bluuebunny/crossref_metadata_2025 huggingface dataset:
Securing Cloud Environments by Analyzing Cryptographic Techniques for Data Integrity and Confidentiality
Authors: Vinod Laxmanrao Mehetre
Published: November 2024

With cloud computing, you'll effectively and rapidly reach a parcel of computer assets. Usually exceptionally valuable in this computerized age. But this alter within the way things work makes it much harder to keep data secure and private. There are numerous reasons why security is critical for cloud settings. This exposition centers on ways to keep information safe from individuals who shouldn't be able to see or alter it. We see at a few of the foremost vital thoughts in cryptography, such as hashing, advanced marks, symmetric and hilter kilter encryption, and how they can be utilized to security the cloud. A few sorts of encryption, like AES and others, keep information secure whereas it's being sent. Other sorts of encryption, like RSA and others, keep information secure whereas it's being put away. Hash calculations, like SHA-256, deliver each set of information a one of a kind advanced stamp that creates it simple to spot changes. This makes beyond any doubt that the information is rectify. Indeed more security is included by computerized marks, which check the data's clarity and reliability to create beyond any doubt it wasn't changed whereas it was being sent. There are more complicated secure strategies that we see at, such as secure multi-party computation and homomorphic encryption. There is solid protection indeed in places where no one can be trusted since these let forms be done on secured information without giving out the essential data. This paper examines how these safety measures can be utilized in cloud-based systems and talks about how well they perform and any speed issues they might lead to. There are case studies from real life that show how safe methods have been used successfully in the cloud. This shows that they can be used in real life. We also talk about the issues that come up when you need to manage encryption keys, which is a key part of keeping data safe. A lot of attention is paid to strong key management tools, such as Hardware Security Modules (HSMs) and key management services (KMS).
Second Degree Quinary Equation xy + XY = (k2 + 2k + 1)w2
Authors: N. Thiruniraiselvi
Published: November 2024

The aim of this is to determine quintuples  in integerssatisfying the quadratic diophantine  . Different sets of solutions in integers are obtained by converting it to a ternary quadratic equation through employing suitable linear transformations for which the solutions  in integers exist.Some  observations are exhibited.
Existence of Fixed Point Theorems in Revised Fuzzy Modular Metric Spaces
Authors: A. Muraliraj
Published: November 2024

Aim of this paper, to established existance of new fixed point theorems in revised fuzzy modular space by deriving variants of Banach contraction principle under the class of inner and outer contractions.
A Revised Fuzzy Differential Equations Using Weakly Compatible Self-Mappings in Revised Fuzzy Metric Spaces
Authors: R. Thangathamizh
Published: November 2024

In this paper, we demonstrated certain coincidence point and Common Fixed Point [briefly, CFP] findings under the rational type weakly compatible revised fuzzy-contraction conditions in full Revised Fuzzy Metric spaces [briefly, RFMSs] utilizing the "triangular property of revised fuzzy metric" with examples in this work. We also provided Revised fuzzy differential equations [briefly, RFDEs] as an application and demonstrated that the solution of the RFDEs has a unique CFP of the integral operators   and . This new path of weakly-compatible Revised fuzzy-contraction with the use of RFDEs in RFM space will be crucial. With different sorts of weakly-compatible Revised fuzzy contraction conditions for self-mappings and different forms of differential equations in the context of RFMSs, this approach may be expanded and developed in diverse directions.
A Revised Fuzzy Differential Equations Using Weakly Compatible Self-Mappings in Revised Fuzzy Metric Spaces
Authors: R.Thangathamizh
Published: November 2024

In this paper, we demonstrated certain coincidence point and Common Fixed Point [briefly, CFP] findings under the rational type weakly compatible revised fuzzy-contraction conditions in full Revised Fuzzy Metric spaces [briefly, RFMSs] utilizing the "triangular property of revised fuzzy metric" with examples in this work. We also provided Revised fuzzy differential equations [briefly, RFDEs] as an application and demonstrated that the solution of the RFDEs has a unique CFP of the integral operators B,C  and g. This new path of weakly-compatible Revised fuzzy-contraction with the use of RFDEs in RFM space will be crucial. With different sorts of weakly-compatible Revised fuzzy contraction conditions for self-mappings and different forms of differential equations in the context of RFMSs, this approach may be expanded and developed in diverse directions.
Enhancements Self-Scaling Quasi-Newton for Unconstrained Optimization
Authors: Basim A.  Hassan
Published: November 2024

A self-scaling for the quasi-Newton tecnique is derive by using a second_order Taylor's expansion to achieve optimal computational performance.   Following this, new updating formulas for the quasi-Newton method are introduced based on the newly derived self-scaling equation. The numerical results confirm this derivation and suggest that the new method could potentially rival the BFGS method in terms of performance.
Novel Step Size for Unconstrained Optimization
Authors: Yoksal A. Laylani
Published: November 2024

The step length size is the basis of gradient descent method with the desirable conjugate property. The aim of this document is to present a fresh gradient descent technique that calculates the step size based on a basic estimation of the Hessian of the function being minimized. The algorithm that results falls within the category of gradient descent methods with linear convergence characteristics.  Our study includes numerical data that demonstrates the significant advantages of our approach compared to traditional gradient descent methods.
A Computational Approach of Singularly Perturbed Singular Boundary Value Problem using an Adaptive Spline
Authors: Swarnakar Dornala
Published: November 2024

This paper develops a different approach for the numerical solution of singularly perturbed two-point singular boundary-value problems using an adaptive spline. The difference method is designed for nonstandard finite differences of the first order derivative and adaptive spline. The suggested scheme is accurate to fourth order and works for both singular and non-singular problems. Convergence analysis of a difference scheme is discussed in this paper. The numerical results are given to illustrate the efficiency of the proposed methods in comparison to the existing method in the literature.
Solving Tricriteria Machine Scheduling Problem using New Efficient Method
Authors: Yasameen M.Mohammed
Published: November 2024

In this study, an MBA model which can select a single machine for production and simultaneously consider many criteria is proposed. In this paper we present the description of the machine scheduling problem (MSP), which is the set of n tasks accomplished by a single machine. The task will be to minimise a function with reference to the criteria that are enumerated below: ∑C_j – total time whereas R_L – range of lateness; T_max – maximum of tardiness. Proofs have been made to conclude that this problem is NP hard. The matter we are concerned with calls for the emergence of a number of subissues, all of which will be discussed in more detail later on. In the theoretical part of our investigation, we have shown that the SPT rule offers a viable solution to the issue that we are attempting to solve. Furthermore, we have shown that it is possible to implement certain manifestations of the dominance rule (DR). In order to solve the suggested MSP tricriteria, the Branch and Bound (BAB) algorithm is used throughout the practical portion of the evaluation process. The objective of this approach is to identify a set of solutions that are not only successful but also efficient for 1//(∑C_j,R_L,T_max) up to n=18 jobs. In addition, the BAB method is used in conjunction with DR for a maximum of n=39 tasks within a reasonable amount of time in order to uncover estimated effective solutions for the problem at hand.
Integrated Methodology for Multi – Person – Multi Objective Decision Making in Intuitionistic Fuzzy Environment
Authors: K. Sundar
Published: November 2024

In this paper, multi – person – multi objective decision making in intuitionistic fuzzy environment is discussed using integrated methodology. And this new approach is also explained with an application. Many decision making problems involve multiple decision makers and conflicting objectives. This paper refers to this kind of problems as group decision making for multi-objective problems (GDM-MOP). The task of GDM-MOP is to select final solution(s) from a set of non-dominated solutions according to the decision makers′ preferences. However, it is common that the preference could be imprecise. We study the GDM-MOP where preferences are expressed by fuzzy reference points, called as fuzzy GDMMOP (FGDM-MOP). This paper provides a decision support model to simultaneously consider two measures for FGDM-MOP: consensus measure and robustness measure. The former is used to reflect the acceptable degree of a solution by the decision making group, while the latter indicates a solution′s ability to cope with any change on preferences. A multi-objective evolutionary approach is presented to solve the problem. Finally, a modified benchmark function is studied to illustrate the proposed approach.
Seagrass Detection and Classification from Uderwater Images Using Deep Transfer Learning
Authors: Yogesh S. Deshmukh
Published: November 2024

Introduction: Seagrass is an important marine ecosystem that provides habitat for many species of fish, crustaceans, and other marine animals. However, a number of issues, such as pollution, overfishing, and climate change, pose a threat to seagrass beds and may cause the environments that support them to deteriorate. One of the main challenges in the conservation of seagrass ecosystems is the accurate classification of seagrass species using computer vision techniques. Convolutional Neural Networks (CNNs), one type of machine learning approach, have garnered more attention lately for the purpose of classifying seagrass species. However, there are several challenges associated with using CNNs for seagrass classification. One of the main challenges is the lack of high-quality, labeled data. Another challenge is the variability in seagrass appearance and its complex growth. Therefore, it is obvious to keep track and identify the types of seagrass present in the coastal region. The proposed image classifier with CNN based on VGG-16 network is effectively used to classify the seagrass into five categories like Syringodium Isoetifolium, Halophila Ovalis, Enhalus Acoroides and Cymodocea Serrulata. By comparing the suggested categorization approach with the most advanced techniques, an overall accuracy of 90.50% is achieved.
Weakly Quasi-Primary K- Modules
Authors: Muntaha Abdul- Razaq Hasan
Published: November 2024

Important areas for study in the field of modules include prime modules .There has already been an introduction  to the idea of quasi-prime modules .Our focus recently  has been on weakly quasi-prime modules as a means for investigating one of these generalizations .The main objective of this work is to introduce a new type of module , namely weakly quasi-primary K-modules . A module D whenever 〖ann〗_K D=√(〖ann〗_K  r D)For each r ∉〖ann〗_K D, is called weakly quasi-primary   K-module , which is a generalization of weakly quasi-prime K-module ,whenever  〖ann〗_K D=〖ann〗_K r D for every r∉〖ann〗_K  D , then D is weakly quasi-prime K-module . Here the author obtain some characterize of weakly quasi-primary K-modules and investigated some properties of weakly quasi-primary K-modules .We prove relationships between weakly quasi-primary K-module and other modules , like prime module, quasi-prime module , weakly quasi-prime module and primary module are study in this paper .
Hybrid Lexicon and Transformer-Based Sentiment Analysis of Student Feedback for Faculty Evaluation: A Speech-to-Text Approach
Authors: Helaria Maria
Published: November 2024

The conventional methods for assessing the performance of educators in academic settings have often been hindered by outdated feedback systems, leading to assessments that can be either skewed or in- complete. This study unveils an innovative, dual-approach methodology that fuses lexicon-driven sentiment analysis with cutting-edge Transformer architectures, specifically focusing on BERT (Bidirectional Encoder Representations from Transformers). This approach provides a more sophisticated, real-time assessment of educators based on student evaluations. One of the standout features of this study is the integration of Speech- to-Text technologies, which allows for the immediate transformation of verbal feedback into text that can be analyzed. The approach makes use of a specialized Educational Sentiment Lexicon for preliminary sentiment evaluation, which subsequently refines the performance of a pre-existing BERT model. This dual-model is proficient in scrutinizing both text-based and verbal feedback—the latter being converted into text through advanced Speech-to-Text techniques. Our results indicate that this approach significantly outperforms existing lexicon-based and machine learning methods in terms of accuracy and comprehensiveness. By providing a real-time, versatile sentiment analysis tool tailored for educational settings, this research marks a paradigm shift in the scope and quality of faculty evaluations, thereby contributing substantially to the field of educational technology and sentiment analysis.
FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices
Authors: Preeti R. Lawhale
Published: November 2024

The security implications of the emerging world of smart devices brought about by the Internet of things' recent rise are enormous. IoT devices typically have restricted resources, such as minimal memory, low processing power, and a short battery life, in addition to their stated security level. In the context of IoT, lightweight cryptographic primitives are presented taking into account the trade-off between speed and security assurance. In this research, we proposed lightweight cryptographic hash methods and optimized hardware for devices with limitations. The size, speed, efficiency, and power consumption of our suggested solutions are examined on FPGA systems. Bit permutation, linear transformation, and S-Box functionality are used in the suggested design. To analyze large data sets and Internet of Things applications, conventional hash algorithms need memory and time. Thus, the demand arises for a lightweight cryptographic protocol that is both rapid and safe. This technique offers the necessary security criteria of conventional hash functions and the parameters for lightweight cryptographic protocols. The proposed architecture is tested and validated based on three key criteria: memory, speed, and power consumption, which are important factors in determining its lightweight nature. Based on the findings, the proposed architecture outperforms other lightweight protocols regarding memory usage, performance, and power consumption.  For an appropriate FPGA-based application deployment, a thorough comparison of our suggested designs is detailed on various FPGA families.
Advanced Deep Learning Techniques for Information Security Vulnerability Detection Using Machine Learning
Authors: Champa Tanga
Published: November 2024

The increasing rate on the complexity and amount of security threats demand more advanced information security vulnerability detection techniques. Traditional methods have limited capability to respond to such diverse and evolving threats in real-time. This paper presents a complete solution by using new machine learning models in combine with advanced mathematical approaches to organize the early detection and prediction of vulnerabilities that later content will be about the information security systems. We investigate the accuracy of different models (with an emphasis on modern approaches that go beyond the standard SVM, CNN, and GNN). It initiates with Bayesian Networks that use probabilistic graphical models for showing the interactions of diverse security characteristics to support reasoning about vulnerabilities as a function on the conditional dependencies. Decision Trees and ensemble techniques like Extreme Gradient Boosting (XGBoost) are able to deal with both heterogeneous data types and are more capable of modelling complex interaction between security features. For unsupervised cases, we use things like Isolation Forests for anomaly detection and Gaussian Mixture Models (GMM) to detect rare patterns in network packets that may be a sign of security attacks. We further investigate the possibility of reinforcement learning (i.e., Q-Learning) to continue to evolve with network changes and identify threats in real-world threatening environment. The learning process is streamlined and the interpretability of results is improved by integrating mathematical techniques such as Markov Chains (for modelling probabilistic transitions within network states) and optimization methods like Lasso Regression (for feature selection). In addition to this, the approach explores Autoencoder-based models but most specifically Variational Autoencoders (VAEs) for their unsupervised learning ability in identifying rare and new zero-day vulnerabilities. Extensive experiments on a variety of cybersecurity datasets demonstrate the effectiveness and efficiency of our approach, where substantial enhancements in detection speed and accuracy are achieved. The cloud-based cybersecurity model introduced in this research, backed by advanced machine learning models and mathematical frameworks could potentially serve to lay a foundation for the future of real-time cyber security defence mechanisms.
Growth of Optimizational Principles in Portfolio Analysis through Discrete Entropic Models
Authors: Vikramjeet Singh
Published: November 2024

There exists an extensive collection of parametric and non-parametric discrete and continuous information models but still inevitability arises to broaden supplementary parametric models to encourage flexibility in the system under study. Moreover, there appears well-built relation between information entropy and the theory of “Portfolio Analysis”. Further, various approaches of measuring risk in portfolio analysis including entropy method, divergence technique and integrated methodology etc. are accessible in the existing literature of portfolio analysis. In the present communication, our intention is to provide advancement regarding certain well convinced optimizational principles by means of new discrete entropic models, and consequently to deliver their solicitations in portfolio analysis. Additionally, the well-established principle has been enlightened through the assistance of a numerical illustration.
Virtual Piano using Assembly Language Programming
Authors: Gopal D. Upadhye
Published: November 2024

In the field of music technology and computer programming, the concept of a virtual piano represents a fascinating intersection. This project aims to develop software using assembly language and TASM (Turbo Assembler), emulating the functionality of a physical piano. The goal is to replicate the production of musical notes similar to those generated by pressing keys on an actual piano. At its core, the virtual piano program orchestrates various components seamlessly. The audio processing module synthesizes distinct notes, each defined by a unique frequency, as users interact with the program by pressing mapped keyboard keys. A thoughtfully designed user interface is crucial, as it bridges the gap between the user and the underlying program, facilitating smooth interaction. By integrating these elements harmoniously, the virtual piano emerges as a comprehensive tool that merges creativity with technology. Harnessing the intricacies of low-level programming allows for the accurate translation of user input into coherent musical notes. Additionally, a solid foundation in audio processing is essential for achieving an authentic rendition of piano sounds. Ultimately, upon completion, the virtual piano program transcends mere software, embodying a transformative musical experience. It not only serves as a functional instrument but also showcases the potential of assembly language in creating sophisticated and engaging digital applications. Through detailed audio synthesis and a user-friendly interface, this project highlights the convergence of technology and art, offering users an immersive and educational encounter with music and programming.
Design of an Efficient QOS Aware Trust-Based Security Model with Bioinspired Sidechains for Healthcare Deployments
Authors: Smruti P. Patil
Published: November 2024

Increased adoption of blockchain technology in healthcare systems has paved the way for transparent and secure data management. However, the inherent difficulties of scalability, performance, and security in blockchain networks necessitate the development of efficient models to ensure Quality of Service (QoS) and reliability. In this paper, we propose a novel QoS-aware trust-based security model for healthcare blockchain deployments that addresses these challenges by taking temporal energy consumption, temporal delay, temporal throughput, and temporal Packet Delivery Ratio (PDR) levels into account when selecting miner nodes. For efficient miner node selection, our model employs trust-based analysis. While for the formation of sidechains an efficient Grey Wolf Optimizer (GWO) Model is used, which is a metaheuristic technique inspired by hunting behaviour of Wolves in real-time scenarios. Effectively balancing the trade-off between exploration and exploitation, the GWO algorithm enables the selection of optimal miner nodes that meet the desired QoS requirements. By incorporating temporal metrics, our model adapts dynamically to changing network conditions, ensuring optimal resource utilization and enhanced network performance levels. To assess the efficacy of our proposed model, we ran extensive simulations and compared its performance to that of existing sidechaining models. The outcomes demonstrate significant enhancements in multiple aspects. In comparison to state-of-the-art sidechaining models, our model achieves an impressive 8.5% reduction in delay, 3.9% reduction in energy consumption, 4.5% increase in throughput, and 2.5% improvement in PDR levels. These enhancements demonstrate the efficacy and efficiency of our healthcare blockchain deployment models. The proposed model has applications in a variety of real-time healthcare scenarios. It can be used in electronic health record (EHR) systems where data integrity, confidentiality, and accessibility are crucial. By ensuring QoS-aware miner node selection, our model contributes to dependable and efficient data management, allowing for streamlined access to patient records while maintaining the necessary security standards. Moreover, the trust-based approach of our model improves the overall security of healthcare blockchain deployments. Our model reduces the risks associated with malicious or compromised miner nodes by incorporating trustworthiness metrics such as reputation and behavior analysis. This is especially important in the healthcare industry, where the sensitive nature of patient data necessitates stringent security measures.
Improving QoS Parameters in Fog Computing Environment using QCSO Framework
Authors: Meena Rani
Published: November 2024

Cloud computing is a complex infrastructure because heterogeneous devices are involved to share the resources on it. In recent years, the Internet of Things (IoT) is being used by a huge number of different devices which create large amounts of data. Due to their remote location, IoT devices create delays in processing the data. To ensure the Quality of Service (QoS) of the network, the real time applications need low latency and high bandwidth which in turn improves the quality of life. To solve this problem, Fog Computing (FC) is introduced which plays a vital role in computing paradise. Fog computing processes the request locally and uses available resources to reduce the latency and enhance the technological innovation and capabilities. To allocate and manage the resources is the primary goal of fog computing. To achieve this, Queuing theory method is used in which tasks are assigned to the resources. Proposed study presents an algorithm named Queue-Cuckoo Search Optimization Framework (QCSOF) to improve the quality of service through resource optimization. The aim of the proposed framework is to enhance resource allocation, energy utilization.
Cloud Guardian: A Comprehensive Approach to Cloud Security Posture Management with Automated Multi-Cloud and Container Security
Authors: G Sreenivasa Yadav
Published: November 2024

As organizations increasingly adopt multi-cloud environments and containerized applications to improve scalability and operational efficiency, managing security across these diverse platforms has become a critical challenge. Traditional security tools and manual processes are insufficient to handle the complexity and dynamic nature of modern cloud infrastructures. This research introduces Cloud Guardian, a cloud security posture management (CSPM) platform that automates security assessments, policy enforcement, and threat detection across multi-cloud platforms and containerized environments. Cloud Guardian addresses the key security challenges associated with multi-cloud and container environments, including misconfigurations, unpatched vulnerabilities, and inconsistent security policies. By integrating real-time threat intelligence and automated remediation capabilities, Cloud Guardian provides continuous protection, ensuring compliance with industry standards such as CIS and NIST. Additionally, the platform’s self-healing mechanisms and rollback capabilities allow for secure and uninterrupted operations by automatically fixing security issues and reverting changes if necessary. This study evaluates Cloud Guardian's effectiveness in securing complex cloud infrastructures through real-world case studies. The findings demonstrate significant improvements in operational efficiency, compliance, and security posture, reducing the risk of breaches and operational disruptions. Cloud Guardian offers a scalable and automated solution for organizations seeking to safeguard their cloud and container environments while minimizing manual intervention and ensuring continuous security and compliance.
Bounded Closed Interval-Valued Decagonal Fuzzy Number and its Application
Authors: T. Gunasekar
Published: November 2024

In this paper, we introduced the notation of Algebraic operations of bounded closed interval-valued decagonal fuzzy number. To overcome the uncertainty here we used fuzzy numbers. Many researchers have focused their research with Triangular and Trapezoidal fuzzy numbers, in some cases it is not fit to the problem when the vagueness arises in ten different opinions so our motto is to develop the theorems for bounded closed interval using Algebraic operations such as Addition, Subtraction, Multiplication and Division via Decagonal Fuzzy Number.
Analysis of Graph Inverses and Algebraic Connectivity in a Systematic Way
Authors: R. Stella Maragatham
Published: November 2024

an exhaustive and methodical study of algebraic connectedness and graph inverses. In many contexts, graph theory plays a crucial role, particularly when understanding intricate systems. This paper delves into the fundamental concepts of graph inverses and explains their significance for network analysis and connectivity evaluation. The Laplacian lattice's second-smallest eigenvalue, or algebraic connectivity µN−1, plays a crucial role in some features like network heartiness, synchronisation security, and diffusion processes. In this study, we focus on the algebraic connectedness in the network-of-networks (NoN), which is the general context of linked networks.  A key component of science is the graph hypothesis. Algebraic graph hypothesis refers to the use of algebraic techniques to graph problems. This article concludes a focus on algebraic graph hypothesis and presents and examines a different algebraic and mathematical variety idea to regard as the greatest matching of an undirected graph.
New approach towards complex bipolar intuitionistic fuzzy subbisemiring applied to homomorphism
Authors: R. Raghavendran
Published: November 2024

We establish the concept of complex bipolar intuitionistic fuzzy subbisemiring (CBIFSBS). We analyze the homomorphic features and important properties of CBIFSBS. We construct the CBIFSBS level sets for bisemirings. Suppose that L is a subset of B. Then R = (μT − L ·eδχT − L , μF − L ·eδχF − L , μT + L ·eδχT + L , μF + L · eδχF + L ) is a CBIFSBS of B if and only if μ(t,s) is a SBS of B for all t, s ∈ [−1, 0] × [0, 1]. It is shown that there are homomorphic pre-images and homomorphic images for every CBIFSBS. We illustrate the results based on numerical examples
Account for the Role of Digital Literacy and Behavior Intention on Electronic Payment and Its Impact on Customer Satisfaction
Authors: Ari Dwi Astono
Published: November 2024

Digital literacy and behavioral intentions have emerged as critical elements within the Micro, Small, and Medium Enterprises (MSMEs) ecosystem, particularly due to their importance in improving electronic payment systems and ensuring customer satisfaction. This study aims to examine how digital literacy and behavioral intentions influence electronic payment systems and assess their impact on customer satisfaction. A survey methodology was employed, gathering responses from 260 participants. Data were analyzed using Structural Equation Modelling (SEM) through Smart-PLS (Partial Least Square) software. The findings suggest that both digital literacy and behavioral intentions have a positive effect on electronic payments, which in turn significantly enhances customer satisfaction. The study provides valuable insights for MSME operators on improving e-payment processes and increasing customer satisfaction with their offerings.
A New Heuristic Hybrid Model for Cancer-Based P-Glycoprotein Drug Classification: Automated Prediction
Authors: Mylavarapu Kalyan Ram
Published: November 2024

As the population improves in real-time applications, drug identification becomes an increasingly difficult task. Various biotechnological applications based on in vitro fertilization can benefit from better drug identification. For many nations, agriculture is the backbone of cancer medication development; increasing crop yields is the key to better drug identification in agricultural contexts. Computer vision applications suffer as a result of the efficient degradation of production quality and quantity caused by p-glycoprotein drugs used in cancer treatment. The identification of cancer-based p-glycoprotein drugs is an aggressive concept in the present day, due to the various symptoms present in cancer. Various authors use soft computing techniques and machine learning approaches to predict these drugs. For the purpose of automatically identifying based p-glycoprotein drugs in cancers, this paper proposes a Hybrid Novel Heuristic Model (HNHM) that combines Self-Organizing Maps (SOMs) with Convolution neural networks (CNNs). The primary challenge in identifying cancer drugs based on p-glycoprotein is feature extraction; hence, dimensionality reduction is crucial in filtering out noise from the cancer sub-cancer drug dataset and separating the subset of the cancer drug dataset affected by the p-glycoprotein drug using support vector machines (SOM). It is a promising approach to automatically identify based p-glycoprotein drugs in cancers by exploring protein features in cancer and predicting which patches are affected by virus-bacteria. CNN is used to match cancer sub-cancer drug data sets. In this study, we use sub-Cancer drug data sets to develop a hybrid heuristic approach to identify based p-glycoprotein drugs found in cancers. Clinical trials of the suggested hybrid model were conducted using sub-cancer drug data sets derived from peach cancers and the publicly available Cancer's drug datasets housed in the UCI repository. With less training required to explore based on p-glycoprotein drugs from sub-Cancer drug datasets, the suggested hybrid model achieves an accuracy of nearly 99.93% when tested on different testing datasets for sub cancer drugs. When compared to other state-of-the-art methods, this one significantly reduces running time while providing efficient performance in a number of p-glycoprotein drug prediction metrics, including recall, sensitivity, and specificity
Enhancing Cognitive Radio Network Through the Novel Optimization Process and Deep Learning Model in the Transverse System
Authors: K. Rama Krishna
Published: November 2024

The dynamic distribution of spectrum, made possible by next-generation networks that utilise Cognitive Radio technology, helps to alleviate spectrum shortages. Additionally, these networks may be dynamically operated to save energy. The innovative cloud-sharing-decision-based method for optimising cognitive radio networks' wireless networks is introduced in this research. Optimisation was achieved via 3 key performance indicators: spectrum utilisation, power utilisation, and human revelation. Researcher find the best key performance indicators for a realistic India’s suburban situation. An optimisation technique in the cloud-based design for architecture simultaneously decreases networks power usage for about 27.6%, mean worldwide revelation by 34.4%, & spectrum utilisation for about 34.6% when related to a standard Cognitive Radio net. For the areas of networks power consumption (4.9%), spectrum utilisation (7.4%), and global exposure (4.4%), method outperforms the old design, even in the worst-case optimisation scenario.
Single-Phase Multilevel Inverter Design with Low Voltage Stress and Optimized Control Model
Authors: Vijayalaxmi Biradar
Published: November 2024

The research explicitly examines the power control of a single-phase renewable energy sources battery energy storage system (RES-BESS)mixed-distributing network designed for domestic usage. The system utilizes a cascaded H-bridge (CHB) conversion architecture as its grid connection.  The CHB uses a hierarchical power management design consisting of a single centralized regulator for the top layer and several dispersed regulators for the bottom layer. The higher stratum produces the reference signals that need to be monitored. Under optimal circumstances, the CHB should operate efficiently with all solar panels at their Maximum Power Point (MPP) and with an electrical factor of 1. However, several functional limitations prevent this from always being feasible, such as limits related to voltage and current, biased shadowing, and State of Charge (SOC) incompatibilities.  Hence, a novel optimization technique is introduced to take into account these impacts directly and calculate a collection of reference parameters to be monitored. This approach aims to maximize the long-term viability of the structure while ensuring compliance with the given limitations. The optimization method in this structure incorporates many functional needs, prioritizing them based on the specific operating circumstances. The decentralized management layer utilizes traditional feedback-controlling methods to monitor the best referencing.  Experimental findings are conducted to verify the best method under normal conditions for operation and evaluate the efficacy of the suggested CHB arrangement with the complete control technique.
A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage
Authors: Shailesh Madhavrao Deshmukh
Published: November 2024

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors.  The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.
Blockchain-Based Drug Supply Chain Recommendation System (B-DSCRS) with Machine Learning for Supply Chain Management
Authors: F. Rahman
Published: November 2024

The pharmaceutical industry has been struggling to keep tabs on its goods across the supply chain for the last decade, which has opened the door for counterfeiters to sell their phoney medications. Integrated product supply chain management mostly focuses on distribution. The pharmaceutical business places a premium on the drug distribution process because of the importance of ensuring the safe handling, storage, and distribution of medical items. The global pharmaceutical sector is facing a significant threat in the form of counterfeit pharmaceuticals. Blockchain technology can manage and monitor the supply chain process with remarkable efficiency when used to its maximum capacity.This paper proposed and implemented a Blockchain-based Drug Supply Chain Recommendation System (B-DSCRS). A pharmaceutical supply chain management system built on the blockchain and a consumer medication recommendation system powered by machine learning comprises our proposed system's two primary components. As a first step in the smart pharmaceutical industry's medication delivery process tracking and monitoring.Conversely, the machine learning module suggests the finest medications for pharmaceutical sector clients. Lastly, our suggested system goes through the testing process to ensure it is efficient and user-friendly.
Employing Evolutionary Computing and Hybrid Artificial Neural Networks to Improve Drug Discovery
Authors: Nidhi Mishra
Published: November 2024

An ultimate relevant translational scientific endeavour that contributes to human vulnerability and happiness might be the creation and advancement of medications. Fast drug discovery procedures necessitate using contemporary computational approaches to tackle pharma data's complexities and high complexity. By combining Evolutionary Computing with Hybrid Artificial Neural Networks (EC-HANNs), this research introduces a novel strategy for optimizing and speeding up the drug development process. To handle various drug-target relations, predict the efficacy of compounds, and discover more accurate potential medications, the proposed model employs EC to evolve the computational construction and hyperparameters of HANNs in real-time. This innovation makes the model a flexible framework. Through repeated refinement of EC- HANN designs, the EC Module uses Particle Swarm Optimization (PSO) to choose the best configuration for individual drug discovery tasks. The model's distinctive feature is that it dynamically adjusts the network configuration to match the details of each drug discovery activity by using PSO to optimize the HANN's architecture and hyperparameters. Regarding sequential biological data, the HANN Module employs Convolutional Neural Networks (CNNs) to glean features for pattern recognition over time. By extracting high-level spatial information from genetic data, CNN can better identify prospective medication candidates. When tested on several benchmark datasets, the proposed framework demonstrates superior performance over traditional neural networks across the board regarding prediction accuracy, convergence speed, and model durability. As a powerful tool, this hybrid approach can simplify drug discovery, leading to more efficient drug development.
AI Predictive Analytics for Verifying Pharmaceutical Authenticity and Combating Drug Counterfeiting
Authors: Aakansha Soy
Published: November 2024

A significant worry in recent years has been the counterfeiting of medicines. The distribution and manufacture of fake or falsified drugs are both criminal and harmful to the public's health. The severity of this issue differs significantly from one country to another, primarily due to variations in the adherence to national regulations and processes. Thus, preventing the sale of fake drugs has become an urgent matter, particularly in poor and developing nations. The study presents a new multi-layered validation structure for pharmaceutical authenticity verification that uses Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and blockchain technology for artificial intelligence predictive analytics. The goal is to tackle the widespread problem of counterfeit drugs. Using GANs to sift through past data, the suggested strategy improves the detection of minor package variations by identifying trends and traits linked to counterfeit drugs. Using the GAN-generated enhanced dataset, a CNN is trained to accurately and specifically categorize drug packaging.Further, the framework uses blockchain technology to provide trustworthy and transparent recordkeeping of drugs in realtime as they move through the supply chain. Increased patient safety directly results from our system's thorough audit trail, which solves the problems of fake medications and regulatory compliance. The suggested approach reduces counterfeit dangers and provides a scalable paradigm for other industries to use when dealing with similar authenticity verification issues, which means it can find more uses in safety-critical fields. The success of the multi-layered validation structure model is evaluated using performance metrics such as accuracy, recall, and F1-score. It achieves a fantastic accuracy rate of over 95%.
Integrating Blockchain Technology in Pharmaceutical Supply Chains
Authors: Kamlesh Kumar Yadav
Published: November 2024

This paper presents a transformative approach to enhancing transparency, security, and traceability. By utilizing a decentralized ledger system, stakeholders can access real-time data about drug provenance and distribution, thereby increasing consumer confidence and improving regulatory compliance. Existing pharmaceutical supply chains face significant challenges, including counterfeit drugs, lack of visibility, and inefficiencies in tracking products through complex logistics networks. These issues lead to financial losses, compromised patient safety, and regulatory penalties. This study proposes a novel framework, the Pharmaceutical Supply Chain using Blockchain Technology (PSC-BCT), designed to address these existing challenges. The PSC-BCT framework leverages smart contracts, distributed ledgers, and IoT integration to create a secure, transparent, and efficient supply chain ecosystem. This framework enables all participants, from manufacturers to pharmacists, to share and verify transaction data, thereby enhancing accountability and reducing the risk of fraud. The proposed method is utilized to streamline operations, reduce delays, and enhance data integrity within the pharmaceutical supply chain. By ensuring that every transaction is recorded immutably, stakeholders can verify product authenticity and track the drug's journey from production to the end consumer. The findings indicate that the PSC-BCT framework significantly improves supply chain transparency and efficiency while reducing instances of counterfeit drugs. Furthermore, the implementation of blockchain technology fosters trust among stakeholders and enhances compliance with regulatory standards, ultimately leading to improved patient outcomes and safety.
Secure Electronic Health Records Against Insider Attacks Using Blockchain
Authors: Uruj Jaleel
Published: November 2024

Patient health information is provided by Electronic Health Record (EHR) systems. The security, integrity, and management of electronic health records are critical issues. EHRs facilitate online data storage and lessen human labour by making data maintenance simple. Traditional EHRs are currently vulnerable to a variety of assaults, such as internet and insider transactions. Diverse fraudulent schemes attempt to manipulate data in order to profit from it globally. Usually, this entails manipulating or hacking the data to produce fictitious reports. This is primarily due to the fact that patients are unable to follow their records. Here, a unique framework utilising the blockchain concept in the healthcare industry has been introduced. Implementing Block Chain for electronic health records (EHR) is the main objective of this proposed study. The decentralised nature of Block Chain lessens the possibility of a single point of failure, resulting in a robust system. Together with addressing the extensibility issue that Block Chain shares, the proposed approach makes use of an off-chain data source. The findings show that, as compared to the conventional health record system, computers is far more safe and scam-free. Lastly, the suggested method offers situations, like in EHR, where the given method ought to work well.