bluuebunny/crossref_metadata_2025 huggingface dataset:
Information Analysis of the Development and Problems of High-entropy Alloys
Authors:   NPJSC «Abylkas Saginov Karaganda Technical University», KVON Svetlana , ISAGULOV Aristotle ,   NPJSC «Abylkas Saginov Karaganda Technical University», ARINOVA Sania ,   NPJSC «Abylkas Saginov Karaganda Technical University», AUBAKIROV Dastan ,   NPJSC «Abylkas Saginov Karaganda Technical University»
Published: November 2024

An information analysis of the development and problems of high-entropy and qua si-high-entropy alloys is carried out. 30 sources have been analyzed, which are fundamental works or the latest developments in this field. It is shown that, unlike the first representations, the structure of the wind farm can also be multiphase, including the phases of implementa tion. The analysis showed that the development of wind farms based on d-transition metal systems is, in fact, the next step in the development of high-alloy steels and alloys of austen itic, martensitic-aging classes or DH-steels. The main problem of WPP production is the use of high-purity metals, which causes the high cost of WPP. A solution to this problem is proposed by partially replacing pure metals with appropriate ferroalloys, which will significantly simplify the smelting technology and reduce the cost.
Review of Completeness of Diagnosis Filling in Medical Record for Inpatients at Mental Hospital. Prof. HB. Saanin Padang
Authors: Mandia Sayati , Nasution Nurhasanah
Published: November 2024

Hospitals are health care institutions that provide comprehensive individual health services that provide inpatient, outpatient, and emergency services. To realize this, hospitals need to organize medical record services to support the provision of health services. The important thing that must be considered by medical record personnel in maintaining the quality of medical records is the completeness of medical information related to the patient's medical history starting from the beginning of treatment until discharge from the hospital. The research aim that the results of this study can improve the quality of medical records and hospital accreditation. This research was conducted in June, 2024 at RSJ. Prof. HB. Saanin and continued with data processing at the Academy of Recorders and Health Information IRIS Padang. The type of research used in this study is descriptive research with quantitative methods and direct observation of medical record documents. Population of study was medical record documents for inpatients in in January - March 2024 totaling 600 medical record documents. The sample size using the Slovin formula was 99 samples. Result of research show that highest distribution of diagnoses based on ICD-10 at Prof. Hb Saanin Padang was paranoid schizophrenia with 35 patients, completeness of filling in the main diagnosis and diagnosis code in cases of mental disorders was 96%, Percentage of readability of diagnosis writing, where the writing of the main diagnosis and secondary diagnosis on the medical record is 100%.

Keywords: Completeness, Medical record, diagnose.
Changepoint Detection in Highly-Atributed Dynamic Graphs
Authors: Penaloza Emiliano , Stevens Nathaniel
Published: November 2024

Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network’s modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot’s modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks by analyzing shifts in modularity. Moreover, we find our method is able to detect a real-world event within the #Iran Twitter reply network, where each node has high-dimensional textual attributes. We additionally make our code available on Github.
Tasa de Egreso hospitalario por Absceso Periamigdalino en hombres y mujeres mayores o igual 1 año en Chile entre los años 2020-2023
Authors: Martinez Afani Ana Maria , Valenzuela Magun Carlos Natanael , Rozada Castro Camila Andrea , Cuellar Kauer Alonso Andrés , Moscoso Sandoval Amira Paz
Published: November 2024

Introducción: El absceso periamigdalino es la complicación más común de amigdalitis. Es de causa polimicrobiana, altamente frecuente en pacientes jóvenes. Objetivo: Calcular la Tasa de Egresos Hospitalarios por absceso periamigdalino, en hombres y mujeres mayores a 1 año, durante el 2020-2023 en Chile. Metodología: Diseño observacional ecológico. Se estudiaron los egresos hospitalarios por absceso periamigdalino, según edad y sexo entre 2020-2023 (n= 7.286), con datos provenientes del DEIS y del Ministerio de Salud de Chile. No se requirió Comité de Ética. Resultados: 2023 fue el año con mayor Tasa de Egresos Hospitalarios, con 14,72 por 100.000 habitantes. Los hombres representaron la mayor tasa con un promedio de 11,57. El grupo etario que predomina es el de 15 a 19 años con una tasa de 20,46. Discusión: La mayor Tasa de Egresos Hospitalarios fue en los años 2022 y 2023, lo que podría explicarse por el aumento en la disponibilidad de camas posterior a la pandemia. El sexo masculino presento mayor Tasa de Egresos Hospitalarios, hecho que puede deberse a una mayor prevalencia de Streptococcus Pyogenes en hombres. El grupo etario con mayor tasa se ubicó entre los 15-19 años, lo que puede originarse en los cambios propios de desarrollo hormonal y sistema inmune en ese periodo. Conclusión: Aumento progresivo de la Tasa de Egresos Hospitalarios entre 2020-2023 en Chile, con una mayor tasa en el sexo masculino y en el grupo etario 15-19 años. Estos resultados proporcionan una actualización epidemiológica sobre el absceso periamigdalino en Chile, facilitando comprender su comportamiento.
A Study of Covid-19 Cases using SIR Model in Nepal
Authors: Prem Sagar Bhandari
Published: November 2024

Due to the growing world population and their mobility all over the world, the risk of infectious disease is high. From 2019, there is a common big issue of COVID-19. Nepal is also a country affected by COVID-19. For the study, the secondary data of COVID-19 from January 2021 to January 2023 were extracted from the Government of Nepal, Ministry of Health, and population Internet sites https://covid19.mohp.gov.np/ or https://heoc.mohp.gov.np, the WHO Coronavirus (COVID-19) Dashboard website: https://www.who.int, and the COVID-Coronavirus Statistics—Worldometer web site: https://www.worldometers.info>coronavirus. The secondary COVID-19 data collected for this study from official offices and sources was analyzed for readability and comprehension. The solution of a renowned model, SIR, was also taken. The computer software MATLAB was used to plot the graphs with the help of parametric values obtained from the real data. From the graphical results, we found that the trend of COVID-19 cases is increasing in May 2020 to November 2020, decreasing in November 2020 to April 2021, increasing in 2021 to June 2021, and decreasing in June 2021 to July 2021. Infected cases are high in November 2020 and in May to June 2021. Recovered cases are high in November to December 2020 and in May to June 2021. We also found that the predicted infected and recovered disease cases from the model are almost matched with the infected and recovered disease cases of real data for COVID-19 in Nepal.
A Novel Perspective on the Quality Index for Fused Satellite Images
Authors: Milind S. Patil
Published: November 2024

Blind Image Quality Assessment for Remote Sensing Applications has garnered a significant amount of interest from the community, presenting a problem that is both persistent and difficult to solve. The current quality indicators are, for the most part, in agreement with each individual's subjective perception. Traditional handcrafted quality measures focus on detecting low-level features such as contour, edge, color, texture, and shape. These are the attributes that will be measured. Nevertheless, it is possible that they will disregard the essential semantics that lie behind the warped image. Within the scope of popular deep learning, the process of acquiring multilayer features is a straightforward exercise. The unfortunate reality is that a significant number of these models either ignore superficial traits or focus only on high-level variables, which ultimately leads to poor prediction performance. In order to successfully portray varying degrees of distortion, the fusion features make advantage of the laws that govern the human visual system in order to extract both local and global components of information. In conclusion, the ultimate quality score is determined by considering the features of both the local and global levels. In this investigation, we developed two different NR-IQA methods. In one, a markov random field is used, while in the other, a sparse approximation variational autoencoder is established as the foundation. The effectiveness of our work is shown by the evaluation of multiple IQA datasets, where our model consistently generates findings that are at the leading edge of the field. This evaluation does not make use of any quality standards; rather, it compares it against a variety of well-known cutting-edge techniques and average opinion ratings based on the opinions of different people. Quality ratings are accurately predicted by the regression models that have been provided.
Design, Simulation and Mathematical Modelling of Slotted Microstrip Patch Reconfigurable Antenna Array for Future Wireless Communication
Authors: Vinaya Deshmukh
Published: November 2024

A multi-slotted Microstrip frequency reconfigurable antenna is designed-simulated and analyzed in this paper for MIMO applications. The antenna demonstrates the impact of parasitic patches, Slots, and  PIN diode on the radiating surface. Designed to operate at five frequencies 3.7 GHz/ 5 GHz/ 6 GHz /9.8 GHz with S11 < -10 dB and VSWR < 2. The communicating bands and the bandwidth achieved by the reconfigurable antenna are 3.72 - 3.8 GHz / 2.1% bandwidth, 4.89 – 5 GHz / 2.2% bandwidth, 5.85 – 6.28 GHz / 7.2% bandwidth, and 9.63 – 9.97 GHz / 3.4% bandwidth. The antenna holds potential applications in areas such as Cognitive radio, C and X band (Microwave communications), MIMO, WiMAX, Satellite Communication, etc.
A Comparative Analysis and Mathematical Modelling of Data Migration Tools to Evaluate Performance Parameters using Machine Learning Methods
Authors: Suchit A. Sapate
Published: November 2024

Abstract: Transferring data between storage systems, platforms, or places is a common reason why data movement is an important part of modern computing. The effectiveness and dependability of these tasks can be greatly affected by how well data transfer tools work. Using machine learning techniques, we compare and contrast different data transfer tools in this work. First, we will talk about some important performance metrics that are needed to properly evaluate data transfer tools. Such elements include the type of data, its size, the network's speed, and other important factors like delay and flow. Next, we get data from different data transfer tools and prepare it before reviewing their performance in a range of situations and setups. The data is analyzed using machine learning techniques like classification, regression, and grouping. Finding groups of tools that work similarly is easier with clustering. This gives you information about which tools are best for different data transfer jobs. We can describe the link between performance factors and tool performance under different situations using regression analysis. The performance traits of tools help with classification, which makes it easier to choose the right tool for a transfer job. Our findings demonstrate that machine learning techniques can correctly evaluate and contrast data transfer tools by looking at how well they do certain tasks. The paper study shows how these approaches can help companies pick the best tool for their data transfer needs by giving them useful information about tool choices. Furthermore, our research shows how important it is to use machine learning when assessing data transfer tools and sets the stage for more research to be done in this area in the future.
Computational Forecasting of Power Prices Using Artificial Neural Networks
Authors: Ganesh Wakte
Published: November 2024

In the restructured power markets, the primary responsibility is setting the price of electricity. Thus, it has become increasingly important to accurately and precisely forecast power prices. An Artificial Neural Network (ANN) that was developed specifically for temporary price prediction in restructured electricity markets is presented in this paper. An input level, two hidden layers, and output layer comprise the four levels of the suggested ANN model, which is a perceptron neural network. Instead of using traditional back propagation for ANN training, using Levenberg-Marquardt retrogression (LMBP) methodology is used to accelerate convergence. The performance and efficacy of the suggested ANN model may be shown by training it on the Ontario power market. MATLAB is used to train the model.
Designing and Developing novel methods for Enhancing the Accuracy of Water Quality Prediction for Aquaponic Farming
Authors: Govinda Khandelwal
Published: November 2024

The Internet of Things (IoT)-based automated water surveillance system is crucial for fish farming, as it helps control risks and improve output and productivity. However, the industry faces challenges such as a lack of understanding of organism selection based on water purity indicators, a shortage of premium seeds and species, and imbalanced datasets. Existing systems also lack optimal feature selection methods, as well as modern machine learning and deep learning approaches. To address these issues, a novel system is proposed that integrates an aquaponic ecosystem containing fish, plants, and bacteria to balance water quality parameters and boost productivity. The system collects data from IoT devices, performs a data cleaning process using missing values, and outliers handling. Then, it performs a feature extraction process to select the optimum features. Next, it employs the novel H-SMOTE approach to tackle the issue of imbalanced datasets. The system uses multi-model categorization to cultivate fish in cold waters, warm-water aquatic plants, and bacteria in an aquaponic environment. The system uses a voting principle to identify the most effective prediction model. The proposed system achieves 99.50% accuracy for water quality prediction for aquaponic farming, addressing the limitations of existing systems and improving prediction accuracy and overall aquaponic farming productivity.
Utilizing Blockchain Technology for Enhanced Cybersecurity in Financial Transactions
Authors: Shobha Agrawal
Published: November 2024

The blockchain technology has become a revolutionary new idea that has a lot of promise to make financial activities safer. This essay looks at how blockchain can be used to make financial systems safer by focusing on how it is independent and can't be changed. When it comes to standard financial transactions, monopoly comes with risks like single points of failure and being open to hacking. Blockchain solves these problems by spreading transaction records among a network of nodes, with each node keeping a log that is always up to date. This autonomous design makes sure that no one organization has access to all past transactions. This lowers the chance of scam or changing data without permission. Also, the fact that blockchain records can't be changed makes sure that financial deals are open and trustworthy. Once a transaction is recorded, it can't be changed after the fact without agreement from everyone in the network. This makes blockchain a strong tool for making financial processes auditable and accountable. Adding smart contracts to blockchain makes it even more useful for keeping financial activities safe. Smart contracts are deals that run themselves and have rules and conditions that are written into the blockchain network. These contracts handle tasks and make sure they are done correctly, which cuts down on the need for middlemen and the chance of mistakes made by people when transactions are settled. Blockchain's cryptographic methods also offer high-level security features like digital signatures and cryptographic hashing. These systems make sure that players are who they say they are and that data is correct throughout the entire transaction process. This makes the system more resistant to hacking and illegal access. This essay looks at examples and case studies of how blockchain has been used successfully in the financial sector. It shows how it can stop fraud, lower trade costs, and make operations run more smoothly.
Dynamic Question Generation using NER with various Feature Extraction and NLP Techniques
Authors: Kanchan Babaji Dhomse
Published: November 2024

The purpose of the Automatic Question Generator is to produce novel questions from the given text that are both linguistically natural, semantically precise, and syntactically coherent. Unlike activities such as summarization and paraphrase, replies play a vital role in question-answering tasks. The creation of multiple-choice questions involves the use of distractions of high quality and the formulation of successful questions. This technology enables instructors to generate multiple-choice assessment questions including both right answers and distractors. An instructor may promptly evaluate a student's understanding of the material. This method enables students to assess their own comprehension level of the topic matter. This technique is beneficial for generating assessment papers for evaluation in the educational industry. Teachers may develop questions about their topics by duplicating or transferring one or more paragraphs. In this paper we proposed an dynamic question generation using Named Entity Recognition (NER), Natural Language Processing (NLP) based approach with conventional learning techniques. The proposed NLP-NER based approach is applicable on heterogeneous as well as large text dataset. However, a compilation of inquiries is generated, derived from the texts that were identified as noteworthy or informative. Various methods of inquiry often yield questions that are primarily focused on gathering factual information, such as inquiries about individuals, time, location, reasons, and details. A natural language processing programme facilitates the comprehension of language and spoken communication by machines. The approach dissects the material into its constituent elements, deciphers the meaning of the language, selects the appropriate actions, and ultimately delivers the content to the user in a comprehensible language.
Comparative Analysis of Machine Learning Models for Enhanced Software Fault Detection: Performance and Accuracy Evaluation
Authors: Shikha Gautam
Published: November 2024

Software defect prediction expectation is a critical portion of program designing that tries to discover and anticipate bugs or imperfections in program frameworks some time recently they happen. The objective is to come up with strategies and models that offer assistance program advancement groups choose which testing assignments are most imperative and how to best utilize their assets. A few machine learning (ML) strategies are utilized to do this. To create forecasts, these frameworks utilize a parcel of distinctive information, like code measures, past imperfection information, and data approximately the coder. The reason of this study is to use different machine learning strategies to make a determining demonstrate for finding computer program bugs. The proposed demonstrate is utilized in tests on the KC2 dataset from NASA's Guarantee library as portion of the study. It was attempted and compared how well diverse machine learning strategies worked. This consider utilized the KC2 dataset and found that the Decision Tree strategy got 73.28% accuracy, Naïve Bayes got 85.96%, K-Nearest Neighbour (KNN) got 82.57%, Support Vector Machine (SVM) got 86.74%, and Random Forest got 84.20%. The study about appeared that these different models can precisely anticipate program bugs when the Guarantee dataset KC2 is utilized. These comes about appear that machine learning models can offer assistance anticipate bugs superior, which can offer assistance groups speed up their tests and make way better utilize of their assets. The study appears that employing a run of machine learning models can enormously move forward the precision of foreseeing computer program abandons. This gives us valuable data almost regions of program frameworks that are more likely to have bugs, which helps keep software quality high.
An Approach for Solving Fuzzy Sequencing Problem with Triangular Fuzzy Neutrosophic Numbers Using Ranking Function
Authors: G. Sudha
Published: November 2024

This study presents a modified approach to address the sequencing problem, specifically when the values are expressed using Triangular Fuzzy Neutrosophic Numbers. First, we have to convert Triangular Neutrosophic fuzzy numbers into crisp one by using Score function. A numerical example has been considered and solved for illustration purpose.
A Glimpse on the Integer Solutions to the Hyperbola 8x2 = 5y2 + 27
Authors: R. Sathiyapriya
Published: November 2024

The hyperbolas given by 8x2 = 5y2 + 27 is considered to obtain its solutions in integers.Various relations amoung the solutions are exhibited.Using the known solutions of the equations in title hyperbolas,parabolas,special Pythagorean triangle and second order Ramanujan numbers are determind.Facinating properties involving figurate numbers are given.
On Solving Cubic Equation x^3+y^3 =7(z-w)^2 (z+w)
Authors: P. Jamuna Devi
Published: November 2024

The cubic Quadratic Equation  is analyzed for its non-zero distinct integer solutions. Five different patterns of non-zero distinct integer solutions to the equation under consideration are obtained. A few applications of Diophantine equations are also presented.
Some Results using E.A Properties in Multiplicative Metric Space
Authors: Thirupathi Thota
Published: November 2024

The aim of this paper is to establish three common fixed point theorems in Multiplicative Metric Space (MMS) by utilizing the conditions E.A property, E.A like properties and common E.A like prperty.Three unique common fixed point theorems are generated and supported it with relevant examples. In the first theorem, we use the concept E.A property and weakly compatible mappings, in the second theorem we use the notion of E.A like property, weakly compatible mappings and in the third theorem we use the notion of common E.A like property, weakly compatible mappings. These conditions are weaker than the existing conditions like compatible mappings and continuous which generalizes the theorem of Afrah A. N. Abdou.
Data Encryption and Decryption using some Integral Transforms
Authors: Gobburi Rekha
Published: November 2024

Ever since the beginning of time, people have valued privacy and information security. Information security is becoming increasingly vital in our digital society. The authentication system's protection of hidden information is crucial. One of the most significant methods used to protect data and secure message transmission is cryptography. There are the two components for the encoding and decoding process which includes the algorithm and key. Cryptography is made more secured by utilising a key for both encryption and decryption.
Introduction: In many circumstances, the sender wants to keep the message private from the public or from unauthorised users. Information is protected using encryption, and the original message is unlocked using decryption. Various Techniques are found in literature and further we extend the findings by combining the Elzaki and Aboodh transforms to encrypt the message and apply their inverse transforms to decrypt it with the ASCII values under modulo 255.
Objectives: In this paper we focused on two stages of encryption and two stages of decryption under modulo 255.
Methods: Using different transformation techniques, we have encrypted and decrypted the data. One is Laplace transform technique while the other is Aboodh-Elzaki transform technique.
Results: The text message is converted to coded message by using both Laplace transform technique and Aboodh-Elzaki transforms technique.
Conclusions: In the current paper, we present a novel method that encrypts data using Laplace transform and decrypts it using the private key, which is under modulo255. The two integral transforms are used to increase the message's level of security during decryption by selecting a new function and altering the other elements.
Enhancing System Security Through Signature-Based Ransomware Detection and Automated Data Backup: A Comprehensive Approach to Mitigating Ransomware Attacks
Authors: Srijita Bhattacharjee
Published: November 2024

Attacks utilizing ransomware have developed into a major hacking hazard, causing enormous misfortunes in cash and information breaches in numerous areas. To bargain with this rising stress, we require a checking framework that works well and can be checked on. In this consider, we propose a way to discover ransomware that works with a programmed reinforcement framework to create it more secure from these sorts of dangers. Employing a huge library of known malware fingerprints, our strategy employments signature-based examination to discover and partitioned hurtful code. By comparing unused records that come in with these signs, the framework can rapidly discover malware and begin a reinforcement and response handle right absent. This strategy based on marks is exceptionally great at finding known sorts of ransomware, so offer assistance can be given rapidly. The expansion of a programmed reinforcement framework too protects the information and makes it accessible, indeed in the event that there's an assault. Once malware is found, the framework rapidly makes secure duplicates of all the information, so no information is misplaced and the assault has less of an impact. Being able to rapidly recoup harmed records from a reinforcement source makes ransomware Attacks much less compelling by giving hoodlums less control to request deliver installments. This strategy of utilizing signature-based ransomware location and programmed reinforcement together may be a solid way to secure frameworks from ransomware dangers. The strategy progresses generally security and brings down the hurt that ransomware seem do by rapidly finding dangers and rapidly recovering lost information.
Local Neutrosophic Rough Similarity Measure based tangent function
Authors: S. Bharathi
Published: November 2024

This work studies the information brought by the degree of truth, falsity, and indeterminacy Membership in the Neutrosophic set (NS) defined as a vector representation. The tangent similarity measure into the Local Neutrosophic Rough set (LNRS) is proposed by tangent similarity measure for NSs and is also proved with some of its properties and an algorithm is generated for the proposed approach between two LNRS. Further, the proposed similarity measure is validated with the school selection problem.
Analysis of Adaptive Strategies: Blood Inventory Optimization in Hospital Blood Banks amidst Uncertain Environments
Authors: Hemant Kumar
Published: November 2024

This paper delves into the critical realm of blood inventory management within hospital blood banks, focusing on the development and optimization of adaptive strategies in the face of uncertain environments. In healthcare systems, ensuring an efficient and reliable blood supply is imperative, yet challenges such as fluctuating demand, supply chain uncertainties, and unforeseen events pose significant obstacles. This study investigates innovative approaches to enhance the resilience and responsiveness of hospital blood banks, ultimately contributing to the establishment of robust adaptive strategies. By leveraging data-driven methodologies and advanced analytics, the research aims to provide actionable insights that empower healthcare institutions to proactively address uncertainties, minimize wastage, and optimize blood inventory management practices. The findings of this study hold the potential to enhance the overall effectiveness of blood supply chains, ensuring a more agile and sustainable approach in meeting the dynamic demands of healthcare scenarios characterized by uncertainty.
Applying "AEM" Transform on Partial Differential Equations
Authors: Baneen Sadeq Mohammed Ali
Published: November 2024

This work demonstrates the (Ahmed-Emad-Murat) "AEM" transform technique, where its important properties and its capability to evaluate a particular solution for partial differential equations have been introduced and shown via the solution of fundamental examples and physical partial differential equations.
An Inflation-Affected Supply-Chain Dual Warehouse Inventory Model for Decaying Things by Investment in Preservation Technology
Authors: S.Ramya
Published: November 2024

It is common to observe that when demand increases, an industry with limited storage capacity (OW) must rent a warehouse (RW). We must purchase more product in larger quantities due to the COVID-19 epidemic, sales discounts, or other factors. When OW's capacity is reached, extra stock is held in RW at a high holding charge compared to OW. The dual warehouse inventory problem of a supply chain constrained by limited storage capacity in OW for non-instantaneously decaying commodities under inflationary conditions has been investigated in the current study. There is a partial backlog in shortages, and the demand rate is depending on inventory. Because of the quick changes in the environment, preservation technology has become increasingly crucial in today's global society. In order to slow down the deterioration of goods with a high rate of deterioration, preservation technology is specifically used in this article. Our primary goal is to reduce the suggested model's overall cost function. At the conclusion of this work, A sensitivity analysis and numerical illustration have been performed to showcase the model.
Techniques for Solving Bound-Constrained Convex Optimization Using Spectral Projected Conjugate Gradient Based on Barzilai-Borwein Step Length
Authors: Sudaba A. Mohammed
Published: November 2024

This work aims to create an effective spectral conjugate gradient for non-linear optimization problems and to project the solution into a bounded convex set for largescale optimization problems. We do this by combining the classical spectral conjugated gradient direction with the projected Barzilai and Borwein step lengths. The efficacy and convergence of the new method are illustrated through a series of problems.
Enhanced Deep Learning Methodology for Detection and Identification of Brain Tumor using CNN
Authors: K. Vijetha
Published: November 2024

Introduction: Brain tumours are a significant global public health issue that require precise and effective diagnosis techniques for well-thought-out treatment strategies. Three separate works for the automatic identification and categorization of brain tumours in magnetic resonance imaging (MR) datasets are presented in this dissertation.
Objectives: The primary objective of this work was to explore uses of the version of the discrete orthonormal S-transform (DOST) to extract texture characteristics and fuzzy C-means (FCM) for image segmentation. By using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) we can minimize the dimensional of the recovered features. The proposed Adaboost method is run with the reduced features, and a Random Forest (ADBRF) classifier is then employed. The   proposed Adaboost method is run with the reduced features, and a Random Forest (ADBRF) classifier is then employed.
Methods: A range of machine learning algorithms Reduced Complexity Spatial Fusion CNN is applied for the brain to classify and identify the brain tumor.
Results: The results revealed significant performance enhancements, particularly for boosting algorithms. Mean, Variance, standard deviation, entropy, energy has been calculated and by using proposed design it has high accuracy rate.
Conclusions: This study highlights the potential of advanced machine learning techniques for classifying and identification of brain tumor. Accurate analysis is often hampered by the complexity and diversity of image data. To tackle this, we created the RCSF-CNN, a proposed method that combines the extraction of complexity features with CNN for brain cancer detection and classification. Notably, our methodology enhances the resilience and accuracy of detection and classification procedures by utilizing the DOST as an intermediary stage. 21% of the dataset was set aside for rigorous testing, while the remaining 79% was used for training
Hypersurfaces of a Finsler Manifold with the Square Root Metric
Authors: Robin Kumar
Published: November 2024

The study of Finsler manifold with (α,β)-metric is very imporatant aspect of Finsler geometry in terms of its applications. Finsler metrics are extensively used in special theory of relativelty, information science, thermodynamics and geometric optics of anisotropic media. The study of hypersurfaces of a Finsler manifold with some important and special (α,β)-metric has already been studied. The emergence of square root metrics in Finsler geometry can be attributed to various classification concerning (α,β) metric. They have also excellent geometric properties in Finsler geometry. In this paper, we have charecterized the hypersurfaces of a Finsler space with square root (α,β)-metric denoted by F=√(α(α+β)), where α=√(a_ij (x)y^i y^j ) is a Riemannian manifold and β=b_i (x)y^i is a differential 1-form on the Finsler manifold (M,F). Furthermore, it has been found the criterion of hypersurface to be a hyperplane of first kind, hyperplane of second kind and not the hyperplane of the third kind.
Complex cubic intuitionistic fuzzy set applied to subbisemirings of bisemirings using homomorphism
Authors: Raed Hatamleh
Published: November 2024

We develop and analyze the concept of complex cubic intuitionistic fuzzy subbisemiring (ComCIFSBS). We investigate ComCIFSBS its characteristics and homomorphic proper-ties. We suggest the ComCIFSBS level sets of bisemiring. A cubic complex intuitionistic fuzzy set subset Ξ of bisemiring B, if and only if each non-empty level set R(t,s), where R = (μ̂ Z · ei2πβ̂ Z , ν̂ Z · ei2πγ̂ Z , μZ · ei2πβZ , νZ · ei2πγZ ) is a ComCIFSBS of B. Let υ be a ComCIFSBS bisemiring B. If Υ is a ComCIFSBS of B × B, then Z is a ComCIFSBS of bisemiring B. Let Z denote the strongest complex intuitionistic fuzzy relation bisemiring B. It is proved that all ComCIFSBSs have homomorphic images as well as homomorphic pre-images. Examples are presented to demonstrate how our findings are applied.
Enhancing the Medical Diagnosis System and Treatment by Counterfactual Diagnostic Algorithm
Authors: K. Rama Krishna
Published: November 2024

Clinical diagnosis and decision-making stand to be radically altered by machine learning. By identifying the underlying medical conditions, doctors may better explain their patients' symptoms via medical diagnosis. There is a substantial correlation between a patient's symptoms and medical history, yet current diagnostic algorithms only detect illnesses that are associated with each other. Suboptimal or even harmful diagnoses may arise from this failure to differentiate correlation from cause, as this article demonstrates. To get around this, they create novel algorithms for counterfactual diagnostics and reframe diagnosis as a work of counterfactual inference. In this work, researchers demonstrate that this method greatly enhances the reliability and security of the subsequent diagnosis while getting closer to the way doctors think about health problems. Using a battery of clinical scenarios, they evaluate the counterfactual method against 45 medical professionals, the gold standard Bayesian diagnosis system, and other algorithms. Compared to the counterfactual approach, which reaches expert clinical accuracy, the Bayesian algorithm obtains correctness, ranking in the top 26% of clinicians in the study unit. Changing this querying strategy alone yields this improvement, eliminating the need for any further model enhancements. Based on these results, the use of machine learning in medical diagnosis is incomplete without counterfactual thinking.
NEW FORM OF SOFT SETS AND ITS PROPERTIES
Authors: Basavaraj M Ittanagi
Published: November 2024

Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. Many researchers have studied this theory and developed several models to solve decision-making and medical diagnostic problems, but most of these models deal only one set of parameters. This causes problems for users, especially with those who use questionnaires in their work and studies. Also Alkhazaleh and Salleh, also introduced the concept of soft-expert sets. This structure can be considered as a generalization of soft-sets in which experts and their opinions have been added to make decision analysis easier to handle. In our model, is more generalization of soft- set and soft-expert set, the collection of more specific information about object sets using mappings. This concept is more powerful for information tables, since collection of the information is very particular to define by mapping and also this model is approaches to rough set theory and information system.
Adaptive Blockchain-Integrated Nonlinear Federated Learning Framework for Real-Time Intrusion Detection in IoT Fog Networks ABFL-RTID
Authors: R. Sushmitha
Published: November 2024

This research presents the Adaptive Blockchain-Integrated Nonlinear Federated Learning (ABFL-RTID) model, designed for real-time intrusion detection in IoT fog networks. The framework leverages nonlinear learning techniques, such as deep neural networks, to enhance detection capabilities in complex and dynamic network environments. Integrating blockchain ensures decentralized security, while federated learning preserves data privacy by enabling local model training on edge devices. The nonlinear models improve adaptability, accurately identifying sophisticated intrusion patterns while securely validating updates through blockchain. Simulations show the framework achieving 96.8% detection accuracy with response times of 150 ms, demonstrating superior scalability and adaptability under changing network conditions. This study provides a practical approach for building resilient and secure intrusion detection systems, enhancing data integrity and privacy without the delays of traditional, centralized models.
Enhancing Cloudburst Prediction System Using Bi-LSTM in Realtime: Machine Learning
Authors: B.Ramasubba Reddy
Published: November 2024

Nowadays Technology is enhanced, but still facing floods and toofan due to uncertain rainfall conditions and artificial rains, it is a challenging task for hydrology and meteorology. Now a new cloudburst prediction system is a requirement for hydrology and meteorology. cloudburst prediction holds significant importance in fields like hydrology and meteorology, aiding farmers, water resource managers, and emergency responders in preparing for and managing the impacts of extreme weather events like droughts and floods. One effective technique for rainfall prediction involves employing machine learning models such as Bi-LSTM (Bidirectional Long Short-Term Memory). Bi-LSTM, a neural network architecture known for its prowess in sequence modeling tasks like voice recognition and natural language processing (NLP), operates by processing data bidirectionally, facilitating a thorough understanding of sequential patterns. By analyzing historical rainfall data as a sequence, a Bi-LSTM model can effectively forecast future rainfall values with notable accuracy.In this proposed methodology, a Bi-LSTM model is trained using a CSV dataset comprising historical rainfall data. The performance of the Bi-LSTM model is then compared with that of an LSTM (Long Short-Term Memory) model, another widely used architecture for sequence prediction tasks. The results indicate that the Bi-LSTM model achieves an accuracy of 96.55%, surpassing the accuracy of the LSTM model, which stands at 94.36%. This comparison underscores the effectiveness of the Bi-LSTM approach in rainfall prediction, showcasing its potential to provide valuable insights for decision-making in various domains affected by weather variability.
Energy-Efficient IoT Networks: Optimizing Resource Management through Machine Learning
Authors: Arulananth T S
Published: November 2024

The rapid expansion of Internet of Things (IoT) devices has brought out novel prospects and obstacles, namely in the domains of energy management and resource optimisation. Ensuring energy efficiency becomes essential as IoT networks grow in order to promote sustainable deployments, save operating costs, and extend the life of devices. The increasing intricacy and dynamic character of contemporary IoT systems sometimes prove to be too much for traditional resource management techniques to handle. The potential of machine learning (ML) to improve resource management in energy-efficient Internet of Things networks is examined in this research. Through the use of machine learning techniques, such as deep learning, supervised learning, and reinforcement learning, Internet of Things systems are able to forecast network traffic, allocate resources intelligently, and reduce energy usage in real time. The study examines cutting-edge methods, addresses important issues, and indicates areas for further investigation. In the end, this research highlights how machine learning may play a revolutionary role in creating IoT networks that are scalable, energy-efficient, sustainable, and able to satisfy the expectations of a world that is becoming more and more connected.
Intelligent Hybrid Model for Predicting and Monitoring Heart Disease
Authors: Janpreet Singh
Published: November 2024

When it comes to the most important and critical aspect in Health care industry, so procedure of Heart Disease Diagnosis can be more conceptualised as this is only way by which we come to know about heart disease patients life – for such kind a risk factor helps patient department downfall disease at an stage. However, this process also causes errors in many cases and abounds with unexpected consequences or a patient dies by it. Therefore, the main difficult predictions in medical sector for heart disease diagnosis by doctors. So, in this technical generation we can very easily appreciate and value the role of artificial intelligence in healthcare. Accordingly, the essence of this study was to introduce a heart disease monitoring model that applies neuro fuzzy as hybrid ML methodology. In the proposed intelligent hybrid inference system, there are input variables used to diagnose disease at different stages. The system produces the output that yields the three different disease stages or levels. Thus, on this generated result base professional doctors of the heart can diagnose over patient and even decide better operation for treatment according to disease state. The k-fold cross validation technique does the same partitioning of the dataset and for testing. It also estimates the performance of system and by its results it accurately predicts stage of heart disease from which a patient is suffering with accuracy 98.90%.
3D Convolutional Neural Networks for Video Recognition
Authors: P. Sunil Prem Kumar
Published: November 2024

3D CNNs have proven to be an effective technique for analysing spatiotemporal data particularly in video recognition. By applying convolutions across consecutive video frames, 3D CNNs take into account both spatial and temporal dimensions, in contrast to typical 2D CNNs that process frames one at a time.
This makes the network extremely useful for comprehending motion and temporal patterns since it enables it to record both static visual information and the dynamic changes between frames. Robust computational resources, significant labelled video data, and advanced regularization techniques are necessary for the efficient training of 3D CNNs.
However, 3D CNNs' capacity to incorporate feature learning throughout time and space presents a number of advantages over conventional techniques, establishing them as a key technology in the advancement of video analysis skills. We have measured the efficiency of the classifier with various parameters accuracy, precision, recall, area under the ROC curve, mean average precision and loss metrics. The proposed classifier reports an accuracy of 98.64% which is promising.
Can Automobile Brands Optimize their Print Advertisements through Eye-Tracking Analysis?
Authors: Joyeeta Chatterjee
Published: November 2024

Introduction: Eye tracking technology has become an essential tool for assessing the effectiveness of visual marketing strategies across various industries. By capturing eye movements, gaze points, and timing variations, it enables researchers to explore how people interact with visual stimuli like images and text. This technology offers valuable insights into which elements of an advertisement or visual content draw the most attention and the order in which they are observed.
Objectives: The purpose of this research is to explore the use of eye tracking in print advertisements and identify the most appealing images and designs for attracting consumers.
Methods :This qualitative exploratory study involved twenty participants who viewed six print advertisements for four-wheeler vehicles. Data was collected using Tobii eye-tracking technology, generating heat maps and gaze plots to analyse the impact of these advertisements on consumer behaviour.
Results :The findings revealed that viewers often overlook body copy in favour of the main image, advertisement headline, and celebrity endorsements. Most participants, especially men, preferred product specifications. Advertisements that achieved top-of-mind recall successfully utilized eye-catching visuals to break sensory adaptation.
This data can identify patterns in visual engagement and inform strategic marketing plans, enhancing the effectiveness of print advertisements. The implications are significant for automotive marketers, who may need to reconsider the style and format of their ads to capture consumer attention. By understanding how consumers interact with advertisements, marketers can create more engaging campaigns, ultimately leading to increased sales. As India emerges as a key player in the global automobile sector, these insights will be valuable for improving the return on investment in advertising.
Conclusion: The findings indicate that viewers frequently bypass body copy in favor of the main image, advertisement headline, and celebrity endorsements. Notably, many participants, particularly men, showed a preference for product specifications. Advertisements that effectively captured attention through striking visuals were more successful in overcoming sensory adaptation and achieving top-of-mind recall. These insights into visual engagement patterns can guide the development of strategic marketing plans, ultimately enhancing the effectiveness of print advertisements.
Error Estimates for Three-Dimensional Singularly Perturbed Convection-Diffusion Problem
Authors: Brehmit Kaur
Published: November 2024

The main aim of the present work is to propose a posteriori error estimates for singularly perturbed convection-diffusion problem in three-dimensions. It has been observed that for small singular perturbation parameter, the problem under consideration displays nonphysical oscillations in the small subregions of the boundary layers. Hughes stabilization strategy along with the Streamline upwind/Petrov-Galerkin (SUPG) method has been proposed to approximate the solution of the problem. Reliable a posteriori error estimates in energy norm on anisotropic meshes have been developed for the proposed method.