bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Cloud Computing Environments with AI-Driven Resource Allocation Models
Authors: Rajesh Kedarnath Navandar
Published: November 2024

Cloud computing has changed the way businesses work by letting them handle resources in ways that were never possible before. But managing these resources well is still a big problem that affects speed, cost-effectiveness, and user happiness. Many old ways of doing things depend on static provisioning or heuristic-based methods, which can cause resources to be underused or over-provisioned. AI-driven resource distribution models, on the other hand, use machine learning algorithms to move resources around based on real-time data and predictive analytics. This method makes things more flexible and quick to respond by making sure that resources are distributed in a way that matches changing needs and work habits. Key parts of AI-driven models include using data on past usage to figure out what resources will be needed in the future. By looking at patterns, these models make sure that there aren't any resource shortages during times of high usage and that resources aren't provisioned when they aren't needed, which saves money. AI also makes it possible for resources to be scaled up automatically when the task changes. The system changes how resources are used in real time by keeping an eye on performance metrics and workload patterns all the time. This makes sure that performance and user experience are always at their best. AI-driven models also help optimize resources by making the best use of them while still following service level agreements (SLAs). These models make the whole system more efficient and lower operational costs by moving resources around between applications and services on the fly.
Neural Networks and Their Application in Natural Language Processing for Social Media Analysis
Authors: Ketaki Naik
Published: November 2024

Natural language processing (NLP) has been changed by neural networks, which make it possible to analyze social media data in more detail, even though it is very large, not organized, and changes all the time. This abstract looks at how neural networks can be used in natural language processing (NLP) to analyze social media. Every day, social media sites produce huge amounts of written content that covers a wide range of topics, feelings, and writing styles. The subtleties and complexity of this data are often too much for traditional NLP methods to handle. Neural networks, on the other hand, offer strong answers because they can learn complex patterns and models from raw text. Sentiment analysis is one of the main ways that neural networks are used in social media research. Deep learning designs like recurrent neural networks (RNNs) or more advanced models like transformer-based architectures (e.g., BERT, GPT) can correctly describe how people feel about social media posts. Businesses can use this feature to find out what the public thinks, keep track of how people feel about their brand, and spot new trends in real time. Neural networks are useful for more than just analyzing mood. They can also help with named entity recognition (NER), topic modeling, and even figuring out humor and irony in text, which is hard for traditional rule-based systems because they rely on set rules and definitions. Also, neural networks are great at processing data in more than one language, which is important because social media is used all over the world. When learned on big datasets, models can be used across languages, giving us information about different language groups without a lot of human tweaking. Another big benefit of neural network methods is that they can be scaled up or down, which is very important for handling the huge amounts of data that social media sites create.
Web of Threats: A Comprehensive Analysis of Various Injection Attacks on Web Application Software
Authors: R Ravinder Singh
Published: November 2024

With the ever-increasing reliance on software systems for critical functions across various domains, the threat landscape has evolved to exploit vulnerabilities within these systems. This research paper explores the clandestine domain of software injection attacks, presenting a comprehensive analysis of their methodologies and severity. The study begins by providing an in-depth exploration of various software injection techniques, such as SQL injection, cross-site scripting (XSS), and code injection etc elucidating the mechanisms through which attackers exploit vulnerabilities to compromise the integrity and confidentiality of sensitive data. Through a detailed examination of Dynamic Application Security Testing reports of different websites over the past two years, the paper sheds light on the diverse ways in which software injection attacks have been employed to compromise systems in different industries. This research paper provides a holistic understanding of software injection attacks, aiming to raise awareness among developers, security practitioners, and policymakers about the gravity of this threat. By elucidating the intricacies of these attacks and proposing effective countermeasures, the paper contributes to the ongoing efforts to bolster the resilience of software systems against evolving cyber threats.
Hybrid Machine Learning Approach using Real Time Data Processing for Educational Data Mining
Authors: Mukul V Khasne
Published: November 2024

Educational data produced by various systems, such as e-learning, student admission process, online student exam, and automated significance management solutions, may be successfully analyzed using educational data mining (EDM) methods to provide beneficial for the academic student’s progress. A highly desired use of EDM is the prediction of standardized testing from previous academic data. In this intelligence, it is critical to establish an automated approach for predicting student success. The majority of existing research on academic prediction problem use traditional feature extraction and illustration approaches, in which retrieved characteristics are feed to machine learning classifier. Supervised learning has recently allowed academics to extract high-level characteristics from raw data automatically. The proposed enhanced feature representation approaches allow for better performance on real time heterogenous data. In this paper, we demonstrate the Long Short-Term Memory (LSTM) with Recurrent Neural Network (RNN) called as RNN-LSTM. By examining current research difficulties based on evolving feature classification and prediction, we applied the most sophisticated LSTM paired with an attentiveness process approach in this work. Academicians, institutions, and governmental organizations benefit from this study since it allows them to anticipate achievement early. In extensive experimental analysis proposed work compared with numerous state-of-the-art, the better recurrent neural network functionalities of LSTM matching with the hybrid mechanism give higher performance. Predictive performance of 90.50% has achieved using the proposed algorithm.
Survey paper on Secured Data Retrieval from a distributed database using custom blockchain and Role Base Access Control.
Authors: Pranati Waghodekar
Published: November 2024

These days, there has been a paradigm shift in the upkeep of Electronic Medical Records (EMRs) in mobile cloud environments. This involves integrating cloud computing with mobile devices to facilitate the transmission of healthcare information between patients and healthcare professionals. With the help of this innovative approach, healthcare may be provided with a high degree of flexibility, minimal overhead costs, and electronic health record access. This new strategy also raises concerns about the security of the networks that enable e-health systems and the privacy of patient data. Finding a solution to efficiently distribute EMRs across mobile users in mobile cloud settings while also upholding the highest security standards is a challenging task. In this paper, explored two different distributed blockchain and cloud frameworks for EMR sharing that combine blockchain technology with a mobile cloud's Decentralised Interplanetary File System (DIFS). Based on the empirical findings, it seems that the idea provides a practical way to protect critical patient data from possible threats and guarantee the dependability of data transfers on mobile clouds. There are notable performance improvements in areas like data privacy protections, minimal network latency, maximum security levels, and lightweight access control when comparing the results of this security analysis and performance assessment to those of earlier data sharing models.
Fuzzy Ranking for Enhanced Security: Optimizing Feature Selection in VANET Intrusion Detection
Authors: Raghunath M. Kawale
Published: November 2024

In Vehicular Ad-Hoc Networks (VANETs), ensuring efficient intrusion detection while minimizing false alarms is paramount for maintaining network security and reliability. Traditional methods often struggle to strike this balance effectively. This study proposes a novel approach that employs fuzzy ranking to enhance intrusion detection efficiency in VANETs while mitigating false positive and negative factors.
At the core of our approach lies the development of an innovative fuzzy ranking mechanism to identify optimal features extracted from VANET nodes. These features serve as critical indicators of potential security threats within the network. By systematically collecting and analyzing data from various sources including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, our approach aims to capture nuanced patterns and anomalies indicative of malicious behavior. The fuzzy ranking technique enables us to prioritize features based on their relevance to intrusion detection, thereby facilitating more effective feature selection. This process ensures that only the most informative features are utilized, enhancing the overall efficiency of the intrusion detection system. Features such as packet transmission rates, signal strength variations, route deviation patterns, and network topology characteristics are carefully evaluated and ranked to optimize detection accuracy.
To train our intrusion detection model, we leverage machine learning algorithms such as deep neural networks, support vector machines, and decision trees. By utilizing labeled datasets containing examples of both benign and malicious network activities, our model learns to distinguish between normal and anomalous behavior, thus enabling timely threat detection. Furthermore, we integrate fuzzy logic-based anomaly detection techniques to identify previously unseen or zero-day attacks, enhancing the system's robustness. In addition to improving detection accuracy, our approach focuses on preventing harmful warnings caused by false alarms. We achieve this by implementing a comprehensive filtering mechanism that distinguishes between transient anomalies and genuine security threats. Contextual factors such as traffic conditions, environmental variables, and historical network behavior are considered to reduce false positives while ensuring reliable threat detection.Through extensive experimentation and evaluation, we demonstrate the effectiveness of our approach in enhancing intrusion detection efficiency and reducing false positive/negative factors in VANETs. The proposed methodology offers a promising solution to the security challenges faced by VANETs, paving the way for safer and more secure vehicular communication systems in the future.
Optimizing Data Extraction using Preprocessing for Enhanced Efficiency
Authors: Santosh V. Chobe
Published: November 2024

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web.
To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications.
In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs.
Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.
Optimization of Traffic Flow Using Computational Fluid Dynamics and AI Algorithms
Authors: Uday Shankar Patil
Published: November 2024

The effective administration of urban activity may be a basic challenge in contemporary city arranging, driven by the got to diminish blockage, minimize travel time, and lower natural affect. This paper presents a novel approach to optimizing activity stream through the integration of Computational Fluid Dynamics (CFD) and Artificial Insights (AI) calculations. By leveraging CFD, we demonstrate activity as a liquid, permitting for the exact reenactment of activity elements beneath different conditions. This fluid-based modeling approach captures the complexities of vehicular development and intuitive in a more nuanced way than conventional activity recreation strategies. To upgrade the viability of our CFD models, we consolidate AI calculations, counting machine learning procedures such as fortification learning and neural systems. These AI calculations analyze expansive datasets of activity designs, anticipate blockage focuses, and propose ideal activity flag timings and routing strategies. The cooperative energy between CFD and AI encourages real-time versatile activity administration, empowering the framework to reply powerfully to changing activity conditions. Our approach is approved through broad recreations and real-world case thinks about in a major metropolitan zone. The comes about illustrate noteworthy enhancements in activity stream, decreased blockage, and lower emanations. Also, the versatile nature of the AI calculations guarantees persistent optimization as activity patterns evolve over time. This investigate contributes to the developing body of information in cleverly transportation frameworks, advertising a adaptable and vigorous arrangement for urban activity administration. The integration of CFD and AI presents a effective apparatus for city organizers and activity engineers, clearing the way for more brilliant, more maintainable urban portability arrangements. Future work will investigate the integration of extra information sources, such as IoT sensors and vehicle-to-infrastructure communications, to advance upgrade the system's prescient capabilities and responsiveness.
Analyzing the State-of-the-Art in Descriptive Statistics, Storytelling, and Performance Parameter Confirmation for IoT Applications
Authors: Prashant C. Dhas
Published: November 2024

This paper gives thorough look at the most up-to-date methods in describing statistics, telling stories, and confirming performance parameters for Internet of Things (IoT) apps. It is very important to use descriptive statistics to summarize and explain the features of IoT data. We talk about different statistical measures, like mean, median, mode, variance, and standard deviation, and stress how important they are for understanding how IoT data is distributed. In the Internet of Things (IoT), storytelling means using stories to share lessons gained from analyzing data. We talk about how important storytelling methods are for helping people understand and use complex IoT data. We also look at different ways to tell stories, such as data visualization, story frameworks, and ways to get people involved. Confirming performance parameters is important for making sure that IoT applications are reliable and accurate. We look at different ways to check performance factors, like computer modeling, hypothesis testing, and confidence ranges. We also talk about the problems that can come up and the best ways to handle them when confirming performance parameters in IoT settings. We find important trends and problems in summary statistics, telling stories, and confirming performance parameters for IoT applications through this research. We stress the need for advanced analysis methods to handle the huge amount, speed, and range of IoT data that is being collected. We also stress how important it is to have good communication strategies when turning data ideas into choices that IoT users can act on. This paper gives researchers, practitioners, and decision-makers who work with IoT systems useful information by giving a full picture of the most recent progress in descriptive statistics, stories, and confirming performance parameters for IoT.
Comparative Analysis of Machine Learning Models for Detecting Abnormal Driving Behaviour in Two-Wheeler Kinematics
Authors: Nupur Giri
Published: November 2024

Road accidents are a major global concern, particularly affecting vulnerable two-wheeler riders. This research investigates driving behaviour using time-series data from real-world scenarios to de- velop machine learning models for anomaly detection and risk as- sessment. The purpose of this study is to explore the performance of five predictive models—Long Short-Term Memory-Residual (LSTM- R), Gated Recurrent Unit (GRU), Adaboost, XGBoost, and a Mul- tilayer Perceptron (MLP) with Random Forest (RF) ensemble—are evaluated based on nine parameters: accelerometer (X, Y, Z), gyro rotation (X, Y, Z), motion yaw, pitch, and roll. A threshold rule-based approach using multi-output regression is employed for predictive modeling.
The study makes three key contributions: First, it assesses the predictive accuracy of the five models, with the MLP-RF ensem- ble achieving a low MAE of 0.1157 on test data. Second, it identi- fies optimal hyperparameters for each model through systematic tuning. Third, it introduces a threshold-based anomaly detection method using residuals, which reduces overfitting and requires fewer instances of abnormal behaviour. The results indicate that these models, especially the MLP-RF ensemble and GRU, effectively predict and detect abnormal driving behaviours, enhancing safety for two-wheeler riders and contributing to sustainable urban devel- opment.
Improving Shear Strength and Microstructural Behavior of Black Cotton Soil Treated with Construction Demolished Waste
Authors: Manjushree V.Gaikwad
Published: November 2024

With its expansive character and low bearing capacity, black cotton soil presents considerable obstacles to construction projects. These challenges can be difficult to overcome. There is a tendency for it to rise and compress in response to variations in the amount of moisture present, and it also has weak strength properties, which makes construction activities more difficult. This study explores the possibility of stabilizing black cotton soil with recycled construction and demolition (C&D) waste in order to address these issues. Various C&D waste materials, like concrete, bricks, mortar, and other debris, are examined for their potential to enhance the shear strength of black cotton soil. The paper assesses the effectiveness of incorporating C&D waste blends ranging from 5% to 25% to mitigate soil swelling and shrinkage with improve shear strength. Previous to determining the soil's shear strength, index properties were assessed.  Unconfined compression strength tests (UCS) are carried out on compacted specimens handled with C&D waste blends, with curing periods of 1 day, 7 days, and 28 days at room temperature. A comparison investigation showed that there was a comparable increase in UCS and a decrease in axial strain at failure as the amount of C&D waste in the soil rose. Micro-analyses displayed that the main factors prompting the interacting behavior of soil amended with C&D waste include changes in the gradation, cohesiveness, and the interplay between the particles, the mineralogical makeup, and the elemental, chemical, and microstructural components. The research aims to offer insights into sustainable methods for strengthening the performance of black cotton soil while reducing waste generation in construction activities.
Mathematical Modeling and Comparative Analysis of Noise and Vibration Characteristics of CI Engine Fueled with Biodiesel
Authors: Ramesh Gawande
Published: November 2024

As an alternative to fossil fuels, renewable energy has the potential to replace diesel in CI engines, and therefore biodiesels such as nahar, jatropha, karanja, etc. are being used as a blend with diesel in CI engines. Since biodiesel is a sustainable fuel, numerous researchers have evaluated and contrasted its performance attributes with those of diesel. The end user’s comfort and level of fatigue following the usage of biodiesel blended with diesel, however, is an interesting area that requires further investigation. The current study aims to investigate the noise and vibration characteristics of unmodified and constant-speed CI engines powered by jatropha, nahar, or karanja biodiesel as a blend with diesel and also developed a mathematical model for noise and vibration responses. The vibration and sound pressure level were recorded using the FFT analyzer Brüel & Kjær Photon+ portable measuring system with RTPro software for different input parameters such as load, density, flash point, and calorific value at different levels for diesel D100 and biodiesel blends. Mathematical non-linear model was developed to predict the relationship between fuel properties and engine noise-vibration characteristics because the R square and adj. R square values of model is found to be close to each other. However, higher adj. R square value and p-value within 0.05 are the good criteria for selection of model.
Dimensionality Reduction of Hyperspectral Images Using Pixel Block Distance for Efficient Classification
Authors: Amit R Pathare
Published: November 2024

Hyperspectral imaging provides useful information in the field of satellite imaging. The extensive spectral and spatial data contained within hyperspectral images (HSI) needs to be analyzed for retrieving the insights about the geographical details. The redundancy in spectral bands results in high dimensionality. The high dimensionality of HSI results in increased computational complexity. This subsequently influences the classification accuracy. Consequently, dimensionality reduction (DR) of HSI is essential before the classification. State-of-the-art DR techniques fail to recognize the nonlinearity in hyperspectral data. The proposed distance measure utilizes information derived from both spatial and spectral domains. The existing DR methods can be included with the proposed distance measure to address the issue of nonlinearity in HSI data. The tests are carried out on DR methods included with the proposed distance measure to assess the results of classification utilizing Support Vector Machine (SVM). Results of classification show that DR techniques incorporated with proposed measures address the nonlinearity present in HSI data.
Hybrid DL Models for Improved Accuracy in Diagnosing Chronic Obstructive Pulmonary Disease
Authors: Nikhil Raje
Published: November 2024

Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory disorder marked by enduring airflow obstruction, leading to considerable illness and death rates. Timely and precise diagnosis is essential for proper management and treatment. In this study, we present a novel hybrid deep learning (DL)  model leveraging an Autoencoder-GAN (Generative Adversarial Network) architecture to improve the accuracy of COPD diagnosis. Our approach incorporates a cutting-edge preprocessing method, Adaptive Histogram Equalization with Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance the contrast and detail in CXR images, facilitating more precise feature extraction. The proposed Autoencoder-GAN Hybrid Model significantly outperforms traditional models, achieving an impressive accuracy of 98.3%. By enhancing image quality and focusing on key features through CLAHE preprocessing, our model is able to better distinguish between healthy and COPD-affected lungs. We compared the performance of our model with standard DL models, including CNN, SVM and Random Forest Classifiers, demonstrating superior results across various evaluation metrics. This study highlights the potential of advanced DL techniques and innovative preprocessing methods to enhance the accuracy of COPD diagnosis, offering a promising tool for healthcare professionals in the early detection and management of this chronic disease.
A Novel Mathematical Investigation of Synthetic Precipitation Employing Diverse Variable -Order Non-linear Fractional Derivatives
Authors: T. Saradhadevi
Published: November 2024

An inadequate rainfall pattern results in water scarcity, groundwater depletion, pollution escalation, and ecological disruption. Addressing these challenges, comprehensive water management strategies will be necessary to ensure sustainability. This paper proposes a nonlinear fractional variable-order model of artificial rain making in the sense of Liouville-Caputo, Caputo-Fabrizio and Atangana –Baleanu fractional derivatives .The existence and uniqueness of fractional solutions has been established and numerical simulations have been carried out to study the influence of different parameters on the process of artificial rainmaking leading to rainfall.
The Application of Integrated Neutrosophic CRITIC and Preference Selection Index Methods for Multi Criteria Decision Making Over the Implementation of An Integrated Fish Farming System
Authors: S. Rubeela Mary
Published: November 2024

This paper presents the application of integrated Neutrosophic CRITIC (The Criteria Importance Through Inter-criteria Correlation) and PSI (Preference Selection Index) methods using by the linguistic variables are converted to Neutrosophic set, which is an effective decision making model in a real life environment. This integrated methods is used to rank the 5 alternatives (Labeo rohita , Cirrhina mrigala , Catfishes, Catla catla, Murrels or Snakeheads) and 6 main criteria( Azolla with Duck Cum-Fish Culture, Paddy -Cum -Fish Culture, Duck- Cum -Fish farming, Fish - Cum -Poultry farming, Fish- cum Pig Culture, Cattle -Fish integrated farming system) for the growth of different varieties of fish by using Neutrosophic Environment.
Unveiling Special Numbers Arising from Diophantine Equations: Exploring Solutions and Ramifications
Authors: J. Sivasankari
Published: November 2024

This paper delves into the exploration of special numbers stemming from the resolution of Diophantine equations—mathematical challenges centered around identifying integer solutions to specific polynomial equations. Investigating various instances of Diophantine scenarios, we meticulously examine and analyze the special numbers that naturally manifest during the process of solving these equations.  We scrutinize the distinctive features and implications of each special number within the framework of Diophantine solutions. The discoveries illuminate the intricate relationships between specific numerical patterns and the underlying algebraic structures inherent in Diophantine equations, presenting avenues for further inquiry and potential applications.
An Application on Fuzzy Relation Using in Difficult Problem Solved
Authors: Sophiya A
Published: November 2024

As a basic concept in fuzzy theory, fuzzy relations are used in a variety of fields, such as fuzzy clustering, uncertainty reasoning, and fuzzy control. When fuzzy relations are applied in practise, it might be difficult to estimate and compare them. This method was therefore applied in this study to tackle a challenging issue. It turns out that a combination of this strategy and the three prior expanded ones can have a beneficial effect on the actual issue. Finally, using fuzzy quantifiers, we give a theoretical investigation into the capacity to solve systems of fuzzy relation equations. As we shall see, solutions to such systems are given in building a fashion, and they may be solvable.
AI-Driven Resource Provisioning: Enhancing Elasticity and Efficiency with Hybrid RNN-LSTM Models
Authors: P. Christopher
Published: November 2024

Automatic resource provisioning techniques that dynamically modify resources in response to service demand are essential to implementing elasticity services. For systems with strict latency or reaction time requirements, such as Enterprise Resource Planning (ERP) systems under high traffic loads, this flexibility is crucial for lowering power consumption and guaranteeing quality of service (QoS). Determining the best point at which to scale resources is still a difficult task. In this research, we provide an AI-integrated system that adjusts resources according to anticipated demand. To predict load requests, the system uses an LSTM model and a Hybrid Recurrent Neural Network (RNN). This strategy seeks to minimize overprovisioning, which will lower the cost of infrastructure and energy usage. The deep learning model aims to estimate the resources required to improve service response time and satisfy customer requests, as well as to anticipate with high accuracy the processing load of distributed servers. Proactive provisioning decisions are made for the servers based on the anticipated load. Our tests on a common server request dataset show that the suggested RNN+LSTM model performs better than traditional deep learning models in terms of efficient resource management and prediction accuracy.
Radial Radio Pell Mean Labeling of Subdivision of Graphs
Authors: K.Sunitha
Published: November 2024

A one-one mapping ϕ : V (G) → N for a connected graph G is defined as follows: d(x,y)+⌈(ϕ(x)+2ϕ(y))/2⌉≥1+r(G), where radius is denoted by r(G). Any vertex in G has a radial radio pell mean number of ϕ which is the maximum number and is represented by rrpmn(ϕ). Here, we look at the labeling of various graphs using the radial radio pell mean of subdivisions such as subdivision of star graph S(K_(1,n)) , subdivision of path graph S(Pn) , subdivision of friendship graph S(Fn), subdivision of wheel graph S(Wn), subdivision of quadrilateral book graph S(QB(4,3)), subdivision of closed helm graph S(CHn), subdivision of helm graph S(Hn), subdivision of double fan graph S(DFn) and subdivision of triangular book graph S(TB(3,n)).
K - Sharp Ordering on Intuitionistic Fuzzy Matrices
Authors: P. Jenita
Published: November 2024

Aim of this article is to develop the concept of k-Sharp ordering as a generalization of k-minus ordering [1]. A relationship between the k-minus ordering and k-Sharp ordering is estabilished. We derive some properties of intuitionistic fuzzy matrices (IFM) under k-sharp ordering. In general k-minus ordering need not imply k-sharp ordering and this is illustrated with suitable example.
Enhancing Energy Efficiency in HVAC Systems through Intelligent Control Strategies
Authors: F. Jerald
Published: November 2024

Their operation is importance to provide comfort in indoor environment in residential and industrial buildings through Heating, Ventilation, and Air Conditioning (HVAC). Nonetheless, HVAC systems remain one of the most energy-consuming elements of buildings, contributing to more than 40 percent of a buildings energy usage. In this paper, the various intelligent control approaches deployed to improve the energy management of HVAC systems are discussed. Precise data acquisition in real time, machine learning algorithm, predictive control, as well as technology that contemplates demand response, help intelligent HVAC systems to decrease energy use while keeping proper indoor climate. The paper describes the contemporary technologies, their possibilities, and barriers for the developing more sustainable HVAC systems operation.
Automated Violence Detection in Surveillance Networks with Deep Learning
Authors: Rohini Temkar
Published: November 2024

The prevalence of violent incidents around the world can be quite overwhelming, posing serious threats to personal safety and social stability. Violence manifests in various forms, including communal, caste, and political conflicts, affecting both rural and urban areas. Moreover, college youth are increasingly influenced by these activities. To combat violence, several strategies have been implemented, including the installation of surveillance systems. This paper proposes a deep learning-based approach for the automatic detection of violent activities using Convolutional Neural Networks (CNNs). Specifically, it utilizes MobileNetV2, which offers high accuracy and serves as a foundational model for our system. The surveillance network processes real-time video from CCTV cameras installed on college campuses, enabling prompt alerts to be sent to relevant authorities via a Telegram network. The proposed methodology aims to overcome limitations found in existing violence detection techniques by employing frame grouping and Temporal Squeeze and Excitation Blocks. This enhances the extraction of spatio-temporal features and improves accuracy and efficiency, especially in scenarios involving occlusions and crowded environments.
AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications
Authors: Naga Tirumala Rao Chillapalli
Published: November 2024

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.
Recent Advances in High-Performance Membrane Materials for Fuel Cells
Authors: Pramod Ambadas Karole
Published: November 2024

Fuel cells have emerged as a key technology for clean energy production, with proton exchange membrane (PEM) fuel cells being one of the most promising types due to their high efficiency and low emissions. A critical component of PEM fuel cells is the membrane, which plays a crucial role in ion transport and overall system performance. Recent advances in membrane materials have focused on improving proton conductivity, chemical stability, and durability while reducing production costs. This review discusses recent developments in high-performance membrane materials, including polymer blends, hybrid membranes, and metal-organic frameworks (MOFs). These innovative materials have shown significant potential in overcoming the limitations of traditional membranes like Nafion®. Advances in acid-base membranes, such as polybenzimidazole (PBI)-based systems, have demonstrated improvements in high-temperature performance. Enhanced cross-linking techniques, the use of antioxidant additives, and nanofillers have further contributed to improved membrane durability. Despite these advancements, challenges remain in scaling up production and reducing costs. Future research must focus on addressing these issues to enable the widespread commercial use of fuel cells. The progress in membrane material technology is critical to realizing the potential of fuel cells as a viable solution for sustainable energy.
Row Column Reduction Method for Solving Trapezoidal Fuzzy Transportation Problem
Authors: J. Jeyanthi
Published: November 2024

The primary goal of the transportation issue is to reduce the cost of moving goods from several warehouses to various showrooms. Fuzzy approaches are frequently employed to eliminate uncertainty. When the cost of transportation is uncertain, we sometimes use a fuzzy approach to resolve the issue. In this paper the values used in constraint equation are trapezoidal number and applied our proposed method, row column reduction method to get an optimal transportation cost.
Analysis and Simulation of Misinformation Spread in Social Networks: A Hybrid Stochastic-Deterministic Approach with NEAB Model
Authors: Ghada A. Ahmed
Published: November 2024

This paper examined and simulated the dynamics of misinformation spread within social networks using the NEAB model [32]. In this model, (N) represents non-believers, (E) denotes individuals exposed to the information but undecided, (A) refers to those who accept the information and may propagate it, and (B) are the believers. Similar to epidemiological models like SEIR (Susceptible-Exposed-Infected-Recovered), the NEAB model adapts to study the spread of various types of information and behaviors within a population [20]. By combining elements of network theory [28] and epidemiology, the model investigates how behaviors and network structures influence information transmission. Our simulations, particularly when applied to real-world scenarios like misinformation spread during events such as COVID-19 or natural disasters, highlight key factors influencing dissemination [7]. Rates of exposure to misinformation, the transition of individuals from exposed to active spreaders, and the rate of recovery all play crucial roles in shaping how misinformation spreads [33]. Misinformation propagates more easily when exposure rates are high, while higher recovery rates help limit its spread. Sensitivity analysis shows that variations in (β) and (δ) significantly impact the spread, emphasizing the importance of targeted interventions during these critical stages [22]. The objective of this study is to evaluate strategies for reducing the effects of misinformation on public discourse and health outcomes while offering insights into the key factors that drive its dissemination based on model simulations.
Design of an Improved Model for Multimodal Data Fusion Using XGBoost-LSTM-CNN and Proximal Policy Optimizations
Authors: Vijaya Kamble
Published: November 2024

Applications in healthcare, finance, and real-time sensor applications call for much stronger demands of improving accuracy and efficiency in the analysis of multimodal data. Current methods have deficiencies in fusing multiple types of data such as time-series, spatial, and categorical data, arising due to limitations in capturing sequential dependencies, spatial patterns, and feature importance simultaneously. It also discusses these challenges through a novel solution based on a hybrid ML-DL approach, with an integration of reinforcement learning and advanced probabilistic models. First, the method that comes to mind is the XGBoost-LSTM-CNN Hybrid Model, wherein three drivers of performance improvements come together, namely, XGBoost, with much-appreciated capability for outstanding handling of structured tabular data, LSTM due to its proficiency in capturing long-term temporal dependencies, and the strength of CNN in spatial feature extraction. These results improve the predictive accuracy for multimodal datasets. After that, further enhanced multimodal fusion by the CAMT selectively pays attention to the critical features across the modalities, enhancing the accuracy of the contextual time-series predictions. Subsequently, PPO enables real-time model adaptation by dynamic optimization of model parameters and improvement of predictions through continuous learning. KF-BNN reduces noise and uncertainty in time-series data by fusing the filtering capability of a Kalman filter with probabilistic modeling via Bayesian neural networks to give reliable predictions. It also provides federated learning via FedAvg with online gradient descent for distributed model training in a manner that ensures privacy, continuous model updates without having to centralize sensitive data samples. These approaches show significant improvements along many axes, with accuracy improvements of up to 4.5% and MAE reductions of 12% relative to the baseline models. Proposed models provide a valid framework for analyzing multimodal data, thereby increasing precision, recall, and overall adaptiveness in dynamic real time, hence pushing the state-of-the-art in multimodal fusion and timeseries prediction. This research probably will influence those areas dependent on distributed, multimodal, and sequential data samples.
Stability, and Almost Sensitivity of Induced Maps
Authors: Amalraj. P
Published: November 2024

Suppose that X is a compact Hausdorff space and f : X → X is continuous. We consider the space K(X), the space of all compact subsets of X with Hausdorff metric H. Let f˜: K(X) → K(X) defined by  f ̃(K)=f(K) . We discuss some interconnections between the orbit of f ̃ and the orbit of  f. By assuming the transitivity of f ̃, we conclude that X contains a cantor set C with (orb(f ̃,C)) ̅ = K(X). That is, orbit of a nowhere dense set is dense in the hyperspace.  Along with this we introduce ”almost sensitivity” and ”stability” in K(X). We prove that f is stable in X if and only if f ̃  is stable in K(X). Again we prove that transitive maps are always ’almost sensitive’ in K(X) and hence the base map is ’almost sensitive’ in X.
Fuzzy Double S – Hausdorff Spaces
Authors: Sowmya.R
Published: November 2024

This study introduces and explores the concept of fuzzy double s-Hausdorff spaces within the field of fuzzy topology. Fuzzy double s-Hausdorff spaces extend the classical Hausdorff condition by incorporating two distinct fuzzy topologies on a single set, allowing for a more nuanced approach to separation where uncertainty and gradation are involved. The research develops formal definitions and characterizations of these spaces, establishing key theorems that outline their properties and behavior.
In this article, a meaning of FDS -T2 remains presented through covering the description of S- T2   introduce by Srivastava [3] and obtain result analogues towards the result of S- T2.
An Analytical Study on Account Holders' Perceptions and Satisfaction with reference to Banking Service Quality Post Merger of selected Public Sector Bank in India
Authors: Kantharaju G
Published: November 2024

Public Sector Banks in India have undergone significant mergers, which enable them to expand their activities, develop further, and strengthen their position as a key sector in the economy. Despite challenges such as rising non-performing assets, increasing frauds, political interference, and favouritism, mergers and acquisitions are graceful to benefit the newly formed banks, the economy, and the industry as a whole. This study examines the impact of recent bank mergers on customer awareness, perceptions, and satisfaction levels, focusing on four major public sector banks: Bank of Baroda, Canara Bank, Union Bank of India, and Indian Bank. Data were collected using structured questionnaires and personal interviews, incorporating 16 variables measured on a five-point Likert scale. The reliability of the data was confirmed with a Cronbach's Alpha value of 0.816, indicating high internal consistency. Analysis using One-Way ANOVA and descriptive statistics revealed significant variations in service quality perceptions across different age groups and bank affiliations, while awareness, digital banking, product/service efficiency, and trust/confidence showed consistent perceptions. The study highlights the positive reception of technological improvements post-merger and provides insights for banks to tailor their communication and service strategies to different customer demographics.
Performance of Logarithmic Magnitude of Centre-Element-Based Steganography over OFDM constituted Wireless Communication Channel
Authors: Mukund D. Maid
Published: November 2024

Hiding crucial information is today's utmost concern and transmitting through wired or wireless medium is another attentive subject. Quality secret hiding requires robustness and better perceptual visuals of the host image. Also, a secret embedding mechanism should offer higher immunity to noises. The work proposes a robust and efficient patch-based secured steganography (P-BSS) mechanism. The stego image is transmitted over an orthogonal frequency division multiplexing (OFDM) based Wireless Communication system governed by 802.11 WLAN. The stenography framework uses center element (CE) information to embed secret bits in its 3x3 neighborhood. The strategy is based on the magnitude and natural logarithm of the center element. The statistical operation on the center element defines the position in the neighborhood from where the embedding process should be initiated. The secret binary information is then hidden using the LSB embedding approach. The stego image when transmitted over a Binary phase shift keying (BPSK) modulated OFDM wireless communication system, the stego image was immune to Additive White Gaussian Noise (AWGN) above 10 dB. The proposed logarithmic magnitude of the center-element-based steganography (LM-CEB-S) framework maintains a proper balance between imperceptibility and security.
Cryptography in Network Security: a Mini Review
Authors: Pooja G. Dhawane
Published: November 2024

Network security and cryptography are crucial components in safeguarding data and communication in the digital age. Cryptography plays a vital role in network security by converting plain text into unreadable formats to protect confidentiality and integrity. As organizations generate vast amounts of data daily, ensuring secure data transfer over the internet becomes increasingly important, with network security challenges growing as society transitions to the digital era. Techniques such as encryption are used to protect enterprise information and communication from cyber threats, ensuring the integrity, confidentiality, and accessibility of computer networks and data. Encryption and network security are employed to protect data stored in systems and exchanged over wireless networks, with cryptography utilizing various algorithms and approaches to secure networks and data effectively.
Stability of a General Quadratic-Cubic Functional Equation in Non-Archimedean 2-Normed Spaces
Authors: Elumalai P
Published: November 2024

This research aims to investigate the Hyers-Ulam stability of the mixed-type quadratic-cubic functional equation in non-Archimedean 2-normed spaces using the fixed-point method. Additionally, some counter-examples are illustrated for instability. The exciting possibilities of this cutting-edge research and unlocking new frontiers in mathematical analysis are explored.