bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Renovascular Hypertension using Hybrid Deep Learning Technique
Authors: Arthi K
Published: January 2025

Renovascular hypertension is a type of secondary hypertension that is difficult to diagnose and monitor due to its complexity and dependence on many weak bodies, resulting from the narrowing of the arteries feeding the kidney. This study presents a novel hybrid deep learning model that combines convolutional neural network (CNN) and short-term temporal (LSTM) network to predict renovascular blood pressure based on data. The proposed model uses the spatial feature extraction capabilities of CNNs to identify significant patterns in various health data such as heart rate, blood pressure, and blood oxygen, while the LSTM network manages physical time to monitor specific patients in real time. IoT devices continuously collect medical data and instantly transmit it to the model, helping to predict renovascular hypertension early and accurately. Experimental results demonstrate the effectiveness of the model with over 90% accuracy in predicting the onset of hypertension compared to deep learning models. Furthermore, this hybrid CNN-LSTM approach performs well in handling noisy and incomplete data that are common in the IoT environment. This study demonstrates the potential of a hybrid deep learning model to improve the content of care, personalize treatment, and provide large-scale, cost-effective solutions for early intervention in the management of renovascular hypertension.
A Review on Quantum Key Distribution for Wireless Networks: Current Status and Future Prospects
Authors: Mohammed. I. Alghamdi
Published: January 2025

As wireless networks become increasingly integral to modern communication infrastructures, the need for robust security mechanisms to protect sensitive information has never been more critical. Quantum Key Distribution (QKD) presents a revolutionary approach to secure communications by leveraging the principles of quantum mechanics to ensure the theoretical unbreakability of cryptographic keys. This study provides a comprehensive review of the current status of QKD technologies within the context of wireless networks, utilizing secondary data sources to analyze recent advancements, implementations, and challenges. The paper begins by outlining the fundamental principles of QKD and its superiority over classical cryptographic methods. It then examines recent developments in QKD protocols and their adaptability to wireless environments, highlighting successful case studies and experimental trials. Key challenges, including technological limitations, integration hurdles with existing network infrastructures, and cost considerations, are discussed in detail. The review also explores emerging trends and innovations, such as hybrid QKD systems and satellite-based implementations, which promise to expand the applicability and feasibility of QKD in wireless networks. Finally, the study delineates future prospects and potential research directions, emphasizing the need for interdisciplinary collaboration to overcome existing limitations and fully realize the potential of QKD in enhancing wireless network security.
Energy Management in Isolated DC Microgrid Systems Using ARO-NN for PV, Battery, and Supercapacitor Integration
Authors: Saurabh V Kumar
Published: January 2025

This article describes how to utilise a battery and supercapacitor to control the energy in a stand-alone DC microgrid. This strategy makes use of an artificial rabbits optimised neural network (ARONN) control mechanism. This power management strategy's key objectives are to balance production and consumption, fulfil power demand, and maintain a steady DC bus voltage. By adjusting the real power available on the shared common bus, this method has the amazing advantage of correcting for losses in power modulator design. ARONN control, which regulates the power modulator in the storage system, and the incremental conductance maximum power point tracking (MPPT) approach, which maximises power extraction from PV sources, are integrated in the isolated DC microgrid regulator. The bus DC voltage is kept constant with little deviation from the reference voltage by efficiently managing the power flow on the shared DC bus. This strategy also lessens stress on the battery by assigning higher-frequency current control to the supercapacitor and lower-frequency current control to the battery. The simulation's outcomes confirm that the suggested energy management and regulator strategies are effective.
Intelligent Generation of New Media Art Based on User Experience Design
Authors: Caixian Ye
Published: January 2025

New media art (NMA) usually requires users of interaction and participation, and users expect to get real-time feedback in the process of their participation. This paper proposes using sensors to collect the data generated in the process of user experience in real time combined with internal data mining and the deep-learning model,GAN, to evaluate and obtain the standard data set as prompt transmitted to AIGC for real-time generated content. The design team uses the AIGC technology to reorganize and continuously optimize the model to get a new media art that meets the needs of the user experience. This method can help artists to avoid the exhaustion of inspiration. And to help enterprises quickly produce different new media art works with different characteristics, so as to improve the penetration rate of new media art in the society.
Development of Dynamic Nonlinear Water Demand Equation and Comprehensive Dataset for Residential
Authors: Loukik S. Salvi
Published: January 2025

This study introduces an innovative approach to modeling residential water demand in India by developing a dynamic nonlinear water demand function. Our primary contribution is the creation of a comprehensive and dynamic dataset, which addresses the absence of such data tailored to urban settings. Utilizing this dataset, we developed a sophisticated water demand equation that enhances the accuracy of demand forecasts. Key influences analyzed include water price elasticity, household infrastructure, climatic conditions, income levels, population density, elevation, water flow, socio-economic status, peer effects, and energy costs. Our findings highlight the need to revise water pricing strategies to manage demand effectively, particularly given the significant disparities in income and infrastructure between urban and rural areas. This study underscores the importance of policy interventions in fostering sustainable water management practices that meet local needs. Additionally, we explore the anticipated impacts of climate change on future water demand in urban India, providing critical insights for policymakers. By proposing this dynamic dataset and a newly formulated water demand equation, we present a robust framework for addressing India’s water sustainability challenges, supporting informed policy decisions to mitigate water scarcity and enhance resilience in water resource management.
Enhancing Precision Agriculture: IoT-Enabled Soil Nutrient Analysis and Deep Learning-Based Crop Recommendation Models
Authors: Amita Shukla
Published: January 2025

The emergence of the Internet of Things (IoT) has brought about a revolution in a number of fields, most notably agriculture, where precision farming techniques that improve sustainability and production have been made possible. In the context of precision agriculture, this review paper focusses on the integration of IoT technology for crop recommendation models and soil nutrient analysis. Farmers can accurately determine nutrient requirements and levels by using real-time soil health monitoring made possible by IoT-enabled sensors and data analytics. This study conducts a thorough analysis of the body of research on the several Internet of Things applications that support efficient crop management and soil nutrient optimisation. It examines cutting-edge sensor technologies—such as spectrometry and electrochemical sensors—used in soil monitoring and emphasises how important it is to get precise soil data. The study also looks at the creation of predictive models that use machine learning algorithms to suggest appropriate crop varieties depending on environmental factors and soil nutrient profiles. The results highlight how the Internet of Things (IoT) may help make better decisions in agriculture, which will eventually increase crop yields and resource efficiency. The knowledge gained from this review not only advances our understanding of precision agriculture enabled by the Internet of Things, but also opens up new avenues for future research focused on combining cutting-edge technology to address the problems facing contemporary farming methods.
Facial Skincare Product Recommendation Using Deep Learning Techniques
Authors: S. Nithyadevi
Published: January 2025

Skincare products are essential cosmetics, particularly in this day and age. Numerous online retailers offer a wide range of skincare products in their inventory. One issue with online skincare product purchases is that consumers are unable to test the product and must rely on reviews and ratings from other customers. To make this process easier and more effective, an innovative skincare product recommendation system has been developed. The system revolutionizes personalized skincare solutions by seamlessly integrating image processing and advanced deep learning techniques like Efficient Net B0. The system goes beyond traditional classifications, accurately identifying diverse skin types, including normal, oily, dry, sensitive or combination. It also takes a meticulous approach to assess skin tones. Users can effortlessly engage with this system through a user- friendly web interface, where they upload facial images. In return, they receive intricate recommendations tailored not only to their specific skin types but also to address individual concerns such as acne, pigmentation and dark circles. The suggestions provided are comprehensive, spanning a range of products including cleansers, moisturizers, serums and more. The system says the routine to the user. By offering personalized recommendations and valuable skincare routine with an overall accuracy of 92.34%.
Reinforced Multi-Path Graph Attention Network (MPGA-RL-Net) for Adaptive Robotic Vision with Task-Specific Region Prioritization
Authors: B. Rama Subbaiah
Published: January 2025

Robotic vision plays a crucial role in enabling autonomous systems to perceive, understand, and interact with complex environ- ments. However, accurately segmenting and prioritizing visual regions of interest in dynamic scenes is challenging due to variations in object shapes, sizes, and spatial relationships. Traditional methods, such as Convolutional Neural Networks (CNNs) and Fully Convolutional Net- works (FCNs), have shown promise but often struggle to integrate both global context and fine-grained details required for tasks like object recognition and navigation. To address these challenges, we present a Multi-Path Graph Attention Network with Reinforcement Learning (MPGA- RL-Net) adapted for robotic vision. This framework leverages a Multi- Path Feature Extraction (MPFE) module to capture multi-scale features at low, medium, and high resolutions, fusing them using an adaptive attention mechanism that assigns task-specific weights to each resolu- tion level. Super pixel-based segmentation is then applied to the fused feature map, representing regions as graph nodes, with Graph Convo- lutional Networks (GCNs) employed to model spatial relationships be- tween regions. Reinforcement Learning (RL) is further incorporated to dynamically adjust attention, allowing the model to focus on critical ar- eas, such as target objects or pathways, in real-time. Performance eval- uations demonstrate that MPGA-RL-Net enhances accuracy in robotic vision tasks, particularly in cluttered or dynamically changing environ- ments, achieving higher precision in object detection and adaptive focus on critical regions compared to conventional methods
The Influence of Social Media Algorithms on Consumer Buying Behaviour
Authors: S. Prabha Arockia Joans
Published: January 2025

Social media algorithms significantly shape 72% of consumer purchasing choices by customizing content to align with user interests. This research investigates the connection between engagement metrics—such as likes, comments, and shares—and consumer purchases, employing structured questionnaires and sophisticated statistical methods like regression analysis and Spearman's rank correlation, analyzing responses from 224 participants. The findings indicate a notable link between personalized content and buying behavior, with algorithms enhancing engagement by adjusting content visibility. Importantly, over half of consumers believe that personalized advertisements directly affect their purchasing decisions, while higher user engagement boosts brand trust. The research emphasizes the necessity for digital marketers to align their content strategies with algorithmic trends to enhance visibility, engagement, and sales, especially as around 54% of users acknowledge the strong influence of algorithms on their content. This study highlights the imperative for marketers to stay proactive in adapting to algorithmic changes to optimize their digital marketing initiatives.
Minimal and Maximal gη-Continuous Functions in Topological Spaces
Authors: D. Subbulakshmi
Published: January 2025

The concept of maximal and minimal gη-continuous functions and some new results are given.
Simulation Insights into Perovskite Active Layers for Enhancing Solar Cell Efficiency
Authors: Naeema Nazar
Published: January 2025

This study explores the performance of perovskite solar cells with various absorber materials, including MASnI3, CH3NH3SnI3, CsPbI3, and CsSnGeI3, analyzing key metrics such as efficiency, open-circuit voltage (Voc), short-circuit current density (Jsc), and fill factor (FF). Simulations using SCAPS software provided baseline data, which were further validated and extended using advanced computational techniques. Sensitivity analyses revealed the impact of parameters like bandgap energy and carrier mobility, while layer optimization and circuit modeling offered insights into enhancing device performance. Comparative analysis and real-world simulations bridged the gap between lab results and practical applications, supported by machine learning models to predict efficiencies for novel materials. This comprehensive approach aids in optimizing perovskite solar cells for future applications.
AI in Agriculture: Precision Farming and Crop Monitoring
Authors: Kamatchi Sundravadivelu
Published: January 2025

This research delves into the application of artificial intelligence in precision farming and crop monitoring, focusing on four AI algorithms: “Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN)”. The objective of improving crop disease detection accuracy and prediction efficiency, along with yield prediction and resource optimization, is targeted. The models were trained to predict crop health and optimize agricultural practices, using a dataset of crop images and environmental data. For the test result, CNN took the leads with an accuracy of 92.5% in disease detection, followed by RF with an accuracy of 89.3%, SVM with an accuracy of 86.7%, and KNN with an accuracy of 81.5%. Additionally, crop yield prediction using a hybrid AI model incorporating meteorological and soil data showed an R-squared value of 0.88, demonstrating strong prediction capabilities. The integration of AI with UAVs and remote sensing technologies allowed for real-time monitoring of crops, providing farmers with actionable insights to optimize resource use. These results demonstrate the possible significant impact of AI on facilitating sustainable farming practices through cost savings, reduced environmental impact, and improved productivity. In general, AI applications in agriculture will revolutionize precision farming by coming up with intelligent data-driven solutions for crop management.
Optimized Energy-Aware Routing for Internet of Things Enabled WSNS using Neuro-Fuzzy Clustering and Quantum Firefly Algorithm
Authors: J. Karthikeyini
Published: January 2025

Researchers and industry professionals are highly interested in Wireless Sensor Networks (WSNs) due to their importance in utilizing low-cost, low-power microelements, such as radios, computers, and sensors, which are often integrated onto a single chip. Recently, the integration of the Internet of Things (IoT) with WSNs has been extensively explored. Effective routing techniques are crucial for optimizing power usage, ensuring Quality of Service (QoS), and maintaining network reliability in IoT-enabled WSNs. This paper presents an enhanced energy-aware navigation system that employs the Quantum Firefly Optimization (QFO) method and Neuro-Fuzzy Clustering for IoT-enabled WSNs. The Neuro-Fuzzy Clustering method extends the system's lifetime by automatically grouping sensor nodes into energy-efficient clusters. The QFO method is used to determine the optimal routing paths by considering factors such as energy consumption, QoS, and trust metrics. By incorporating these advanced methodologies, the proposed solution outperforms existing approaches in terms of energy efficiency, routing accuracy, and overall network stability. Simulation results demonstrate that this novel approach has the potential to significantly improve current routing protocols and expand the capabilities of IoT-enabled WSNs. Additionally, to enhance efficiency in mobile computing environments, the security of the intrusion detection system was strengthened through the use of deep learning techniques.
Automatic Urban Change Detection using LANDSAT-8 Satellite Images and Deep Learning Techniques in Australian Capital Territory (ACT), Australia
Authors: Ninad More
Published: January 2025

The Remote Sensing based Satellite data is useful as a very powerful tools for providing a complete information for an accurate urban area monitoring. Identifying the urban change detection is a main part of professional urban drafting, regional based urban development in fraction of low cost effective along with less time duration compared to common methods (e.g. manually field survey report, Aerial Imagery etc.). The main goal of the proposed work is to monitoring various changes in features on the complete surface of earth of various pixel resolution can be carried out by low-cost remote sensing-based LANDSAT-8 satellite images.  The urban change detection is examining the various changes in urban area between two periods: 1) Baseline period 2) More recent period. In the previously, the classical change detection (CD-Algorithm) is inaccurately identifying both the spatial information and various scale variations within satellite images. To overcome the problem, the present research work introduces an advanced Deep Learning based novel Change Detection (Bitemporal_CD) technique that accurately extracts complete spatial information at various level of scales variations to simply address both issues. The new proposed model is based on Multi Scale and Multi Depth (MSMD) approach deep neural network that generates all binary information change based map that simply integrates with complete information of different sizes of patches at various decision parameter. It is used LANDSAT-8 satellites images originally from Australian Capital Territory (ACT), Suburban Nicholls, and District: Gungahlin- Australia, because total area of the ACT is 2,351.7 km2 of which 61% area is hilly or complete mountainous. The proposed advanced deep learning model is estimated in comparison with both Bi_Temporal Change Vector Analysis (Bi_Temporal CVA) and Support-based Vector Machine (SVM). On the basis of Deep learning change detection results, our proposed model shows a notable advancement in the performance of Cohen’s_kappa coefficient (KC) compared to both SVM as well as Bi_Temporal CVA, with high increases of approximately 12.87% and 30.37% individually. Finally, the proposed Multi Scale and Multi Depth approach deep neural network model performs superior in detecting various changes including across all level of metrics with high accuracy.
Analysis of Combination Power Generation Using Renewable Energy Sources and Its Applications
Authors: Karrar Hussain
Published: January 2025

In this study, we discuss effective energy management for a micro grid powered by solar PV, wind, and batteries. Both the solar fuzzy logic controller-based MPPT technique and the use of wind PMSG generators to increase power output are being studied. Bi-directional de-de converters and diode-based bridge rectifiers are used to integrate solar, wind, and battery energy sources. The efficiency with which these power converters operate will have an impact on how well a hybrid power generation performs. In this study, more precise fuzzy controllers are suggested for use with these power converters. Two situations, such as variation in RE sources with a constant load and fluctuation in load demand with constant RE sources, are examined for the proposed fuzzy controller-based hybrid micro grid. The proposed fuzzy logic controllers-based hybrid micro grid energy management approach makes stable energy management converters of power energy management. The technologies employed with solar hybrid systems for energy storage are thoroughly detailed in this chapter. Particularly discussed are several varieties of rechargeable batteries, as well as their chemical and physical compositions and electrical output properties.
Applications and Future Directions of Fuzzy BRK Topological Groups in Mathematics and AI
Authors: S.Kousalya
Published: January 2025

The study of fuzzy topological groups has garnered significant attention due to their applications in various fields of mathematics and computational theory. This paper introduces an in-depth exploration of Fuzzy BRK (Banach-Riemann-Klein) topological groups, emphasizing both the theoretical foundations and potential extensions of the concept. We first establish a rigorous framework that unifies fuzzy set theory with BRK topological groups, providing new insights into their structural properties. By employing fuzzy relations and fuzzy sets, we redefine the notion of continuity, closure, and neighborhood within BRK topological groups, leading to more generalized topological structures that can accommodate fuzziness. The main contribution of this work lies in the development of new extensions that address key limitations in classical BRK topological group theory. Specifically, we propose a novel method of constructing fuzzy BRK topological groups, allowing for more flexibility in handling uncertainties and imprecise data. Additionally, we investigate homomorphisms and isomorphisms in the context of fuzzy BRK groups, highlighting their role in preserving topological properties under fuzzy transformations. The results presented have broad implications for both pure and applied mathematics, particularly in fields requiring a blend of algebraic and topological techniques, such as fuzzy logic, decision-making processes, and artificial intelligence. Finally, we outline potential avenues for further research and applications of fuzzy BRK topological groups in real-world scenarios.
A Knowledge Engineering on Spam Review by using Selected Deductive Learning Approaches
Authors: Sripathi.S
Published: January 2025

The rapidly growing digital marketplace continuously generates a substantial volume of online reviews, such as those found on Yelp and Amazon. Consumers increasingly adopt the practice of reading previous reviews before making purchasing decisions for various products. This study addresses the critical challenge of detecting spam reviews in e-commerce platforms, a problem that significantly impacts consumer trust and business integrity. We propose a novel comparative analysis of six machine learning models for spam review detection: Naive Bayes Multinomial Updatable (NBMU), Stochastic Gradient Descent (SGD), Classification Via Regression (CVR), PART, Random Tree (RT), and K-Star. Our research contributes a comprehensive evaluation of these models on a large-scale Amazon product review dataset, consisting of 26.7 million reviews, implementing a robust feature selection process to enhance model performance and efficiency. Through in-depth analysis using multiple metrics including accuracy, precision, recall, F-measure, ROC, and PRC, we identify the CVR model as the top performer, achieving 82.70% accuracy, 0.83 precision, 0.82 recall, and 0.90 ROC. Additionally, we provide a detailed error analysis comparing Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Relative Root Squared Error (RRSE) across all models. Our findings demonstrate the superiority of the CVR model in spam review detection, offering a promising approach for e-commerce platforms to maintain review integrity. This research provides valuable insights for both academia and industry in combating online review fraud and enhancing the reliability of e-commerce ecosystems.
Open Maps via δ-open Sets in Pythagorean Fuzzy Topological Spaces and its Applications
Authors: A. Vadivel
Published: January 2025

In this paper, we introduce the concept of Pythagorean fuzzy  (resp. , ,    or )-open mappings are introduced and their properties are discussed. In current scenario people with symptom of Covid-19 like fever, cough, sneezing, sore throat, loss of taste and smell etc., were panic about the disease, and the diagnosis of Covid-19 takes many hours and people cannot go for the test frequently. Some other diseases like flu, pneumonia, cold etc., also has the same symptoms. Each patients has unique experience of that particular symptom and some time they may not experience that symptom even though they were affected by the Covid-19. Also, in this paper we tried to diagnosis Covid-19 with the help of picture fuzzy sets which helps to record all symptoms in prÃ©cised manner.
Introduction: This paper introduces the concept of Pythagorean fuzzy open mappings and their relevance to COVID-19 symptom diagnosis. Due to the overlapping symptoms of COVID-19 with other illnesses, a Pythagorean fuzzy set approach is utilized to more accurately capture and analyze symptom patterns.
Objectives: The objective is to develop a precise mapping model that utilizes Pythagorean fuzzy sets to differentiate between COVID-19 symptoms and other similar conditions, aiming to improve diagnostic speed and accuracy.
Methods: The method involves formulating fuzzy sets based on COVID-19 symptoms, computing distances (Hamming, Euclidean) between patients' symptoms and ideal symptom patterns, and identifying cases with higher COVID-19 risk.
Results: The approach successfully identifies patients with symptoms closest to COVID-19 indicators, offering a more systematic and timely diagnosis. Results suggest that this fuzzy set model can enhance early detection and intervention for affected individuals
Conclusions: In this paper, Some new notions of strongly Pythagorean fuzzy open (closed) maps called Pythagorean fuzzy -open and Pythagorean fuzzy -closed maps are introduced and discussed their relationship between their near mappings with examples. Also, we have tried to diagnosis Covid-19 with the help of Pythagorean fuzzy sets which helps to record all symptoms in prÃ©cised manner. In future, researchers can extend this model to other extensions of fuzzy sets such as rough sets and utilize the interdependency among the various evaluation criteria for better judgement.
Hybrid Textural Feature Descriptors for Ovarian Cyst Classification Using Machine Learning
Authors: Aditi Gupta
Published: January 2025

A computer aided diagnosis system for classification of ovarian cyst is a crucial step towards treatment of women suffering from cysts in ovaries. Ultrasound or sonography is an efficacious technique that assists healthcare providers in diagnosis and therapeutics for cysts in ovaries. In this research, an automated ovarian cyst diagnosis system based on textural features from ultrasound images is designed. Local binary pattern and fractal dimension textual features are combined to deduce features related to texture from ultrasound images and these textural features are given as input to machine learning classifiers like, decision tree, logistic regression and support vector machine. This novel method assists healthcare providers to distinguish cystic ovaries from normal ovaries in ultrasound images. Evaluation metrics namely, precision, accuracy, recall, F1-score, specificity and receiver operating characteristics – area under curve are used to validate the efficacy of the classifiers. Decision tree, logistic regression and support vector machine classifiers furnished an accuracy of 83.42%, 89.12% and 91.19%, respectively on a dataset of 1203 ultrasound images procured from a medical diagnostic centre in India. Support vector machine has proved to be a better classifier in relation to accuracy (91.19%), precision (97.98%), F1-score (94.49%), specificity (90.90%) and area under curve (0.93) compared to decision tree and logistic regression. This computer aided diagnosis system helps healthcare providers in making efficient and accurate decisions towards treatment plan of patients.
Analytical and Computational Investigation of Non-Steady State Reaction and Kinetics at Spherical Ultramicroelectrodes Concerning Conducting Polymer Modification using Homotopy Perturbation Method
Authors: A. Uma
Published: January 2025

In this work,  mathematical modelling of non-steady state reaction and kinetics at spherical  ultramicroelectrodes within conducting polymer modification is considered. The main objective of this work is to propose a new analytical formulation for the system of nonlinear non-steady state reaction diffusion equation in spherical ultramicroelectrodes.  Employing Homotopy Perturbation Method, the concentrations of species, mediator and current may all be obtained analytically for all conceivable experimental results of the parameter. The accumulated analytical outcomes are analyzed with numerical simulations and implemented to investigate various parameters. By comparing the analytical solution with numerical findings, the accuracy of the method is presented. In order to better understand the system dynamics, a numerical simulation of the issue is also provided through Matlab Software. The new analytical results contribute to optimizing the consistency of this model. These novel approaches produce a compact set of analytical approximations that possess straightforward to compute and verify as well.
Characterization of Pythagorean Fuzzy Bi-Interior Ideal and Bi-Quasi-Ideal in Γ-Semirings
Authors: T. Anitha
Published: January 2025

In this paper, we introduce the Pythagorean fuzzy bi-interior-ideals and Pythagorean fuzzy bi-quasi-ideals in Γ - semiring. More over we prove the every Pythagorean fuzzy left and right ideals are Pythagorean fuzzy bi-interior -ideal in Γ - semiring. Also we study the notion of Pythagorean fuzzy bi-quasi-ideal in Γ - semiring and characterize Pythagorean fuzzy bi-quasi-ideal in Γ - semiring.
Towards Improved Biometric Security: EEG-Based Person Identification Enhanced by Deep Learning and Facial Recognition
Authors: Shalu Verma
Published: January 2025

Due to their inherent qualities of being secretive, vivid, and unpredictable, electroencephalogram (EEG) signals are considered a valuable tool for security-related identification. However, research on using EEG signals for person identification is still in its early stages. The challenges lie in decoding these signals accurately and implementing effective EEG-based identification methods. In recent years, EEG has been at the forefront of scientific research on User Authentication (UA), leading to innovative experiments that aim to identify individuals based on their unique brain activity in specific usage scenarios. The utilization of EEG signals, which are derived from brain activity, holds great potential for addressing contemporary security concerns in conventional knowledge-based user authentication, including the vulnerability to shoulder surfing. This research investigates a new method for person identification that combines electroencephalogram (EEG) signals with facial video. A hybrid model is proposed, incorporating features from both MobileNet and a Convolutional Neural Network with Long short-term memory (LSTM-CNN) architecture giving a person identification accuracy of 99.81%. The model is trained and tested on the 'DEAP' dataset to identify individuals by leveraging unique EEG patterns and facial features, thereby improving biometric identification through the integration of these insights.
Blockchain-Enabled Patient-Centric Framework for Secure Health Data Exchange
Authors: Shankar Gadhve
Published: January 2025

Information and communication technology (ICT) has become integral to various facets of society, including healthcare, where it supports digitization and the development of sophisticated solutions. The adoption of digital health technologies, such as electronic health records (EHRs), has accelerated with the increasing volume of patient data. However, traditional EHR systems face critical challenges, including potential data loss, concerns about the confidentiality and integrity of medical records, inefficient clinical data retrieval, and the lack of effective communication among participating hospitals.To address these limitations, blockchain technology offers a decentralized and secure approach to managing healthcare data. It ensures data integrity, enhances privacy, and facilitates seamless sharing across stakeholders. A patient-centric design for a decentralized healthcare management system has been developed using JavaScript-based smart contracts and blockchain-based EHRs. A functional prototype leveraging Hyperledger Fabric and Composer technologies demonstrates how these innovations can transform healthcare by ensuring data security, interoperability, and efficiency.
Inverse Independent Outer Connected Domination Number of Few Classes of Graphs
Authors: G Menon
Published: January 2025

In  a  graph  G  =  (V, E),  a  vertex  set  D  ⊆ V   such  that  for  every  vertex  v  ∈/  D  the vertex v is adjacent to atleast one vertex in D and no vertex in D is adjacent to each other, then ,D is the independent dominating set.when the vertex set V D V con- tains a independent dominating set, say D′, then D′ is called the inverse independent domiating set. If the subgraph induced by V − D′ is connected then D′ is called the inverse independent outer connected dominating set. The minimum cardinality of such set of vertices is called the Inverse Independent Outer Connected Domination Number denoted  by  γ˜I−OC (G).  In  this  paper  we  will  be  discussing  γ˜I−OC S(n, k),  γ˜I−OC S+(n, k), γ˜I−OC S++(n, k),  γ˜I−OC (Sn),  γ˜I−OC (G1 ◦ G2)  where  S(n, k),  S+(n, k),  S++(n, k),  Sn  are Sierpinski  Graph,  Extended   Sierpinski   Graphs  and   Sierpinski  Gasket   Graph  respectively and G1 and G2 are two standard graphs.
Extorial in RL Circuits and Heat Flows
Authors: T. Sathinathan
Published: January 2025

The newly defined -extorial function was used in this paper to generate the solution of the RL circuit. When an inductor and a resistor are linked across a voltage source, the resulting circuit is known as an RL circuit. Depending on how the resistor and inductor are connected, this circuit may be in series or parallel. One of the solutions to RL’s mathematical difference problem is the extorial function. To solve the RL circuit, we thus develop the theory of the extorial function and employ it.
Customer Impatience and Feedback Mechanism in MX/G/1 Retrial Queue with, Unreliable Server, Bernoulli Vacation and Customer Search Strategies
Authors: K. Kaarunya
Published: January 2025

This article examines a complex retrial queueing system characterized by unreliable batch arrival, three phases of service, customer impatience, feedback mechanism, Bernoulli vacation policy and customer search behaviors. In this system, customer arrivals follow a Poisson process. When the server is idle, one customer from the batch receives service while the others enter a retrial group. The server offers essential service to all the incoming customers. If the server is busy upon arrival, customers may choose to leave (balk) or wait in the retrial group for the server to become available. Customers who retry either rejoin the service if the server is free or abandon the system if their retrial attempts fail (renege). The server may randomly breakdown during any phases of service and the repair starts immediately. After completion of repair, the server continues to serve the interrupted customer in the system. After completing each phase of service, the customer may either opt for optional service, or join the retrial group as a feedback customer, or leave the system. Upon completion of the first essential phase or second optional phase, if the server is idle, it searches for customers if available in the retrial group with a certain probability. After completing the third optional phase, the server goes on a Bernoulli vacation with certain probability. The retrial, service, repair and vacation times are arbitrarily distributed. By using the supplementary variable technique, various system state measures and reliability measures are derived. The effects of various parameters on these system measures are analyzed through numerical examples.
A Characterization of Rings in which every Semi-Hopfian Module is Noetherian
Authors: Mankagna Albert Diompy
Published: January 2025

Let  be a ring and  an -module. It is well known that Hopfian modules are semi-Hopfian, but the converse is not always true. For instance, if  is a prime and  is any direct sum of copies of , then  is semi-Hopfian but not Noetherian. In this paper, we introduce and study a new class of rings, called SHN-rings, defined as rings for which every semi-Hopfian module is Noetherian. We investigate the structural properties of SHN-rings and explore their relationships with other well-known classes of rings and modules. Over commutative rings, we establish that SHN-rings coincide with Köthe rings, -rings, pure-semisimple rings, and Artinian principal ideal rings. Furthermore, we show that over commutative SHN-rings, the notions of multiplication modules, cyclic modules, finitely generated modules, and Noetherian modules are equivalent. Additionally, we prove that in the setting of regular rings, semiprime Artinian rings, -rings, and SHN-rings are equivalent.
A Deep Learning Based Adaptive Model for Blocking Incoming Calls Based on the Caller's Voice Commands
Authors: Kalyani Ghuge
Published: January 2025

The proliferation of intrusive and potentially harmful incoming calls necessitates innovative solutions that transcend traditional blocking mechanisms. This research introduces a groundbreaking deep learning-powered adaptive model that revolutionizes call management through sophisticated voice command analysis. By leveraging advanced machine learning techniques, we develop a context-aware system capable of dynamically interpreting caller intent with unprecedented precision.
Our novel methodology integrates multimodal feature extraction, sentiment analysis, and reinforcement learning to create an intelligent call-blocking mechanism. Utilizing state-of-the-art convolutional and transformer-based neural architectures, we process voice commands to classify potential spam or unwanted communications with 97.6% accuracy. The proposed adaptive framework demonstrates remarkable user-centric flexibility, reducing false positive rates by 62% compared to existing rule-based systems.
The research significantly contributes to privacy protection technologies, offering a robust, real-time solution that learns and adapts to individual user preferences. By transforming call management from static filtering to dynamic, intelligent screening, we address critical challenges in telecommunications privacy and user experience.
Comprehensive and Effective Techniques Used to Improve Low Latency in 5G Communication
Authors: Varunkumar Mishra
Published: January 2025

Introduction: The advent of 5G technology is poised to revolutionize a wide range of industries by enabling ultra-low-latency communications. However, reducing latency remains a significant challenge due to the complexities of traditional network infrastructures and the propagation delays associated with long-distance signal transmission. Latency is a critical factor in the performance of applications such as autonomous vehicles, industrial IoT, and real-time communications, where even minimal delays can result in performance degradation. This paper explores innovative techniques to address the challenges associated with reducing latency in 5G networks.
Objectives: The primary objective of this paper is to identify and analyze two key approaches for minimizing latency in 5G networks: (1) reducing propagation delay and (2) optimizing network architecture. These objectives are central to achieving the low-latency performance required for next-generation 5G applications.
Methods: For network architecture optimization, edge computing is integrated to process data closer to the source, minimizing the time spent in backhaul transmission. Additionally, we advise using Software-Defined Networking (SDN) for dynamic traffic management, which enables real-time adjustments to improve latency, and putting in place effective routing algorithms that minimise packet processing delays.
Results: The integration of small cell base stations and mmWave frequency bands is expected to substantially reduce signal propagation delays, as these technologies shorten the transmission distance between the source and destination. The implementation of edge computing contributes to a significant reduction in backhaul latency, while efficient routing algorithms and SDN-based traffic management ensure optimized data flow and minimal processing delays. Preliminary simulations and analysis suggest that these combined techniques can effectively meet the stringent latency requirements of 5G applications, particularly in scenarios demanding high throughput and real-time communication.
Conclusions: Achieving low-latency performance in 5G networks is essential for the successful deployment of future technologies that rely on real-time communication. This paper demonstrates that reducing propagation delay and optimizing network architecture through strategies like small cell deployment, mmWave utilization, edge computing, and dynamic traffic management can significantly enhance latency performance. These findings contribute to the ongoing effort to design 5G networks that can support the diverse and demanding applications of the next-generation digital landscape.
Multi-Class Deep Learning-Based Enhanced Image Segmentation and Feature Extraction Technique for Early Classification of Skin Diseases
Authors: Ritika Sharma
Published: January 2025

Skin disorders pose great health issues among millions of people across the globe and thus require accurate diagnosis and treatment. This research article proposed an enhanced deep learning-based image segmentation and feature extraction approach for the early categorization of human skin diseases utilizing the Dermnet dataset of image samples classified under 23 categories. Input images are applied during the preprocessing stage to an enhanced image segmentation technique known as enhanced holistically nested edge detection (EHNED) toward detail enrichment in edges.  Further, for multiple feature extraction, the model used EfficientNet-B0 and extracted 1280 features from an image represented as a 1D array. The extracted features form a baseline for training and testing CNNs on the diagnosis of skin diseases. It tries to bridge all the loopholes from previous research by observing an extremely vast and heterogeneous dataset and uses better techniques for segmentation and feature extraction. The proposed framework is likely to increase the recall performance metric to 91%, specificity to 96%, and ability to enable early diagnosis in case of skin diseases. This enhancement is supposed to follow more effective management and better patient outcomes. Findings in this study are likely to contribute toward dermatology research and promote intelligent systems in diagnostics for automatic classification of skin diseases.
ML and Legal Analytics: A Computational Approach to Case Outcome Prediction in Legal Management
Authors: Bhanu Pratap Singh
Published: January 2025

The legal industry has undergone a transformation through the combination of machine learning and artificial intelligence techniques. This work focuses the application of such approaches in legal management and also explores how these techniques are useful in various aspects of legal service. With this work, there is an analysis done using case studies from leading organisations such as Lex Machina, JP Morgan, Deloitte, IBM Watson, All State insurance and others. We show that the potential of machine learning is useful in improving the efficiency and decision making in these processes applied to critical legal domains such as contract intelligence, IPR analysis, litigation risk assessment and other our work. The potential of ML to combine with traditional legal practices offer a lot of advantages in the field of data analytics, and pattern recognition.
A Cloud-Enabled IoT Framework for Liver Disease Detection Using ML and Embedded Electronics
Authors: Brajesh Kumar Singh
Published: January 2025

The increasing prevalence of liver diseases worldwide necessitates innovative and efficient diagnostic approaches. This study presents a cloud-enabled Internet of Things (IoT) framework integrating machine learning (ML) algorithms and embedded electronics for real-time liver disease detection. The framework combines wearable sensors and embedded devices to collect vital physiological data, including liver enzyme levels, bilirubin concentration, and patient demographics. These data are transmitted to a cloud-based server through IoT communication protocols, where advanced ML models analyze the information to predict liver disease with high accuracy. The system employs lightweight algorithms to ensure low-latency processing and scalability for remote deployment in resource-constrained settings. Rigorous validation using clinical datasets demonstrates the framework's efficacy, achieving substantial precision and recall metrics. Furthermore, the integration of cloud computing enhances data accessibility, storage, and computational capabilities, while IoT components ensure continuous monitoring and seamless patient-doctor communication. This study highlights the potential of ML-powered IoT systems in improving early detection and personalized healthcare solutions for liver diseases, offering a transformative shift toward proactive and patient-centered medical care. The proposed framework addresses challenges such as real-time data synchronization, energy efficiency, and robust data security, paving the way for scalable, cost-effective, and reliable liver disease detection solutions.
Magnetohydrodynamic Effect on 3D Nanofluid Flow via Stretching/Shrinking Surface
Authors: S. Rakmaiah
Published: January 2025

This study presents a numerical technique to analyze the MHD effect on the motion of 3D NFs via shrinking or SS under convective BCs. The investigation incorporates convective and VSC. The gov. eqs. are transformed into a set of coupled NLODEs using appropriate ST. These transformed nonlinear equations are calculated using R-K-F method combined with the shooting technique. The influence of various physical parameters on  ,   and  distributions is illustrated graphically.  Other that, the study evaluates SF coefficient and HTR for different NFs parameters.