with minimal variance. These results establish new benchmarks for resource-efficient video recommendation systems, proving that adaptive architecture switching can effectively balance recommendation accuracy with computational efficiency.
Numerical Analysis of Integrals with the Quadrature Based Haar Wavelet Technique
Authors: Umesha V
Published: February 2025

This paper presents a quadrature rule based on uniform Haar wavelets to approximate the values of definite integrals. The key advantages of this method lie in its effectiveness and ease of application. Numerical examples and error estimates are provided to demonstrate the convergence and accuracy of the proposed method, validating its effectiveness for practical use.
Effects of Heat Source and Thermal Radiation on MHD Casson and Williamson Nanofluid Flow in a Porous Media
Authors: Tanuja Rachakonda
Published: February 2025

A non-Newtonian Casson-Williamson nanofluid flow's mass and heat transport characteristics were examined in this study. We investigate how the velocity boundary condition and viscous dissipation impact the mass and heat transfer mechanism in the presence of a stretching sheet immersed in a porous material that generates heat in response to thermal radiation and a uniform magnetic field. Casson-Williamson nanofluid is assumed to have constant physicochemical properties. As the nanofluid particles flow through the system, their concentration is studied in relation to the chemical consequences. This physical problem is quantitatively modelled using a series of boundary-conditioned nonlinear partial differential equations. By combining the Runge-Kutta method with the shooting methodology, we were able to numerically solve the differential equations with the associated boundary conditions. Next, the numerical analysis is shown graphically to demonstrate how different controlling parameters affect concentration, temperature, and velocity. Without a magnetic field, the non-Newtonian nanofluid moves more quickly than with one, while the temperature field shows the reverse tendency. The rate of heat transfer decreased while the skin-friction coefficient rose with an increase in the porosity parameter.
Revolutionizing Fuzzy Modelling through Graded Mean Integration using Python
Authors: V. Sundhari
Published: February 2025

The research introduces a novel approach to optimize the Economic Order Quantity (EOQ) models in fuzzy modelling systems by incorporating Graded Mean Integration (GMI). Traditional EOQ models are extended by considering fuzzy parameters, such as the ordering cost and holding cost, which are represented by triangular fuzzy numbers. The total inventory cost is formulated with fuzzy parameters and then integrated using the GMI approach to handle the uncertainty in the cost components. The resulting fuzzy modelling cost function is differentiated to obtain the optimal lot size under fuzzy conditions. The study presents the solution to the problem through partial derivatives, considering the defuzzification process using GMI. The optimization process is carried out by solving for the fuzzy optimal order quantity, and the results demonstrate the effectiveness of the GMI approach in determining the most cost-effective modelling policy under uncertain conditions. Python-based visualizations are utilized to illustrate the fuzzy modelling cost curve, the impact of fuzzy parameters on the optimal order quantity, and the defuzzification process, providing a clear understanding of the model's behavior. These visual tools enhance the decision-making process by offering graphical representations of the cost dynamics in fuzzy environments. This method provides a more realistic and flexible approach to inventory management, particularly when dealing with imprecise data in real-world scenarios.
Indo-HateSpeech Analysis: A Multi-Level Hate Speech Classification Framework Using BERT Features and Machine Learning Models
Authors: Pravin D. Kaware
Published: February 2025

The rise of social media has enabled individuals to express opinions widely, but it has also facilitated the spread of hate speech, posing a significant societal challenge. Effective automated hate speech detection systems are crucial to mitigating this issue. Addressing this issue in the context of diverse languages like those in India, this paper presented Indo-HateSpeech, a multi-level framework that combines contextualized embeddings from Bidirectional Encoder Representations from Transformers (BERT) with classical machine learning models for hate speech classification. The comprehensive dataset including emojis, emoticons, hashtags, and slang is necessary to identify hate speech on social media based on current trends. Therefore, the Indo-HateSpeech dataset is developed and collected from various posts on Instagram. This dataset contains hate speech sentences in English and Hindi in Indian Context for classifying hate speech into three categories: No Hate (HS0), Hate (HS1), and Extreme Hate (HSN). Contextual embeddings derived from the pre-trained BERT model to extract high-quality text features from text data with sentiment features. These embeddings are subsequently fed into traditional machine learning classifiers, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM), for classification. Experimental results demonstrate the effectiveness of BERT embeddings in enhancing classification performance in terms of accuracy, F1-score, and recall over traditional approaches, highlighting the framework's potential for scalable deployment in real-world applications.
Energy Efficient Clustering Protocol for Green IoT using Improved Tunicate Swarm Intelligence for Benchmark Texas Data
Authors: Umakant L Tupe
Published: February 2025

The sixth generation (6G) is expected to be a crucial technology that will enable the pervasive and seamless connectivity of a large count of Internet-of-Things (IoT) devices. The need to alleviate the worry of lowering energy consumption, i.e., green communication, is fueled by the extraordinarily fast data rate, low end-to-end delay, and high mobility of IoT devices. Sensor-enabled IoT relies heavily on green communication. Energy is at the centre of the smart IoT application that allows the sensors to function. The sensors' ability to work efficiently is hampered by rapid energy loss. To prevent the quicker loss of energy from sensors in the IoT, an energy-efficient system is necessary. Since heuristic approaches are not suited for such issues and may turn into NP-hard problems in the case of dense area sensor networks, meta-heuristic approaches are most successful in solving these kinds of problems with near-optimal solutions. Extending the longevity as well as stability of WSNs via energy efficiency is a difficult task. Clustering has increased energy efficiency by selecting Cluster Heads (CHs), but its application is yet difficult. The areas where CHs are desirable are initially sought in traditional CH selection algorithms. This paper plans to design and develop a new framework of Green IoT communication via a new, improved meta-heuristic model. Here, a novel energy-aware objective function is derived based on the constraints like “distance, delay, energy, transmission energy, reception energy, and data aggregation energy”. The Adaptive Random Variable-based Tunicate Swarm Algorithm (ARV-TSA) is adopted for performing the novel green communication in IoT. The experimentation is performed for the parametric data from Texas for “temperature sensor, humidity sensor, Thermistor, ultrasonic sensor with minimum voltage, maximum voltage, minimum current, maximum current etc”. The simulation findings have demonstrated a substantial increase in energy conservation and network stability period improvement when compared to identical methods.
Cross-Sectional Texture and Pattern Analysis of Burmese and Assamese Areca Nuts Using CNN Model
Authors: Prodipto Dasa,
Published: February 2025

This paper endeavours to distinguish between Burmese areca nut and Assamese areca nut based on their cross-sectional texture and pattern. Employing advanced machine learning techniques, specifically Convolutional Neural Networks (CNNs), we constructed a model capable of accurately classifying these two variants using images of their cross-sections. The paper explores two distinct datasets: one comprising unedited images and the other generated by applying edge detection algorithms to these images. A custom Python script utilizing OpenCV libraries was developed to create edge maps, providing enhanced feature representations for the CNN model. The model architecture involves convolutional layers, max-pooling layers, and dropout layers for regularization. We conducted extensive experimentation with varied batch sizes and epochs to optimize the model's performance. Our results reveal that the model achieved high accuracy in distinguishing between the two areca nut variants, demonstrating the potential for machine learning to contribute to quality control and identification in this domain. This paper not only presents a successful classification model but also sheds light on the efficacy of edge maps as input data for image-based classification tasks.
Design of an Improved Model for Diabetes Detection Combining Deep Dyna-Q Learning with Ensemble Classification
Authors: Priyanka Deshmukh,
Published: February 2025

The burgeoning prevalence of diabetes worldwide necessitates advancements in early detection techniques to curb its impacts on public health. Traditional methods, while effective to a degree, often suffer from limitations such as low detection speed, moderate accuracy, and high false positive rates, which could delay timely intervention and management of the disease. This research introduces an innovative approach combining deep reinforcement learning and ensemble classification to enhance the efficiency of diabetes detection. In addressing the limitations of existing detection systems, our study integrates a Deep Dyna-Q Learning Network with an ensemble of classical classifiers, namely Naive Bayes, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbors (kNN). The Deep Dyna-Q Network is meticulously designed with multiple layers that include dense units with ReLU activation, batch normalization, and dropout for regularization, specifically configured to optimize learning from complex, non-linear medical data samples. The ensemble approach leverages the distinct statistical strengths of each classifier, allowing for a more robust and comprehensive analysis of clinical data samples. This ensemble is tailored to improve the generalizability and reliability of predictions by mitigating individual biases inherent in single-model predictions. Our model processes a wide array of clinical parameters such as Glucose levels, Blood Pressure, Body Mass Index (BMI), and lifestyle factors like Smoking and Physical Activity, which are critical for predicting diabetes onset. The use of such diverse data helps in capturing a holistic view of risk factors associated with diabetes, thereby enhancing the model's diagnostic precision. The impact of this hybrid model is significant, demonstrating an enhancement in precision by 8.5%, accuracy by 5.9%, recall by 8.3%, and a reduction in diagnosis delay by 2.9%. Furthermore, the Area Under the Curve (AUC), a key metric for classification performance, improved by 9.5%. These improvements underscore the potential of integrating deep learning techniques with traditional statistical methods to create a more accurate, efficient, and responsive diabetes detection system. This work not only advances the technological framework for disease detection but also offers a scalable model that can be adapted to various healthcare settings, potentially leading to better patient outcomes and reduced healthcare costs through timely and accurate diabetes detection.
Machine Learning Approach for Optimal Parameter Estimation in Vibratory Welding
Authors: Rashmi Dwivedi
Published: February 2025

Vibratory treatment during welding process is proved to reduce residual stresses in the weld joint and improve mechanical properties. But optimal setting of vibration parameters such as frequency, amplitude and time is essential to obtain maximum improvement strength of the joint. Identification of such optimal parameter set through series of experimentation is very critical due to severe consumption of resources. The research is aimed to identify optimal setting of parameters through machine learning approach for the welding of SS304 in the gas metal arc welding (GMAW) process. Supervised learning is preferred to run in the convolution neural networks (CNN) by feeding the algorithm with essential data about the mechanical properties. The response is observed for varying the frequency of workpiece, electrode at different voltages as the time kept constant and amplitude found with adverse-affect over weld strength. The observed readings are tested through experimental investigation and confirmed the response in mechanical properties such as tensile strength and hardness are obtained within the acceptable range of deviation. Microstructural characterization is performed to record the evidence over the grain refinement to support the maximum enhancement in mechanical properties of weld joint.
A Deep Neural Network Model for Forecasting Variable Stock Movement Intervals
Authors: Dinesh Singh Dhakar
Published: February 2025

Forecasting results for stock markets exhibit variabilities in forecasting accuracy as the tenure of prediction is varied. Typically stocks are predicted for long, short as well as mid term tenures based on the tenure of prediction. While longer tenures have relatively much larger data to be trained as training data, divergences are also potentially large as the forecasting period may render higher randomness due to unprecedented events. The short term forecasting is relatively less prone to unprecedented events due to the tenure of forecasting. However, the lesser amount of training data may result in less accurate pattern recognition. A common ground is typically found in terms of mid term forecasting. This paper presents an experimental evaluation of all three formats of forecasting based on the training, testing split. The deep neural network model is used for the forecasting purpose and the forecasting MAPE and accuracy has been tabulated for a multitude of stocks. It has been shown that the proposed approach attains an MAPE of only 2.18% outperforming existing work in the domain.
Advances in Deep Learning Image Inpainting Architectures for the Effective Reconstruction of Damaged Regions- A Systematic Review
Authors: Suma N
Published: February 2025

Image inpainting is the method of reconstructing the missed or damaged region of the picture with certain rules that play a vital role in computer vision applications. Numerous image inpainting approaches have been recently proposed that effectively reconstruct the missed regions. Traditionally the missed regions are restored by schemes that are derived from the diffusion models, exemplar approaches, and sparsity approaches. Different hybrid schemes are derived from these traditional schemes that show reasonable performance in missed region reconstruction. Recently deep learning-based approaches which are derived from the architectures of Generative adversarial networks (GAN), Convolutional neural networks (CNN), Transformers, and U-Net are commonly used for effective image painting. More specifically numerous approaches have been derived using the advantage of GAN and CNN architectures. These schemes focus on the reconstruction of global structures and local texture components of the missed region from the known region. Therefore, the paper provides a review of different traditional, and recent deep-learning schemes.
Cybersecurity: A Survey of Threats, Technologies, and Solutions
Authors: Mohammed. I. Alghamdi
Published: February 2025

This study provides a comprehensive survey of the current landscape in cybersecurity, focusing on the prevalent threats, emerging technologies, and viable solutions. Utilizing secondary data from a wide array of sources including academic publications, industry reports, and government documents, the research delineates the evolving nature of cyber threats such as malware, phishing, and ransomware. It also examines the role of advanced technologies like artificial intelligence, blockchain, and quantum computing in enhancing cybersecurity measures. Furthermore, the study explores strategic solutions and best practices adopted by organizations worldwide to safeguard digital assets and ensure data integrity. By synthesizing existing literature and data, this survey aims to offer a holistic understanding of the challenges faced in cybersecurity and the technological innovations that are driving progress in the field. The findings provide valuable insights for policymakers, industry stakeholders, and researchers seeking to strengthen cybersecurity frameworks and devise proactive defense mechanisms against cyber adversaries.
Transformational Leadership, Self-Efficacy, Organizational Climate on Innovative Behavior in Indonesia
Authors: Ulya Amelia
Published: February 2025

This study investigated the influence of transformational leadership, self-efficacy, and organizational climate on teachers' innovative behavior. The focus was on idea generation, idea search, idea communication, implementation initiation activities, involving others, and overcoming obstacles. The method used was a survey involving 160 civil servant teachers in junior high schools in Bukittinggi City. PLS-SEM is a statistical tool used to examine data and test research hypotheses in a structural model. The findings of this study revealed (1) a significant correlation between innovative behavior, transformational leadership, self-efficacy, and organizational climate. Self-efficacy showed the highest positive coefficient in showing innovative behavior (0.234). (2) Transformational leadership had a direct positive and significant effect on self-efficacy. (3) Transformational leadership had direct positive and significant effect on organizational climate. (4) Organizational climate had direct positive and significant effect on self-efficacy. (5) Transformational leadership had an indirect positive and significant effect on teachers’ innovative behavior through self-efficacy. (6) Transformational leadership had an indirect positive and significant effect on teachers’ innovative behavior through organizational climate. (7) Organizational climate had an indirect positive and significant effect on teachers’ innovative behavior through self-efficacy. The use of technology can support innovative behavior. The principal must be able to be a role model and provide inspiring motivation for teachers to create a sense of togetherness and improve innovative behavior. Teachers must understand the subject matter well. Consistent in achieving goals and optimistic in facing challenges. Giving rewards and punishments and collaboration can help improve teachers' innovative behavior. This study can be developed with a wider sample area and a more complex method, namely the mix method.
Improving Recommendation Systems with Machine Learning-Based Noise Management
Authors: Kausar Attar
Published: February 2025

Recommendation systems have now adopted a central role in digital services in the contemporary world given its effectiveness in increasing user interest and value. However, these systems will always face the problem of handling natural noise; a situation that counterfeits the actual preferences of the users, leading to low accuracy of the recommended items. This research introduces a new framework CNN and ANN which provides an efficient way of handling natural noise to increase the efficiency of recommendations. The method that is proposed here classifies user interaction data using CNN, rejecting noise but embracing relevant inputs. ANN is then used on the denoised data for further enhancement and for generating individual product recommendations. This means that by combining CNNs ability to extract features and ANNs ability to make predictions, this two-structure model greatly decreases the chances of the system being easily fooled by noise and/or increases the accuracy of the response to true user preferences. We apply our approach with varying levels of natural noise on several datasets and obtain significant improvements over other recommendation approaches. Not only does this hybrid solution decrement the injurious repercussions of noise, it also provides suggestions for future progress of the recommendation system.
Retail Complaint Patterns: A Statistical and Predictive Analysis Framework
Authors: Archana Ratnaparkhi
Published: February 2025

Customer complaints provide essential insights for improving service quality and customer satisfaction in retail. This study analyzes complaint patterns across multiple store locations to identify significant variations and potential causes. Using a chi- square goodness-of-fit test, we evaluate whether observed complaint frequencies differ from expected distributions, high- lighting stores with atypical complaint rates. Data visualization techniques reveal temporal and spatial trends, while classification models predict the likelihood of complaints based on product and store characteristics. To enhance model performance, we apply Recursive Feature Elimination (RFE) to identify influential factors, such as store size and product category. The results offer actionable insights for retailers, enabling targeted strategies to reduce complaints and improve the customer experience. This study underscores the value of complaint data in strategic decision-making and resource allocation for enhanced operational efficiency.
Extension of Primary and Semiprimary Bi-Ideals of Semirings
Authors: G. Shanmugam
Published: February 2025

The 1PBID, 2PBID, and 3PBID of semirings are introduced. Additionally, we communicate with a number of the various SPBIs’ attributes. 2PBID and 3PBID are generalizations of 1PBID and 2PBID, respectively, which we explain. We go over the m1, m2 and m3-systems and BI generator. It is possible to generalize the m1-system to the m2-system and the m2-system to the m3-system.
θ-separation Axioms on Fuzzy Hypersoft Topological Spaces
Authors: P. Revathi,
Published: February 2025

In this article, the concept of fuzzy hypersoft  (resp.  semi   pre)-separation axioms in fuzzy hypersoft topological spaces are introduced by developing fuzzy hypersoft  (resp.  semi   pre)-neighbourhood with respect to fuzzy hypersoft points. Also, the properties and relations between fuzzy hypersoft  (resp.  semi   pre)- -spaces  are discussed.
Prediction and Assessment of Software Engineering Skill Set among Computer Science Students using Random Forest Algorithm
Authors: Jasmin Nizar
Published: February 2025

Introduction: Skill assessment of students is an evaluation process that captures the ability of each student, whether they are new to a skill or have mastered it. People working on software development projects need to possess three skills: - Soft skills, Life skills, and Technical skills. It is globally accepted in the IT industry that for computer science students to succeed in their careers to the fullest extent, project management training is required. The skill-based education in software engineering helps to produce go-to-market talent and, in turn, achieve job satisfaction and potential career growth. In the current educational system, it is essential to predict students' skill sets, including Soft, Life, and Technical skills. The skill set of software engineering among computer science students can be accurately predicted using machine learning approaches.
Objectives: To identify skill gaps among students, promote skill-based education within software engineering programs, and enhance workforce readiness by bridging the knowledge gap between academia and the software industry.
Methods: The students' Soft, Life, and Technical skill levels are predicted using the Random Forest (RF) method. To enhance the RF model's effectiveness, this research incorporates Principal Component Analysis (PCA) as an additional step. PCA dissects the input features of the highest quality into their constituent elements. Accumulated Local Effects (ALE), an Explainable AI approach, is used to determine which factors influence skill set predictions. The suggested classification model is assessed using metrics such as accuracy, precision, recall, F1 score, and the AUC value.
Results: The modelling outcomes show that the suggested PCA-based Random Forest model has a higher prediction accuracy of 86.5% for Soft skills, 84.6% for Life skills, and 87% for Technical skills than the standard machine learning models used for comparisons.
Conclusions: Experimental results indicate that the Random Forest model with PCA demonstrates significant generalization capabilities and outperforms baseline approaches in accurately forecasting computer science students' software engineering talents.
On (α, β)-Total Edge Irregularity Strength of Some Graphs
Authors: P. Pandiaraj
Published: February 2025

Let , and  having cardinality  and  respectively, symbolize the vertex set, and edge set of a simple graph  together with a total – labeling  where . Let . If an invertible function exists, say  specified by  for every edge  with , then is termed as total edge irregular labeling. Also, the value  is said to be the edge weight of .  The least  for which  admits  – edge irregular  is indicated by , known as Total Edge irregularity Strength of the graph The precise value of for standard graph families and Mycielskian path graphs is explored in this work. In addition, an open problem of  and are solved partially.
On Sombor Energy of Graphs with self-loops
Authors: S. H. Pathan
Published: February 2025

The goal of this paper is to broaden the concept of Sombor Energy from a simple graph to one containing self-loops. Let G be a simple nth-order graph, and  be the graph generated by adding  self-loops to G. Sombor matrix of  is defined as
  
If  are eigenvalues of , then Sombor Energy of  is defined as
  
 where  is the degree of vertex with self loop
Mathematical Analysis of Thermodynamic Model to Predict the Engine Parameters
Authors: Anbuchezian Ashokan
Published: February 2025

To Predict the thermodynamic performance parameters SI engine two zone thermodynamic simulation model was developed. The Engine performance and thermodynamic parameters was predicted using first order mathematical ordinary differential equations such as peak pressure, burned gas temperature, unburned gas temperature, heat transfer, heat leakage, heat flux and Adiabatic flame temperature. The Fuel is specified by the way of C-H-O-N Values, the equilibrium state of combustion products was determined by olikara and borman method. The model was developed for Air cooled, single cylinder, 4 stroke SI Engine with variable compression ratio of 6-8. Curve fit Co-efficient are used to simulate air and fuel data along with residuals. Thermodynamic parameters are plotted with respect to crank angle. The objective of this work was to study the thermodynamic, performance parameters of SI Engine using various mathematical models.
Degree Associated Reconstruction Number of Split Graphs with Some Biregular Independent Set
Authors: A. Anu
Published: February 2025

A vertex-deleted subgraph of a graph G with which the degree of the deleted vertex is given is called a degree associated card of G. The degree associated reconstruction number (or drn) of a graph G is the size of the smallest collection of the degree associated cards of G that uniquely determines G.  A split graph G is a graph in which the vertices can be partitioned into an independent set and a clique. We prove that the drn is 1 or 2 for all split graphs G of order at least seven in which all the vertices in the independent set have degrees r and s whose distinct degrees differ by at least two.
Generalized q-Mittag-Leffler Matrix Function and its Properties
Authors: Cynthia V. Rodrigues
Published: February 2025

Motivated essentially by the success of the applications of the Mittag-Leffler functions and Matrix theory in Science and Engineering, we propose here a unification of certain q-extensions of generalizations of Mittag-Leffler function together with Saxena-Nishimoto's function, Bessel-Maitland function, Dotsenko function, Elliptic Function, etc. We obtain Mellin-Barnes contour integral representation, a q-difference equation, Eigen function property.
Efficient Learning-driven Anomaly Detection and Classification for IoT-based Monitoring Systems
Authors: Anusha R S
Published: February 2025

The Internet of Things (IoT) has revolutionized data collection and analysis from diverse sources. IoT-based monitoring systems are now widespread in manufacturing, healthcare, and smart cities. These systems gather vast amounts of data from sensors and devices, enabling the detection of anomalies and patterns. The IoT has become an integral part of our lives, transforming various industries by enabling seamless connectivity between devices and increasing automation and efficiency. However, the reliability of IoT systems is often compromised due to the complexity and scale of these networks, making them vulnerable to failures and security breaches. To mitigate this problem, anomaly detection using efficient learning in IoT is a promising solution to identify unusual patterns that could indicate system faults or threats.
This paper implements an efficient learning-driven approach for anomaly detection classification in IoT-based monitoring systems. The study aims to improve the accuracy and speed of identifying unusual patterns or behaviours in IoT data streams. By using advanced machine learning techniques, the proposed method can effectively detect anomalies in real-time, enhancing the overall security and reliability of IoT monitoring systems.
The proposed framework enhances IoT system reliability by reducing downtime, improving security, and ensuring consistent performance of connected devices. Experimental results demonstrate the efficiency of machine learning techniques and their capabilities in anomaly detection. Empirical findings show that Decision Trees (DT) and Random Forests (RF) outperform other competitive models like Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Adaptive Boosting (AB) and Voting Classifier (VC) for network intrusion detection in the context of anomaly detection.
Coupling NLP for Intelligent Knowledge Management in Organizations: A Framework for AI-Powered Decision Support
Authors: Vandana Kalra
Published: February 2025

Knowledge management (KM) is crucial component for business development in modern enterprises and this type of management is facilitated through technology. Nevertheless, conventional knowledge management systems (KMS) face problems concerning, but not limited to, information silos, difficulty in accessing data, and the complexity in managing unstructured data. As new advancements are made towards Natural Language Processing (NLP), Artificial Intelligence (AI) technologies that allow for contextual knowledge discovery, intelligent search, automated summarization, and real time content classification become readily available. This research analyzes the application of NLP systems concerning their integration with knowledge systems in business, information retrieval, enterprise search, and knowledge recommendation systems. For these integrations to be successful, Name Entity Recognition (NER), semantic search, Retrieval-Augmented Generation (RAG), Optical Character Reader (OCR), and Explainable AI (XAI) technologies need to be utilized. This will assure that decision-making processes are secure and ethical. This paper also presents an NLP-Driven Knowledge Management Framework (NLP-KMF), which is a novel framework that helps manage knowledge. The paper discusses the real-world usage of NLP-powered knowledge management in corporate learning, customer service, and compliance with Google, Accenture, IBM, and JPMorgan Chase serving as the centers of case studies. Strategies to counter issues such as AI bias and misinformation alongside privacy threats are discussed as well. The last section of the paper analyzes the forthcoming research areas that could include topics such as multimodal AI for knowledge management, AI repositories that continuously learn, and decision intelligence driven by AI. This serves as a constructive and precise plan for organizations that wish to evolve from static knowledge databases to dynamic self-adapting AI systems.
A Proposed Method for Data Size Reduction in WSN: Optimizing Humidity, Temperature, and Pressure Sensing
Authors: Saif Al-Alak
Published: February 2025

The utilizing of Wireless Sensor Network (WSN) for collecting data of humidity, temperature, and pressure is important to avoid natural disasters. However, the using of WSN has many challenges which are represented by node energy and power consumption as well node throughput. This study proposes a scheme to improve node life time and node throughput. The proposed scheme is coding the sensed data in a way that making an efficient compression by Huffman coding. The using of the proposed scheme with Huffman algorithm optimizes humidity, temperature, and pressure transmitted data over WSN, which produces efficient energy and power consumption as well node throughput. The testing result shows that the proposed scheme enhances network life time and network performance.
Reorientation of Sharia Stock Regulations: Integrating Taṣarrufāt al-Rasūl and Maqāṣid al-Sharī‘ah for Justice and Sustainability
Authors: Mukhlis Lubis
Published: February 2025

The Sharia capital market has become a vital instrument in supporting economic growth grounded in Islamic values. However, the regulation of Sharia-compliant stocks in Indonesia faces challenges in fostering social justice, public welfare, and economic sustainability. This study aims to explore the relevance of the concept of taṣarrufāt al-Rasūl, as developed by Ibn ‘Āshūr, in advancing Sharia stock law. Utilizing an interpretative phenomenological approach, data were collected through in-depth interviews with academics, capital market practitioners, and relevant Islamic scholars. The data were analyzed to identify key themes reflecting the integration of maqāṣid al-sharī‘ah values into Sharia stock regulations. The findings reveal that Sharia stock regulations require a philosophical reorientation that not only emphasizes technical compliance but also aims to create collective benefits through adaptive legal flexibility. The taṣarrufāt al-Rasūl-based approach supports the integration of values such as justice, public welfare (maslahat), and sustainability into regulatory frameworks, with potential benefits for strategic sectors like renewable energy, education, and healthcare. The results also underscore the proactive role of the National Sharia Council-Indonesian Ulama Council (DSN-MUI) in crafting innovative and contextual fatwas. This study concludes that Ibn ‘Āshūr’s thought provides a robust philosophical framework for developing inclusive Sharia stock regulations that address modern challenges. The findings not only enrich the body of Islamic legal literature but also offer practical guidance for regulators and capital market stakeholders in creating regulations that prioritize maslahat, sustainability, and social transformation.
Image Based Chronic Renal Disease Diagnosis Using Convolution Neural Network Deep Learning Approach
Authors: Megha Patel
Published: February 2025

Kidney disease presents a major health challenge, with conditions like kidney stones requiring timely diagnosis and treatment. Traditional radiological methods are time-consuming and dependent on expert interpretation. This study proposes an automated kidney stone detection system using deep learning techniques, employing CNN for classification. A diverse dataset, including normal kidney tissue, kidney stones, cysts, and tumors, is utilized. The methodology includes data preprocessing, feature selection, and CNN-based classification, achieving a high accuracy of 88.43%.
Additionally, this research explores Chronic Kidney Disease (CKD) prediction using a modified CNN architecture, incorporating data augmentation and preprocessing for enhanced accuracy. The model is evaluated against existing machine learning approaches, and contour plotting is employed to assess severity levels.Results highlight the potential of deep learning in improving early detection, reducing diagnostic time, and assisting radiologists in kidney disease management.
Automated Classification of Alcoholism Using Discrete Cosine Harmonic Wavelet-Packet Transform
Authors: Aradhana Manekar, Lochan Jolly
Published: February 2025

Alcoholism, characterized by excessive alcohol consumption, leads to addiction and life-threatening health complications. This not only affects an individual's physical health but also their mental and social well-being. While traditional self-reported survey-based methods often lack reliability, neuroimaging studies provide more objective and accurate data. This study introduces the Discrete Cosine Harmonic Wavelet-Packet Transform (DCHW-PT) as an innovative method for automated alcoholism detection using EEG data. To the best of our knowledge, this is the first implementation of DCHW-PT in this domain. The framework leverages DCT’s computational simplicity, producing precise real coefficients, unlike the DFT. It retains HWT’s benefits, such as built-in decimation and interpolation, whereas DWT requires anti-aliasing and anti-imaging filters. WPT enhances multi-resolution analysis, and the shift-invariant nature of DCHW-PT provides an effective solution for detecting transient EEG signals associated with alcoholism. Extracted features—including Hjorth parameters (Activity, Mobility, Complexity), kurtosis, standard deviation, mean, energy, and skewness—offer a comprehensive statistical EEG description. Dimensionality reduction is achieved via a t-test, and model performance is assessed using accuracy, sensitivity, specificity, and F1-score, with 10-fold cross-validation. The Ensemble-Subspace classifier achieves 98.3% accuracy, with sensitivity (98.59%) and specificity (98.01%), surpassing traditional methods and aligning with leading EEG-based alcoholism detection algorithms. Ultimately, this study demonstrates the effectiveness of DCHW-PT in alcoholism detection, setting a foundation for future research in advanced alcoholism diagnostic applications.
Blockchain-Based Decentralized Document Verification and Its Applications
Authors: Ayush Mishra
Published: February 2025

The proliferation of counterfeit academic certificates has engendered unethical practices, thereby depriving meritorious candidates of potential opportunities; this situation subsequently renders conventional document ver- ification methodologies ineffective due to their inherent time-consuming nature, high costs, and susceptibility to manipulation. In response to these pressing challenges, this paper advocates for a blockchain-based decentralized document verification framework that leverages the InterPlanetary File System (IPFS) and Ethereum blockchain, thereby enhancing security, transparency, and operational efficiency. The system adheres to a meticulously struc- tured methodology whereby applicants initially submit their credentials, which are subsequently authenticated by educational institutions prior to their storage within the IPFS for decentralized file management; concurrently, only the hash of these credentials is retained on the blockchain, effectively reducing costs and augmenting scalability. To assess the system’s efficacy, it was subjected to rigorous testing employing multiple concurrent mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, with findings demonstrating that Proof of Stake offers the most optimal equilibrium between velocity and security. The prototype that was developed showcased significant improvements, with verification accuracy, reduced the processing time also minimized the manual effort, furthermore, making the process significantly more efficient than traditional methods. Furthermore, the system accomplished a transaction throughput of 1000 transactions per second, accompanied by an average confirmation time of 5 seconds, thus significantly enhancing operational efficiency for employers and institutions engaged in credential verification. Additionally, a thorough comparative analysis was conducted against traditional methodologies concerning security, processing velocity, and cost-effectiveness, ensuring validation through tamper- proof mechanisms and the mitigation of fraud risks associated with certificates. This research not only bolsters the reliability of document verification but also lays the groundwork for future advancements, including cross-chain integration, AI-driven fraud detection systems, and mobile-based verification applications, all of which aspire to further optimize efficiency and accessibility within academic credential verification processes.